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Python Reference

Custom Charts

Create custom charts and visualizations.

Analytics and Query API

Query and analyze data logged to W&B.

Automations

Automate your W&B workflows.

Python SDK

Train and fine-tune models, manage models from experimentation to production.

1 - Custom Charts

Create custom charts and visualizations.

1.1 - bar()

View the source code

function bar

bar(
    table: 'wandb.Table',
    label: 'str',
    value: 'str',
    title: 'str' = '',
    split_table: 'bool' = False
) → CustomChart

Constructs a bar chart from a wandb.Table of data.

Args:

  • table: A table containing the data for the bar chart.
  • label: The name of the column to use for the labels of each bar.
  • value: The name of the column to use for the values of each bar.
  • title: The title of the bar chart.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Example:

import random
import wandb

# Generate random data for the table
data = [
    ["car", random.uniform(0, 1)],
    ["bus", random.uniform(0, 1)],
    ["road", random.uniform(0, 1)],
    ["person", random.uniform(0, 1)],
]

# Create a table with the data
table = wandb.Table(data=data, columns=["class", "accuracy"])

# Initialize a W&B run and log the bar plot
with wandb.init(project="bar_chart") as run:
    # Create a bar plot from the table
    bar_plot = wandb.plot.bar(
         table=table,
         label="class",
         value="accuracy",
         title="Object Classification Accuracy",
    )

    # Log the bar chart to W&B
    run.log({"bar_plot": bar_plot})

1.2 - confusion_matrix()

View the source code

function confusion_matrix

confusion_matrix(
    probs: 'Sequence[Sequence[float]] | None' = None,
    y_true: 'Sequence[T] | None' = None,
    preds: 'Sequence[T] | None' = None,
    class_names: 'Sequence[str] | None' = None,
    title: 'str' = 'Confusion Matrix Curve',
    split_table: 'bool' = False
) → CustomChart

Constructs a confusion matrix from a sequence of probabilities or predictions.

Args:

  • probs: A sequence of predicted probabilities for each class. The sequence shape should be (N, K) where N is the number of samples and K is the number of classes. If provided, preds should not be provided.
  • y_true: A sequence of true labels.
  • preds: A sequence of predicted class labels. If provided, probs should not be provided.
  • class_names: Sequence of class names. If not provided, class names will be defined as “Class_1”, “Class_2”, etc.
  • title: Title of the confusion matrix chart.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Raises:

  • ValueError: If both probs and preds are provided or if the number of predictions and true labels are not equal. If the number of unique predicted classes exceeds the number of class names or if the number of unique true labels exceeds the number of class names.
  • wandb.Error: If numpy is not installed.

Examples: Logging a confusion matrix with random probabilities for wildlife classification:

import numpy as np
import wandb

# Define class names for wildlife
wildlife_class_names = ["Lion", "Tiger", "Elephant", "Zebra"]

# Generate random true labels (0 to 3 for 10 samples)
wildlife_y_true = np.random.randint(0, 4, size=10)

# Generate random probabilities for each class (10 samples x 4 classes)
wildlife_probs = np.random.rand(10, 4)
wildlife_probs = np.exp(wildlife_probs) / np.sum(
    np.exp(wildlife_probs),
    axis=1,
    keepdims=True,
)

# Initialize W&B run and log confusion matrix
with wandb.init(project="wildlife_classification") as run:
    confusion_matrix = wandb.plot.confusion_matrix(
         probs=wildlife_probs,
         y_true=wildlife_y_true,
         class_names=wildlife_class_names,
         title="Wildlife Classification Confusion Matrix",
    )
    run.log({"wildlife_confusion_matrix": confusion_matrix})

In this example, random probabilities are used to generate a confusion matrix.

Logging a confusion matrix with simulated model predictions and 85% accuracy:

import numpy as np
import wandb

# Define class names for wildlife
wildlife_class_names = ["Lion", "Tiger", "Elephant", "Zebra"]

# Simulate true labels for 200 animal images (imbalanced distribution)
wildlife_y_true = np.random.choice(
    [0, 1, 2, 3],
    size=200,
    p=[0.2, 0.3, 0.25, 0.25],
)

# Simulate model predictions with 85% accuracy
wildlife_preds = [
    y_t
    if np.random.rand() < 0.85
    else np.random.choice([x for x in range(4) if x != y_t])
    for y_t in wildlife_y_true
]

# Initialize W&B run and log confusion matrix
with wandb.init(project="wildlife_classification") as run:
    confusion_matrix = wandb.plot.confusion_matrix(
         preds=wildlife_preds,
         y_true=wildlife_y_true,
         class_names=wildlife_class_names,
         title="Simulated Wildlife Classification Confusion Matrix",
    )
    run.log({"wildlife_confusion_matrix": confusion_matrix})

In this example, predictions are simulated with 85% accuracy to generate a confusion matrix.

1.3 - histogram()

View the source code

function histogram

histogram(
    table: 'wandb.Table',
    value: 'str',
    title: 'str' = '',
    split_table: 'bool' = False
) → CustomChart

Constructs a histogram chart from a W&B Table.

Args:

  • table: The W&B Table containing the data for the histogram.
  • value: The label for the bin axis (x-axis).
  • title: The title of the histogram plot.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Example:

import math
import random
import wandb

# Generate random data
data = [[i, random.random() + math.sin(i / 10)] for i in range(100)]

# Create a W&B Table
table = wandb.Table(
    data=data,
    columns=["step", "height"],
)

# Create a histogram plot
histogram = wandb.plot.histogram(
    table,
    value="height",
    title="My Histogram",
)

# Log the histogram plot to W&B
with wandb.init(...) as run:
    run.log({"histogram-plot1": histogram})

1.4 - line_series()

View the source code

function line_series

line_series(
    xs: 'Iterable[Iterable[Any]] | Iterable[Any]',
    ys: 'Iterable[Iterable[Any]]',
    keys: 'Iterable[str] | None' = None,
    title: 'str' = '',
    xname: 'str' = 'x',
    split_table: 'bool' = False
) → CustomChart

Constructs a line series chart.

Args:

  • xs: Sequence of x values. If a singular array is provided, all y values are plotted against that x array. If an array of arrays is provided, each y value is plotted against the corresponding x array.
  • ys: Sequence of y values, where each iterable represents a separate line series.
  • keys: Sequence of keys for labeling each line series. If not provided, keys will be automatically generated as “line_1”, “line_2”, etc.
  • title: Title of the chart.
  • xname: Label for the x-axis.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Examples: Logging a single x array where all y series are plotted against the same x values:

import wandb

# Initialize W&B run
with wandb.init(project="line_series_example") as run:
    # x values shared across all y series
    xs = list(range(10))

    # Multiple y series to plot
    ys = [
         [i for i in range(10)],  # y = x
         [i**2 for i in range(10)],  # y = x^2
         [i**3 for i in range(10)],  # y = x^3
    ]

    # Generate and log the line series chart
    line_series_chart = wandb.plot.line_series(
         xs,
         ys,
         title="title",
         xname="step",
    )
    run.log({"line-series-single-x": line_series_chart})

In this example, a single xs series (shared x-values) is used for all ys series. This results in each y-series being plotted against the same x-values (0-9).

Logging multiple x arrays where each y series is plotted against its corresponding x array:

import wandb

# Initialize W&B run
with wandb.init(project="line_series_example") as run:
    # Separate x values for each y series
    xs = [
         [i for i in range(10)],  # x for first series
         [2 * i for i in range(10)],  # x for second series (stretched)
         [3 * i for i in range(10)],  # x for third series (stretched more)
    ]

    # Corresponding y series
    ys = [
         [i for i in range(10)],  # y = x
         [i**2 for i in range(10)],  # y = x^2
         [i**3 for i in range(10)],  # y = x^3
    ]

    # Generate and log the line series chart
    line_series_chart = wandb.plot.line_series(
         xs, ys, title="Multiple X Arrays Example", xname="Step"
    )
    run.log({"line-series-multiple-x": line_series_chart})

In this example, each y series is plotted against its own unique x series. This allows for more flexibility when the x values are not uniform across the data series.

Customizing line labels using keys:

import wandb

# Initialize W&B run
with wandb.init(project="line_series_example") as run:
    xs = list(range(10))  # Single x array
    ys = [
         [i for i in range(10)],  # y = x
         [i**2 for i in range(10)],  # y = x^2
         [i**3 for i in range(10)],  # y = x^3
    ]

    # Custom labels for each line
    keys = ["Linear", "Quadratic", "Cubic"]

    # Generate and log the line series chart
    line_series_chart = wandb.plot.line_series(
         xs,
         ys,
         keys=keys,  # Custom keys (line labels)
         title="Custom Line Labels Example",
         xname="Step",
    )
    run.log({"line-series-custom-keys": line_series_chart})

This example shows how to provide custom labels for the lines using the keys argument. The keys will appear in the legend as “Linear”, “Quadratic”, and “Cubic”.

1.5 - line()

View the source code

function line

line(
    table: 'wandb.Table',
    x: 'str',
    y: 'str',
    stroke: 'str | None' = None,
    title: 'str' = '',
    split_table: 'bool' = False
) → CustomChart

Constructs a customizable line chart.

Args:

  • table: The table containing data for the chart.
  • x: Column name for the x-axis values.
  • y: Column name for the y-axis values.
  • stroke: Column name to differentiate line strokes (e.g., for grouping lines).
  • title: Title of the chart.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Example:

import math
import random
import wandb

# Create multiple series of data with different patterns
data = []
for i in range(100):
     # Series 1: Sinusoidal pattern with random noise
     data.append([i, math.sin(i / 10) + random.uniform(-0.1, 0.1), "series_1"])
     # Series 2: Cosine pattern with random noise
     data.append([i, math.cos(i / 10) + random.uniform(-0.1, 0.1), "series_2"])
     # Series 3: Linear increase with random noise
     data.append([i, i / 10 + random.uniform(-0.5, 0.5), "series_3"])

# Define the columns for the table
table = wandb.Table(data=data, columns=["step", "value", "series"])

# Initialize wandb run and log the line chart
with wandb.init(project="line_chart_example") as run:
     line_chart = wandb.plot.line(
         table=table,
         x="step",
         y="value",
         stroke="series",  # Group by the "series" column
         title="Multi-Series Line Plot",
     )
     run.log({"line-chart": line_chart})

1.6 - plot

View the source code

module wandb

Chart Visualization Utilities

This module offers a collection of predefined chart types, along with functionality for creating custom charts, enabling flexible visualization of your data beyond the built-in options.

1.7 - plot_table()

View the source code

function plot_table

plot_table(
    vega_spec_name: 'str',
    data_table: 'wandb.Table',
    fields: 'dict[str, Any]',
    string_fields: 'dict[str, Any] | None' = None,
    split_table: 'bool' = False
) → CustomChart

Creates a custom charts using a Vega-Lite specification and a wandb.Table.

This function creates a custom chart based on a Vega-Lite specification and a data table represented by a wandb.Table object. The specification needs to be predefined and stored in the W&B backend. The function returns a custom chart object that can be logged to W&B using wandb.log().

Args:

  • vega_spec_name: The name or identifier of the Vega-Lite spec that defines the visualization structure.
  • data_table: A wandb.Table object containing the data to be visualized.
  • fields: A mapping between the fields in the Vega-Lite spec and the corresponding columns in the data table to be visualized.
  • string_fields: A dictionary for providing values for any string constants required by the custom visualization.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Raises:

  • wandb.Error: If data_table is not a wandb.Table object.

Example:

# Create a custom chart using a Vega-Lite spec and the data table.
import wandb

wandb.init()

data = [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]]
table = wandb.Table(data=data, columns=["x", "y"])

fields = {"x": "x", "y": "y", "title": "MY TITLE"}

# Create a custom title with `string_fields`.
my_custom_chart = wandb.plot_table(
   vega_spec_name="wandb/line/v0",
   data_table=table,
   fields=fields,
   string_fields={"title": "Title"},
)

wandb.log({"custom_chart": my_custom_chart})

1.8 - pr_curve()

View the source code

function pr_curve

pr_curve(
    y_true: 'Iterable[T] | None' = None,
    y_probas: 'Iterable[numbers.Number] | None' = None,
    labels: 'list[str] | None' = None,
    classes_to_plot: 'list[T] | None' = None,
    interp_size: 'int' = 21,
    title: 'str' = 'Precision-Recall Curve',
    split_table: 'bool' = False
) → CustomChart

Constructs a Precision-Recall (PR) curve.

The Precision-Recall curve is particularly useful for evaluating classifiers on imbalanced datasets. A high area under the PR curve signifies both high precision (a low false positive rate) and high recall (a low false negative rate). The curve provides insights into the balance between false positives and false negatives at various threshold levels, aiding in the assessment of a model’s performance.

Args:

  • y_true: True binary labels. The shape should be (num_samples,).
  • y_probas: Predicted scores or probabilities for each class. These can be probability estimates, confidence scores, or non-thresholded decision values. The shape should be (num_samples, num_classes).
  • labels: Optional list of class names to replace numeric values in y_true for easier plot interpretation. For example, labels = ['dog', 'cat', 'owl'] will replace 0 with ‘dog’, 1 with ‘cat’, and 2 with ‘owl’ in the plot. If not provided, numeric values from y_true will be used.
  • classes_to_plot: Optional list of unique class values from y_true to be included in the plot. If not specified, all unique classes in y_true will be plotted.
  • interp_size: Number of points to interpolate recall values. The recall values will be fixed to interp_size uniformly distributed points in the range [0, 1], and the precision will be interpolated accordingly.
  • title: Title of the plot. Defaults to “Precision-Recall Curve”.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Raises:

  • wandb.Error: If NumPy, pandas, or scikit-learn is not installed.

Example:

import wandb

# Example for spam detection (binary classification)
y_true = [0, 1, 1, 0, 1]  # 0 = not spam, 1 = spam
y_probas = [
    [0.9, 0.1],  # Predicted probabilities for the first sample (not spam)
    [0.2, 0.8],  # Second sample (spam), and so on
    [0.1, 0.9],
    [0.8, 0.2],
    [0.3, 0.7],
]

labels = ["not spam", "spam"]  # Optional class names for readability

with wandb.init(project="spam-detection") as run:
    pr_curve = wandb.plot.pr_curve(
         y_true=y_true,
         y_probas=y_probas,
         labels=labels,
         title="Precision-Recall Curve for Spam Detection",
    )
    run.log({"pr-curve": pr_curve})

1.9 - roc_curve()

View the source code

function roc_curve

roc_curve(
    y_true: 'Sequence[numbers.Number]',
    y_probas: 'Sequence[Sequence[float]] | None' = None,
    labels: 'list[str] | None' = None,
    classes_to_plot: 'list[numbers.Number] | None' = None,
    title: 'str' = 'ROC Curve',
    split_table: 'bool' = False
) → CustomChart

Constructs Receiver Operating Characteristic (ROC) curve chart.

Args:

  • y_true: The true class labels (ground truth) for the target variable. Shape should be (num_samples,).
  • y_probas: The predicted probabilities or decision scores for each class. Shape should be (num_samples, num_classes).
  • labels: Human-readable labels corresponding to the class indices in y_true. For example, if labels=['dog', 'cat'], class 0 will be displayed as ‘dog’ and class 1 as ‘cat’ in the plot. If None, the raw class indices from y_true will be used. Default is None.
  • classes_to_plot: A subset of unique class labels to include in the ROC curve. If None, all classes in y_true will be plotted. Default is None.
  • title: Title of the ROC curve plot. Default is “ROC Curve”.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Raises:

  • wandb.Error: If numpy, pandas, or scikit-learn are not found.

Example:

import numpy as np
import wandb

# Simulate a medical diagnosis classification problem with three diseases
n_samples = 200
n_classes = 3

# True labels: assign "Diabetes", "Hypertension", or "Heart Disease" to
# each sample
disease_labels = ["Diabetes", "Hypertension", "Heart Disease"]
# 0: Diabetes, 1: Hypertension, 2: Heart Disease
y_true = np.random.choice([0, 1, 2], size=n_samples)

# Predicted probabilities: simulate predictions, ensuring they sum to 1
# for each sample
y_probas = np.random.dirichlet(np.ones(n_classes), size=n_samples)

# Specify classes to plot (plotting all three diseases)
classes_to_plot = [0, 1, 2]

# Initialize a W&B run and log a ROC curve plot for disease classification
with wandb.init(project="medical_diagnosis") as run:
   roc_plot = wandb.plot.roc_curve(
        y_true=y_true,
        y_probas=y_probas,
        labels=disease_labels,
        classes_to_plot=classes_to_plot,
        title="ROC Curve for Disease Classification",
   )
   run.log({"roc-curve": roc_plot})

1.10 - scatter()

View the source code

function scatter

scatter(
    table: 'wandb.Table',
    x: 'str',
    y: 'str',
    title: 'str' = '',
    split_table: 'bool' = False
) → CustomChart

Constructs a scatter plot from a wandb.Table of data.

Args:

  • table: The W&B Table containing the data to visualize.
  • x: The name of the column used for the x-axis.
  • y: The name of the column used for the y-axis.
  • title: The title of the scatter chart.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Example:

import math
import random
import wandb

# Simulate temperature variations at different altitudes over time
data = [
   [i, random.uniform(-10, 20) - 0.005 * i + 5 * math.sin(i / 50)]
   for i in range(300)
]

# Create W&B table with altitude (m) and temperature (°C) columns
table = wandb.Table(data=data, columns=["altitude (m)", "temperature (°C)"])

# Initialize W&B run and log the scatter plot
with wandb.init(project="temperature-altitude-scatter") as run:
   # Create and log the scatter plot
   scatter_plot = wandb.plot.scatter(
        table=table,
        x="altitude (m)",
        y="temperature (°C)",
        title="Altitude vs Temperature",
   )
   run.log({"altitude-temperature-scatter": scatter_plot})

1.11 - visualize()

View the source code

function visualize

visualize(id: 'str', value: 'Table') → Visualize

2 - Analytics and Query API

Query and analyze data logged to W&B.

2.1 - api

View the source code

module wandb.apis.public

Use the Public API to export or update data that you have saved to W&B.

Before using this API, you’ll want to log data from your script — check the Quickstart for more details.

You might use the Public API to

  • update metadata or metrics for an experiment after it has been completed,
  • pull down your results as a dataframe for post-hoc analysis in a Jupyter notebook, or
  • check your saved model artifacts for those tagged as ready-to-deploy.

For more on using the Public API, check out our guide.

class RetryingClient

method RetryingClient.__init__

__init__(client: wandb_gql.client.Client)

property RetryingClient.app_url

property RetryingClient.server_info

method RetryingClient.execute

execute(*args, **kwargs)

method RetryingClient.version_supported

version_supported(min_version: str) → bool

class Api

Used for querying the W&B server.

Examples:

import wandb

wandb.Api()

method Api.__init__

__init__(
    overrides: Optional[Dict[str, Any]] = None,
    timeout: Optional[int] = None,
    api_key: Optional[str] = None
) → None

Initialize the API.

Args:

  • overrides (dict[str, Any] | None): You can set base_url if you are
  • using a W&B server other than https: //api.wandb.ai. You can also set defaults for entity, project, and run.
  • timeout (int | None): HTTP timeout in seconds for API requests. If not specified, the default timeout will be used.
  • api_key (str | None): API key to use for authentication. If not provided, the API key from the current environment or configuration will be used.

property Api.api_key

Returns W&B API key.

property Api.client

Returns the client object.

property Api.default_entity

Returns the default W&B entity.

property Api.user_agent

Returns W&B public user agent.

property Api.viewer

Returns the viewer object.

method Api.artifact

artifact(name: str, type: Optional[str] = None)

Returns a single artifact.

Args:

  • name: The artifact’s name. The name of an artifact resembles a filepath that consists, at a minimum, the name of the project the artifact was logged to, the name of the artifact, and the artifact’s version or alias. Optionally append the entity that logged the artifact as a prefix followed by a forward slash. If no entity is specified in the name, the Run or API setting’s entity is used.
  • type: The type of artifact to fetch.

Returns: An Artifact object.

Raises:

  • ValueError: If the artifact name is not specified.
  • ValueError: If the artifact type is specified but does not match the type of the fetched artifact.

Examples: In the proceeding code snippets “entity”, “project”, “artifact”, “version”, and “alias” are placeholders for your W&B entity, name of the project the artifact is in, the name of the artifact, and artifact’s version, respectively.

import wandb

# Specify the project, artifact's name, and the artifact's alias
wandb.Api().artifact(name="project/artifact:alias")

# Specify the project, artifact's name, and a specific artifact version
wandb.Api().artifact(name="project/artifact:version")

# Specify the entity, project, artifact's name, and the artifact's alias
wandb.Api().artifact(name="entity/project/artifact:alias")

# Specify the entity, project, artifact's name, and a specific artifact version
wandb.Api().artifact(name="entity/project/artifact:version")

Note:

This method is intended for external use only. Do not call api.artifact() within the wandb repository code.

method Api.artifact_collection

artifact_collection(type_name: str, name: str) → public.ArtifactCollection

Returns a single artifact collection by type.

You can use the returned ArtifactCollection object to retrieve information about specific artifacts in that collection, and more.

Args:

  • type_name: The type of artifact collection to fetch.
  • name: An artifact collection name. Optionally append the entity that logged the artifact as a prefix followed by a forward slash.

Returns: An ArtifactCollection object.

Examples: In the proceeding code snippet “type”, “entity”, “project”, and “artifact_name” are placeholders for the collection type, your W&B entity, name of the project the artifact is in, and the name of the artifact, respectively.

import wandb

collections = wandb.Api().artifact_collection(
    type_name="type", name="entity/project/artifact_name"
)

# Get the first artifact in the collection
artifact_example = collections.artifacts()[0]

# Download the contents of the artifact to the specified root directory.
artifact_example.download()

method Api.artifact_collection_exists

artifact_collection_exists(name: str, type: str) → bool

Whether an artifact collection exists within a specified project and entity.

Args:

  • name: An artifact collection name. Optionally append the entity that logged the artifact as a prefix followed by a forward slash. If entity or project is not specified, infer the collection from the override params if they exist. Otherwise, entity is pulled from the user settings and project will default to “uncategorized”.
  • type: The type of artifact collection.

Returns: True if the artifact collection exists, False otherwise.

Examples: In the proceeding code snippet “type”, and “collection_name” refer to the type of the artifact collection and the name of the collection, respectively.

import wandb

wandb.Api.artifact_collection_exists(type="type", name="collection_name")

method Api.artifact_collections

artifact_collections(
    project_name: str,
    type_name: str,
    per_page: int = 50
) → public.ArtifactCollections

Returns a collection of matching artifact collections.

Args:

  • project_name: The name of the project to filter on.
  • type_name: The name of the artifact type to filter on.
  • per_page: Sets the page size for query pagination. None will use the default size. Usually there is no reason to change this.

Returns: An iterable ArtifactCollections object.

method Api.artifact_exists

artifact_exists(name: str, type: Optional[str] = None) → bool

Whether an artifact version exists within the specified project and entity.

Args:

  • name: The name of artifact. Add the artifact’s entity and project as a prefix. Append the version or the alias of the artifact with a colon. If the entity or project is not specified, W&B uses override parameters if populated. Otherwise, the entity is pulled from the user settings and the project is set to “Uncategorized”.
  • type: The type of artifact.

Returns: True if the artifact version exists, False otherwise.

Examples: In the proceeding code snippets “entity”, “project”, “artifact”, “version”, and “alias” are placeholders for your W&B entity, name of the project the artifact is in, the name of the artifact, and artifact’s version, respectively.

import wandb

wandb.Api().artifact_exists("entity/project/artifact:version")
wandb.Api().artifact_exists("entity/project/artifact:alias")

method Api.artifact_type

artifact_type(
    type_name: str,
    project: Optional[str] = None
) → public.ArtifactType

Returns the matching ArtifactType.

Args:

  • type_name: The name of the artifact type to retrieve.
  • project: If given, a project name or path to filter on.

Returns: An ArtifactType object.

method Api.artifact_types

artifact_types(project: Optional[str] = None) → public.ArtifactTypes

Returns a collection of matching artifact types.

Args:

  • project: The project name or path to filter on.

Returns: An iterable ArtifactTypes object.

method Api.artifact_versions

artifact_versions(type_name, name, per_page=50)

Deprecated. Use Api.artifacts(type_name, name) method instead.

method Api.artifacts

artifacts(
    type_name: str,
    name: str,
    per_page: int = 50,
    tags: Optional[List[str]] = None
) → public.Artifacts

Return an Artifacts collection.

Args: type_name: The type of artifacts to fetch. name: The artifact’s collection name. Optionally append the entity that logged the artifact as a prefix followed by a forward slash. per_page: Sets the page size for query pagination. If set to None, use the default size. Usually there is no reason to change this. tags: Only return artifacts with all of these tags.

Returns: An iterable Artifacts object.

Examples: In the proceeding code snippet, “type”, “entity”, “project”, and “artifact_name” are placeholders for the artifact type, W&B entity, name of the project the artifact was logged to, and the name of the artifact, respectively.

import wandb

wandb.Api().artifacts(type_name="type", name="entity/project/artifact_name")

method Api.automation

automation(name: str, entity: Optional[str] = None) → Automation

Returns the only Automation matching the parameters.

Args:

  • name: The name of the automation to fetch.
  • entity: The entity to fetch the automation for.

Raises:

  • ValueError: If zero or multiple Automations match the search criteria.

Examples: Get an existing automation named “my-automation”:

    import wandb

    api = wandb.Api()
    automation = api.automation(name="my-automation")
    ``` 

Get an existing automation named "other-automation", from the entity "my-team": 

```python
    automation = api.automation(name="other-automation", entity="my-team")
    ``` 

---

### <kbd>method</kbd> `Api.automations`

```python
automations(
    entity: Optional[str] = None,
    name: Optional[str] = None,
    per_page: int = 50
) → Iterator[ForwardRef('Automation')]

Returns an iterator over all Automations that match the given parameters.

If no parameters are provided, the returned iterator will contain all Automations that the user has access to.

