Run

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

Description

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). W&B recommends using a context (with statement) manager to automatically finish the run.

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.Run.log(). Anything you log using wandb.Run.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.

Attributes: summary: (Summary) A summary of the run, which is a dictionary-like object. For more information, see Log summary metrics.

Args

• settings:
• config:
• sweep_config:
• launch_config:

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})

Properties

property settings

A frozen copy of run’s Settings object.

property dir

The directory where files associated with the run are saved.

property config

Config object associated with this run.

property config_static

Static config object associated with this run.

property 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 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 tags

Tags associated with the run, if there are any.

property id

Identifier for this run.

property sweep_id

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

property path

Path to the run.

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

property start_time

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

property resumed

True if the run was resumed, False otherwise.

property offline

True if the run is offline, False otherwise.

property disabled

True if the run is disabled, False otherwise.

property group

Returns the name of the group associated with this run.

Grouping runs together allows related experiments to be organized and visualized collectively in the W&B UI. This is especially useful for scenarios such as distributed training or cross-validation, where multiple runs should be viewed and managed as a unified experiment.

In shared mode, where all processes share the same run object, setting a group is usually unnecessary, since there is only one run and no grouping is required.

property job_type

Name of the job type associated with the run.

View a run’s job type in the run’s Overview page in the W&B App.

You can use this to categorize runs by their job type, such as “training”, “evaluation”, or “inference”. This is useful for organizing and filtering runs in the W&B UI, especially when you have multiple runs with different job types in the same project. For more information, see Organize runs.

property project

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

property 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 sweep_url

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

Offline runs do not have a sweep URL.

property url

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

Offline runs will not have a url.

property 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.

Methods

method alert

self,
title: 'str',
text: 'str',
level: 'str | AlertLevel | None' = None,
wait_duration: 'int | float | timedelta | None' = None

Create an alert with the given title and text.

Arguments

• 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 define_metric

self,
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

Customize metrics logged with wandb.Run.log().

Arguments

• 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 wandb.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”, “first”, “best”, “copy” and “none”. “none” prevents a summary from being generated. “best” is used together with the goal parameter, “best” is deprecated and should not be used, use “min” or “max” instead. “copy” is deprecated and should not be used.
• goal: Specify how to interpret the “best” summary type. Supported options are “minimize” and “maximize”. “goal” is deprecated and should not be used, use “min” or “max” instead.
• 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.

method display

self,
height: 'int' = 420,
hidden: 'bool' = False

Display this run in Jupyter.

Arguments

• height:
• hidden:

method finish

self,
exit_code: 'int | None' = None,
quiet: 'bool | 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.

Arguments

• 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 finish_artifact

self,
artifact_or_path: 'Artifact | str',
name: 'str | None' = None,
type: 'str | None' = None,
aliases: 'list[str] | None' = None,
distributed_id: 'str | None' = None

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

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

Arguments

• artifact_or_path: A path to the contents of this artifact,
• name: An artifact name. May be prefixed with entity/project.
• 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.

method link_artifact

self,
artifact: 'Artifact',
target_path: 'str',
aliases: 'list[str] | None' = None

Link the artifact to a collection.

The term “link” refers to pointers that connect where W&B stores the artifact and where the artifact is accessible in the registry. W&B does not duplicate artifacts when you link an artifact to a collection.

View linked artifacts in the Registry UI for the specified collection.

Arguments

• artifact: The artifact object to link to the collection.
• target_path: The path of the collection. Path consists of the prefix “wandb-registry-” along with the registry name and the collection name wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}.
• aliases: Add one or more aliases to the linked artifact. The “latest” alias is automatically applied to the most recent artifact you link.

method link_model

self,
path: 'StrPath',
registered_model_name: 'str',
name: 'str | None' = None,
aliases: 'list[str] | None' = 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.

Arguments

• path: (str) A path to the contents of this model, can be in the
• 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.

method log

self,
data: 'dict[str, Any]',
step: 'int | None' = None,
commit: 'bool | 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”:

with wandb.init() as run:
    # Log metrics in the "train" section.
    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”.

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.

with wandb.init() as run:
    # 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 wandb.Run.log() invocations to log to the same step with the step and commit parameters. The following are all equivalent:

with wandb.init() as run:
    # 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, commit=True)

Arguments

• data: A dict with str keys and values that are serializable
• 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.

Raises

• wandb.Error: If called before wandb.init().
• ValueError: If invalid data is passed.

