log()

5 minute read

`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.

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Last modified June 2, 2025

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log()

function wandb.log

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`function` `wandb.log`