Args:

  • entity: The entity to fetch the automations for.
  • name: The name of the automation to fetch.
  • per_page: The number of automations to fetch per page. Defaults to 50. Usually there is no reason to change this.

Returns: A list of automations.

Examples: Fetch all existing automations for the entity “my-team”:

    import wandb

    api = wandb.Api()
    automations = api.automations(entity="my-team")
    ``` 

---

### <kbd>method</kbd> `Api.create_automation`

```python
create_automation(
    obj: 'NewAutomation',
    fetch_existing: bool = False,
    **kwargs: typing_extensions.Unpack[ForwardRef('WriteAutomationsKwargs')]
) → Automation

Create a new Automation.

Args: obj: The automation to create. fetch_existing: If True, and a conflicting automation already exists, attempt to fetch the existing automation instead of raising an error. **kwargs: Any additional values to assign to the automation before creating it. If given, these will override any values that may already be set on the automation: - name: The name of the automation. - description: The description of the automation. - enabled: Whether the automation is enabled. - scope: The scope of the automation. - event: The event that triggers the automation. - action: The action that is triggered by the automation.

Returns: The saved Automation.

Examples: Create a new automation named “my-automation” that sends a Slack notification when a run within a specific project logs a metric exceeding a custom threshold:

     import wandb
     from wandb.automations import OnRunMetric, RunEvent, SendNotification

     api = wandb.Api()

     project = api.project("my-project", entity="my-team")

     # Use the first Slack integration for the team
     slack_hook = next(api.slack_integrations(entity="my-team"))

     event = OnRunMetric(
         scope=project,
         filter=RunEvent.metric("custom-metric") > 10,
     )
     action = SendNotification.from_integration(slack_hook)

     automation = api.create_automation(
         event >> action,
         name="my-automation",
         description="Send a Slack message whenever 'custom-metric' exceeds 10.",
     )
    ``` 

---

### <kbd>method</kbd> `Api.create_project`

```python
create_project(name: str, entity: str) → None

Create a new project.

Args:

  • name: The name of the new project.
  • entity: The entity of the new project.

method Api.create_registry

create_registry(
    name: str,
    visibility: Literal['organization', 'restricted'],
    organization: Optional[str] = None,
    description: Optional[str] = None,
    artifact_types: Optional[List[str]] = None
) → Registry

Create a new registry.

Args:

  • name: The name of the registry. Name must be unique within the organization.
  • visibility: The visibility of the registry.
  • organization: Anyone in the organization can view this registry. You can edit their roles later from the settings in the UI.
  • restricted: Only invited members via the UI can access this registry. Public sharing is disabled.
  • organization: The organization of the registry. If no organization is set in the settings, the organization will be fetched from the entity if the entity only belongs to one organization.
  • description: The description of the registry.
  • artifact_types: The accepted artifact types of the registry. A type is no
  • more than 128 characters and do not include characters /or ``:. If not specified, all types are accepted. Allowed types added to the registry cannot be removed later.

Returns: A registry object.

Examples:

   import wandb

   api = wandb.Api()
   registry = api.create_registry(
       name="my-registry",
       visibility="restricted",
       organization="my-org",
       description="This is a test registry",
       artifact_types=["model"],
   )
   ``` 

---

### <kbd>method</kbd> `Api.create_run`

```python
create_run(
   run_id: Optional[str] = None,
   project: Optional[str] = None,
   entity: Optional[str] = None
) → public.Run

Create a new run.

Args:

  • run_id: The ID to assign to the run. If not specified, W&B creates a random ID.
  • project: The project where to log the run to. If no project is specified, log the run to a project called “Uncategorized”.
  • entity: The entity that owns the project. If no entity is specified, log the run to the default entity.

Returns: The newly created Run.

method Api.create_run_queue

create_run_queue(
    name: str,
    type: 'public.RunQueueResourceType',
    entity: Optional[str] = None,
    prioritization_mode: Optional[ForwardRef('public.RunQueuePrioritizationMode')] = None,
    config: Optional[dict] = None,
    template_variables: Optional[dict] = None
) → public.RunQueue

Create a new run queue in W&B Launch.

Args:

  • name: Name of the queue to create
  • type: Type of resource to be used for the queue. One of “local-container”, “local-process”, “kubernetes”,“sagemaker”, or “gcp-vertex”.
  • entity: Name of the entity to create the queue. If None, use the configured or default entity.
  • prioritization_mode: Version of prioritization to use. Either “V0” or None.
  • config: Default resource configuration to be used for the queue. Use handlebars (eg. {{var}}) to specify template variables.
  • template_variables: A dictionary of template variable schemas to use with the config.

Returns: The newly created RunQueue.

Raises: ValueError if any of the parameters are invalid wandb.Error on wandb API errors

method Api.create_team

create_team(team: str, admin_username: Optional[str] = None) → public.Team

Create a new team.

Args:

  • team: The name of the team
  • admin_username: Username of the admin user of the team. Defaults to the current user.

Returns: A Team object.

method Api.create_user

create_user(email: str, admin: Optional[bool] = False)

Create a new user.

Args:

  • email: The email address of the user.
  • admin: Set user as a global instance administrator.

Returns: A User object.

method Api.delete_automation

delete_automation(obj: Union[ForwardRef('Automation'), str]) → Literal[True]

Delete an automation.

Args:

  • obj: The automation to delete, or its ID.

Returns: True if the automation was deleted successfully.

method Api.flush

flush()

Flush the local cache.

The api object keeps a local cache of runs, so if the state of the run may change while executing your script you must clear the local cache with api.flush() to get the latest values associated with the run.

method Api.from_path

from_path(path: str)

Return a run, sweep, project or report from a path.

Args:

  • path: The path to the project, run, sweep or report

Returns: A Project, Run, Sweep, or BetaReport instance.

Raises: wandb.Error if path is invalid or the object doesn’t exist.

Examples: In the proceeding code snippets “project”, “team”, “run_id”, “sweep_id”, and “report_name” are placeholders for the project, team, run ID, sweep ID, and the name of a specific report, respectively.

import wandb

api = wandb.Api()

project = api.from_path("project")
team_project = api.from_path("team/project")
run = api.from_path("team/project/runs/run_id")
sweep = api.from_path("team/project/sweeps/sweep_id")
report = api.from_path("team/project/reports/report_name")

method Api.integrations

integrations(
    entity: Optional[str] = None,
    per_page: int = 50
) → Iterator[ForwardRef('Integration')]

Return an iterator of all integrations for an entity.

Args:

  • entity: The entity (e.g. team name) for which to fetch integrations. If not provided, the user’s default entity will be used.
  • per_page: Number of integrations to fetch per page. Defaults to 50. Usually there is no reason to change this.

Yields:

  • Iterator[SlackIntegration | WebhookIntegration]: An iterator of any supported integrations.

method Api.job

job(name: Optional[str], path: Optional[str] = None) → public.Job

Return a Job object.

Args:

  • name: The name of the job.
  • path: The root path to download the job artifact.

Returns: A Job object.

method Api.list_jobs

list_jobs(entity: str, project: str) → List[Dict[str, Any]]

Return a list of jobs, if any, for the given entity and project.

Args:

  • entity: The entity for the listed jobs.
  • project: The project for the listed jobs.

Returns: A list of matching jobs.

method Api.project

project(name: str, entity: Optional[str] = None) → public.Project

Return the Project with the given name (and entity, if given).

Args:

  • name: The project name.
  • entity: Name of the entity requested. If None, will fall back to the default entity passed to Api. If no default entity, will raise a ValueError.

Returns: A Project object.

method Api.projects

projects(entity: Optional[str] = None, per_page: int = 200) → public.Projects

Get projects for a given entity.

Args:

  • entity: Name of the entity requested. If None, will fall back to the default entity passed to Api. If no default entity, will raise a ValueError.
  • per_page: Sets the page size for query pagination. If set to None, use the default size. Usually there is no reason to change this.

Returns: A Projects object which is an iterable collection of Projectobjects.

method Api.queued_run

queued_run(
    entity: str,
    project: str,
    queue_name: str,
    run_queue_item_id: str,
    project_queue=None,
    priority=None
)

Return a single queued run based on the path.

Parses paths of the form entity/project/queue_id/run_queue_item_id.

method Api.registries

registries(
    organization: Optional[str] = None,
    filter: Optional[Dict[str, Any]] = None
) → Registries

Returns a Registry iterator.

Use the iterator to search and filter registries, collections, or artifact versions across your organization’s registry.

Examples: Find all registries with the names that contain “model” ```python import wandb

 api = wandb.Api()  # specify an org if your entity belongs to multiple orgs
 api.registries(filter={"name": {"$regex": "model"}})
```

Find all collections in the registries with the name “my_collection” and the tag “my_tag” python api.registries().collections(filter={"name": "my_collection", "tag": "my_tag"})

Find all artifact versions in the registries with a collection name that contains “my_collection” and a version that has the alias “best” python api.registries().collections( filter={"name": {"$regex": "my_collection"}} ).versions(filter={"alias": "best"})

Find all artifact versions in the registries that contain “model” and have the tag “prod” or alias “best” python api.registries(filter={"name": {"$regex": "model"}}).versions( filter={"$or": [{"tag": "prod"}, {"alias": "best"}]} )

Args:

  • organization: (str, optional) The organization of the registry to fetch. If not specified, use the organization specified in the user’s settings.
  • filter: (dict, optional) MongoDB-style filter to apply to each object in the registry iterator. Fields available to filter for collections are name, description, created_at, updated_at. Fields available to filter for collections are name, tag, description, created_at, updated_at Fields available to filter for versions are tag, alias, created_at, updated_at, metadata

Returns: A registry iterator.

method Api.registry

registry(name: str, organization: Optional[str] = None) → Registry

Return a registry given a registry name.

Args:

  • name: The name of the registry. This is without the wandb-registry- prefix.
  • organization: The organization of the registry. If no organization is set in the settings, the organization will be fetched from the entity if the entity only belongs to one organization.

Returns: A registry object.

Examples: Fetch and update a registry ```python import wandb

api = wandb.Api()
registry = api.registry(name="my-registry", organization="my-org")
registry.description = "This is an updated description"
registry.save()
```

method Api.reports

reports(
    path: str = '',
    name: Optional[str] = None,
    per_page: int = 50
) → public.Reports

Get reports for a given project path.

Note: wandb.Api.reports() API is in beta and will likely change in future releases.

Args:

  • path: The path to project the report resides in. Specify the entity that created the project as a prefix followed by a forward slash.
  • name: Name of the report requested.
  • per_page: Sets the page size for query pagination. If set to None, use the default size. Usually there is no reason to change this.

Returns: A Reports object which is an iterable collection of BetaReport objects.

Examples:

import wandb

wandb.Api.reports("entity/project")

method Api.run

run(path='')

Return a single run by parsing path in the form entity/project/run_id.

Args:

  • path: Path to run in the form entity/project/run_id. If api.entity is set, this can be in the form project/run_id and if api.project is set this can just be the run_id.

Returns: A Run object.

method Api.run_queue

run_queue(entity: str, name: str)

Return the named RunQueue for entity.

See Api.create_run_queue for more information on how to create a run queue.

method Api.runs

runs(
    path: Optional[str] = None,
    filters: Optional[Dict[str, Any]] = None,
    order: str = '+created_at',
    per_page: int = 50,
    include_sweeps: bool = True
)

Return a set of runs from a project that match the filters provided.

Fields you can filter by include:

  • createdAt: The timestamp when the run was created. (in ISO 8601 format, e.g. “2023-01-01T12:00:00Z”)
  • displayName: The human-readable display name of the run. (e.g. “eager-fox-1”)
  • duration: The total runtime of the run in seconds.
  • group: The group name used to organize related runs together.
  • host: The hostname where the run was executed.
  • jobType: The type of job or purpose of the run.
  • name: The unique identifier of the run. (e.g. “a1b2cdef”)
  • state: The current state of the run.
  • tags: The tags associated with the run.
  • username: The username of the user who initiated the run

Additionally, you can filter by items in the run config or summary metrics. Such as config.experiment_name, summary_metrics.loss, etc.

For more complex filtering, you can use MongoDB query operators. For details, see: https://docs.mongodb.com/manual/reference/operator/query The following operations are supported:

  • $and
  • $or
  • $nor
  • $eq
  • $ne
  • $gt
  • $gte
  • $lt
  • $lte
  • $in
  • $nin
  • $exists
  • $regex

Args:

  • path: (str) path to project, should be in the form: “entity/project”
  • filters: (dict) queries for specific runs using the MongoDB query language. You can filter by run properties such as config.key, summary_metrics.key, state, entity, createdAt, etc.
  • For example: {"config.experiment_name": "foo"} would find runs with a config entry of experiment name set to “foo”
  • order: (str) Order can be created_at, heartbeat_at, config.*.value, or summary_metrics.*. If you prepend order with a + order is ascending. If you prepend order with a - order is descending (default). The default order is run.created_at from oldest to newest.
  • per_page: (int) Sets the page size for query pagination.
  • include_sweeps: (bool) Whether to include the sweep runs in the results.

Returns: A Runs object, which is an iterable collection of Run objects.

Examples:

# Find runs in project where config.experiment_name has been set to "foo"
api.runs(path="my_entity/project", filters={"config.experiment_name": "foo"})

# Find runs in project where config.experiment_name has been set to "foo" or "bar"
api.runs(
    path="my_entity/project",
    filters={
         "$or": [
             {"config.experiment_name": "foo"},
             {"config.experiment_name": "bar"},
         ]
    },
)

# Find runs in project where config.experiment_name matches a regex
# (anchors are not supported)
api.runs(
    path="my_entity/project",
    filters={"config.experiment_name": {"$regex": "b.*"}},
)

# Find runs in project where the run name matches a regex
# (anchors are not supported)
api.runs(
    path="my_entity/project", filters={"display_name": {"$regex": "^foo.*"}}
)

# Find runs in project sorted by ascending loss
api.runs(path="my_entity/project", order="+summary_metrics.loss")

method Api.slack_integrations

slack_integrations(
    entity: Optional[str] = None,
    per_page: int = 50
) → Iterator[ForwardRef('SlackIntegration')]

Returns an iterator of Slack integrations for an entity.

Args:

  • entity: The entity (e.g. team name) for which to fetch integrations. If not provided, the user’s default entity will be used.
  • per_page: Number of integrations to fetch per page. Defaults to 50. Usually there is no reason to change this.

Yields:

  • Iterator[SlackIntegration]: An iterator of Slack integrations.

Examples: Get all registered Slack integrations for the team “my-team”: ```python import wandb

api = wandb.Api()
slack_integrations = api.slack_integrations(entity="my-team")
```

Find only Slack integrations that post to channel names starting with “team-alerts-”: python slack_integrations = api.slack_integrations(entity="my-team") team_alert_integrations = [ ig for ig in slack_integrations if ig.channel_name.startswith("team-alerts-") ]

method Api.sweep

sweep(path='')

Return a sweep by parsing path in the form entity/project/sweep_id.

Args:

  • path: Path to sweep in the form entity/project/sweep_id. If api.entity is set, this can be in the form project/sweep_id and if api.project is set this can just be the sweep_id.

Returns: A Sweep object.

method Api.sync_tensorboard

sync_tensorboard(root_dir, run_id=None, project=None, entity=None)

Sync a local directory containing tfevent files to wandb.

method Api.team

team(team: str) → public.Team

Return the matching Team with the given name.

Args:

  • team: The name of the team.

Returns: A Team object.

method Api.update_automation

update_automation(
    obj: 'Automation',
    create_missing: bool = False,
    **kwargs: typing_extensions.Unpack[ForwardRef('WriteAutomationsKwargs')]
) → Automation

Update an existing automation.

Args:

  • obj: The automation to update. Must be an existing automation. create_missing (bool): If True, and the automation does not exist, create it. **kwargs: Any additional values to assign to the automation before updating it. If given, these will override any values that may already be set on the automation: - name: The name of the automation. - description: The description of the automation. - enabled: Whether the automation is enabled. - scope: The scope of the automation. - event: The event that triggers the automation. - action: The action that is triggered by the automation.

Returns: The updated automation.

Examples: Disable and edit the description of an existing automation (“my-automation”):

    import wandb

    api = wandb.Api()

    automation = api.automation(name="my-automation")
    automation.enabled = False
    automation.description = "Kept for reference, but no longer used."

    updated_automation = api.update_automation(automation)
    ``` 

OR: 

```python
    import wandb

    api = wandb.Api()

    automation = api.automation(name="my-automation")

    updated_automation = api.update_automation(
         automation,
         enabled=False,
         description="Kept for reference, but no longer used.",
    )
    ``` 

---

### <kbd>method</kbd> `Api.upsert_run_queue`

```python
upsert_run_queue(
    name: str,
    resource_config: dict,
    resource_type: 'public.RunQueueResourceType',
    entity: Optional[str] = None,
    template_variables: Optional[dict] = None,
    external_links: Optional[dict] = None,
    prioritization_mode: Optional[ForwardRef('public.RunQueuePrioritizationMode')] = None
)

Upsert a run queue in W&B Launch.

Args:

  • name: Name of the queue to create
  • entity: Optional name of the entity to create the queue. If None, use the configured or default entity.
  • resource_config: Optional default resource configuration to be used for the queue. Use handlebars (eg. {{var}}) to specify template variables.
  • resource_type: Type of resource to be used for the queue. One of “local-container”, “local-process”, “kubernetes”, “sagemaker”, or “gcp-vertex”.
  • template_variables: A dictionary of template variable schemas to be used with the config.
  • external_links: Optional dictionary of external links to be used with the queue.
  • prioritization_mode: Optional version of prioritization to use. Either “V0” or None

Returns: The upserted RunQueue.

Raises: ValueError if any of the parameters are invalid wandb.Error on wandb API errors

method Api.user

user(username_or_email: str) → Optional[ForwardRef('public.User')]

Return a user from a username or email address.

This function only works for local administrators. Use api.viewer to get your own user object.

Args:

  • username_or_email: The username or email address of the user.

Returns: A User object or None if a user is not found.

method Api.users

users(username_or_email: str) → List[ForwardRef('public.User')]

Return all users from a partial username or email address query.

This function only works for local administrators. Use api.viewer to get your own user object.

Args:

  • username_or_email: The prefix or suffix of the user you want to find.

Returns: An array of User objects.

method Api.webhook_integrations

webhook_integrations(
    entity: Optional[str] = None,
    per_page: int = 50
) → Iterator[ForwardRef('WebhookIntegration')]

Returns an iterator of webhook integrations for an entity.

Args:

  • entity: The entity (e.g. team name) for which to fetch integrations. If not provided, the user’s default entity will be used.
  • per_page: Number of integrations to fetch per page. Defaults to 50. Usually there is no reason to change this.

Yields:

  • Iterator[WebhookIntegration]: An iterator of webhook integrations.

Examples: Get all registered webhook integrations for the team “my-team”: ```python import wandb

api = wandb.Api()
webhook_integrations = api.webhook_integrations(entity="my-team")
```

Find only webhook integrations that post requests to “https://my-fake-url.com”: python webhook_integrations = api.webhook_integrations(entity="my-team") my_webhooks = [ ig for ig in webhook_integrations if ig.url_endpoint.startswith("https://my-fake-url.com") ]

2.2 - artifacts

View the source code

module wandb.apis.public

W&B Public API for Artifact objects.

This module provides classes for interacting with W&B artifacts and their collections.

function server_supports_artifact_collections_gql_edges

server_supports_artifact_collections_gql_edges(
    client: 'RetryingClient',
    warn: 'bool' = False
) → bool

Check if W&B server supports GraphQL edges for artifact collections.

class ArtifactTypes

method ArtifactTypes.__init__

__init__(client: 'Client', entity: 'str', project: 'str', per_page: 'int' = 50)

property ArtifactTypes.cursor

Returns the cursor for the next page of results.

property ArtifactTypes.length

Returns None.

property ArtifactTypes.more

Returns whether there are more artifact types to fetch.

method ArtifactTypes.convert_objects

convert_objects() → list[ArtifactType]

Convert the raw response data into a list of ArtifactType objects.

method ArtifactTypes.update_variables

update_variables() → None

Update the cursor variable for pagination.

class ArtifactType

An artifact object that satisfies query based on the specified type.

Args:

  • client: The client instance to use for querying W&B.
  • entity: The entity (user or team) that owns the project.
  • project: The name of the project to query for artifact types.
  • type_name: The name of the artifact type.
  • attrs: Optional mapping of attributes to initialize the artifact type. If not provided, the object will load its attributes from W&B upon initialization.

method ArtifactType.__init__

__init__(
    client: 'Client',
    entity: 'str',
    project: 'str',
    type_name: 'str',
    attrs: 'Mapping[str, Any] | None' = None
)

property ArtifactType.id

The unique identifier of the artifact type.

property ArtifactType.name

The name of the artifact type.

method ArtifactType.collection

collection(name: 'str') → ArtifactCollection

Get a specific artifact collection by name.

Args:

  • name (str): The name of the artifact collection to retrieve.

method ArtifactType.collections

collections(per_page: 'int' = 50) → ArtifactCollections

Get all artifact collections associated with this artifact type.

Args:

  • per_page (int): The number of artifact collections to fetch per page. Default is 50.

method ArtifactType.load

load() → Mapping[str, Any]

Load the artifact type attributes from W&B.

class ArtifactCollections

Artifact collections of a specific type in a project.

Args:

  • client: The client instance to use for querying W&B.
  • entity: The entity (user or team) that owns the project.
  • project: The name of the project to query for artifact collections.
  • type_name: The name of the artifact type for which to fetch collections.
  • per_page: The number of artifact collections to fetch per page. Default is 50.

method ArtifactCollections.__init__

__init__(
    client: 'Client',
    entity: 'str',
    project: 'str',
    type_name: 'str',
    per_page: 'int' = 50
)

property ArtifactCollections.cursor

Returns the cursor for the next page of results.

property ArtifactCollections.length

property ArtifactCollections.more

Returns whether there are more artifacts to fetch.

method ArtifactCollections.convert_objects

convert_objects() → list[ArtifactCollection]

Convert the raw response data into a list of ArtifactCollection objects.

method ArtifactCollections.update_variables

update_variables() → None

Update the cursor variable for pagination.

class ArtifactCollection

An artifact collection that represents a group of related artifacts.

Args:

  • client: The client instance to use for querying W&B.
  • entity: The entity (user or team) that owns the project.
  • project: The name of the project to query for artifact collections.
  • name: The name of the artifact collection.
  • type: The type of the artifact collection (e.g., “dataset”, “model”).
  • organization: Optional organization name if applicable.
  • attrs: Optional mapping of attributes to initialize the artifact collection. If not provided, the object will load its attributes from W&B upon initialization.

method ArtifactCollection.__init__

__init__(
    client: 'Client',
    entity: 'str',
    project: 'str',
    name: 'str',
    type: 'str',
    organization: 'str | None' = None,
    attrs: 'Mapping[str, Any] | None' = None,
    is_sequence: 'bool | None' = None
)

property ArtifactCollection.aliases

Artifact Collection Aliases.

property ArtifactCollection.created_at

The creation date of the artifact collection.

property ArtifactCollection.description

A description of the artifact collection.

property ArtifactCollection.id

The unique identifier of the artifact collection.

property ArtifactCollection.name

The name of the artifact collection.

property ArtifactCollection.tags

The tags associated with the artifact collection.

property ArtifactCollection.type

Returns the type of the artifact collection.

method ArtifactCollection.artifacts

artifacts(per_page: 'int' = 50) → Artifacts

Get all artifacts in the collection.

method ArtifactCollection.change_type

change_type(new_type: 'str') → None

Deprecated, change type directly with save instead.

method ArtifactCollection.delete

delete() → None

Delete the entire artifact collection.

method ArtifactCollection.is_sequence

is_sequence() → bool

Return whether the artifact collection is a sequence.

method ArtifactCollection.load

load()

Load the artifact collection attributes from W&B.

method ArtifactCollection.save

save() → None

Persist any changes made to the artifact collection.

class Artifacts

An iterable collection of artifact versions associated with a project.

Optionally pass in filters to narrow down the results based on specific criteria.

Args:

  • client: The client instance to use for querying W&B.
  • entity: The entity (user or team) that owns the project.
  • project: The name of the project to query for artifacts.
  • collection_name: The name of the artifact collection to query.
  • type: The type of the artifacts to query. Common examples include “dataset” or “model”.
  • filters: Optional mapping of filters to apply to the query.
  • order: Optional string to specify the order of the results.
  • per_page: The number of artifact versions to fetch per page. Default is 50.
  • tags: Optional string or list of strings to filter artifacts by tags.

method Artifacts.__init__

__init__(
    client: 'Client',
    entity: 'str',
    project: 'str',
    collection_name: 'str',
    type: 'str',
    filters: 'Mapping[str, Any] | None' = None,
    order: 'str | None' = None,
    per_page: 'int' = 50,
    tags: 'str | list[str] | None' = None
)

property Artifacts.cursor

Returns the cursor for the next page of results.

property Artifacts.length

Returns the total number of artifacts in the collection.

property Artifacts.more

Returns whether there are more files to fetch.

method Artifacts.convert_objects

convert_objects() → list[Artifact]

Convert the raw response data into a list of wandb.Artifact objects.

class RunArtifacts

method RunArtifacts.__init__

__init__(
    client: 'Client',
    run: 'Run',
    mode: "Literal['logged', 'used']" = 'logged',
    per_page: 'int' = 50
)

property RunArtifacts.cursor

Returns the cursor for the next page of results.

property RunArtifacts.length

Returns the total number of artifacts in the collection.

property RunArtifacts.more

Returns whether there are more artifacts to fetch.

method RunArtifacts.convert_objects

convert_objects() → list[Artifact]

Convert the raw response data into a list of wandb.Artifact objects.

class ArtifactFiles

method ArtifactFiles.__init__

__init__(
    client: 'Client',
    artifact: 'Artifact',
    names: 'Sequence[str] | None' = None,
    per_page: 'int' = 50
)

property ArtifactFiles.cursor

Returns the cursor for the next page of results.

property ArtifactFiles.length

Returns the total number of files in the artifact.

property ArtifactFiles.more

Returns whether there are more files to fetch.

property ArtifactFiles.path

Returns the path of the artifact.

method ArtifactFiles.convert_objects

convert_objects() → list[public.File]

Convert the raw response data into a list of public.File objects.

method ArtifactFiles.update_variables

update_variables() → None

Update the variables dictionary with the cursor.