Examples

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

Basic usage

import wandb

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

Incremental logging

import wandb

with wandb.init() as run:
    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)
with wandb.init() as run:
    run.log({"gradients": wandb.Histogram(gradients)})

Image from NumPy

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})

Image from PIL

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})

Video from NumPy

import numpy as np
import wandb

with wandb.init() as run:
    # 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

with wandb.init() as run:
    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

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

3D Object

import wandb

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

method log_artifact

self,
artifact_or_path: 'Artifact | StrPath',
name: 'str | None' = None,
type: 'str | None' = None,
aliases: 'list[str] | None' = None,
tags: 'list[str] | None' = None

Declare an artifact as an output of a run.

Arguments

• artifact_or_path: (str or Artifact) A path to the contents of this artifact,
• name: (str, optional) An artifact name. Valid names can be in the following forms:
• type: (str) The type of artifact to log, examples include dataset, model
• aliases: (list, optional) Aliases to apply to this artifact, defaults to ["latest"]
• tags: (list, optional) Tags to apply to this artifact, if any.

method log_code

self,
root: 'str | None' = '.',
name: 'str | None' = None,
include_fn: 'Callable[[str, str], bool] | Callable[[str], bool]' = _is_py_requirements_or_dockerfile,
exclude_fn: 'Callable[[str, str], bool] | Callable[[str], bool]' = exclude_wandb_fn

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.

Arguments

• root: The relative (to os.getcwd()) or absolute path to recursively find code from.
• name: (str, optional) 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
• 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/"
        ),
    )

method log_model

self,
path: 'StrPath',
name: 'str | None' = None,
aliases: 'list[str] | None' = None

Logs a model artifact containing the contents inside the ‘path’ to a run and marks it as an output to this run.

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

Arguments

• path: (str) A path to the contents of this model,
• name: A name to assign to the model artifact that the file contents will be added to. 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"]

Raises

• ValueError: If name has invalid special characters.

method mark_preempting

self

Mark this run as preempting.

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

method pin_config_keys

self,
keys: 'Sequence[str]' = ()

Pin config keys to display in the References section on Run Overview.

Pinned keys appear prominently above Notes on the Run Overview page. String values are rendered as markdown; non-strings are rendered as plain text. Calling this again replaces the previously pinned list.

Arguments

• keys: Config key names to pin, matching keys set via run.config. These are exact key strings (dots and slashes are treated literally, not as path separators). Order is preserved and determines display order.

method restore

self,
name: 'str',
run_path: 'str | None' = None,
replace: 'bool' = False,
root: 'str | None' = None

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.

Arguments

• 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.

Raises

• 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 save

self,
glob_str: 'str | os.PathLike',
base_path: 'str | os.PathLike | None' = None,
policy: 'PolicyName' = 'live'

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.

Files are automatically deduplicated: calling save() multiple times on the same file without modifications will not re-upload it.

Arguments

• 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.

method status

self

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

method unwatch

self,
models: 'torch.nn.Module | Sequence[torch.nn.Module] | None' = None

Remove pytorch model topology, gradient and parameter hooks.

Arguments

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

method upsert_artifact

self,
artifact_or_path: 'Artifact | str',
name: 'str | None' = None,
type: 'str | None' = None,
aliases: 'list[str] | None' = None,
distributed_id: 'str | None' = None

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.

Arguments

• artifact_or_path: A path to the contents of this artifact,
• name: An artifact name. May be prefixed with “entity/project”. Defaults to the basename of the path prepended with the current run ID
• 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.

method use_artifact

self,
artifact_or_name: 'str | Artifact',
type: 'str | None' = None,
aliases: 'list[str] | None' = None,
use_as: 'str | None' = None

Declare an artifact as an input to a run.

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

Arguments

• artifact_or_name: The name of the artifact to use. May be prefixed with the name of the project the artifact was logged to (“entity” or “entity/project”). 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
• type: The type of artifact to use.
• aliases: Aliases to apply to this artifact
• use_as: This argument is deprecated and does nothing.

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>"
)

# Explicitly finish the run since a context manager is not used.
run.finish()

method use_model

self,
name: 'str'

Download the files logged in a model artifact ‘name’.

Arguments

• 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

Raises

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

method watch

self,
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

Hook into given PyTorch model to monitor gradients and the model’s computational graph.

This function can track parameters, gradients, or both during training.

Arguments

• 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.

method write_logs

self,
text: 'str'

Write text to the run’s Logs tab.

Use write_logs to directly write text to the Logs tab instead of relying on automatic stdout/stderr capture. Calls after the run has finished are silently ignored.

Consider using the capture_loggers setting which integrates with Python’s logging module.

Arguments

• text: The text to write. A trailing newline is added if not present.