2.3 - automations

View the source code

module wandb.apis.public

W&B Public API for Automation objects.

class Automations

An iterable collection of Automation objects.

method Automations.__init__

__init__(
    client: '_Client',
    variables: 'Mapping[str, Any]',
    per_page: 'int' = 50,
    _query: 'Document | None' = None
)

property Automations.cursor

The start cursor to use for the next page.

property Automations.more

Whether there are more items to fetch.

method Automations.convert_objects

convert_objects() → Iterable[Automation]

Parse the page data into a list of objects.

2.4 - files

View the source code

module wandb.apis.public

W&B Public API for File objects.

This module provides classes for interacting with files stored in W&B.

Example:

from wandb.apis.public import Api

# Initialize API
api = Api()

# Get files from a specific run
run = api.run("entity/project/run_id")
files = run.files()

# Work with files
for file in files:
    print(f"File: {file.name}")
    print(f"Size: {file.size} bytes")
    print(f"Type: {file.mimetype}")

    # Download file
    if file.size < 1000000:  # Less than 1MB
        file.download(root="./downloads")

    # Get S3 URI for large files
    if file.size >= 1000000:
        print(f"S3 URI: {file.path_uri}")

Note:

This module is part of the W&B Public API and provides methods to access, download, and manage files stored in W&B. Files are typically associated with specific runs and can include model weights, datasets, visualizations, and other artifacts.

class Files

An iterable collection of File objects.

Access and manage files uploaded to W&B during a run. Handles pagination automatically when iterating through large collections of files.

Args:

  • client: The run object that contains the files
  • run: The run object that contains the files
  • names (list, optional): A list of file names to filter the files
  • per_page (int, optional): The number of files to fetch per page
  • upload (bool, optional): If True, fetch the upload URL for each file

Example:

from wandb.apis.public.files import Files
from wandb.apis.public.api import Api

# Initialize the API client
api = Api()

# Example run object
run = api.run("entity/project/run-id")

# Create a Files object to iterate over files in the run
files = Files(api.client, run)

# Iterate over files
for file in files:
   print(file.name)
   print(file.url)
   print(file.size)

   # Download the file
   file.download(root="download_directory", replace=True)

method Files.__init__

__init__(client, run, names=None, per_page=50, upload=False)

property Files.cursor

Returns the cursor position for pagination of file results.

property Files.length

The number of files saved to the specified run.

property Files.more

Returns whether there are more files to fetch.

method Files.convert_objects

convert_objects()

Converts GraphQL edges to File objects.

method Files.update_variables

update_variables()

Updates the GraphQL query variables for pagination.

class File

File saved to W&B.

Represents a single file stored in W&B. Includes access to file metadata. Files are associated with a specific run and can include text files, model weights, datasets, visualizations, and other artifacts. You can download the file, delete the file, and access file properties.

Specify one or more attributes in a dictionary to fine a specific file logged to a specific run. You can search using the following keys:

  • id (str): The ID of the run that contains the file
  • name (str): Name of the file
  • url (str): path to file
  • direct_url (str): path to file in the bucket
  • sizeBytes (int): size of file in bytes
  • md5 (str): md5 of file
  • mimetype (str): mimetype of file
  • updated_at (str): timestamp of last update
  • path_uri (str): path to file in the bucket, currently only available for files stored in S3

Args:

  • client: The run object that contains the file
  • attrs (dict): A dictionary of attributes that define the file
  • run: The run object that contains the file

Example:

from wandb.apis.public.files import File
from wandb.apis.public.api import Api

# Initialize the API client
api = Api()

# Example attributes dictionary
file_attrs = {
   "id": "file-id",
   "name": "example_file.txt",
   "url": "https://example.com/file",
   "direct_url": "https://example.com/direct_file",
   "sizeBytes": 1024,
   "mimetype": "text/plain",
   "updated_at": "2025-03-25T21:43:51Z",
   "md5": "d41d8cd98f00b204e9800998ecf8427e",
}

# Example run object
run = api.run("entity/project/run-id")

# Create a File object
file = File(api.client, file_attrs, run)

# Access some of the attributes
print("File ID:", file.id)
print("File Name:", file.name)
print("File URL:", file.url)
print("File MIME Type:", file.mimetype)
print("File Updated At:", file.updated_at)

# Access File properties
print("File Size:", file.size)
print("File Path URI:", file.path_uri)

# Download the file
file.download(root="download_directory", replace=True)

# Delete the file
file.delete()

method File.__init__

__init__(client, attrs, run=None)

property File.path_uri

Returns the URI path to the file in the storage bucket.

property File.size

Returns the size of the file in bytes.

method File.delete

delete()

Delete the file from the W&B server.

method File.download

download(
    root: str = '.',
    replace: bool = False,
    exist_ok: bool = False,
    api: Optional[wandb.apis.public.api.Api] = None
) → TextIOWrapper

Downloads a file previously saved by a run from the wandb server.

Args:

  • root: Local directory to save the file. Defaults to “.”.
  • replace: If True, download will overwrite a local file if it exists. Defaults to False.
  • exist_ok: If True, will not raise ValueError if file already exists and will not re-download unless replace=True. Defaults to False.
  • api: If specified, the Api instance used to download the file.

Raises: ValueError if file already exists, replace=False and exist_ok=False.

2.5 - history

View the source code

module wandb.apis.public

W&B Public API for Run History.

This module provides classes for efficiently scanning and sampling run history data.

Note:

This module is part of the W&B Public API and provides methods to access run history data. It handles pagination automatically and offers both complete and sampled access to metrics logged during training runs.

class HistoryScan

Iterator for scanning complete run history.

Args:

  • client: (wandb.apis.internal.Api) The client instance to use
  • run: (wandb.sdk.internal.Run) The run object to scan history for
  • min_step: (int) The minimum step to start scanning from
  • max_step: (int) The maximum step to scan up to
  • page_size: (int) Number of samples per page (default is 1000)

method HistoryScan.__init__

__init__(client, run, min_step, max_step, page_size=1000)

class SampledHistoryScan

Iterator for sampling run history data.

Args:

  • client: (wandb.apis.internal.Api) The client instance to use
  • run: (wandb.sdk.internal.Run) The run object to sample history from
  • keys: (list) List of keys to sample from the history
  • min_step: (int) The minimum step to start sampling from
  • max_step: (int) The maximum step to sample up to
  • page_size: (int) Number of samples per page (default is 1000)

method SampledHistoryScan.__init__

__init__(client, run, keys, min_step, max_step, page_size=1000)

2.6 - integrations

View the source code

module wandb.apis.public

W&B Public API for integrations.

This module provides classes for interacting with W&B integrations.

class Integrations

method Integrations.__init__

__init__(client: '_Client', variables: 'dict[str, Any]', per_page: 'int' = 50)

property Integrations.cursor

The start cursor to use for the next page.

property Integrations.more

Whether there are more Integrations to fetch.

method Integrations.convert_objects

convert_objects() → Iterable[Integration]

Parse the page data into a list of integrations.

class WebhookIntegrations

method WebhookIntegrations.__init__

__init__(client: '_Client', variables: 'dict[str, Any]', per_page: 'int' = 50)

property WebhookIntegrations.cursor

The start cursor to use for the next page.

property WebhookIntegrations.more

Whether there are more webhook integrations to fetch.

method WebhookIntegrations.convert_objects

convert_objects() → Iterable[WebhookIntegration]

Parse the page data into a list of webhook integrations.

class SlackIntegrations

method SlackIntegrations.__init__

__init__(client: '_Client', variables: 'dict[str, Any]', per_page: 'int' = 50)

property SlackIntegrations.cursor

The start cursor to use for the next page.

property SlackIntegrations.more

Whether there are more Slack integrations to fetch.

method SlackIntegrations.convert_objects

convert_objects() → Iterable[SlackIntegration]

Parse the page data into a list of Slack integrations.

2.7 - jobs

View the source code

module wandb.apis.public

W&B Public API for management Launch Jobs and Launch Queues.

This module provides classes for managing W&B jobs, queued runs, and run queues.

class Job

method Job.__init__

__init__(api: 'Api', name, path: Optional[str] = None) → None

property Job.name

The name of the job.

method Job.call

call(
    config,
    project=None,
    entity=None,
    queue=None,
    resource='local-container',
    resource_args=None,
    template_variables=None,
    project_queue=None,
    priority=None
)

Call the job with the given configuration.

Args:

  • config (dict): The configuration to pass to the job. This should be a dictionary containing key-value pairs that match the input types defined in the job.
  • project (str, optional): The project to log the run to. Defaults to the job’s project.
  • entity (str, optional): The entity to log the run under. Defaults to the job’s entity.
  • queue (str, optional): The name of the queue to enqueue the job to. Defaults to None.
  • resource (str, optional): The resource type to use for execution. Defaults to “local-container”.
  • resource_args (dict, optional): Additional arguments for the resource type. Defaults to None.
  • template_variables (dict, optional): Template variables to use for the job. Defaults to None.
  • project_queue (str, optional): The project that manages the queue. Defaults to None.
  • priority (int, optional): The priority of the queued run. Defaults to None.

method Job.set_entrypoint

set_entrypoint(entrypoint: List[str])

Set the entrypoint for the job.

class QueuedRun

A single queued run associated with an entity and project.

Args:

  • entity: The entity associated with the queued run.
  • project (str): The project where runs executed by the queue are logged to.
  • queue_name (str): The name of the queue.
  • run_queue_item_id (int): The id of the run queue item.
  • project_queue (str): The project that manages the queue.
  • priority (str): The priority of the queued run.

Call run = queued_run.wait_until_running() or run = queued_run.wait_until_finished() to access the run.

method QueuedRun.__init__

__init__(
    client,
    entity,
    project,
    queue_name,
    run_queue_item_id,
    project_queue='model-registry',
    priority=None
)

property QueuedRun.entity

The entity associated with the queued run.

property QueuedRun.id

The id of the queued run.

property QueuedRun.project

The project associated with the queued run.

property QueuedRun.queue_name

The name of the queue.

property QueuedRun.state

The state of the queued run.

method QueuedRun.delete

delete(delete_artifacts=False)

Delete the given queued run from the wandb backend.

method QueuedRun.wait_until_finished

wait_until_finished()

Wait for the queued run to complete and return the finished run.

method QueuedRun.wait_until_running

wait_until_running()

Wait until the queued run is running and return the run.

class RunQueue

Class that represents a run queue in W&B.

Args:

  • client: W&B API client instance.
  • name: Name of the run queue
  • entity: The entity (user or team) that owns this queue
  • prioritization_mode: Queue priority mode Can be “DISABLED” or “V0”. Defaults to None.
  • _access: Access level for the queue Can be “project” or “user”. Defaults to None.
  • _default_resource_config_id: ID of default resource config
  • _default_resource_config: Default resource configuration

method RunQueue.__init__

__init__(
    client: 'RetryingClient',
    name: str,
    entity: str,
    prioritization_mode: Optional[Literal['DISABLED', 'V0']] = None,
    _access: Optional[Literal['project', 'user']] = None,
    _default_resource_config_id: Optional[int] = None,
    _default_resource_config: Optional[dict] = None
) → None

property RunQueue.access

The access level of the queue.

property RunQueue.default_resource_config

The default configuration for resources.

property RunQueue.entity

The entity that owns the queue.

External resource links for the queue.

property RunQueue.id

The id of the queue.

property RunQueue.items

Up to the first 100 queued runs. Modifying this list will not modify the queue or any enqueued items!

property RunQueue.name

The name of the queue.

property RunQueue.prioritization_mode

The prioritization mode of the queue.

Can be set to “DISABLED” or “V0”.

property RunQueue.template_variables

Variables for resource templates.

property RunQueue.type

The resource type for execution.

classmethod RunQueue.create

create(
    name: str,
    resource: 'RunQueueResourceType',
    entity: Optional[str] = None,
    prioritization_mode: Optional[ForwardRef('RunQueuePrioritizationMode')] = None,
    config: Optional[dict] = None,
    template_variables: Optional[dict] = None
) → RunQueue

Create a RunQueue.

Args:

  • name: The name of the run queue to create.
  • resource: The resource type for execution.
  • entity: The entity (user or team) that will own the queue. Defaults to the default entity of the API client.
  • prioritization_mode: The prioritization mode for the queue. Can be “DISABLED” or “V0”. Defaults to None.
  • config: Optional dictionary for the default resource configuration. Defaults to None.
  • template_variables: Optional dictionary for template variables used in the resource configuration.

method RunQueue.delete

delete()

Delete the run queue from the wandb backend.

2.8 - projects

View the source code

module wandb.apis.public

W&B Public API for Project objects.

This module provides classes for interacting with W&B projects and their associated data.

Example:

from wandb.apis.public import Api

# Initialize API
api = Api()

# Get all projects for an entity
projects = api.projects("entity")

# Access project data
for project in projects:
    print(f"Project: {project.name}")
    print(f"URL: {project.url}")

    # Get artifact types
    for artifact_type in project.artifacts_types():
        print(f"Artifact Type: {artifact_type.name}")

    # Get sweeps
    for sweep in project.sweeps():
        print(f"Sweep ID: {sweep.id}")
        print(f"State: {sweep.state}")

Note:

This module is part of the W&B Public API and provides methods to access and manage projects. For creating new projects, use wandb.init() with a new project name.

class Projects

An iterable collection of Project objects.

An iterable interface to access projects created and saved by the entity.

Args:

  • client (wandb.apis.internal.Api): The API client instance to use.
  • entity (str): The entity name (username or team) to fetch projects for.
  • per_page (int): Number of projects to fetch per request (default is 50).

Example:

from wandb.apis.public.api import Api

# Initialize the API client
api = Api()

# Find projects that belong to this entity
projects = api.projects(entity="entity")

# Iterate over files
for project in projects:
   print(f"Project: {project.name}")
   print(f"- URL: {project.url}")
   print(f"- Created at: {project.created_at}")
   print(f"- Is benchmark: {project.is_benchmark}")

method Projects.__init__

__init__(client, entity, per_page=50)

property Projects.cursor

Returns the cursor position for pagination of project results.

property Projects.length

Returns the total number of projects.

Note: This property is not available for projects.

property Projects.more

Returns True if there are more projects to fetch. Returns False if there are no more projects to fetch.

method Projects.convert_objects

convert_objects()

Converts GraphQL edges to File objects.

class Project

A project is a namespace for runs.

Args:

  • client: W&B API client instance.
  • name (str): The name of the project.
  • entity (str): The entity name that owns the project.

method Project.__init__

__init__(client, entity, project, attrs)

property Project.id

property Project.path

Returns the path of the project. The path is a list containing the entity and project name.

property Project.url

Returns the URL of the project.

method Project.artifacts_types

artifacts_types(per_page=50)

Returns all artifact types associated with this project.

method Project.sweeps

sweeps()

Fetches all sweeps associated with the project.

2.9 - query_generator

View the source code

module wandb.apis.public

method QueryGenerator.filter_to_mongo

filter_to_mongo(filter)

Returns dictionary with filter format converted to MongoDB filter.

classmethod QueryGenerator.format_order_key

format_order_key(key: str)

Format a key for sorting.

method QueryGenerator.key_to_server_path

key_to_server_path(key)

Convert a key dictionary to the corresponding server path string.

method QueryGenerator.keys_to_order

keys_to_order(keys)

Convert a list of key dictionaries to an order string.

method QueryGenerator.mongo_to_filter

mongo_to_filter(filter)

Returns dictionary with MongoDB filter converted to filter format.

method QueryGenerator.order_to_keys

order_to_keys(order)

Convert an order string to a list of key dictionaries.

method QueryGenerator.server_path_to_key

server_path_to_key(path)

Convert a server path string to the corresponding key dictionary.

2.10 - reports

View the source code

module wandb.apis.public

W&B Public API for Report objects.

This module provides classes for interacting with W&B reports and managing report-related data.

class Reports

Reports is an iterable collection of BetaReport objects.

Args:

  • client (wandb.apis.internal.Api): The API client instance to use.
  • project (wandb.sdk.internal.Project): The project to fetch reports from.
  • name (str, optional): The name of the report to filter by. If None, fetches all reports.
  • entity (str, optional): The entity name for the project. Defaults to the project entity.
  • per_page (int): Number of reports to fetch per page (default is 50).

method Reports.__init__

__init__(client, project, name=None, entity=None, per_page=50)

property Reports.cursor

Returns the cursor position for pagination of file results.

property Reports.length

The number of reports in the project.

property Reports.more

Returns whether there are more files to fetch.

method Reports.convert_objects

convert_objects()

Converts GraphQL edges to File objects.

method Reports.update_variables

update_variables()

Updates the GraphQL query variables for pagination.

class BetaReport

BetaReport is a class associated with reports created in W&B.

WARNING: this API will likely change in a future release

Attributes:

  • name (string): The name of the report.
  • description (string): Report description.
  • user (User): The user that created the report.
  • spec (dict): The spec off the report.
  • updated_at (string): timestamp of last update.

method BetaReport.__init__

__init__(client, attrs, entity=None, project=None)

property BetaReport.sections

Get the panel sections (groups) from the report.

property BetaReport.updated_at

Timestamp of last update

property BetaReport.url

URL of the report.

Contains the entity, project, display name, and id.

method BetaReport.runs

runs(section, per_page=50, only_selected=True)

Get runs associated with a section of the report.

method BetaReport.to_html

to_html(height=1024, hidden=False)

Generate HTML containing an iframe displaying this report.

2.11 - runs

View the source code

module wandb.apis.public

W&B Public API for Runs.

This module provides classes for interacting with W&B runs and their associated data.

Example:

from wandb.apis.public import Api

# Initialize API
api = Api()

# Get runs matching filters
runs = api.runs(
    path="entity/project", filters={"state": "finished", "config.batch_size": 32}
)

# Access run data
for run in runs:
    print(f"Run: {run.name}")
    print(f"Config: {run.config}")
    print(f"Metrics: {run.summary}")

    # Get history with pandas
    history_df = run.history(keys=["loss", "accuracy"], pandas=True)

    # Work with artifacts
    for artifact in run.logged_artifacts():
        print(f"Artifact: {artifact.name}")

Note:

This module is part of the W&B Public API and provides read/write access to run data. For logging new runs, use the wandb.init() function from the main wandb package.

class Runs

An iterable collection of runs associated with a project and optional filter.

This is generally used indirectly using the Api.runs namespace.

Args:

  • client: (wandb.apis.public.RetryingClient) The API client to use for requests.
  • entity: (str) The entity (username or team) that owns the project.
  • project: (str) The name of the project to fetch runs from.
  • filters: (Optional[Dict[str, Any]]) A dictionary of filters to apply to the runs query.
  • order: (Optional[str]) The order of the runs, can be “asc” or “desc” Defaults to “desc”.
  • per_page: (int) The number of runs to fetch per request (default is 50).
  • include_sweeps: (bool) Whether to include sweep information in the runs. Defaults to True.

Examples:

from wandb.apis.public.runs import Runs
from wandb.apis.public import Api

# Initialize the API client
api = Api()

# Get all runs from a project that satisfy the filters
filters = {"state": "finished", "config.optimizer": "adam"}

runs = Runs(
   client=api.client,
   entity="entity",
   project="project_name",
   filters=filters,
)

# Iterate over runs and print details
for run in runs:
   print(f"Run name: {run.name}")
   print(f"Run ID: {run.id}")
   print(f"Run URL: {run.url}")
   print(f"Run state: {run.state}")
   print(f"Run config: {run.config}")
   print(f"Run summary: {run.summary}")
   print(f"Run history (samples=5): {run.history(samples=5)}")
   print("----------")

# Get histories for all runs with specific metrics
histories_df = runs.histories(
   samples=100,  # Number of samples per run
   keys=["loss", "accuracy"],  # Metrics to fetch
   x_axis="_step",  # X-axis metric
   format="pandas",  # Return as pandas DataFrame
)

method Runs.__init__

__init__(
    client: 'RetryingClient',
    entity: str,
    project: str,
    filters: Optional[Dict[str, Any]] = None,
    order: Optional[str] = None,
    per_page: int = 50,
    include_sweeps: bool = True
)

property Runs.cursor

Returns the cursor position for pagination of runs results.

property Runs.length

Returns the total number of runs.

property Runs.more

Returns whether there are more runs to fetch.

method Runs.convert_objects

convert_objects()

Converts GraphQL edges to Runs objects.

method Runs.histories

histories(
    samples: int = 500,
    keys: Optional[List[str]] = None,
    x_axis: str = '_step',
    format: Literal['default', 'pandas', 'polars'] = 'default',
    stream: Literal['default', 'system'] = 'default'
)

Return sampled history metrics for all runs that fit the filters conditions.

Args:

  • samples: The number of samples to return per run
  • keys: Only return metrics for specific keys
  • x_axis: Use this metric as the xAxis defaults to _step
  • format: Format to return data in, options are “default”, “pandas”, “polars”
  • stream: “default” for metrics, “system” for machine metrics

Returns:

  • pandas.DataFrame: If format="pandas", returns a pandas.DataFrame of history metrics.
  • polars.DataFrame: If format="polars", returns a polars.DataFrame of history metrics.
  • list of dicts: If format="default", returns a list of dicts containing history metrics with a run_id key.

class Run

A single run associated with an entity and project.

Args:

  • client: The W&B API client.
  • entity: The entity associated with the run.
  • project: The project associated with the run.
  • run_id: The unique identifier for the run.
  • attrs: The attributes of the run.
  • include_sweeps: Whether to include sweeps in the run.

Attributes:

  • tags ([str]): a list of tags associated with the run
  • url (str): the url of this run
  • id (str): unique identifier for the run (defaults to eight characters)
  • name (str): the name of the run
  • state (str): one of: running, finished, crashed, killed, preempting, preempted
  • config (dict): a dict of hyperparameters associated with the run
  • created_at (str): ISO timestamp when the run was started
  • system_metrics (dict): the latest system metrics recorded for the run
  • summary (dict): A mutable dict-like property that holds the current summary. Calling update will persist any changes.
  • project (str): the project associated with the run
  • entity (str): the name of the entity associated with the run
  • project_internal_id (int): the internal id of the project
  • user (str): the name of the user who created the run
  • path (str): Unique identifier [entity]/[project]/[run_id]
  • notes (str): Notes about the run
  • read_only (boolean): Whether the run is editable
  • history_keys (str): Keys of the history metrics that have been logged
  • with wandb.log({key: value})
  • metadata (str): Metadata about the run from wandb-metadata.json

method Run.__init__

__init__(
    client: 'RetryingClient',
    entity: str,
    project: str,
    run_id: str,
    attrs: Optional[Mapping] = None,
    include_sweeps: bool = True
)

Initialize a Run object.

Run is always initialized by calling api.runs() where api is an instance of wandb.Api.

method Run.delete

delete(delete_artifacts=False)

Delete the given run from the wandb backend.

Args:

  • delete_artifacts (bool, optional): Whether to delete the artifacts associated with the run.

method Run.file

file(name)

Return the path of a file with a given name in the artifact.

Args:

  • name (str): name of requested file.

Returns: A File matching the name argument.

method Run.files

files(names=None, per_page=50)

Return a file path for each file named.

Args:

  • names (list): names of the requested files, if empty returns all files
  • per_page (int): number of results per page.

Returns: A Files object, which is an iterator over File objects.

method Run.history

history(samples=500, keys=None, x_axis='_step', pandas=True, stream='default')

Return sampled history metrics for a run.

This is simpler and faster if you are ok with the history records being sampled.

Args:

  • samples : (int, optional) The number of samples to return
  • pandas : (bool, optional) Return a pandas dataframe
  • keys : (list, optional) Only return metrics for specific keys
  • x_axis : (str, optional) Use this metric as the xAxis defaults to _step
  • stream : (str, optional) “default” for metrics, “system” for machine metrics

Returns:

  • pandas.DataFrame: If pandas=True returns a pandas.DataFrame of history metrics.
  • list of dicts: If pandas=False returns a list of dicts of history metrics.

method Run.load

load(force=False)

Fetch and update run data from GraphQL database.

Ensures run data is up to date.

Args:

  • force (bool): Whether to force a refresh of the run data.

method Run.log_artifact

log_artifact(
    artifact: 'wandb.Artifact',
    aliases: Optional[Collection[str]] = None,
    tags: Optional[Collection[str]] = None
)

Declare an artifact as output of a run.

Args:

  • artifact (Artifact): An artifact returned from wandb.Api().artifact(name).
  • aliases (list, optional): Aliases to apply to this artifact.
  • tags: (list, optional) Tags to apply to this artifact, if any.

Returns: A Artifact object.

method Run.logged_artifacts

logged_artifacts(per_page: int = 100) → RunArtifacts

Fetches all artifacts logged by this run.

Retrieves all output artifacts that were logged during the run. Returns a paginated result that can be iterated over or collected into a single list.

Args:

  • per_page: Number of artifacts to fetch per API request.

Returns: An iterable collection of all Artifact objects logged as outputs during this run.

Example:

import wandb
import tempfile

with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as tmp:
   tmp.write("This is a test artifact")
   tmp_path = tmp.name
run = wandb.init(project="artifact-example")
artifact = wandb.Artifact("test_artifact", type="dataset")
artifact.add_file(tmp_path)
run.log_artifact(artifact)
run.finish()

api = wandb.Api()

finished_run = api.run(f"{run.entity}/{run.project}/{run.id}")

for logged_artifact in finished_run.logged_artifacts():
   print(logged_artifact.name)

method Run.save

save()

Persist changes to the run object to the W&B backend.

method Run.scan_history

scan_history(keys=None, page_size=1000, min_step=None, max_step=None)

Returns an iterable collection of all history records for a run.

Args:

  • keys ([str], optional): only fetch these keys, and only fetch rows that have all of keys defined.
  • page_size (int, optional): size of pages to fetch from the api.
  • min_step (int, optional): the minimum number of pages to scan at a time.
  • max_step (int, optional): the maximum number of pages to scan at a time.

Returns: An iterable collection over history records (dict).

Example: Export all the loss values for an example run

run = api.run("entity/project-name/run-id")
history = run.scan_history(keys=["Loss"])
losses = [row["Loss"] for row in history]

method Run.to_html

to_html(height=420, hidden=False)

Generate HTML containing an iframe displaying this run.

method Run.update

update()

Persist changes to the run object to the wandb backend.

method Run.upload_file

upload_file(path, root='.')

Upload a local file to W&B, associating it with this run.

Args:

  • path (str): Path to the file to upload. Can be absolute or relative.
  • root (str): The root path to save the file relative to. For example, if you want to have the file saved in the run as “my_dir/file.txt” and you’re currently in “my_dir” you would set root to “../”. Defaults to current directory (".").

Returns: A File object representing the uploaded file.

method Run.use_artifact

use_artifact(artifact, use_as=None)

Declare an artifact as an input to a run.

Args:

  • artifact (Artifact): An artifact returned from wandb.Api().artifact(name)
  • use_as (string, optional): A string identifying how the artifact is used in the script. Used to easily differentiate artifacts used in a run, when using the beta wandb launch feature’s artifact swapping functionality.

Returns: An Artifact object.

method Run.used_artifacts

used_artifacts(per_page: int = 100) → RunArtifacts

Fetches artifacts explicitly used by this run.

Retrieves only the input artifacts that were explicitly declared as used during the run, typically via run.use_artifact(). Returns a paginated result that can be iterated over or collected into a single list.

Args:

  • per_page: Number of artifacts to fetch per API request.

Returns: An iterable collection of Artifact objects explicitly used as inputs in this run.

Example:

import wandb

run = wandb.init(project="artifact-example")
run.use_artifact("test_artifact:latest")
run.finish()

api = wandb.Api()
finished_run = api.run(f"{run.entity}/{run.project}/{run.id}")
for used_artifact in finished_run.used_artifacts():
   print(used_artifact.name)
test_artifact

method Run.wait_until_finished

wait_until_finished()

Check the state of the run until it is finished.

2.12 - sweeps

View the source code

module wandb.apis.public

W&B Public API for Sweeps.

This module provides classes for interacting with W&B hyperparameter optimization sweeps.

Example:

from wandb.apis.public import Api

# Initialize API
api = Api()

# Get a specific sweep
sweep = api.sweep("entity/project/sweep_id")

# Access sweep properties
print(f"Sweep: {sweep.name}")
print(f"State: {sweep.state}")
print(f"Best Loss: {sweep.best_loss}")

# Get best performing run
best_run = sweep.best_run()
print(f"Best Run: {best_run.name}")
print(f"Metrics: {best_run.summary}")

Note:

This module is part of the W&B Public API and provides read-only access to sweep data. For creating and controlling sweeps, use the wandb.sweep() and wandb.agent() functions from the main wandb package.

class Sweep

The set of runs associated with the sweep.

Attributes:

  • runs (Runs): List of runs
  • id (str): Sweep ID
  • project (str): The name of the project the sweep belongs to
  • config (dict): Dictionary containing the sweep configuration
  • state (str): The state of the sweep. Can be “Finished”, “Failed”, “Crashed”, or “Running”.
  • expected_run_count (int): The number of expected runs for the sweep

method Sweep.__init__

__init__(client, entity, project, sweep_id, attrs=None)

property Sweep.config

The sweep configuration used for the sweep.

property Sweep.entity

The entity associated with the sweep.

property Sweep.expected_run_count

Return the number of expected runs in the sweep or None for infinite runs.

property Sweep.name

The name of the sweep.

If the sweep has a name, it will be returned. Otherwise, the sweep ID will be returned.

property Sweep.order

Return the order key for the sweep.

property Sweep.path

Returns the path of the project.

The path is a list containing the entity, project name, and sweep ID.

property Sweep.url

The URL of the sweep.

The sweep URL is generated from the entity, project, the term “sweeps”, and the sweep ID.run_id. For SaaS users, it takes the form of https://wandb.ai/entity/project/sweeps/sweeps_ID.

property Sweep.username

Deprecated. Use Sweep.entity instead.

method Sweep.best_run

best_run(order=None)

Return the best run sorted by the metric defined in config or the order passed in.

classmethod Sweep.get

get(
    client,
    entity=None,
    project=None,
    sid=None,
    order=None,
    query=None,
    **kwargs
)

Execute a query against the cloud backend.

method Sweep.load

load(force: bool = False)

Fetch and update sweep data logged to the run from GraphQL database.

method Sweep.to_html

to_html(height=420, hidden=False)

Generate HTML containing an iframe displaying this sweep.

2.13 - teams

View the source code

module wandb.apis.public

W&B Public API for managing teams and team members.

This module provides classes for managing W&B teams and their members.

Note:

This module is part of the W&B Public API and provides methods to manage teams and their members. Team management operations require appropriate permissions.

class Member

A member of a team.

Args:

  • client (wandb.apis.internal.Api): The client instance to use
  • team (str): The name of the team this member belongs to
  • attrs (dict): The member attributes

method Member.__init__

__init__(client, team, attrs)

method Member.delete

delete()

Remove a member from a team.

Returns: Boolean indicating success

class Team

A class that represents a W&B team.

This class provides methods to manage W&B teams, including creating teams, inviting members, and managing service accounts. It inherits from Attrs to handle team attributes.

Args:

  • client (wandb.apis.public.Api): The api instance to use
  • name (str): The name of the team
  • attrs (dict): Optional dictionary of team attributes

Note:

Team management requires appropriate permissions.

method Team.__init__

__init__(client, name, attrs=None)

classmethod Team.create

create(api, team, admin_username=None)

Create a new team.

Args:

  • api: (Api) The api instance to use
  • team: (str) The name of the team
  • admin_username: (str) optional username of the admin user of the team, defaults to the current user.

Returns: A Team object

method Team.create_service_account

create_service_account(description)

Create a service account for the team.

Args:

  • description: (str) A description for this service account

Returns: The service account Member object, or None on failure

method Team.invite

invite(username_or_email, admin=False)

Invite a user to a team.

Args:

  • username_or_email: (str) The username or email address of the user you want to invite.
  • admin: (bool) Whether to make this user a team admin. Defaults to False.

Returns: True on success, False if user was already invited or didn’t exist.

method Team.load

load(force=False)

Return members that belong to a team.

2.14 - users

View the source code

module wandb.apis.public

W&B Public API for managing users and API keys.

This module provides classes for managing W&B users and their API keys.

Note:

This module is part of the W&B Public API and provides methods to manage users and their authentication. Some operations require admin privileges.

class User

A class representing a W&B user with authentication and management capabilities.

This class provides methods to manage W&B users, including creating users, managing API keys, and accessing team memberships. It inherits from Attrs to handle user attributes.

Args:

  • client: (wandb.apis.internal.Api) The client instance to use
  • attrs: (dict) The user attributes

Note:

Some operations require admin privileges

method User.__init__

__init__(client, attrs)

property User.api_keys

List of API key names associated with the user.

Returns:

  • list[str]: Names of API keys associated with the user. Empty list if user has no API keys or if API key data hasn’t been loaded.

property User.teams

List of team names that the user is a member of.

Returns:

  • list (list): Names of teams the user belongs to. Empty list if user has no team memberships or if teams data hasn’t been loaded.

property User.user_api

An instance of the api using credentials from the user.

classmethod User.create

create(api, email, admin=False)

Create a new user.

Args:

  • api (Api): The api instance to use
  • email (str): The name of the team
  • admin (bool): Whether this user should be a global instance admin

Returns: A User object

method User.delete_api_key

delete_api_key(api_key)

Delete a user’s api key.

Args:

  • api_key (str): The name of the API key to delete. This should be one of the names returned by the api_keys property.

Returns: Boolean indicating success

Raises: ValueError if the api_key couldn’t be found

method User.generate_api_key

generate_api_key(description=None)

Generate a new api key.

Args:

  • description (str, optional): A description for the new API key. This can be used to identify the purpose of the API key.

Returns: The new api key, or None on failure

3 - Automations

Automate your W&B workflows.

3.1 - ActionType

View the source code

class ActionType

The type of action triggered by an automation.

3.2 - ArtifactEvent

View the source code

class ArtifactEvent

3.3 - Automation

View the source code

class Automation

A local instance of a saved W&B automation.

property Automation.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property Automation.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

3.4 - DoNothing

View the source code

class DoNothing

Defines an automation action that intentionally does nothing.

property DoNothing.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property DoNothing.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

3.5 - EventType

View the source code

class EventType

The type of event that triggers an automation.

3.6 - MetricChangeFilter

View the source code

class MetricChangeFilter

Defines a filter that compares a change in a run metric against a user-defined threshold.

The change is calculated over “tumbling” windows, i.e. the difference between the current window and the non-overlapping prior window.

property MetricChangeFilter.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property MetricChangeFilter.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

3.7 - MetricThresholdFilter

View the source code

class MetricThresholdFilter

Defines a filter that compares a run metric against a user-defined threshold value.

property MetricThresholdFilter.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property MetricThresholdFilter.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

3.8 - NewAutomation

View the source code

class NewAutomation

A new automation to be created.

property NewAutomation.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property NewAutomation.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

property NewAutomation.scope

The scope in which the triggering event must occur.

3.9 - OnAddArtifactAlias

View the source code

class OnAddArtifactAlias

A new alias is assigned to an artifact.

property OnAddArtifactAlias.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property OnAddArtifactAlias.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

method OnAddArtifactAlias.then

then(action: 'InputAction') → NewAutomation

Define a new Automation in which this event triggers the given action.

3.10 - OnCreateArtifact

View the source code

class OnCreateArtifact

A new artifact is created.

property OnCreateArtifact.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property OnCreateArtifact.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

method OnCreateArtifact.then

then(action: 'InputAction') → NewAutomation

Define a new Automation in which this event triggers the given action.

3.11 - OnLinkArtifact

View the source code

class OnLinkArtifact

A new artifact is linked to a collection.

property OnLinkArtifact.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property OnLinkArtifact.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

method OnLinkArtifact.then

then(action: 'InputAction') → NewAutomation

Define a new Automation in which this event triggers the given action.

3.12 - OnRunMetric

View the source code

class OnRunMetric

A run metric satisfies a user-defined condition.

property OnRunMetric.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property OnRunMetric.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

method OnRunMetric.then

then(action: 'InputAction') → NewAutomation

Define a new Automation in which this event triggers the given action.

3.13 - ProjectScope

View the source code

class ProjectScope

An automation scope defined by a specific Project.

property ProjectScope.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property ProjectScope.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

3.14 - RunEvent

View the source code

class RunEvent

method RunEvent.metric

metric(name: 'str') → MetricVal

Define a metric filter condition.

3.15 - ScopeType

View the source code

class ScopeType

The kind of scope that triggers an automation.

3.16 - SendNotification

View the source code

class SendNotification

Defines an automation action that sends a (Slack) notification.

property SendNotification.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property SendNotification.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

classmethod SendNotification.from_integration

from_integration(
    integration: 'SlackIntegration',
    title: 'str' = '',
    text: 'str' = '',
    level: 'AlertSeverity' = <AlertSeverity.INFO: 'INFO'>
) → Self

Define a notification action that sends to the given (Slack) integration.

3.17 - SendWebhook

View the source code

class SendWebhook

Defines an automation action that sends a webhook request.

property SendWebhook.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property SendWebhook.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

classmethod SendWebhook.from_integration

from_integration(
    integration: 'WebhookIntegration',
    payload: 'Optional[SerializedToJson[dict[str, Any]]]' = None
) → Self

Define a webhook action that sends to the given (webhook) integration.

3.18 - SlackIntegration

View the source code

class SlackIntegration

property SlackIntegration.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property SlackIntegration.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

3.19 - WebhookIntegration

View the source code

class WebhookIntegration

property WebhookIntegration.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property WebhookIntegration.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

4 - SDK v(0.19.11)

Train and fine-tune models, manage models from experimentation to production. For guides and examples, see https://docs.wandb.ai.

4.1 - Actions

Use during training to log experiments, track metrics, and save model artifacts.

4.1.1 - Classes

4.1.1.1 - Artifact

View the source code

class Artifact

Flexible and lightweight building block for dataset and model versioning.

Construct an empty W&B Artifact. Populate an artifacts contents with methods that begin with add. Once the artifact has all the desired files, you can call wandb.log_artifact() to log it.

Args:

  • name (str): A human-readable name for the artifact. Use the name to identify a specific artifact in the W&B App UI or programmatically. You can interactively reference an artifact with the use_artifact Public API. A name can contain letters, numbers, underscores, hyphens, and dots. The name must be unique across a project.
  • type (str): The artifact’s type. Use the type of an artifact to both organize and differentiate artifacts. You can use any string that contains letters, numbers, underscores, hyphens, and dots. Common types include dataset or model. Include model within your type string if you want to link the artifact to the W&B Model Registry. Note that some types reserved for internal use and cannot be set by users. Such types include job and types that start with wandb-.
  • description (str | None) = None: A description of the artifact. For Model or Dataset Artifacts, add documentation for your standardized team model or dataset card. View an artifact’s description programmatically with the Artifact.description attribute or programmatically with the W&B App UI. W&B renders the description as markdown in the W&B App.
  • metadata (dict[str, Any] | None) = None: Additional information about an artifact. Specify metadata as a dictionary of key-value pairs. You can specify no more than 100 total keys.
  • incremental: Use Artifact.new_draft() method instead to modify an existing artifact.
  • use_as: Deprecated.
  • is_link: Boolean indication of if the artifact is a linked artifact(True) or source artifact(False).

Returns: An Artifact object.

method Artifact.__init__

__init__(
    name: 'str',
    type: 'str',
    description: 'str | None' = None,
    metadata: 'dict[str, Any] | None' = None,
    incremental: 'bool' = False,
    use_as: 'str | None' = None
) → None

property Artifact.aliases

List of one or more semantically-friendly references or

identifying “nicknames” assigned to an artifact version.

Aliases are mutable references that you can programmatically reference. Change an artifact’s alias with the W&B App UI or programmatically. See Create new artifact versions for more information.

property Artifact.collection

The collection this artifact was retrieved from.

A collection is an ordered group of artifact versions. If this artifact was retrieved from a portfolio / linked collection, that collection will be returned rather than the collection that an artifact version originated from. The collection that an artifact originates from is known as the source sequence.

property Artifact.commit_hash

The hash returned when this artifact was committed.

property Artifact.created_at

Timestamp when the artifact was created.

property Artifact.description

A description of the artifact.

property Artifact.digest

The logical digest of the artifact.

The digest is the checksum of the artifact’s contents. If an artifact has the same digest as the current latest version, then log_artifact is a no-op.

property Artifact.distributed_id

property Artifact.entity

The name of the entity that the artifact collection belongs to.

If the artifact is a link, the entity will be the entity of the linked artifact.

property Artifact.file_count

The number of files (including references).

property Artifact.history_step

The nearest step at which history metrics were logged for the source run of the artifact.

Examples:

    run = artifact.logged_by()
    if run and (artifact.history_step is not None):
        history = run.sample_history(
            min_step=artifact.history_step,
            max_step=artifact.history_step + 1,
            keys=["my_metric"],
        )
   ``` 

---

### <kbd>property</kbd> Artifact.id

The artifact's ID. 

---

### <kbd>property</kbd> Artifact.incremental





---

### <kbd>property</kbd> Artifact.is_link

Boolean flag indicating if the artifact is a link artifact. 

True: The artifact is a link artifact to a source artifact. False: The artifact is a source artifact. 

---

### <kbd>property</kbd> Artifact.linked_artifacts

Returns a list of all the linked artifacts of a source artifact. 

If the artifact is a link artifact (`artifact.is_link == True`), it will return an empty list. Limited to 500 results. 

---

### <kbd>property</kbd> Artifact.manifest

The artifact's manifest. 

The manifest lists all of its contents, and can't be changed once the artifact has been logged. 

---

### <kbd>property</kbd> Artifact.metadata

User-defined artifact metadata. 

Structured data associated with the artifact. 

---

### <kbd>property</kbd> Artifact.name

The artifact name and version of the artifact. 

A string with the format `{collection}:{alias}`. If fetched before an artifact is logged/saved, the name won't contain the alias. If the artifact is a link, the name will be the name of the linked artifact. 

---

### <kbd>property</kbd> Artifact.project

The name of the project that the artifact collection belongs to. 

If the artifact is a link, the project will be the project of the linked artifact. 

---

### <kbd>property</kbd> Artifact.qualified_name

The entity/project/name of the artifact. 

If the artifact is a link, the qualified name will be the qualified name of the linked artifact path. 

---

### <kbd>property</kbd> Artifact.size

The total size of the artifact in bytes. 

Includes any references tracked by this artifact. 

---

### <kbd>property</kbd> Artifact.source_artifact

Returns the source artifact. The source artifact is the original logged artifact. 

If the artifact itself is a source artifact (`artifact.is_link == False`), it will return itself. 

---

### <kbd>property</kbd> Artifact.source_collection

The artifact's source collection. 

The source collection is the collection that the artifact was logged from. 

---

### <kbd>property</kbd> Artifact.source_entity

The name of the entity of the source artifact. 

---

### <kbd>property</kbd> Artifact.source_name

The artifact name and version of the source artifact. 

A string with the format `{source_collection}:{alias}`. Before the artifact is saved, contains only the name since the version is not yet known. 

---

### <kbd>property</kbd> Artifact.source_project

The name of the project of the source artifact. 

---

### <kbd>property</kbd> Artifact.source_qualified_name

The source_entity/source_project/source_name of the source artifact. 

---

### <kbd>property</kbd> Artifact.source_version

The source artifact's version. 

A string with the format `v{number}`. 

---

### <kbd>property</kbd> Artifact.state

The status of the artifact. One of: "PENDING", "COMMITTED", or "DELETED". 

---

### <kbd>property</kbd> Artifact.tags

List of one or more tags assigned to this artifact version. 

---

### <kbd>property</kbd> Artifact.ttl

The time-to-live (TTL) policy of an artifact. 

Artifacts are deleted shortly after a TTL policy's duration passes. If set to `None`, the artifact deactivates TTL policies and will be not scheduled for deletion, even if there is a team default TTL. An artifact inherits a TTL policy from the team default if the team administrator defines a default TTL and there is no custom policy set on an artifact. 



**Raises:**

- `ArtifactNotLoggedError`:  Unable to fetch inherited TTL if the artifact has not been logged or saved. 

---

### <kbd>property</kbd> Artifact.type

The artifact's type. Common types include `dataset` or `model`. 

---

### <kbd>property</kbd> Artifact.updated_at

The time when the artifact was last updated. 

---

### <kbd>property</kbd> Artifact.url

Constructs the URL of the artifact. 



**Returns:**

- `str`:  The URL of the artifact. 

---

### <kbd>property</kbd> Artifact.use_as

Deprecated. 

---

### <kbd>property</kbd> Artifact.version

The artifact's version. 

A string with the format `v{number}`. If the artifact is a link artifact, the version will be from the linked collection. 



---

### <kbd>method</kbd> `Artifact.add`

```python
add(
   obj: 'WBValue',
   name: 'StrPath',
   overwrite: 'bool' = False
) → ArtifactManifestEntry

Add wandb.WBValue obj to the artifact.

Args:

  • obj: The object to add. Currently support one of Bokeh, JoinedTable, PartitionedTable, Table, Classes, ImageMask, BoundingBoxes2D, Audio, Image, Video, Html, Object3D
  • name: The path within the artifact to add the object.
  • overwrite: If True, overwrite existing objects with the same file path if applicable.

Returns: The added manifest entry

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.

method Artifact.add_dir

add_dir(
    local_path: 'str',
    name: 'str | None' = None,
    skip_cache: 'bool | None' = False,
    policy: "Literal['mutable', 'immutable'] | None" = 'mutable',
    merge: 'bool' = False
) → None

Add a local directory to the artifact.

Args:

  • local_path: The path of the local directory.
  • name: The subdirectory name within an artifact. The name you specify appears in the W&B App UI nested by artifact’s type. Defaults to the root of the artifact.
  • skip_cache: If set to True, W&B will not copy/move files to the cache while uploading
  • policy: By default, “mutable”.
    • mutable: Create a temporary copy of the file to prevent corruption during upload.
    • immutable: Disable protection, rely on the user not to delete or change the file.
  • merge: If False (default), throws ValueError if a file was already added in a previous add_dir call and its content has changed. If True, overwrites existing files with changed content. Always adds new files and never removes files. To replace an entire directory, pass a name when adding the directory using add_dir(local_path, name=my_prefix) and call remove(my_prefix) to remove the directory, then add it again.

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.
  • ValueError: Policy must be “mutable” or “immutable”

method Artifact.add_file

add_file(
    local_path: 'str',
    name: 'str | None' = None,
    is_tmp: 'bool | None' = False,
    skip_cache: 'bool | None' = False,
    policy: "Literal['mutable', 'immutable'] | None" = 'mutable',
    overwrite: 'bool' = False
) → ArtifactManifestEntry

Add a local file to the artifact.

Args:

  • local_path: The path to the file being added.
  • name: The path within the artifact to use for the file being added. Defaults to the basename of the file.
  • is_tmp: If true, then the file is renamed deterministically to avoid collisions.
  • skip_cache: If True, do not copy files to the cache after uploading.
  • policy: By default, set to “mutable”. If set to “mutable”, create a temporary copy of the file to prevent corruption during upload. If set to “immutable”, disable protection and rely on the user not to delete or change the file.
  • overwrite: If True, overwrite the file if it already exists.

Returns: The added manifest entry.

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.
  • ValueError: Policy must be “mutable” or “immutable”

method Artifact.add_reference

add_reference(
    uri: 'ArtifactManifestEntry | str',
    name: 'StrPath | None' = None,
    checksum: 'bool' = True,
    max_objects: 'int | None' = None
) → Sequence[ArtifactManifestEntry]

Add a reference denoted by a URI to the artifact.

Unlike files or directories that you add to an artifact, references are not uploaded to W&B. For more information, see Track external files.

By default, the following schemes are supported:

  • http(s): The size and digest of the file will be inferred by the Content-Length and the ETag response headers returned by the server.
  • s3: The checksum and size are pulled from the object metadata. If bucket versioning is enabled, then the version ID is also tracked.
  • gs: The checksum and size are pulled from the object metadata. If bucket versioning is enabled, then the version ID is also tracked.
  • https, domain matching *.blob.core.windows.net
  • Azure: The checksum and size are be pulled from the blob metadata. If storage account versioning is enabled, then the version ID is also tracked.
  • file: The checksum and size are pulled from the file system. This scheme is useful if you have an NFS share or other externally mounted volume containing files you wish to track but not necessarily upload.

For any other scheme, the digest is just a hash of the URI and the size is left blank.

Args:

  • uri: The URI path of the reference to add. The URI path can be an object returned from Artifact.get_entry to store a reference to another artifact’s entry.
  • name: The path within the artifact to place the contents of this reference.
  • checksum: Whether or not to checksum the resource(s) located at the reference URI. Checksumming is strongly recommended as it enables automatic integrity validation. Disabling checksumming will speed up artifact creation but reference directories will not iterated through so the objects in the directory will not be saved to the artifact. We recommend setting checksum=False when adding reference objects, in which case a new version will only be created if the reference URI changes.
  • max_objects: The maximum number of objects to consider when adding a reference that points to directory or bucket store prefix. By default, the maximum number of objects allowed for Amazon S3, GCS, Azure, and local files is 10,000,000. Other URI schemas do not have a maximum.

Returns: The added manifest entries.

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.

method Artifact.checkout

checkout(root: 'str | None' = None) → str

Replace the specified root directory with the contents of the artifact.

WARNING: This will delete all files in root that are not included in the artifact.

Args:

  • root: The directory to replace with this artifact’s files.

Returns: The path of the checked out contents.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.delete

delete(delete_aliases: 'bool' = False) → None

Delete an artifact and its files.

If called on a linked artifact, only the link is deleted, and the source artifact is unaffected.

Use artifact.unlink() instead of artifact.delete() to remove a link between a source artifact and a linked artifact.

Args:

  • delete_aliases: If set to True, deletes all aliases associated with the artifact. Otherwise, this raises an exception if the artifact has existing aliases. This parameter is ignored if the artifact is linked (a member of a portfolio collection).

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.download

download(
    root: 'StrPath | None' = None,
    allow_missing_references: 'bool' = False,
    skip_cache: 'bool | None' = None,
    path_prefix: 'StrPath | None' = None,
    multipart: 'bool | None' = None
) → FilePathStr

Download the contents of the artifact to the specified root directory.

Existing files located within root are not modified. Explicitly delete root before you call download if you want the contents of root to exactly match the artifact.

Args:

  • root: The directory W&B stores the artifact’s files.
  • allow_missing_references: If set to True, any invalid reference paths will be ignored while downloading referenced files.
  • skip_cache: If set to True, the artifact cache will be skipped when downloading and W&B will download each file into the default root or specified download directory.
  • path_prefix: If specified, only files with a path that starts with the given prefix will be downloaded. Uses unix format (forward slashes).
  • multipart: If set to None (default), the artifact will be downloaded in parallel using multipart download if individual file size is greater than 2GB. If set to True or False, the artifact will be downloaded in parallel or serially regardless of the file size.

Returns: The path to the downloaded contents.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.file

file(root: 'str | None' = None) → StrPath

Download a single file artifact to the directory you specify with root.

Args:

  • root: The root directory to store the file. Defaults to ./artifacts/self.name/.

Returns: The full path of the downloaded file.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.
  • ValueError: If the artifact contains more than one file.

method Artifact.files

files(names: 'list[str] | None' = None, per_page: 'int' = 50) → ArtifactFiles

Iterate over all files stored in this artifact.

Args:

  • names: The filename paths relative to the root of the artifact you wish to list.
  • per_page: The number of files to return per request.

Returns: An iterator containing File objects.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.finalize

finalize() → None

Finalize the artifact version.

You cannot modify an artifact version once it is finalized because the artifact is logged as a specific artifact version. Create a new artifact version to log more data to an artifact. An artifact is automatically finalized when you log the artifact with log_artifact.

method Artifact.get

get(name: 'str') → WBValue | None

Get the WBValue object located at the artifact relative name.

Args:

  • name: The artifact relative name to retrieve.

Returns: W&B object that can be logged with wandb.log() and visualized in the W&B UI.

Raises:

  • ArtifactNotLoggedError: if the artifact isn’t logged or the run is offline.

method Artifact.get_added_local_path_name

get_added_local_path_name(local_path: 'str') → str | None

Get the artifact relative name of a file added by a local filesystem path.

Args:

  • local_path: The local path to resolve into an artifact relative name.

Returns: The artifact relative name.

method Artifact.get_entry

get_entry(name: 'StrPath') → ArtifactManifestEntry

Get the entry with the given name.

Args:

  • name: The artifact relative name to get

Returns: A W&B object.

Raises:

  • ArtifactNotLoggedError: if the artifact isn’t logged or the run is offline.
  • KeyError: if the artifact doesn’t contain an entry with the given name.

method Artifact.get_path

get_path(name: 'StrPath') → ArtifactManifestEntry

Deprecated. Use get_entry(name).

method Artifact.is_draft

is_draft() → bool

Check if artifact is not saved.

Returns: Boolean. False if artifact is saved. True if artifact is not saved.

method Artifact.json_encode

json_encode() → dict[str, Any]

Returns the artifact encoded to the JSON format.

Returns: A dict with string keys representing attributes of the artifact.

link(target_path: 'str', aliases: 'list[str] | None' = None) → Artifact | None

Link this artifact to a portfolio (a promoted collection of artifacts).

Args:

  • target_path: The path to the portfolio inside a project. The target path must adhere to one of the following schemas {portfolio}, {project}/{portfolio} or {entity}/{project}/{portfolio}. To link the artifact to the Model Registry, rather than to a generic portfolio inside a project, set target_path to the following schema {"model-registry"}/{Registered Model Name} or {entity}/{"model-registry"}/{Registered Model Name}.
  • aliases: A list of strings that uniquely identifies the artifact inside the specified portfolio.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

Returns: The linked artifact if linking was successful, otherwise None.

method Artifact.logged_by

logged_by() → Run | None

Get the W&B run that originally logged the artifact.

Returns: The name of the W&B run that originally logged the artifact.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.new_draft

new_draft() → Artifact

Create a new draft artifact with the same content as this committed artifact.

Modifying an existing artifact creates a new artifact version known as an “incremental artifact”. The artifact returned can be extended or modified and logged as a new version.

Returns: An Artifact object.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.new_file

new_file(
    name: 'str',
    mode: 'str' = 'x',
    encoding: 'str | None' = None
) → Iterator[IO]

Open a new temporary file and add it to the artifact.

Args:

  • name: The name of the new file to add to the artifact.
  • mode: The file access mode to use to open the new file.
  • encoding: The encoding used to open the new file.

Returns: A new file object that can be written to. Upon closing, the file is automatically added to the artifact.

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.

method Artifact.remove

remove(item: 'StrPath | ArtifactManifestEntry') → None

Remove an item from the artifact.

Args:

  • item: The item to remove. Can be a specific manifest entry or the name of an artifact-relative path. If the item matches a directory all items in that directory will be removed.

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.
  • FileNotFoundError: If the item isn’t found in the artifact.

method Artifact.save

save(
    project: 'str | None' = None,
    settings: 'wandb.Settings | None' = None
) → None

Persist any changes made to the artifact.

If currently in a run, that run will log this artifact. If not currently in a run, a run of type “auto” is created to track this artifact.

Args:

  • project: A project to use for the artifact in the case that a run is not already in context.
  • settings: A settings object to use when initializing an automatic run. Most commonly used in testing harness.
unlink() → None

Unlink this artifact if it is currently a member of a promoted collection of artifacts.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.
  • ValueError: If the artifact is not linked, in other words, it is not a member of a portfolio collection.

method Artifact.used_by

used_by() → list[Run]

Get a list of the runs that have used this artifact and its linked artifacts.

Returns: A list of Run objects.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.verify

verify(root: 'str | None' = None) → None

Verify that the contents of an artifact match the manifest.

All files in the directory are checksummed and the checksums are then cross-referenced against the artifact’s manifest. References are not verified.

Args:

  • root: The directory to verify. If None artifact will be downloaded to ‘./artifacts/self.name/’.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.
  • ValueError: If the verification fails.

method Artifact.wait

wait(timeout: 'int | None' = None) → Artifact

If needed, wait for this artifact to finish logging.

Args:

  • timeout: The time, in seconds, to wait.

Returns: An Artifact object.

4.1.1.2 - ArtifactTTL

View the source code

class ArtifactTTL

An enumeration.

4.1.1.3 - Error

View the source code

class Error

Base W&B Error.

method Error.__init__

__init__(message, context: Optional[dict] = None) → None

4.1.1.4 - Run

View the source code

class Run

A unit of computation logged by W&B. Typically, this is an ML experiment.

Call wandb.init() to create a new run. wandb.init() starts a new run and returns a wandb.Run object. Each run is associated with a unique ID (run ID). There is only ever at most one active wandb.Run in any process.

For distributed training experiments, you can either track each process separately using one run per process or track all processes to a single run. See Log distributed training experiments for more information.

You can log data to a run with wandb.log(). Anything you log using wandb.log() is sent to that run. See Create an experiment or wandb.init API reference page or more information.

There is a another Run object in the wandb.apis.public namespace. Use this object is to interact with runs that have already been created.

Finish active runs before starting new runs. Use a context manager (with statement) to automatically finish the run or use wandb.finish() to finish a run manually. W&B recommends using a context manager to automatically finish the run.

Attributes:

  • summary: (Summary) Single values set for each wandb.log() key. By default, summary is set to the last value logged. You can manually set summary to the best value, like max accuracy, instead of the final value.

Examples: Create a run with wandb.init():

import wandb

# Start a new run and log some data
# Use context manager (`with` statement) to automatically finish the run
with wandb.init(entity="entity", project="project") as run:
    run.log({"accuracy": acc, "loss": loss})

method Run.__init__

__init__(
    settings: 'Settings',
    config: 'dict[str, Any] | None' = None,
    sweep_config: 'dict[str, Any] | None' = None,
    launch_config: 'dict[str, Any] | None' = None
) → None

property Run.config

Config object associated with this run.

property Run.config_static

Static config object associated with this run.

property Run.dir

The directory where files associated with the run are saved.

property Run.disabled

True if the run is disabled, False otherwise.

property Run.entity

The name of the W&B entity associated with the run.

Entity can be a username or the name of a team or organization.

property Run.group

Name of the group associated with the run.

Setting a group helps the W&B UI organize runs. If you are doing a distributed training you should give all of the runs in the training the same group. If you are doing cross-validation you should give all the cross-validation folds the same group.

property Run.id

Identifier for this run.

property Run.job_type

Name of the job type associated with the run.

property Run.name

Display name of the run.

Display names are not guaranteed to be unique and may be descriptive. By default, they are randomly generated.

property Run.notes

Notes associated with the run, if there are any.

Notes can be a multiline string and can also use markdown and latex equations inside $$, like $x + 3$.

property Run.offline

True if the run is offline, False otherwise.

property Run.path

Path to the run.

Run paths include entity, project, and run ID, in the format entity/project/run_id.

property Run.project

Name of the W&B project associated with the run.

property Run.project_url

URL of the W&B project associated with the run, if there is one.

Offline runs do not have a project URL.

property Run.resumed

True if the run was resumed, False otherwise.

property Run.settings

A frozen copy of run’s Settings object.

property Run.start_time

Unix timestamp (in seconds) of when the run started.

property Run.starting_step

The first step of the run.

property Run.step

Current value of the step.

This counter is incremented by wandb.log.

property Run.sweep_id

Identifier for the sweep associated with the run, if there is one.

property Run.sweep_url

URL of the sweep associated with the run, if there is one.

Offline runs do not have a sweep URL.

property Run.tags

Tags associated with the run, if there are any.

property Run.url

The url for the W&B run, if there is one.

Offline runs will not have a url.

method Run.alert

alert(
    title: 'str',
    text: 'str',
    level: 'str | AlertLevel | None' = None,
    wait_duration: 'int | float | timedelta | None' = None
) → None

Create an alert with the given title and text.

Args:

  • title: The title of the alert, must be less than 64 characters long.
  • text: The text body of the alert.
  • level: The alert level to use, either: INFO, WARN, or ERROR.
  • wait_duration: The time to wait (in seconds) before sending another alert with this title.

method Run.define_metric

define_metric(
    name: 'str',
    step_metric: 'str | wandb_metric.Metric | None' = None,
    step_sync: 'bool | None' = None,
    hidden: 'bool | None' = None,
    summary: 'str | None' = None,
    goal: 'str | None' = None,
    overwrite: 'bool | None' = None
) → wandb_metric.Metric

Customize metrics logged with wandb.log().

Args:

  • name: The name of the metric to customize.
  • step_metric: The name of another metric to serve as the X-axis for this metric in automatically generated charts.
  • step_sync: Automatically insert the last value of step_metric into run.log() if it is not provided explicitly. Defaults to True if step_metric is specified.
  • hidden: Hide this metric from automatic plots.
  • summary: Specify aggregate metrics added to summary. Supported aggregations include “min”, “max”, “mean”, “last”, “best”, “copy” and “none”. “best” is used together with the goal parameter. “none” prevents a summary from being generated. “copy” is deprecated and should not be used.
  • goal: Specify how to interpret the “best” summary type. Supported options are “minimize” and “maximize”.
  • overwrite: If false, then this call is merged with previous define_metric calls for the same metric by using their values for any unspecified parameters. If true, then unspecified parameters overwrite values specified by previous calls.

Returns: An object that represents this call but can otherwise be discarded.

method Run.display

display(height: 'int' = 420, hidden: 'bool' = False) → bool

Display this run in Jupyter.

method Run.finish

finish(exit_code: 'int | None' = None, quiet: 'bool | None' = None) → None

Finish a run and upload any remaining data.

Marks the completion of a W&B run and ensures all data is synced to the server. The run’s final state is determined by its exit conditions and sync status.

Run States:

  • Running: Active run that is logging data and/or sending heartbeats.
  • Crashed: Run that stopped sending heartbeats unexpectedly.
  • Finished: Run completed successfully (exit_code=0) with all data synced.
  • Failed: Run completed with errors (exit_code!=0).
  • Killed: Run was forcibly stopped before it could finish.

Args:

  • exit_code: Integer indicating the run’s exit status. Use 0 for success, any other value marks the run as failed.
  • quiet: Deprecated. Configure logging verbosity using wandb.Settings(quiet=...).

method Run.finish_artifact

finish_artifact(
    artifact_or_path: 'Artifact | str',
    name: 'str | None' = None,
    type: 'str | None' = None,
    aliases: 'list[str] | None' = None,
    distributed_id: 'str | None' = None
) → Artifact

Finishes a non-finalized artifact as output of a run.

Subsequent “upserts” with the same distributed ID will result in a new version.

Args:

  • artifact_or_path: A path to the contents of this artifact, can be in the following forms: - /local/directory - /local/directory/file.txt - s3://bucket/path You can also pass an Artifact object created by calling wandb.Artifact.
  • name: An artifact name. May be prefixed with entity/project. Valid names can be in the following forms: - name:version - name:alias - digest This will default to the basename of the path prepended with the current run id if not specified.
  • type: The type of artifact to log, examples include dataset, model
  • aliases: Aliases to apply to this artifact, defaults to ["latest"]
  • distributed_id: Unique string that all distributed jobs share. If None, defaults to the run’s group name.

Returns: An Artifact object.

method Run.get_project_url

get_project_url() → str | None

This method is deprecated and will be removed in a future release. Use run.project_url instead.

URL of the W&B project associated with the run, if there is one. Offline runs do not have a project URL.

method Run.get_sweep_url

get_sweep_url() → str | None

This method is deprecated and will be removed in a future release. Use run.sweep_url instead.

The URL of the sweep associated with the run, if there is one. Offline runs do not have a sweep URL.

method Run.get_url

get_url() → str | None

This method is deprecated and will be removed in a future release. Use run.url instead.

URL of the W&B run, if there is one. Offline runs do not have a URL.

link_artifact(
    artifact: 'Artifact',
    target_path: 'str',
    aliases: 'list[str] | None' = None
) → Artifact | None

Link the given artifact to a portfolio (a promoted collection of artifacts).

Linked artifacts are visible in the UI for the specified portfolio.

Args:

  • artifact: the (public or local) artifact which will be linked
  • target_path: takes the following forms: {portfolio}, {project}/{portfolio}, or {entity}/{project}/{portfolio}
  • aliases: List[str] - optional alias(es) that will only be applied on this linked artifact inside the portfolio. The alias “latest” will always be applied to the latest version of an artifact that is linked.

Returns: The linked artifact if linking was successful, otherwise None.

link_model(
    path: 'StrPath',
    registered_model_name: 'str',
    name: 'str | None' = None,
    aliases: 'list[str] | None' = None
) → Artifact | None

Log a model artifact version and link it to a registered model in the model registry.

Linked model versions are visible in the UI for the specified registered model.

This method will:

  • Check if ’name’ model artifact has been logged. If so, use the artifact version that matches the files located at ‘path’ or log a new version. Otherwise log files under ‘path’ as a new model artifact, ’name’ of type ‘model’.
  • Check if registered model with name ‘registered_model_name’ exists in the ‘model-registry’ project. If not, create a new registered model with name ‘registered_model_name’.
  • Link version of model artifact ’name’ to registered model, ‘registered_model_name’.
  • Attach aliases from ‘aliases’ list to the newly linked model artifact version.

Args:

  • path: (str) A path to the contents of this model, can be in the following forms:
    • /local/directory
    • /local/directory/file.txt
    • s3://bucket/path
  • registered_model_name: The name of the registered model that the model is to be linked to. A registered model is a collection of model versions linked to the model registry, typically representing a team’s specific ML Task. The entity that this registered model belongs to will be derived from the run.
  • name: The name of the model artifact that files in ‘path’ will be logged to. This will default to the basename of the path prepended with the current run id if not specified.
  • aliases: Aliases that will only be applied on this linked artifact inside the registered model. The alias “latest” will always be applied to the latest version of an artifact that is linked.

Raises:

  • AssertionError: If registered_model_name is a path or if model artifact ’name’ is of a type that does not contain the substring ‘model’.
  • ValueError: If name has invalid special characters.

Returns: The linked artifact if linking was successful, otherwise None.

Examples:

run.link_model(
   path="/local/directory",
   registered_model_name="my_reg_model",
   name="my_model_artifact",
   aliases=["production"],
)

Invalid usage

run.link_model(
    path="/local/directory",
    registered_model_name="my_entity/my_project/my_reg_model",
    name="my_model_artifact",
    aliases=["production"],
)

run.link_model(
    path="/local/directory",
    registered_model_name="my_reg_model",
    name="my_entity/my_project/my_model_artifact",
    aliases=["production"],
)

method Run.log

log(
    data: 'dict[str, Any]',
    step: 'int | None' = None,
    commit: 'bool | None' = None
) → None

Upload run data.

Use log to log data from runs, such as scalars, images, video, histograms, plots, and tables. See Log objects and media for code snippets, best practices, and more.

Basic usage:

import wandb

with wandb.init() as run:
     run.log({"train-loss": 0.5, "accuracy": 0.9})

The previous code snippet saves the loss and accuracy to the run’s history and updates the summary values for these metrics.

Visualize logged data in a workspace at wandb.ai, or locally on a self-hosted instance of the W&B app, or export data to visualize and explore locally, such as in a Jupyter notebook, with the Public API.

Logged values don’t have to be scalars. You can log any W&B supported Data Type such as images, audio, video, and more. For example, you can use wandb.Table to log structured data. See Log tables, visualize and query data tutorial for more details.

W&B organizes metrics with a forward slash (/) in their name into sections named using the text before the final slash. For example, the following results in two sections named “train” and “validate”:

run.log(
     {
         "train/accuracy": 0.9,
         "train/loss": 30,
         "validate/accuracy": 0.8,
         "validate/loss": 20,
     }
)

Only one level of nesting is supported; run.log({"a/b/c": 1}) produces a section named “a/b”.

run.log is not intended to be called more than a few times per second. For optimal performance, limit your logging to once every N iterations, or collect data over multiple iterations and log it in a single step.

By default, each call to log creates a new “step”. The step must always increase, and it is not possible to log to a previous step. You can use any metric as the X axis in charts. See Custom log axes for more details.

In many cases, it is better to treat the W&B step like you’d treat a timestamp rather than a training step.

# Example: log an "epoch" metric for use as an X axis.
run.log({"epoch": 40, "train-loss": 0.5})

It is possible to use multiple log invocations to log to the same step with the step and commit parameters. The following are all equivalent:

# Normal usage:
run.log({"train-loss": 0.5, "accuracy": 0.8})
run.log({"train-loss": 0.4, "accuracy": 0.9})

# Implicit step without auto-incrementing:
run.log({"train-loss": 0.5}, commit=False)
run.log({"accuracy": 0.8})
run.log({"train-loss": 0.4}, commit=False)
run.log({"accuracy": 0.9})

# Explicit step:
run.log({"train-loss": 0.5}, step=current_step)
run.log({"accuracy": 0.8}, step=current_step)
current_step += 1
run.log({"train-loss": 0.4}, step=current_step)
run.log({"accuracy": 0.9}, step=current_step)

Args:

  • data: A dict with str keys and values that are serializable
  • Python objects including: int, float and string; any of the wandb.data_types; lists, tuples and NumPy arrays of serializable Python objects; other dicts of this structure.
  • step: The step number to log. If None, then an implicit auto-incrementing step is used. See the notes in the description.
  • commit: If true, finalize and upload the step. If false, then accumulate data for the step. See the notes in the description. If step is None, then the default is commit=True; otherwise, the default is commit=False.
  • sync: This argument is deprecated and does nothing.

Examples: For more and more detailed examples, see our guides to logging.

Basic usage

import wandb

run = wandb.init()
run.log({"accuracy": 0.9, "epoch": 5})

Incremental logging

import wandb

run = wandb.init()
run.log({"loss": 0.2}, commit=False)
# Somewhere else when I'm ready to report this step:
run.log({"accuracy": 0.8})

Histogram

import numpy as np
import wandb

# sample gradients at random from normal distribution
gradients = np.random.randn(100, 100)
run = wandb.init()
run.log({"gradients": wandb.Histogram(gradients)})

Image from NumPy

import numpy as np
import wandb

run = wandb.init()
examples = []
for i in range(3):
    pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
    image = wandb.Image(pixels, caption=f"random field {i}")
    examples.append(image)
run.log({"examples": examples})

Image from PIL

import numpy as np
from PIL import Image as PILImage
import wandb

run = wandb.init()
examples = []
for i in range(3):
    pixels = np.random.randint(
         low=0,
         high=256,
         size=(100, 100, 3),
         dtype=np.uint8,
    )
    pil_image = PILImage.fromarray(pixels, mode="RGB")
    image = wandb.Image(pil_image, caption=f"random field {i}")
    examples.append(image)
run.log({"examples": examples})

Video from NumPy

import numpy as np
import wandb

run = wandb.init()
# axes are (time, channel, height, width)
frames = np.random.randint(
    low=0,
    high=256,
    size=(10, 3, 100, 100),
    dtype=np.uint8,
)
run.log({"video": wandb.Video(frames, fps=4)})

Matplotlib plot

from matplotlib import pyplot as plt
import numpy as np
import wandb

run = wandb.init()
fig, ax = plt.subplots()
x = np.linspace(0, 10)
y = x * x
ax.plot(x, y)  # plot y = x^2
run.log({"chart": fig})

PR Curve

import wandb

run = wandb.init()
run.log({"pr": wandb.plot.pr_curve(y_test, y_probas, labels)})

3D Object

import wandb

run = wandb.init()
run.log(
    {
         "generated_samples": [
             wandb.Object3D(open("sample.obj")),
             wandb.Object3D(open("sample.gltf")),
             wandb.Object3D(open("sample.glb")),
         ]
    }
)

Raises:

  • wandb.Error: if called before wandb.init
  • ValueError: if invalid data is passed

Examples:

# Basic usage
import wandb

run = wandb.init()
run.log({"accuracy": 0.9, "epoch": 5})

# Incremental logging
import wandb

run = wandb.init()
run.log({"loss": 0.2}, commit=False)
# Somewhere else when I'm ready to report this step:
run.log({"accuracy": 0.8})

# Histogram
import numpy as np
import wandb

# sample gradients at random from normal distribution
gradients = np.random.randn(100, 100)
run = wandb.init()
run.log({"gradients": wandb.Histogram(gradients)})

# Image from numpy
import numpy as np
import wandb

run = wandb.init()
examples = []
for i in range(3):
    pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
    image = wandb.Image(pixels, caption=f"random field {i}")
    examples.append(image)
run.log({"examples": examples})

# Image from PIL
import numpy as np
from PIL import Image as PILImage
import wandb

run = wandb.init()
examples = []
for i in range(3):
    pixels = np.random.randint(
         low=0, high=256, size=(100, 100, 3), dtype=np.uint8
    )
    pil_image = PILImage.fromarray(pixels, mode="RGB")
    image = wandb.Image(pil_image, caption=f"random field {i}")
    examples.append(image)
run.log({"examples": examples})

# Video from numpy
import numpy as np
import wandb

run = wandb.init()
# axes are (time, channel, height, width)
frames = np.random.randint(
    low=0, high=256, size=(10, 3, 100, 100), dtype=np.uint8
)
run.log({"video": wandb.Video(frames, fps=4)})

# Matplotlib Plot
from matplotlib import pyplot as plt
import numpy as np
import wandb

run = wandb.init()
fig, ax = plt.subplots()
x = np.linspace(0, 10)
y = x * x
ax.plot(x, y)  # plot y = x^2
run.log({"chart": fig})

# PR Curve
import wandb

run = wandb.init()
run.log({"pr": wandb.plot.pr_curve(y_test, y_probas, labels)})

# 3D Object
import wandb

run = wandb.init()
run.log(
    {
         "generated_samples": [
             wandb.Object3D(open("sample.obj")),
             wandb.Object3D(open("sample.gltf")),
             wandb.Object3D(open("sample.glb")),
         ]
    }
)

For more and more detailed examples, see our guides to logging.

method Run.log_artifact

log_artifact(
    artifact_or_path: 'Artifact | StrPath',
    name: 'str | None' = None,
    type: 'str | None' = None,
    aliases: 'list[str] | None' = None,
    tags: 'list[str] | None' = None
) → Artifact

Declare an artifact as an output of a run.

Args:

  • artifact_or_path: A path to the contents of this artifact, can be in the following forms
    • /local/directory
    • /local/directory/file.txt
    • s3://bucket/path
  • name: An artifact name. Defaults to the basename of the path prepended with the current run id if not specified. Valid names can be in the following forms:
    • name:version
    • name:alias
    • digest
  • type: The type of artifact to log. Common examples include dataset and model
  • aliases: Aliases to apply to this artifact, defaults to ["latest"]
  • tags: Tags to apply to this artifact, if any.

Returns: An Artifact object.

method Run.log_code

log_code(
    root: 'str | None' = '.',
    name: 'str | None' = None,
    include_fn: 'Callable[[str, str], bool] | Callable[[str], bool]' = <function _is_py_requirements_or_dockerfile at 0x101b8a290>,
    exclude_fn: 'Callable[[str, str], bool] | Callable[[str], bool]' = <function exclude_wandb_fn at 0x1039e3760>
) → Artifact | None

Save the current state of your code to a W&B Artifact.

By default, it walks the current directory and logs all files that end with .py.

Args:

  • root: The relative (to os.getcwd()) or absolute path to recursively find code from.
  • name: The name of our code artifact. By default, we’ll name the artifact source-$PROJECT_ID-$ENTRYPOINT_RELPATH. There may be scenarios where you want many runs to share the same artifact. Specifying name allows you to achieve that.
  • include_fn: A callable that accepts a file path and (optionally) root path and returns True when it should be included and False otherwise. This
  • defaults to lambda path, root: path.endswith(".py").
  • exclude_fn: A callable that accepts a file path and (optionally) root path and returns True when it should be excluded and False otherwise. This defaults to a function that excludes all files within <root>/.wandb/ and <root>/wandb/ directories.

Examples: Basic usage

import wandb

with wandb.init() as run:
    run.log_code()

Advanced usage

import wandb

with wandb.init() as run:
    run.log_code(
         root="../",
         include_fn=lambda path: path.endswith(".py") or path.endswith(".ipynb"),
         exclude_fn=lambda path, root: os.path.relpath(path, root).startswith(
             "cache/"
         ),
    )

Returns: An Artifact object if code was logged

method Run.log_model

log_model(
    path: 'StrPath',
    name: 'str | None' = None,
    aliases: 'list[str] | None' = None
) → None

Logs a model artifact as an output of this run.

The name of model artifact can only contain alphanumeric characters, underscores, and hyphens.

Args:

  • path: A path to the contents of this model, can be in the following forms
    • /local/directory
    • /local/directory/file.txt
    • s3://bucket/path
  • name: A name to assign to the model artifact that the file contents will be added to. The string must contain only alphanumeric characters such as dashes, underscores, and dots. This will default to the basename of the path prepended with the current run id if not specified.
  • aliases: Aliases to apply to the created model artifact, defaults to ["latest"]

Returns: None

Raises:

  • ValueError: if name has invalid special characters.

Examples:

run.log_model(
   path="/local/directory",
   name="my_model_artifact",
   aliases=["production"],
)

Invalid usage

run.log_model(
    path="/local/directory",
    name="my_entity/my_project/my_model_artifact",
    aliases=["production"],
)

method Run.mark_preempting

mark_preempting() → None

Mark this run as preempting.

Also tells the internal process to immediately report this to server.

method Run.project_name

project_name() → str

This method is deprecated and will be removed in a future release. Use run.project instead.

Name of the W&B project associated with the run.

method Run.restore

restore(
    name: 'str',
    run_path: 'str | None' = None,
    replace: 'bool' = False,
    root: 'str | None' = None
) → None | TextIO

Download the specified file from cloud storage.

File is placed into the current directory or run directory. By default, will only download the file if it doesn’t already exist.

Args:

  • name: The name of the file.
  • run_path: Optional path to a run to pull files from, i.e. username/project_name/run_id if wandb.init has not been called, this is required.
  • replace: Whether to download the file even if it already exists locally
  • root: The directory to download the file to. Defaults to the current directory or the run directory if wandb.init was called.

Returns: None if it can’t find the file, otherwise a file object open for reading.

Raises:

  • wandb.CommError: If W&B can’t connect to the W&B backend.
  • ValueError: If the file is not found or can’t find run_path.

method Run.save

save(
    glob_str: 'str | os.PathLike',
    base_path: 'str | os.PathLike | None' = None,
    policy: 'PolicyName' = 'live'
) → bool | list[str]

Sync one or more files to W&B.

Relative paths are relative to the current working directory.

A Unix glob, such as “myfiles/*”, is expanded at the time save is called regardless of the policy. In particular, new files are not picked up automatically.

A base_path may be provided to control the directory structure of uploaded files. It should be a prefix of glob_str, and the directory structure beneath it is preserved.

When given an absolute path or glob and no base_path, one directory level is preserved as in the example above.

Args:

  • glob_str: A relative or absolute path or Unix glob.
  • base_path: A path to use to infer a directory structure; see examples.
  • policy: One of live, now, or end.
    • live: upload the file as it changes, overwriting the previous version
    • now: upload the file once now
    • end: upload file when the run ends

Returns: Paths to the symlinks created for the matched files.

For historical reasons, this may return a boolean in legacy code.

import wandb

wandb.init()

wandb.save("these/are/myfiles/*")
# => Saves files in a "these/are/myfiles/" folder in the run.

wandb.save("these/are/myfiles/*", base_path="these")
# => Saves files in an "are/myfiles/" folder in the run.

wandb.save("/User/username/Documents/run123/*.txt")
# => Saves files in a "run123/" folder in the run. See note below.

wandb.save("/User/username/Documents/run123/*.txt", base_path="/User")
# => Saves files in a "username/Documents/run123/" folder in the run.

wandb.save("files/*/saveme.txt")
# => Saves each "saveme.txt" file in an appropriate subdirectory
#    of "files/".

method Run.status

status() → RunStatus

Get sync info from the internal backend, about the current run’s sync status.

method Run.to_html

to_html(height: 'int' = 420, hidden: 'bool' = False) → str

Generate HTML containing an iframe displaying the current run.

method Run.unwatch

unwatch(
    models: 'torch.nn.Module | Sequence[torch.nn.Module] | None' = None
) → None

Remove pytorch model topology, gradient and parameter hooks.

Args:

  • models: Optional list of pytorch models that have had watch called on them.

method Run.upsert_artifact

upsert_artifact(
    artifact_or_path: 'Artifact | str',
    name: 'str | None' = None,
    type: 'str | None' = None,
    aliases: 'list[str] | None' = None,
    distributed_id: 'str | None' = None
) → Artifact

Declare (or append to) a non-finalized artifact as output of a run.

Note that you must call run.finish_artifact() to finalize the artifact. This is useful when distributed jobs need to all contribute to the same artifact.

Args:

  • artifact_or_path: A path to the contents of this artifact, can be in the following forms:
    • /local/directory
    • /local/directory/file.txt
    • s3://bucket/path
  • name: An artifact name. May be prefixed with “entity/project”. Defaults to the basename of the path prepended with the current run ID if not specified. Valid names can be in the following forms:
    • name:version
    • name:alias
    • digest
  • type: The type of artifact to log. Common examples include dataset, model.
  • aliases: Aliases to apply to this artifact, defaults to ["latest"].
  • distributed_id: Unique string that all distributed jobs share. If None, defaults to the run’s group name.

Returns: An Artifact object.

method Run.use_artifact

use_artifact(
    artifact_or_name: 'str | Artifact',
    type: 'str | None' = None,
    aliases: 'list[str] | None' = None,
    use_as: 'str | None' = None
) → Artifact

Declare an artifact as an input to a run.

Call download or file on the returned object to get the contents locally.

Args:

  • artifact_or_name: The name of the artifact to use. May be prefixed with the name of the project the artifact was logged to ("" or “/”). If no entity is specified in the name, the Run or API setting’s entity is used. Valid names can be in the following forms
    • name:version
    • name:alias
  • type: The type of artifact to use.
  • aliases: Aliases to apply to this artifact
  • use_as: This argument is deprecated and does nothing.

Returns: An Artifact object.

Examples:

import wandb

run = wandb.init(project="<example>")

# Use an artifact by name and alias
artifact_a = run.use_artifact(artifact_or_name="<name>:<alias>")

# Use an artifact by name and version
artifact_b = run.use_artifact(artifact_or_name="<name>:v<version>")

# Use an artifact by entity/project/name:alias
artifact_c = run.use_artifact(
   artifact_or_name="<entity>/<project>/<name>:<alias>"
)

# Use an artifact by entity/project/name:version
artifact_d = run.use_artifact(
   artifact_or_name="<entity>/<project>/<name>:v<version>"
)

method Run.use_model

use_model(name: 'str') → FilePathStr

Download the files logged in a model artifact name.

Args:

  • name: A model artifact name. ’name’ must match the name of an existing logged model artifact. May be prefixed with entity/project/. Valid names can be in the following forms
    • model_artifact_name:version
    • model_artifact_name:alias

Raises:

  • AssertionError: if model artifact name is of a type that does not contain the substring ‘model’.

Returns:

  • path: path to downloaded model artifact file(s).

Examples:

run.use_model(
   name="my_model_artifact:latest",
)

run.use_model(
   name="my_project/my_model_artifact:v0",
)

run.use_model(
   name="my_entity/my_project/my_model_artifact:<digest>",
)

Invalid usage

run.use_model(
    name="my_entity/my_project/my_model_artifact",
)

method Run.watch

watch(
    models: 'torch.nn.Module | Sequence[torch.nn.Module]',
    criterion: 'torch.F | None' = None,
    log: "Literal['gradients', 'parameters', 'all'] | None" = 'gradients',
    log_freq: 'int' = 1000,
    idx: 'int | None' = None,
    log_graph: 'bool' = False
) → None

Hook into given PyTorch model to monitor gradients and the model’s computational graph.

This function can track parameters, gradients, or both during training.

Args:

  • models: A single model or a sequence of models to be monitored.
  • criterion: The loss function being optimized (optional).
  • log: Specifies whether to log “gradients”, “parameters”, or “all”. Set to None to disable logging. (default=“gradients”).
  • log_freq: Frequency (in batches) to log gradients and parameters. (default=1000)
  • idx: Index used when tracking multiple models with wandb.watch. (default=None)
  • log_graph: Whether to log the model’s computational graph. (default=False)

Raises: ValueError: If wandb.init has not been called or if any of the models are not instances of torch.nn.Module.

4.1.1.5 - Settings

View the source code

class Settings

Settings for the W&B SDK.

This class manages configuration settings for the W&B SDK, ensuring type safety and validation of all settings. Settings are accessible as attributes and can be initialized programmatically, through environment variables (WANDB_ prefix), and with configuration files.

The settings are organized into three categories: 1. Public settings: Core configuration options that users can safely modify to customize W&B’s behavior for their specific needs. 2. Internal settings: Settings prefixed with ‘x_’ that handle low-level SDK behavior. These settings are primarily for internal use and debugging. While they can be modified, they are not considered part of the public API and may change without notice in future versions. 3. Computed settings: Read-only settings that are automatically derived from other settings or the environment.

Args:

  • allow_offline_artifacts (bool): Flag to allow table artifacts to be synced in offline mode.
  • allow_val_change (bool): Flag to allow modification of Config values after they’ve been set.
  • anonymous (Optional[Literal[“allow”, “must”, “never”]]): Controls anonymous data logging. Possible values are:
    • “never”: requires you to link your W&B account before tracking the run, so you don’t accidentally create an anonymous run.
    • “allow”: lets a logged-in user track runs with their account, but lets someone who is running the script without a W&B account see the charts in the UI.
    • “must”: sends the run to an anonymous account instead of to a signed-up user account.
  • api_key (Optional[str]): The W&B API key.
  • azure_account_url_to_access_key (Optional[Dict[str, str]]): Mapping of Azure account URLs to their corresponding access keys for Azure integration.
  • base_url (str): The URL of the W&B backend for data synchronization.
  • code_dir (Optional[str]): Directory containing the code to be tracked by W&B.
  • config_paths (Optional[Sequence[str]]): Paths to files to load configuration from into the Config object.
  • console (Literal[“auto”, “off”, “wrap”, “redirect”, “wrap_raw”, “wrap_emu”]): The type of console capture to be applied. Possible values are:
    • “auto” - Automatically selects the console capture method based on the system environment and settings.
    • “off” - Disables console capture.
    • “redirect” - Redirects low-level file descriptors for capturing output.
    • “wrap” - Overrides the write methods of sys.stdout/sys.stderr. Will be mapped to either “wrap_raw” or “wrap_emu” based on the state of the system.
    • “wrap_raw” - Same as “wrap” but captures raw output directly instead of through an emulator. Derived from the wrap setting and should not be set manually.
    • “wrap_emu” - Same as “wrap” but captures output through an emulator. Derived from the wrap setting and should not be set manually.
  • console_multipart (bool): Whether to produce multipart console log files.
  • credentials_file (str): Path to file for writing temporary access tokens.
  • disable_code (bool): Whether to disable capturing the code.
  • disable_git (bool): Whether to disable capturing the git state.
  • disable_job_creation (bool): Whether to disable the creation of a job artifact for W&B Launch.
  • docker (Optional[str]): The Docker image used to execute the script.
  • email (Optional[str]): The email address of the user.
  • entity (Optional[str]): The W&B entity, such as a user or a team.
  • organization (Optional[str]): The W&B organization.
  • force (bool): Whether to pass the force flag to wandb.login().
  • fork_from (Optional[RunMoment]): Specifies a point in a previous execution of a run to fork from. The point is defined by the run ID, a metric, and its value. Only the metric ‘_step’ is supported.
  • git_commit (Optional[str]): The git commit hash to associate with the run.
  • git_remote (str): The git remote to associate with the run.
  • git_remote_url (Optional[str]): The URL of the git remote repository.
  • git_root (Optional[str]): Root directory of the git repository.
  • heartbeat_seconds (int): Interval in seconds between heartbeat signals sent to the W&B servers.
  • host (Optional[str]): Hostname of the machine running the script.
  • http_proxy (Optional[str]): Custom proxy servers for http requests to W&B.
  • https_proxy (Optional[str]): Custom proxy servers for https requests to W&B.
  • identity_token_file (Optional[str]): Path to file containing an identity token (JWT) for authentication.
  • ignore_globs (Sequence[str]): Unix glob patterns relative to files_dir specifying files to exclude from upload.
  • init_timeout (float): Time in seconds to wait for the wandb.init call to complete before timing out.
  • insecure_disable_ssl (bool): Whether to disable SSL verification.
  • job_name (Optional[str]): Name of the Launch job running the script.
  • job_source (Optional[Literal[“repo”, “artifact”, “image”]]): Source type for Launch.
  • label_disable (bool): Whether to disable automatic labeling features.
  • launch (bool): Flag to indicate if the run is being launched through W&B Launch.
  • launch_config_path (Optional[str]): Path to the launch configuration file.
  • login_timeout (Optional[float]): Time in seconds to wait for login operations before timing out.
  • mode (Literal[“online”, “offline”, “dryrun”, “disabled”, “run”, “shared”]): The operating mode for W&B logging and synchronization.
  • notebook_name (Optional[str]): Name of the notebook if running in a Jupyter-like environment.
  • program (Optional[str]): Path to the script that created the run, if available.
  • program_abspath (Optional[str]): The absolute path from the root repository directory to the script that created the run. Root repository directory is defined as the directory containing the .git directory, if it exists. Otherwise, it’s the current working directory.
  • program_relpath (Optional[str]): The relative path to the script that created the run.
  • project (Optional[str]): The W&B project ID.
  • quiet (bool): Flag to suppress non-essential output.
  • reinit (Union[Literal[“default”, “return_previous”, “finish_previous”, “create_new”], bool]): What to do when wandb.init() is called while a run is active. Options are
    • “default”: Use “finish_previous” in notebooks and “return_previous” otherwise.
    • “return_previous”: Return the most recently created run that is not yet finished. This does not update wandb.run; see the “create_new” option.
    • “finish_previous”: Finish all active runs, then return a new run.
    • “create_new”: Create a new run without modifying other active runs. Does not update wandb.run and top-level functions like wandb.log. Because of this, some older integrations that rely on the global run will not work.
  • relogin (bool): Whether to force a new login attempt.
  • resume (Optional[Literal[“allow”, “must”, “never”, “auto”]]): Specifies the resume behavior for the run. The available options are
    • “must”: Resumes from an existing run with the same ID. If no such run exists, it will result in failure.
    • “allow”: Attempts to resume from an existing run with the same ID. If none is found, a new run will be created.
    • “never”: Always starts a new run. If a run with the same ID already exists, it will result in failure.
    • “auto”: Automatically resumes from the most recent failed run on the same machine.
  • resume_from (Optional[RunMoment]): Specifies a point in a previous execution of a run to resume from. The point is defined by the run ID, a metric, and its value. Currently, only the metric ‘_step’ is supported.
  • resumed (bool): Indication from the server about the state of the run. This is different from resume, a user provided flag.
  • root_dir (str): The root directory to use as the base for all run-related paths. Used to derive the wandb directory and the run directory.
  • run_group (Optional[str]): Group identifier for related runs. Used for grouping runs in the UI.
  • run_id (Optional[str]): The ID of the run.
  • run_job_type (Optional[str]): Type of job being run (e.g., training, evaluation).
  • run_name (Optional[str]): Human-readable name for the run.
  • run_notes (Optional[str]): Additional notes or description for the run.
  • run_tags (Optional[Tuple[str, …]]): Tags to associate with the run for organization and filtering.
  • sagemaker_disable (bool): Flag to disable SageMaker-specific functionality.
  • save_code (Optional[bool]): Whether to save the code associated with the run.
  • settings_system (Optional[str]): Path to the system-wide settings file.
  • show_colors (Optional[bool]): Whether to use colored output in the console.
  • show_emoji (Optional[bool]): Whether to show emoji in the console output.
  • show_errors (bool): Whether to display error messages.
  • show_info (bool): Whether to display informational messages.
  • show_warnings (bool): Whether to display warning messages.
  • silent (bool): Flag to suppress all output.
  • start_method (Optional[str]): Method to use for starting subprocesses.
  • strict (Optional[bool]): Whether to enable strict mode for validation and error checking.
  • summary_timeout (int): Time in seconds to wait for summary operations before timing out.
  • summary_warnings (int): Maximum number of summary warnings to display.
  • sweep_id (Optional[str]): Identifier of the sweep this run belongs to.
  • sweep_param_path (Optional[str]): Path to the sweep parameters configuration.
  • symlink (bool): Whether to use symlinks for run directories.
  • sync_tensorboard (Optional[bool]): Whether to synchronize TensorBoard logs with W&B.
  • table_raise_on_max_row_limit_exceeded (bool): Whether to raise an exception when table row limits are exceeded.
  • username (Optional[str]): Username of the user.

property Settings.colab_url

The URL to the Colab notebook, if running in Colab.

property Settings.deployment

property Settings.files_dir

Absolute path to the local directory where the run’s files are stored.

property Settings.is_local

property Settings.log_dir

The directory for storing log files.

property Settings.log_internal

The path to the file to use for internal logs.

The path to the symlink to the internal log file of the most recent run.

The path to the symlink to the user-process log file of the most recent run.

property Settings.log_user

The path to the file to use for user-process logs.

property Settings.model_extra

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to "allow".

property Settings.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns: A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

property Settings.project_url

The W&B URL where the project can be viewed.

property Settings.resume_fname

The path to the resume file.

property Settings.run_mode

The mode of the run. Can be either “run” or “offline-run”.

property Settings.run_url

The W&B URL where the run can be viewed.

property Settings.settings_workspace

The path to the workspace settings file.

property Settings.sweep_url

The W&B URL where the sweep can be viewed.

property Settings.sync_dir

The directory for storing the run’s files.

property Settings.sync_file

Path to the append-only binary transaction log file.

Path to the symlink to the most recent run’s transaction log file.

property Settings.timespec

The time specification for the run.

property Settings.wandb_dir

Full path to the wandb directory.

classmethod Settings.catch_private_settings

catch_private_settings(values)

Check if a private field is provided and assign to the corresponding public one.

This is a compatibility layer to handle previous versions of the settings.

method Settings.update_from_dict

update_from_dict(settings: 'Dict[str, Any]') → None

Update settings from a dictionary.

4.1.2 - Functions

4.1.2.1 - agent()

View the source code

function agent

agent(
    sweep_id: str,
    function: Optional[Callable] = None,
    entity: Optional[str] = None,
    project: Optional[str] = None,
    count: Optional[int] = None
) → None

Start one or more sweep agents.

The sweep agent uses the sweep_id to know which sweep it is a part of, what function to execute, and (optionally) how many agents to run.

Args:

  • sweep_id: The unique identifier for a sweep. A sweep ID is generated by W&B CLI or Python SDK.
  • function: A function to call instead of the “program” specified in the sweep config.
  • entity: The username or team name where you want to send W&B runs created by the sweep to. Ensure that the entity you specify already exists. If you don’t specify an entity, the run will be sent to your default entity, which is usually your username.
  • project: The name of the project where W&B runs created from the sweep are sent to. If the project is not specified, the run is sent to a project labeled “Uncategorized”.
  • count: The number of sweep config trials to try.

4.1.2.2 - controller()

View the source code

function controller

controller(
    sweep_id_or_config: Optional[str, Dict] = None,
    entity: Optional[str] = None,
    project: Optional[str] = None
) → _WandbController

Public sweep controller constructor.

Examples:

import wandb

tuner = wandb.controller(...)
print(tuner.sweep_config)
print(tuner.sweep_id)
tuner.configure_search(...)
tuner.configure_stopping(...)

4.1.2.3 - finish()

View the source code

function finish

finish(exit_code: 'int | None' = None, quiet: 'bool | None' = None) → None

Finish a run and upload any remaining data.

Marks the completion of a W&B run and ensures all data is synced to the server. The run’s final state is determined by its exit conditions and sync status.

Run States:

  • Running: Active run that is logging data and/or sending heartbeats.
  • Crashed: Run that stopped sending heartbeats unexpectedly.
  • Finished: Run completed successfully (exit_code=0) with all data synced.
  • Failed: Run completed with errors (exit_code!=0).

Args:

  • exit_code: Integer indicating the run’s exit status. Use 0 for success, any other value marks the run as failed.
  • quiet: Deprecated. Configure logging verbosity using wandb.Settings(quiet=...).

4.1.2.4 - init()

View the source code

function init

init(
    entity: 'str | None' = None,
    project: 'str | None' = None,
    dir: 'StrPath | None' = None,
    id: 'str | None' = None,
    name: 'str | None' = None,
    notes: 'str | None' = None,
    tags: 'Sequence[str] | None' = None,
    config: 'dict[str, Any] | str | None' = None,
    config_exclude_keys: 'list[str] | None' = None,
    config_include_keys: 'list[str] | None' = None,
    allow_val_change: 'bool | None' = None,
    group: 'str | None' = None,
    job_type: 'str | None' = None,
    mode: "Literal['online', 'offline', 'disabled'] | None" = None,
    force: 'bool | None' = None,
    anonymous: "Literal['never', 'allow', 'must'] | None" = None,
    reinit: "bool | Literal[None, 'default', 'return_previous', 'finish_previous', 'create_new']" = None,
    resume: "bool | Literal['allow', 'never', 'must', 'auto'] | None" = None,
    resume_from: 'str | None' = None,
    fork_from: 'str | None' = None,
    save_code: 'bool | None' = None,
    tensorboard: 'bool | None' = None,
    sync_tensorboard: 'bool | None' = None,
    monitor_gym: 'bool | None' = None,
    settings: 'Settings | dict[str, Any] | None' = None
) → Run

Start a new run to track and log to W&B.

In an ML training pipeline, you could add wandb.init() to the beginning of your training script as well as your evaluation script, and each piece would be tracked as a run in W&B.

wandb.init() spawns a new background process to log data to a run, and it also syncs data to https://wandb.ai by default, so you can see your results in real-time. When you’re done logging data, call wandb.finish() to end the run. If you don’t call run.finish(), the run will end when your script exits.

Run IDs must not contain any of the following special characters / \ # ? % :

Args:

  • entity: The username or team name the runs are logged to. The entity must already exist, so ensure you create your account or team in the UI before starting to log runs. If not specified, the run will default your default entity. To change the default entity, go to your settings and update the “Default location to create new projects” under “Default team”.
  • project: The name of the project under which this run will be logged. If not specified, we use a heuristic to infer the project name based on the system, such as checking the git root or the current program file. If we can’t infer the project name, the project will default to "uncategorized".
  • dir: The absolute path to the directory where experiment logs and metadata files are stored. If not specified, this defaults to the ./wandb directory. Note that this does not affect the location where artifacts are stored when calling download().
  • id: A unique identifier for this run, used for resuming. It must be unique within the project and cannot be reused once a run is deleted. For a short descriptive name, use the name field, or for saving hyperparameters to compare across runs, use config.
  • name: A short display name for this run, which appears in the UI to help you identify it. By default, we generate a random two-word name allowing easy cross-reference runs from table to charts. Keeping these run names brief enhances readability in chart legends and tables. For saving hyperparameters, we recommend using the config field.
  • notes: A detailed description of the run, similar to a commit message in Git. Use this argument to capture any context or details that may help you recall the purpose or setup of this run in the future.
  • tags: A list of tags to label this run in the UI. Tags are helpful for organizing runs or adding temporary identifiers like “baseline” or “production.” You can easily add, remove tags, or filter by tags in the UI. If resuming a run, the tags provided here will replace any existing tags. To add tags to a resumed run without overwriting the current tags, use run.tags += ["new_tag"] after calling run = wandb.init().
  • config: Sets wandb.config, a dictionary-like object for storing input parameters to your run, such as model hyperparameters or data preprocessing settings. The config appears in the UI in an overview page, allowing you to group, filter, and sort runs based on these parameters. Keys should not contain periods (.), and values should be smaller than 10 MB. If a dictionary, argparse.Namespace, or absl.flags.FLAGS is provided, the key-value pairs will be loaded directly into wandb.config. If a string is provided, it is interpreted as a path to a YAML file, from which configuration values will be loaded into wandb.config.
  • config_exclude_keys: A list of specific keys to exclude from wandb.config.
  • config_include_keys: A list of specific keys to include in wandb.config.
  • allow_val_change: Controls whether config values can be modified after their initial set. By default, an exception is raised if a config value is overwritten. For tracking variables that change during training, such as a learning rate, consider using wandb.log() instead. By default, this is False in scripts and True in Notebook environments.
  • group: Specify a group name to organize individual runs as part of a larger experiment. This is useful for cases like cross-validation or running multiple jobs that train and evaluate a model on different test sets. Grouping allows you to manage related runs collectively in the UI, making it easy to toggle and review results as a unified experiment.
  • job_type: Specify the type of run, especially helpful when organizing runs within a group as part of a larger experiment. For example, in a group, you might label runs with job types such as “train” and “eval”. Defining job types enables you to easily filter and group similar runs in the UI, facilitating direct comparisons.
  • mode: Specifies how run data is managed, with the following options:
    • "online" (default): Enables live syncing with W&B when a network connection is available, with real-time updates to visualizations.
    • "offline": Suitable for air-gapped or offline environments; data is saved locally and can be synced later. Ensure the run folder is preserved to enable future syncing.
    • "disabled": Disables all W&B functionality, making the run’s methods no-ops. Typically used in testing to bypass W&B operations.
  • force: Determines if a W&B login is required to run the script. If True, the user must be logged in to W&B; otherwise, the script will not proceed. If False (default), the script can proceed without a login, switching to offline mode if the user is not logged in.
  • anonymous: Specifies the level of control over anonymous data logging. Available options are:
    • "never" (default): Requires you to link your W&B account before tracking the run. This prevents unintentional creation of anonymous runs by ensuring each run is associated with an account.
    • "allow": Enables a logged-in user to track runs with their account, but also allows someone running the script without a W&B account to view the charts and data in the UI.
    • "must": Forces the run to be logged to an anonymous account, even if the user is logged in.
  • reinit: Shorthand for the “reinit” setting. Determines the behavior of wandb.init() when a run is active.
  • resume: Controls the behavior when resuming a run with the specified id. Available options are:
    • "allow": If a run with the specified id exists, it will resume from the last step; otherwise, a new run will be created.
    • "never": If a run with the specified id exists, an error will be raised. If no such run is found, a new run will be created.
    • "must": If a run with the specified id exists, it will resume from the last step. If no run is found, an error will be raised.
    • "auto": Automatically resumes the previous run if it crashed on this machine; otherwise, starts a new run.
    • True: Deprecated. Use "auto" instead.
    • False: Deprecated. Use the default behavior (leaving resume unset) to always start a new run. If resume is set, fork_from and resume_from cannot be used. When resume is unset, the system will always start a new run.
  • resume_from: Specifies a moment in a previous run to resume a run from, using the format {run_id}?_step={step}. This allows users to truncate the history logged to a run at an intermediate step and resume logging from that step. The target run must be in the same project. If an id argument is also provided, the resume_from argument will take precedence. resume, resume_from and fork_from cannot be used together, only one of them can be used at a time. Note that this feature is in beta and may change in the future.
  • fork_from: Specifies a point in a previous run from which to fork a new run, using the format {id}?_step={step}. This creates a new run that resumes logging from the specified step in the target run’s history. The target run must be part of the current project. If an id argument is also provided, it must be different from the fork_from argument, an error will be raised if they are the same. resume, resume_from and fork_from cannot be used together, only one of them can be used at a time. Note that this feature is in beta and may change in the future.
  • save_code: Enables saving the main script or notebook to W&B, aiding in experiment reproducibility and allowing code comparisons across runs in the UI. By default, this is disabled, but you can change the default to enable on your settings page.
  • tensorboard: Deprecated. Use sync_tensorboard instead.
  • sync_tensorboard: Enables automatic syncing of W&B logs from TensorBoard or TensorBoardX, saving relevant event files for viewing in the W&B UI.
  • saving relevant event files for viewing in the W&B UI. (Default: False)
  • monitor_gym: Enables automatic logging of videos of the environment when using OpenAI Gym.
  • settings: Specifies a dictionary or wandb.Settings object with advanced settings for the run.

Raises:

  • Error: if some unknown or internal error happened during the run initialization.
  • AuthenticationError: if the user failed to provide valid credentials.
  • CommError: if there was a problem communicating with the WandB server.
  • UsageError: if the user provided invalid arguments.
  • KeyboardInterrupt: if user interrupts the run.

Returns: A Run object.

Examples: wandb.init() returns a run object, and you can also access the run object with wandb.run:

import wandb

config = {"lr": 0.01, "batch_size": 32}
with wandb.init(config=config) as run:
    run.config.update({"architecture": "resnet", "depth": 34})

    # ... your training code here ...

4.1.2.5 - login()

View the source code

function login

login(
    anonymous: Optional[Literal['must', 'allow', 'never']] = None,
    key: Optional[str] = None,
    relogin: Optional[bool] = None,
    host: Optional[str] = None,
    force: Optional[bool] = None,
    timeout: Optional[int] = None,
    verify: bool = False,
    referrer: Optional[str] = None
) → bool

Set up W&B login credentials.

By default, this will only store credentials locally without verifying them with the W&B server. To verify credentials, pass verify=True.

Args:

  • anonymous: Set to “must”, “allow”, or “never”. If set to “must”, always log a user in anonymously. If set to “allow”, only create an anonymous user if the user isn’t already logged in. If set to “never”, never log a user anonymously. Default set to “never”.
  • key: The API key to use.
  • relogin: If true, will re-prompt for API key.
  • host: The host to connect to.
  • force: If true, will force a relogin.
  • timeout: Number of seconds to wait for user input.
  • verify: Verify the credentials with the W&B server.
  • referrer: The referrer to use in the URL login request.

Returns:

  • bool: If key is configured

Raises:

  • AuthenticationError: If api_key fails verification with the server.
  • UsageError: If api_key cannot be configured and no tty.

4.1.2.6 - restore()

View the source code

function restore

restore(
    name: 'str',
    run_path: 'str | None' = None,
    replace: 'bool' = False,
    root: 'str | None' = None
) → None | TextIO

Download the specified file from cloud storage.

File is placed into the current directory or run directory. By default, will only download the file if it doesn’t already exist.

Args:

  • name: The name of the file.
  • run_path: Optional path to a run to pull files from, i.e. username/project_name/run_id if wandb.init has not been called, this is required.
  • replace: Whether to download the file even if it already exists locally
  • root: The directory to download the file to. Defaults to the current directory or the run directory if wandb.init was called.

Returns: None if it can’t find the file, otherwise a file object open for reading.

Raises:

  • wandb.CommError: If W&B can’t connect to the W&B backend.
  • ValueError: If the file is not found or can’t find run_path.

4.1.2.7 - setup()

View the source code

function setup

setup(settings: 'Settings | None' = None) → _WandbSetup

Prepares W&B for use in the current process and its children.

You can usually ignore this as it is implicitly called by wandb.init().

When using wandb in multiple processes, calling wandb.setup() in the parent process before starting child processes may improve performance and resource utilization.

Note that wandb.setup() modifies os.environ, and it is important that child processes inherit the modified environment variables.

See also wandb.teardown().

Args:

  • settings: Configuration settings to apply globally. These can be overridden by subsequent wandb.init() calls.

Example:

import multiprocessing

import wandb


def run_experiment(params):
   with wandb.init(config=params):
        # Run experiment
        pass


if __name__ == "__main__":
   # Start backend and set global config
   wandb.setup(settings={"project": "my_project"})

   # Define experiment parameters
   experiment_params = [
        {"learning_rate": 0.01, "epochs": 10},
        {"learning_rate": 0.001, "epochs": 20},
   ]

   # Start multiple processes, each running a separate experiment
   processes = []
   for params in experiment_params:
        p = multiprocessing.Process(target=run_experiment, args=(params,))
        p.start()
        processes.append(p)

   # Wait for all processes to complete
   for p in processes:
        p.join()

   # Optional: Explicitly shut down the backend
   wandb.teardown()

4.1.2.8 - sweep()

View the source code

function sweep

sweep(
    sweep: Union[dict, Callable],
    entity: Optional[str] = None,
    project: Optional[str] = None,
    prior_runs: Optional[List[str]] = None
) → str

Initialize a hyperparameter sweep.

Search for hyperparameters that optimizes a cost function of a machine learning model by testing various combinations.

Make note the unique identifier, sweep_id, that is returned. At a later step provide the sweep_id to a sweep agent.

See Sweep configuration structure for information on how to define your sweep.

Args:

  • sweep: The configuration of a hyperparameter search. (or configuration generator). If you provide a callable, ensure that the callable does not take arguments and that it returns a dictionary that conforms to the W&B sweep config spec.
  • entity: The username or team name where you want to send W&B runs created by the sweep to. Ensure that the entity you specify already exists. If you don’t specify an entity, the run will be sent to your default entity, which is usually your username.
  • project: The name of the project where W&B runs created from the sweep are sent to. If the project is not specified, the run is sent to a project labeled ‘Uncategorized’.
  • prior_runs: The run IDs of existing runs to add to this sweep.

Returns:

  • sweep_id: str. A unique identifier for the sweep.

4.1.2.9 - teardown()

View the source code

function teardown

teardown(exit_code: 'int | None' = None) → None

Waits for W&B to finish and frees resources.

Completes any runs that were not explicitly finished using run.finish() and waits for all data to be uploaded.

It is recommended to call this at the end of a session that used wandb.setup(). It is invoked automatically in an atexit hook, but this is not reliable in certain setups such as when using Python’s multiprocessing module.

4.1.3 - Legacy Functions

4.1.3.1 - define_metric()

View the source code

function wandb.define_metric

wandb.define_metric(
    name: 'str',
    step_metric: 'str | wandb_metric.Metric | None' = None,
    step_sync: 'bool | None' = None,
    hidden: 'bool | None' = None,
    summary: 'str | None' = None,
    goal: 'str | None' = None,
    overwrite: 'bool | None' = None
) → wandb_metric.Metric

Customize metrics logged with wandb.log().

Args:

  • name: The name of the metric to customize.
  • step_metric: The name of another metric to serve as the X-axis for this metric in automatically generated charts.
  • step_sync: Automatically insert the last value of step_metric into run.log() if it is not provided explicitly. Defaults to True if step_metric is specified.
  • hidden: Hide this metric from automatic plots.
  • summary: Specify aggregate metrics added to summary. Supported aggregations include “min”, “max”, “mean”, “last”, “best”, “copy” and “none”. “best” is used together with the goal parameter. “none” prevents a summary from being generated. “copy” is deprecated and should not be used.
  • goal: Specify how to interpret the “best” summary type. Supported options are “minimize” and “maximize”.
  • overwrite: If false, then this call is merged with previous define_metric calls for the same metric by using their values for any unspecified parameters. If true, then unspecified parameters overwrite values specified by previous calls.

Returns: An object that represents this call but can otherwise be discarded.

4.1.3.2 - link_model()

View the source code 
wandb.link_model(
    path: 'StrPath',
    registered_model_name: 'str',
    name: 'str | None' = None,
    aliases: 'list[str] | None' = None
) → Artifact | None

Log a model artifact version and link it to a registered model in the model registry.

Linked model versions are visible in the UI for the specified registered model.

This method will:

  • Check if ’name’ model artifact has been logged. If so, use the artifact version that matches the files located at ‘path’ or log a new version. Otherwise log files under ‘path’ as a new model artifact, ’name’ of type ‘model’.
  • Check if registered model with name ‘registered_model_name’ exists in the ‘model-registry’ project. If not, create a new registered model with name ‘registered_model_name’.
  • Link version of model artifact ’name’ to registered model, ‘registered_model_name’.
  • Attach aliases from ‘aliases’ list to the newly linked model artifact version.

Args:

  • path: (str) A path to the contents of this model, can be in the following forms:
    • /local/directory
    • /local/directory/file.txt
    • s3://bucket/path
  • registered_model_name: The name of the registered model that the model is to be linked to. A registered model is a collection of model versions linked to the model registry, typically representing a team’s specific ML Task. The entity that this registered model belongs to will be derived from the run.
  • name: The name of the model artifact that files in ‘path’ will be logged to. This will default to the basename of the path prepended with the current run id if not specified.
  • aliases: Aliases that will only be applied on this linked artifact inside the registered model. The alias “latest” will always be applied to the latest version of an artifact that is linked.

Raises:

  • AssertionError: If registered_model_name is a path or if model artifact ’name’ is of a type that does not contain the substring ‘model’.
  • ValueError: If name has invalid special characters.

Returns: The linked artifact if linking was successful, otherwise None.

Examples:

run.link_model(
   path="/local/directory",
   registered_model_name="my_reg_model",
   name="my_model_artifact",
   aliases=["production"],
)

Invalid usage

run.link_model(
    path="/local/directory",
    registered_model_name="my_entity/my_project/my_reg_model",
    name="my_model_artifact",
    aliases=["production"],
)

run.link_model(
    path="/local/directory",
    registered_model_name="my_reg_model",
    name="my_entity/my_project/my_model_artifact",
    aliases=["production"],
)

4.1.3.3 - log_artifact()

View the source code

function wandb.log_artifact

wandb.log_artifact(
    artifact_or_path: 'Artifact | StrPath',
    name: 'str | None' = None,
    type: 'str | None' = None,
    aliases: 'list[str] | None' = None,
    tags: 'list[str] | None' = None
) → Artifact

Declare an artifact as an output of a run.

Args:

  • artifact_or_path: A path to the contents of this artifact, can be in the following forms
    • /local/directory
    • /local/directory/file.txt
    • s3://bucket/path
  • name: An artifact name. Defaults to the basename of the path prepended with the current run id if not specified. Valid names can be in the following forms:
    • name:version
    • name:alias
    • digest
  • type: The type of artifact to log. Common examples include dataset and model
  • aliases: Aliases to apply to this artifact, defaults to ["latest"]
  • tags: Tags to apply to this artifact, if any.

Returns: An Artifact object.

4.1.3.4 - log_model()

View the source code

function wandb.log_model

wandb.log_model(
    path: 'StrPath',
    name: 'str | None' = None,
    aliases: 'list[str] | None' = None
) → None

Logs a model artifact as an output of this run.

The name of model artifact can only contain alphanumeric characters, underscores, and hyphens.

Args:

  • path: A path to the contents of this model, can be in the following forms
    • /local/directory
    • /local/directory/file.txt
    • s3://bucket/path
  • name: A name to assign to the model artifact that the file contents will be added to. The string must contain only alphanumeric characters such as dashes, underscores, and dots. This will default to the basename of the path prepended with the current run id if not specified.
  • aliases: Aliases to apply to the created model artifact, defaults to ["latest"]

Returns: None

Raises:

  • ValueError: if name has invalid special characters.

Examples:

run.log_model(
   path="/local/directory",
   name="my_model_artifact",
   aliases=["production"],
)

Invalid usage

run.log_model(
    path="/local/directory",
    name="my_entity/my_project/my_model_artifact",
    aliases=["production"],
)

4.1.3.5 - log()

View the source code

function wandb.log

wandb.log(
    data: 'dict[str, Any]',
    step: 'int | None' = None,
    commit: 'bool | None' = None
) → None

Upload run data.

Use log to log data from runs, such as scalars, images, video, histograms, plots, and tables. See Log objects and media for code snippets, best practices, and more.

Basic usage:

import wandb

with wandb.init() as run:
     run.log({"train-loss": 0.5, "accuracy": 0.9})

The previous code snippet saves the loss and accuracy to the run’s history and updates the summary values for these metrics.

Visualize logged data in a workspace at wandb.ai, or locally on a self-hosted instance of the W&B app, or export data to visualize and explore locally, such as in a Jupyter notebook, with the Public API.

Logged values don’t have to be scalars. You can log any W&B supported Data Type such as images, audio, video, and more. For example, you can use wandb.Table to log structured data. See Log tables, visualize and query data tutorial for more details.

W&B organizes metrics with a forward slash (/) in their name into sections named using the text before the final slash. For example, the following results in two sections named “train” and “validate”:

run.log(
     {
         "train/accuracy": 0.9,
         "train/loss": 30,
         "validate/accuracy": 0.8,
         "validate/loss": 20,
     }
)

Only one level of nesting is supported; run.log({"a/b/c": 1}) produces a section named “a/b”.

run.log is not intended to be called more than a few times per second. For optimal performance, limit your logging to once every N iterations, or collect data over multiple iterations and log it in a single step.

By default, each call to log creates a new “step”. The step must always increase, and it is not possible to log to a previous step. You can use any metric as the X axis in charts. See Custom log axes for more details.

In many cases, it is better to treat the W&B step like you’d treat a timestamp rather than a training step.

# Example: log an "epoch" metric for use as an X axis.
run.log({"epoch": 40, "train-loss": 0.5})

It is possible to use multiple log invocations to log to the same step with the step and commit parameters. The following are all equivalent:

# Normal usage:
run.log({"train-loss": 0.5, "accuracy": 0.8})
run.log({"train-loss": 0.4, "accuracy": 0.9})

# Implicit step without auto-incrementing:
run.log({"train-loss": 0.5}, commit=False)
run.log({"accuracy": 0.8})
run.log({"train-loss": 0.4}, commit=False)
run.log({"accuracy": 0.9})

# Explicit step:
run.log({"train-loss": 0.5}, step=current_step)
run.log({"accuracy": 0.8}, step=current_step)
current_step += 1
run.log({"train-loss": 0.4}, step=current_step)
run.log({"accuracy": 0.9}, step=current_step)

Args:

  • data: A dict with str keys and values that are serializable
  • Python objects including: int, float and string; any of the wandb.data_types; lists, tuples and NumPy arrays of serializable Python objects; other dicts of this structure.
  • step: The step number to log. If None, then an implicit auto-incrementing step is used. See the notes in the description.
  • commit: If true, finalize and upload the step. If false, then accumulate data for the step. See the notes in the description. If step is None, then the default is commit=True; otherwise, the default is commit=False.
  • sync: This argument is deprecated and does nothing.

Examples: For more and more detailed examples, see our guides to logging.

Basic usage

import wandb

run = wandb.init()
run.log({"accuracy": 0.9, "epoch": 5})

Incremental logging

import wandb

run = wandb.init()
run.log({"loss": 0.2}, commit=False)
# Somewhere else when I'm ready to report this step:
run.log({"accuracy": 0.8})

Histogram

import numpy as np
import wandb

# sample gradients at random from normal distribution
gradients = np.random.randn(100, 100)
run = wandb.init()
run.log({"gradients": wandb.Histogram(gradients)})

Image from NumPy

import numpy as np
import wandb

run = wandb.init()
examples = []
for i in range(3):
    pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
    image = wandb.Image(pixels, caption=f"random field {i}")
    examples.append(image)
run.log({"examples": examples})

Image from PIL

import numpy as np
from PIL import Image as PILImage
import wandb

run = wandb.init()
examples = []
for i in range(3):
    pixels = np.random.randint(
         low=0,
         high=256,
         size=(100, 100, 3),
         dtype=np.uint8,
    )
    pil_image = PILImage.fromarray(pixels, mode="RGB")
    image = wandb.Image(pil_image, caption=f"random field {i}")
    examples.append(image)
run.log({"examples": examples})

Video from NumPy

import numpy as np
import wandb

run = wandb.init()
# axes are (time, channel, height, width)
frames = np.random.randint(
    low=0,
    high=256,
    size=(10, 3, 100, 100),
    dtype=np.uint8,
)
run.log({"video": wandb.Video(frames, fps=4)})

Matplotlib plot

from matplotlib import pyplot as plt
import numpy as np
import wandb

run = wandb.init()
fig, ax = plt.subplots()
x = np.linspace(0, 10)
y = x * x
ax.plot(x, y)  # plot y = x^2
run.log({"chart": fig})

PR Curve

import wandb

run = wandb.init()
run.log({"pr": wandb.plot.pr_curve(y_test, y_probas, labels)})

3D Object

import wandb

run = wandb.init()
run.log(
    {
         "generated_samples": [
             wandb.Object3D(open("sample.obj")),
             wandb.Object3D(open("sample.gltf")),
             wandb.Object3D(open("sample.glb")),
         ]
    }
)

Raises:

  • wandb.Error: if called before wandb.init
  • ValueError: if invalid data is passed

Examples:

# Basic usage
import wandb

run = wandb.init()
run.log({"accuracy": 0.9, "epoch": 5})

# Incremental logging
import wandb

run = wandb.init()
run.log({"loss": 0.2}, commit=False)
# Somewhere else when I'm ready to report this step:
run.log({"accuracy": 0.8})

# Histogram
import numpy as np
import wandb

# sample gradients at random from normal distribution
gradients = np.random.randn(100, 100)
run = wandb.init()
run.log({"gradients": wandb.Histogram(gradients)})

# Image from numpy
import numpy as np
import wandb

run = wandb.init()
examples = []
for i in range(3):
    pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
    image = wandb.Image(pixels, caption=f"random field {i}")
    examples.append(image)
run.log({"examples": examples})

# Image from PIL
import numpy as np
from PIL import Image as PILImage
import wandb

run = wandb.init()
examples = []
for i in range(3):
    pixels = np.random.randint(
         low=0, high=256, size=(100, 100, 3), dtype=np.uint8
    )
    pil_image = PILImage.fromarray(pixels, mode="RGB")
    image = wandb.Image(pil_image, caption=f"random field {i}")
    examples.append(image)
run.log({"examples": examples})

# Video from numpy
import numpy as np
import wandb

run = wandb.init()
# axes are (time, channel, height, width)
frames = np.random.randint(
    low=0, high=256, size=(10, 3, 100, 100), dtype=np.uint8
)
run.log({"video": wandb.Video(frames, fps=4)})

# Matplotlib Plot
from matplotlib import pyplot as plt
import numpy as np
import wandb

run = wandb.init()
fig, ax = plt.subplots()
x = np.linspace(0, 10)
y = x * x
ax.plot(x, y)  # plot y = x^2
run.log({"chart": fig})

# PR Curve
import wandb

run = wandb.init()
run.log({"pr": wandb.plot.pr_curve(y_test, y_probas, labels)})

# 3D Object
import wandb

run = wandb.init()
run.log(
    {
         "generated_samples": [
             wandb.Object3D(open("sample.obj")),
             wandb.Object3D(open("sample.gltf")),
             wandb.Object3D(open("sample.glb")),
         ]
    }
)

For more and more detailed examples, see our guides to logging.

4.1.3.6 - save()

View the source code

function wandb.save

wandb.save(
    glob_str: 'str | os.PathLike',
    base_path: 'str | os.PathLike | None' = None,
    policy: 'PolicyName' = 'live'
) → bool | list[str]

Sync one or more files to W&B.

Relative paths are relative to the current working directory.

A Unix glob, such as “myfiles/*”, is expanded at the time save is called regardless of the policy. In particular, new files are not picked up automatically.

A base_path may be provided to control the directory structure of uploaded files. It should be a prefix of glob_str, and the directory structure beneath it is preserved.

When given an absolute path or glob and no base_path, one directory level is preserved as in the example above.

Args:

  • glob_str: A relative or absolute path or Unix glob.
  • base_path: A path to use to infer a directory structure; see examples.
  • policy: One of live, now, or end.
    • live: upload the file as it changes, overwriting the previous version
    • now: upload the file once now
    • end: upload file when the run ends

Returns: Paths to the symlinks created for the matched files.

For historical reasons, this may return a boolean in legacy code.

import wandb

wandb.init()

wandb.save("these/are/myfiles/*")
# => Saves files in a "these/are/myfiles/" folder in the run.

wandb.save("these/are/myfiles/*", base_path="these")
# => Saves files in an "are/myfiles/" folder in the run.

wandb.save("/User/username/Documents/run123/*.txt")
# => Saves files in a "run123/" folder in the run. See note below.

wandb.save("/User/username/Documents/run123/*.txt", base_path="/User")
# => Saves files in a "username/Documents/run123/" folder in the run.

wandb.save("files/*/saveme.txt")
# => Saves each "saveme.txt" file in an appropriate subdirectory
#    of "files/".

4.1.3.7 - unwatch()

View the source code

function wandb.unwatch

wandb.unwatch(
    models: 'torch.nn.Module | Sequence[torch.nn.Module] | None' = None
) → None

Remove pytorch model topology, gradient and parameter hooks.

Args:

  • models: Optional list of pytorch models that have had watch called on them.

4.1.3.8 - use_artifact()

View the source code

function wandb.use_artifact

wandb.use_artifact(
    artifact_or_name: 'str | Artifact',
    type: 'str | None' = None,
    aliases: 'list[str] | None' = None,
    use_as: 'str | None' = None
) → Artifact

Declare an artifact as an input to a run.

Call download or file on the returned object to get the contents locally.

Args:

  • artifact_or_name: The name of the artifact to use. May be prefixed with the name of the project the artifact was logged to ("" or “/”). If no entity is specified in the name, the Run or API setting’s entity is used. Valid names can be in the following forms
    • name:version
    • name:alias
  • type: The type of artifact to use.
  • aliases: Aliases to apply to this artifact
  • use_as: This argument is deprecated and does nothing.

Returns: An Artifact object.

Examples:

import wandb

run = wandb.init(project="<example>")

# Use an artifact by name and alias
artifact_a = run.use_artifact(artifact_or_name="<name>:<alias>")

# Use an artifact by name and version
artifact_b = run.use_artifact(artifact_or_name="<name>:v<version>")

# Use an artifact by entity/project/name:alias
artifact_c = run.use_artifact(
   artifact_or_name="<entity>/<project>/<name>:<alias>"
)

# Use an artifact by entity/project/name:version
artifact_d = run.use_artifact(
   artifact_or_name="<entity>/<project>/<name>:v<version>"
)

4.1.3.9 - use_model()

View the source code

function wandb.use_model

wandb.use_model(name: 'str') → FilePathStr

Download the files logged in a model artifact name.

Args:

  • name: A model artifact name. ’name’ must match the name of an existing logged model artifact. May be prefixed with entity/project/. Valid names can be in the following forms
    • model_artifact_name:version
    • model_artifact_name:alias

Raises:

  • AssertionError: if model artifact name is of a type that does not contain the substring ‘model’.

Returns:

  • path: path to downloaded model artifact file(s).

Examples:

run.use_model(
   name="my_model_artifact:latest",
)

run.use_model(
   name="my_project/my_model_artifact:v0",
)

run.use_model(
   name="my_entity/my_project/my_model_artifact:<digest>",
)

Invalid usage

run.use_model(
    name="my_entity/my_project/my_model_artifact",
)

4.1.3.10 - watch()

View the source code

function wandb.watch

wandb.watch(
    models: 'torch.nn.Module | Sequence[torch.nn.Module]',
    criterion: 'torch.F | None' = None,
    log: "Literal['gradients', 'parameters', 'all'] | None" = 'gradients',
    log_freq: 'int' = 1000,
    idx: 'int | None' = None,
    log_graph: 'bool' = False
) → None

Hook into given PyTorch model to monitor gradients and the model’s computational graph.

This function can track parameters, gradients, or both during training.

Args:

  • models: A single model or a sequence of models to be monitored.
  • criterion: The loss function being optimized (optional).
  • log: Specifies whether to log “gradients”, “parameters”, or “all”. Set to None to disable logging. (default=“gradients”).
  • log_freq: Frequency (in batches) to log gradients and parameters. (default=1000)
  • idx: Index used when tracking multiple models with wandb.watch. (default=None)
  • log_graph: Whether to log the model’s computational graph. (default=False)

Raises: ValueError: If wandb.init has not been called or if any of the models are not instances of torch.nn.Module.

4.2 - Data Types

Defines Data Types for logging interactive visualizations to W&B.

4.2.1 - Audio

View the source code

class Audio

W&B class for audio clips.

Attributes:

  • data_or_path (string or numpy array): A path to an audio file or a numpy array of audio data.
  • sample_rate (int): Sample rate, required when passing in raw numpy array of audio data.
  • caption (string): Caption to display with audio.

method Audio.__init__

__init__(
    data_or_path: Union[str, pathlib.Path, list, ForwardRef('np.ndarray')],
    sample_rate: Optional[int] = None,
    caption: Optional[str] = None
)

Accept a path to an audio file or a numpy array of audio data.

4.2.2 - box3d()

View the source code

function box3d

box3d(
    center: 'npt.ArrayLike',
    size: 'npt.ArrayLike',
    orientation: 'npt.ArrayLike',
    color: 'RGBColor',
    label: 'Optional[str]' = None,
    score: 'Optional[numeric]' = None
) → Box3D

Returns a Box3D.

Args:

  • center: The center point of the box as a length-3 ndarray.
  • size: The box’s X, Y and Z dimensions as a length-3 ndarray.
  • orientation: The rotation transforming global XYZ coordinates into the box’s local XYZ coordinates, given as a length-4 ndarray [r, x, y, z] corresponding to the non-zero quaternion r + xi + yj + zk.
  • color: The box’s color as an (r, g, b) tuple with 0 <= r,g,b <= 1.
  • label: An optional label for the box.
  • score: An optional score for the box.

4.2.3 - Html

View the source code

class Html

W&B class for logging HTML content to W&B.

Args:

  • data: HTML to display in wandb
  • inject: Add a stylesheet to the HTML object. If set to False the HTML will pass through unchanged.

method Html.__init__

__init__(
    data: Union[str, pathlib.Path, ForwardRef('TextIO')],
    inject: bool = True,
    data_is_not_path: bool = False
) → None

Creates a W&B HTML object.

It can be initialized by providing a path to a file:

with wandb.init() as run:
     run.log({"html": wandb.Html("./index.html")})

Alternatively, it can be initialized by providing literal HTML, in either a string or IO object:

with wandb.init() as run:
     run.log({"html": wandb.Html("<h1>Hello, world!</h1>")})

Args: data: A string that is a path to a file with the extension “.html”, or a string or IO object containing literal HTML.

  • inject: Add a stylesheet to the HTML object. If set to False the HTML will pass through unchanged.
  • data_is_not_path: If set to False, the data will be treated as a path to a file.

4.2.4 - Image

View the source code

class Image

A class for logging images to W&B.

See https://pillow.readthedocs.io/en/stable/handbook/concepts.html#modes for more information on modes.

Args:

  • data_or_path: Accepts numpy array of image data, or a PIL image. The class attempts to infer the data format and converts it.
  • mode: The PIL mode for an image. Most common are “L”, “RGB”, “RGBA”.
  • caption: Label for display of image.

When logging a torch.Tensor as a wandb.Image, images are normalized. If you do not want to normalize your images, convert your tensors to a PIL Image.

Examples:

# Create a wandb.Image from a numpy array
import numpy as np
import wandb

with wandb.init() as run:
   examples = []
   for i in range(3):
        pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
        image = wandb.Image(pixels, caption=f"random field {i}")
        examples.append(image)
   run.log({"examples": examples})

# Create a wandb.Image from a PILImage
import numpy as np
from PIL import Image as PILImage
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
         pixels = np.random.randint(
             low=0, high=256, size=(100, 100, 3), dtype=np.uint8
         )
         pil_image = PILImage.fromarray(pixels, mode="RGB")
         image = wandb.Image(pil_image, caption=f"random field {i}")
         examples.append(image)
    run.log({"examples": examples})

# log .jpg rather than .png (default)
import numpy as np
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
         pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
         image = wandb.Image(pixels, caption=f"random field {i}", file_type="jpg")
         examples.append(image)
    run.log({"examples": examples})

method Image.__init__

__init__(
    data_or_path: 'ImageDataOrPathType',
    mode: Optional[str] = None,
    caption: Optional[str] = None,
    grouping: Optional[int] = None,
    classes: Optional[ForwardRef('Classes'), Sequence[dict]] = None,
    boxes: Optional[Dict[str, ForwardRef('BoundingBoxes2D')], Dict[str, dict]] = None,
    masks: Optional[Dict[str, ForwardRef('ImageMask')], Dict[str, dict]] = None,
    file_type: Optional[str] = None,
    normalize: bool = True
) → None

Initialize a wandb.Image object.

Args:

  • data_or_path: Accepts numpy array/pytorch tensor of image data, a PIL image object, or a path to an image file.

If a numpy array or pytorch tensor is provided, the image data will be saved to the given file type. If the values are not in the range [0, 255] or all values are in the range [0, 1], the image pixel values will be normalized to the range [0, 255] unless normalize is set to False. - pytorch tensor should be in the format (channel, height, width) - numpy array should be in the format (height, width, channel)

  • mode: The PIL mode for an image. Most common are “L”, “RGB”,
  • "RGBA". Full explanation at https: //pillow.readthedocs.io/en/stable/handbook/concepts.html#modes
  • caption: Label for display of image.
  • grouping: The grouping number for the image.
  • classes: A list of class information for the image, used for labeling bounding boxes, and image masks.
  • boxes: A dictionary containing bounding box information for the image.
  • see: https://docs.wandb.ai/ref/python/data-types/boundingboxes2d/
  • masks: A dictionary containing mask information for the image.
  • see: https://docs.wandb.ai/ref/python/data-types/imagemask/
  • file_type: The file type to save the image as. This parameter has no effect if data_or_path is a path to an image file.
  • normalize: If True, normalize the image pixel values to fall within the range of [0, 255]. Normalize is only applied if data_or_path is a numpy array or pytorch tensor.

Examples:

Create a wandb.Image from a numpy array ```python

import numpy as np
import wandb

with wandb.init() as run:
     examples = []
     for i in range(3):
         pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
         image = wandb.Image(pixels, caption=f"random field {i}")
         examples.append(image)
     run.log({"examples": examples})
```

Create a wandb.Image from a PILImage ```python

import numpy as np
from PIL import Image as PILImage
import wandb

with wandb.init() as run:
     examples = []
     for i in range(3):
         pixels = np.random.randint(
             low=0, high=256, size=(100, 100, 3), dtype=np.uint8
         )
         pil_image = PILImage.fromarray(pixels, mode="RGB")
         image = wandb.Image(pil_image, caption=f"random field {i}")
         examples.append(image)
     run.log({"examples": examples})
```

log .jpg rather than .png (default) ```python

import numpy as np
import wandb

with wandb.init() as run:
     examples = []
     for i in range(3):
         pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
         image = wandb.Image(
             pixels, caption=f"random field {i}", file_type="jpg"
         )
         examples.append(image)
     run.log({"examples": examples})
```

method Image.guess_mode

guess_mode(
    data: Union[ForwardRef('np.ndarray'), ForwardRef('torch.Tensor')],
    file_type: Optional[str] = None
) → str

Guess what type of image the np.array is representing.

4.2.5 - Molecule

View the source code

class Molecule

W&B class for 3D Molecular data.

Args:

  • data_or_path: (pathlib.Path, string, io) Molecule can be initialized from a file name or an io object.
  • caption: (string) Caption associated with the molecule for display.

method Molecule.__init__

__init__(
    data_or_path: Union[str, pathlib.Path, ForwardRef('TextIO')],
    caption: Optional[str] = None,
    **kwargs: str
) → None

4.2.6 - Object3D

View the source code

class Object3D

W&B class for 3D point clouds.

Args:

  • data_or_path: (numpy array, pathlib.Path, string, io) Object3D can be initialized from a file or a numpy array.

Examples: The shape of the numpy array must be one of either

[[x y z],       ...] nx3
[[x y z c],     ...] nx4 where c is a category with supported range [1, 14]
[[x y z r g b], ...] nx6 where is rgb is color

method Object3D.__init__

__init__(
    data_or_path: Union[ForwardRef('np.ndarray'), str, pathlib.Path, ForwardRef('TextIO'), dict],
    caption: Optional[str] = None,
    **kwargs: Optional[str, ForwardRef('FileFormat3D')]
) → None

4.2.7 - Plotly

View the source code

class Plotly

W&B class for Plotly plots.

Args:

  • val: Matplotlib or Plotly figure.

method Plotly.__init__

__init__(
    val: Union[ForwardRef('plotly.Figure'), ForwardRef('matplotlib.artist.Artist')]
)

classmethod Plotly.get_media_subdir

get_media_subdir() → str

classmethod Plotly.make_plot_media

make_plot_media(
    val: Union[ForwardRef('plotly.Figure'), ForwardRef('matplotlib.artist.Artist')]
) → Union[wandb.sdk.data_types.image.Image, ForwardRef('Plotly')]

method Plotly.to_json

to_json(
    run_or_artifact: Union[ForwardRef('LocalRun'), ForwardRef('Artifact')]
) → dict

4.2.8 - Table

View the source code

class Table

The Table class used to display and analyze tabular data.

Unlike traditional spreadsheets, Tables support numerous types of data: scalar values, strings, numpy arrays, and most subclasses of wandb.data_types.Media. This means you can embed Images, Video, Audio, and other sorts of rich, annotated media directly in Tables, alongside other traditional scalar values.

This class is the primary class used to generate the Table Visualizer in the UI: https://docs.wandb.ai/guides/data-vis/tables.

Attributes:

  • columns (List[str]): Names of the columns in the table. Defaults to [“Input”, “Output”, “Expected”].
  • data: (List[List[any]]) 2D row-oriented array of values.
  • dataframe (pandas.DataFrame): DataFrame object used to create the table. When set, data and columns arguments are ignored.
  • optional (Union[bool,List[bool]]): Determines if None values are allowed. Default to True. - If a singular bool value, then the optionality is enforced for all columns specified at construction time. - If a list of bool values, then the optionality is applied to each column - should be the same length as columns. applies to all columns. A list of bool values applies to each respective column.
  • allow_mixed_types (bool): Determines if columns are allowed to have mixed types (disables type validation). Defaults to False.

method Table.__init__

__init__(
    columns=None,
    data=None,
    rows=None,
    dataframe=None,
    dtype=None,
    optional=True,
    allow_mixed_types=False,
    log_mode: Optional[Literal['IMMUTABLE', 'MUTABLE', 'INCREMENTAL']] = 'IMMUTABLE'
)

Initializes a Table object.

The rows is available for legacy reasons and should not be used. The Table class uses data to mimic the Pandas API.

Args:

  • columns: (List[str]) Names of the columns in the table. Defaults to [“Input”, “Output”, “Expected”].
  • data: (List[List[any]]) 2D row-oriented array of values.
  • dataframe: (pandas.DataFrame) DataFrame object used to create the table. When set, data and columns arguments are ignored.
  • optional: (Union[bool,List[bool]]) Determines if None values are allowed. Default to True - If a singular bool value, then the optionality is enforced for all columns specified at construction time - If a list of bool values, then the optionality is applied to each column - should be the same length as columns applies to all columns. A list of bool values applies to each respective column.
  • allow_mixed_types: (bool) Determines if columns are allowed to have mixed types (disables type validation). Defaults to False
  • log_mode: Optional[str] Controls how the Table is logged when mutations occur. Options: - “IMMUTABLE” (default): Table can only be logged once; subsequent logging attempts after the table has been mutated will be no-ops. - “MUTABLE”: Table can be re-logged after mutations, creating a new artifact version each time it’s logged. - “INCREMENTAL”: Table data is logged incrementally, with each log creating a new artifact entry containing the new data since the last log.

method Table.add_column

add_column(name, data, optional=False)

Adds a column of data to the table.

Args:

  • name: (str) - the unique name of the column
  • data: (list | np.array) - a column of homogeneous data
  • optional: (bool) - if null-like values are permitted

method Table.add_computed_columns

add_computed_columns(fn)

Adds one or more computed columns based on existing data.

Args:

  • fn: A function which accepts one or two parameters, ndx (int) and row (dict), which is expected to return a dict representing new columns for that row, keyed by the new column names.

ndx is an integer representing the index of the row. Only included if include_ndx is set to True.

row is a dictionary keyed by existing columns

method Table.add_data

add_data(*data)

Adds a new row of data to the table.

The maximum amount ofrows in a table is determined by wandb.Table.MAX_ARTIFACT_ROWS.

The length of the data should match the length of the table column.

method Table.add_row

add_row(*row)

Deprecated; use add_data instead.

method Table.cast

cast(col_name, dtype, optional=False)

Casts a column to a specific data type.

This can be one of the normal python classes, an internal W&B type, or an example object, like an instance of wandb.Image or wandb.Classes.

Args:

  • col_name (str): The name of the column to cast.
  • dtype (class, wandb.wandb_sdk.interface._dtypes.Type, any): The target dtype.
  • optional (bool): If the column should allow Nones.

method Table.get_column

get_column(name, convert_to=None)

Retrieves a column from the table and optionally converts it to a NumPy object.

Args:

  • name: (str) - the name of the column
  • convert_to: (str, optional) - “numpy”: will convert the underlying data to numpy object

method Table.get_dataframe

get_dataframe()

Returns a pandas.DataFrame of the table.

method Table.get_index

get_index()

Returns an array of row indexes for use in other tables to create links.

4.2.9 - Video

View the source code

class Video

A class for logging videos to W&B.

Args:

  • data_or_path: Video can be initialized with a path to a file or an io object. The format must be “gif”, “mp4”, “webm” or “ogg”. The format must be specified with the format argument. Video can be initialized with a numpy tensor. The numpy tensor must be either 4 dimensional or 5 dimensional. Channels should be (time, channel, height, width) or (batch, time, channel, height width)
  • caption: Caption associated with the video for display.
  • fps: The frame rate to use when encoding raw video frames. Default value is 4. This parameter has no effect when data_or_path is a string, or bytes.
  • format: Format of video, necessary if initializing with path or io object.

Examples: Log a numpy array as a video

import numpy as np
import wandb

run = wandb.init()
# axes are (time, channel, height, width)
frames = np.random.randint(low=0, high=256, size=(10, 3, 100, 100), dtype=np.uint8)
run.log({"video": wandb.Video(frames, fps=4)})

method Video.__init__

__init__(
    data_or_path: Union[str, pathlib.Path, ForwardRef('np.ndarray'), ForwardRef('TextIO'), ForwardRef('BytesIO')],
    caption: Optional[str] = None,
    fps: Optional[int] = None,
    format: Optional[Literal['gif', 'mp4', 'webm', 'ogg']] = None
)

Initialize a W&B Video object.

Args: data_or_path: Video can be initialized with a path to a file or an io object. Video can be initialized with a numpy tensor. The numpy tensor must be either 4 dimensional or 5 dimensional. The dimensions should be (number of frames, channel, height, width) or (batch, number of frames, channel, height, width) The format parameter must be specified with the format argument when initializing with a numpy array or io object.

  • caption: Caption associated with the video for display. fps: The frame rate to use when encoding raw video frames. Default value is 4. This parameter has no effect when data_or_path is a string, or bytes. format: Format of video, necessary if initializing with a numpy array or io object. This parameter will be used to determine the format to use when encoding the video data. Accepted values are “gif”, “mp4”, “webm”, or “ogg”. If no value is provided, the default format will be “gif”.

Examples: Log a numpy array as a video ```python import numpy as np import wandb

with wandb.init() as run: # axes are (number of frames, channel, height, width) frames = np.random.randint( low=0, high=256, size=(10, 3, 100, 100), dtype=np.uint8 ) run.log({“video”: wandb.Video(frames, format=“mp4”, fps=4)})





---

4.3 - Launch Library Reference

A collection of launch APIs for W&B.

4.3.1 - create_and_run_agent()

View the source code

function create_and_run_agent

create_and_run_agent(
    api: wandb.apis.internal.Api,
    config: Dict[str, Any]
) → None

4.3.2 - launch_add()

View the source code

function launch_add

launch_add(
    uri: Optional[str] = None,
    job: Optional[str] = None,
    config: Optional[Dict[str, Any]] = None,
    template_variables: Optional[Dict[str, Union[float, int, str]]] = None,
    project: Optional[str] = None,
    entity: Optional[str] = None,
    queue_name: Optional[str] = None,
    resource: Optional[str] = None,
    entry_point: Optional[List[str]] = None,
    name: Optional[str] = None,
    version: Optional[str] = None,
    docker_image: Optional[str] = None,
    project_queue: Optional[str] = None,
    resource_args: Optional[Dict[str, Any]] = None,
    run_id: Optional[str] = None,
    build: Optional[bool] = False,
    repository: Optional[str] = None,
    sweep_id: Optional[str] = None,
    author: Optional[str] = None,
    priority: Optional[int] = None
) → public.QueuedRun

Enqueue a W&B launch experiment. With either a source uri, job or docker_image.

Arguments:

  • uri: URI of experiment to run. A wandb run uri or a Git repository URI.
  • job: string reference to a wandb.Job eg: wandb/test/my-job:latest
  • config: A dictionary containing the configuration for the run. May also contain resource specific arguments under the key “resource_args”
  • template_variables: A dictionary containing values of template variables for a run queue.
  • Expected format of {“VAR_NAME”: VAR_VALUE}
  • project: Target project to send launched run to
  • entity: Target entity to send launched run to
  • queue: the name of the queue to enqueue the run to
  • priority: the priority level of the job, where 1 is the highest priority
  • resource: Execution backend for the run: W&B provides built-in support for “local-container” backend
  • entry_point: Entry point to run within the project. Defaults to using the entry point used in the original run for wandb URIs, or main.py for git repository URIs.
  • name: Name run under which to launch the run.
  • version: For Git-based projects, either a commit hash or a branch name.
  • docker_image: The name of the docker image to use for the run.
  • resource_args: Resource related arguments for launching runs onto a remote backend. Will be stored on the constructed launch config under resource_args.
  • run_id: optional string indicating the id of the launched run
  • build: optional flag defaulting to false, requires queue to be set if build, an image is created, creates a job artifact, pushes a reference to that job artifact to queue
  • repository: optional string to control the name of the remote repository, used when pushing images to a registry
  • project_queue: optional string to control the name of the project for the queue. Primarily used for back compatibility with project scoped queues

Example:

from wandb.sdk.launch import launch_add

project_uri = "https://github.com/wandb/examples"
params = {"alpha": 0.5, "l1_ratio": 0.01}
# Run W&B project and create a reproducible docker environment
# on a local host
api = wandb.apis.internal.Api()
launch_add(uri=project_uri, parameters=params)

Returns: an instance ofwandb.api.public.QueuedRun which gives information about the queued run, or if wait_until_started or wait_until_finished are called, gives access to the underlying Run information.

Raises: wandb.exceptions.LaunchError if unsuccessful

4.3.3 - launch()

View the source code

function launch

launch(
    api: wandb.apis.internal.Api,
    job: Optional[str] = None,
    entry_point: Optional[List[str]] = None,
    version: Optional[str] = None,
    name: Optional[str] = None,
    resource: Optional[str] = None,
    resource_args: Optional[Dict[str, Any]] = None,
    project: Optional[str] = None,
    entity: Optional[str] = None,
    docker_image: Optional[str] = None,
    config: Optional[Dict[str, Any]] = None,
    synchronous: Optional[bool] = True,
    run_id: Optional[str] = None,
    repository: Optional[str] = None
) → AbstractRun

Launch a W&B launch experiment.

Arguments:

  • job: string reference to a wandb.Job eg: wandb/test/my-job:latest
  • api: An instance of a wandb Api from wandb.apis.internal.
  • entry_point: Entry point to run within the project. Defaults to using the entry point used in the original run for wandb URIs, or main.py for git repository URIs.
  • version: For Git-based projects, either a commit hash or a branch name.
  • name: Name run under which to launch the run.
  • resource: Execution backend for the run.
  • resource_args: Resource related arguments for launching runs onto a remote backend. Will be stored on the constructed launch config under resource_args.
  • project: Target project to send launched run to
  • entity: Target entity to send launched run to
  • config: A dictionary containing the configuration for the run. May also contain resource specific arguments under the key “resource_args”.
  • synchronous: Whether to block while waiting for a run to complete. Defaults to True. Note that if synchronous is False and backend is “local-container”, this method will return, but the current process will block when exiting until the local run completes. If the current process is interrupted, any asynchronous runs launched via this method will be terminated. If synchronous is True and the run fails, the current process will error out as well.
  • run_id: ID for the run (To ultimately replace the :name: field)
  • repository: string name of repository path for remote registry

Example:

   from wandb.sdk.launch import launch

   job = "wandb/jobs/Hello World:latest"
   params = {"epochs": 5}
   # Run W&B project and create a reproducible docker environment
   # on a local host
   api = wandb.apis.internal.Api()
   launch(api, job, parameters=params)
   ``` 





**Returns:**
an instance of`wandb.launch.SubmittedRun` exposing information (e.g. run ID) about the launched run. 



**Raises:**
`wandb.exceptions.ExecutionError` If a run launched in blocking mode is unsuccessful.

4.3.4 - LaunchAgent

View the source code

class LaunchAgent

Launch agent class which polls run given run queues and launches runs for wandb launch.

method LaunchAgent.__init__

__init__(api: wandb.apis.internal.Api, config: Dict[str, Any])

Initialize a launch agent.

Arguments:

  • api: Api object to use for making requests to the backend.
  • config: Config dictionary for the agent.

property LaunchAgent.num_running_jobs

Return the number of jobs not including schedulers.

property LaunchAgent.num_running_schedulers

Return just the number of schedulers.

property LaunchAgent.thread_ids

Returns a list of keys running thread ids for the agent.

method LaunchAgent.check_sweep_state

check_sweep_state(
    launch_spec: Dict[str, Any],
    api: wandb.apis.internal.Api
) → None

Check the state of a sweep before launching a run for the sweep.

method LaunchAgent.fail_run_queue_item

fail_run_queue_item(
    run_queue_item_id: str,
    message: str,
    phase: str,
    files: Optional[List[str]] = None
) → None

method LaunchAgent.finish_thread_id

finish_thread_id(
    thread_id: int,
    exception: Optional[Exception, wandb.sdk.launch.errors.LaunchDockerError] = None
) → None

Removes the job from our list for now.

method LaunchAgent.get_job_and_queue

get_job_and_queue() → Optional[wandb.sdk.launch.agent.agent.JobSpecAndQueue]

classmethod LaunchAgent.initialized

initialized() → bool

Return whether the agent is initialized.

method LaunchAgent.loop

loop() → None

Loop infinitely to poll for jobs and run them.

Raises:

  • KeyboardInterrupt: if the agent is requested to stop.

classmethod LaunchAgent.name

name() → str

Return the name of the agent.

method LaunchAgent.pop_from_queue

pop_from_queue(queue: str) → Any

Pops an item off the runqueue to run as a job.

Arguments:

  • queue: Queue to pop from.

Returns: Item popped off the queue.

Raises:

  • Exception: if there is an error popping from the queue.

method LaunchAgent.print_status

print_status() → None

Prints the current status of the agent.

method LaunchAgent.run_job

run_job(
    job: Dict[str, Any],
    queue: str,
    file_saver: wandb.sdk.launch.agent.run_queue_item_file_saver.RunQueueItemFileSaver
) → None

Set up project and run the job.

Arguments:

  • job: Job to run.

method LaunchAgent.task_run_job

task_run_job(
    launch_spec: Dict[str, Any],
    job: Dict[str, Any],
    default_config: Dict[str, Any],
    api: wandb.apis.internal.Api,
    job_tracker: wandb.sdk.launch.agent.job_status_tracker.JobAndRunStatusTracker
) → None

method LaunchAgent.update_status

update_status(status: str) → None

Update the status of the agent.

Arguments:

  • status: Status to update the agent to.

4.3.5 - load_wandb_config()

View the source code

function load_wandb_config

load_wandb_config() → Config

Load wandb config from WANDB_CONFIG environment variable(s).

The WANDB_CONFIG environment variable is a json string that can contain multiple config keys. The WANDB_CONFIG_[0-9]+ environment variables are used for environments where there is a limit on the length of environment variables. In that case, we shard the contents of WANDB_CONFIG into multiple environment variables numbered from 0.

Returns: A dictionary of wandb config values.

4.3.6 - manage_config_file()

View the source code

function manage_config_file

manage_config_file(
    path: str,
    include: Optional[List[str]] = None,
    exclude: Optional[List[str]] = None,
    schema: Optional[Any] = None
)

Declare an overridable configuration file for a launch job.

If a new job version is created from the active run, the configuration file will be added to the job’s inputs. If the job is launched and overrides have been provided for the configuration file, this function will detect the overrides from the environment and update the configuration file on disk. Note that these overrides will only be applied in ephemeral containers. include and exclude are lists of dot separated paths with the config. The paths are used to filter subtrees of the configuration file out of the job’s inputs.

For example, given the following configuration file: yaml model: name: resnet layers: 18 training: epochs: 10 batch_size: 32

Passing include=['model'] will only include the model subtree in the job’s inputs. Passing exclude=['model.layers'] will exclude the layers key from the model subtree. Note that exclude takes precedence over include.

. is used as a separator for nested keys. If a key contains a ., it should be escaped with a backslash, e.g. include=[r'model\.layers']. Note the use of r to denote a raw string when using escape chars.

Args:

  • path (str): The path to the configuration file. This path must be relative and must not contain backwards traversal, i.e. ...
  • include (List[str]): A list of keys to include in the configuration file.
  • exclude (List[str]): A list of keys to exclude from the configuration file.
  • schema (dict | Pydantic model): A JSON Schema or Pydantic model describing describing which attributes will be editable from the Launch drawer. Accepts both an instance of a Pydantic BaseModel class or the BaseModel class itself.

Raises:

  • LaunchError: If the path is not valid, or if there is no active run.

4.3.7 - manage_wandb_config()

View the source code

function manage_wandb_config

manage_wandb_config(
    include: Optional[List[str]] = None,
    exclude: Optional[List[str]] = None,
    schema: Optional[Any] = None
)

Declare wandb.config as an overridable configuration for a launch job.

If a new job version is created from the active run, the run config (wandb.config) will become an overridable input of the job. If the job is launched and overrides have been provided for the run config, the overrides will be applied to the run config when wandb.init is called. include and exclude are lists of dot separated paths with the config. The paths are used to filter subtrees of the configuration file out of the job’s inputs.

For example, given the following run config contents: yaml model: name: resnet layers: 18 training: epochs: 10 batch_size: 32 Passing include=['model'] will only include the model subtree in the job’s inputs. Passing exclude=['model.layers'] will exclude the layers key from the model subtree. Note that exclude takes precedence over include. . is used as a separator for nested keys. If a key contains a ., it should be escaped with a backslash, e.g. include=[r'model\.layers']. Note the use of r to denote a raw string when using escape chars.

Args:

  • include (List[str]): A list of subtrees to include in the configuration.
  • exclude (List[str]): A list of subtrees to exclude from the configuration.
  • schema (dict | Pydantic model): A JSON Schema or Pydantic model describing describing which attributes will be editable from the Launch drawer. Accepts both an instance of a Pydantic BaseModel class or the BaseModel class itself.

Raises:

  • LaunchError: If there is no active run.