Python SDK

• 1: Global Functions
• 1.1: agent()
• 1.2: controller()
• 1.3: finish()
• 1.4: init()
• 1.5: login()
• 1.6: restore()
• 1.7: setup()
• 1.8: sweep()
• 1.9: teardown()
• 2: Data Types
• 2.1: Audio
• 2.2: box3d()
• 2.3: Html
• 2.4: Image
• 2.5: Molecule
• 2.6: Object3D
• 2.7: Plotly
• 2.8: Table
• 2.9: Video
• 3: Artifact
• 4: Automations
• 4.1: Automation
• 4.2: DoNothing
• 4.3: MetricChangeFilter
• 4.4: MetricThresholdFilter
• 4.5: NewAutomation
• 4.6: OnAddArtifactAlias
• 4.7: OnCreateArtifact
• 4.8: OnLinkArtifact
• 4.9: OnRunMetric
• 4.10: SendNotification
• 4.11: SendWebhook
• 5: Custom Charts
• 5.1: bar()
• 5.2: confusion_matrix()
• 5.3: histogram()
• 5.4: line_series()
• 5.5: line()
• 5.6: plot_table()
• 5.7: pr_curve()
• 5.8: roc_curve()
• 5.9: scatter()
• 6: Public API
• 6.1: Api
• 6.2: Artifacts
• 6.3: Automations
• 6.4: Files
• 6.5: History
• 6.6: Integrations
• 6.7: Projects
• 6.8: Reports
• 6.9: Runs
• 6.10: Sweeps
• 6.11: Teams
• 6.12: Users
• 7: Run
• 8: Settings
• 9: Optional Extras
• 10: System Metrics Reference

pip install wandb

import wandb

# Specify your team entity
entity = "<team_entity>"

# Project that the run is recorded to
project = "my-awesome-project"

with wandb.init(entity=entity, project=project) as run:
   run.log({"accuracy": .90, "loss": .10})

# Using quotes (works in all shells)
pip install "wandb[media]"
pip install "wandb[workspaces,media]"

# Escaping brackets (for zsh/bash)
pip install wandb\[media\]

# Install all common extras
pip install "wandb[media,workspaces,sweeps,launch]"

1 - Global Functions

The W&B Python SDK provides a set of global functions that serve as the primary entry points for interacting with the platform. These functions are called directly on the wandb module and form the foundation of most W&B workflows.

Overview

Global functions in W&B are top-level functions that you call directly, such as wandb.init() or wandb.login(). Unlike methods that belong to specific classes, these functions provide direct access to W&B’s core functionality without needing to instantiate objects first.

Available Functions

Getting Started

The most common workflow begins with authenticating with W&B, initializing a run, and logging values (such as accuracy and loss) from your training loop:

import wandb

# Authenticate with W&B
wandb.login()

config = {
   "learning_rate": 0.01,
   "epochs": 10,
}

# Project where logs
project = "my-awesome-project"

# Start a new run
with wandb.init(project=project, config=config) as run:
   # Your training code here...
   
   # Log values to W&B
   run.log({"accuracy": acc, "loss": loss})

## Key Concepts

- **Runs**: The fundamental unit of tracking in W&B, representing a single execution of your code
- **Authentication**: Required to sync data with the W&B platform
- **Configuration**: Store hyperparameters and metadata for your experiments
- **Sweeps**: Automated hyperparameter optimization across multiple runs

For detailed information about each function, click on the function names above to view their complete documentation, including parameters, examples, and usage patterns.

1.1 - agent()

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.

1.2 - controller()

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(...)

1.3 - finish()

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=...).

1.4 - init()

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', 'shared'] | 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.Run.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.
  • "shared": (This is an experimental feature). Allows multiple processes, possibly on different machines, to simultaneously log to the same run. In this approach you use a primary node and one or more worker nodes to log data to the same run. Within the primary node you initialize a run. For each worker node, initialize a run using the run ID used by the primary node.
• 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.

Returns: A Run object.

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.

Examples: wandb.init() returns a Run object. Use the run object to log data, save artifacts, and manage the run lifecycle.

import wandb

config = {"lr": 0.01, "batch_size": 32}
with wandb.init(config=config) as run:
    # Log accuracy and loss to the run
    acc = 0.95  # Example accuracy
    loss = 0.05  # Example loss
    run.log({"accuracy": acc, "loss": loss})

1.5 - login()

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”. Defaults to None.
• 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.

1.6 - restore()

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:

• 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.

1.7 - setup()

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()

1.8 - sweep()

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:

• str: A unique identifier for the sweep.

1.9 - teardown()

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.

2 - Data Types

The W&B Python SDK includes data types for logging various forms of media and structured data.

Overview

Data Types in W&B are classes that wrap media and structured data for logging to runs. They include visualization components in the W&B UI and handle data serialization, storage, and retrieval.

Available Data Types

Getting Started

Using Image Data Type to log an image:

import wandb
import matplotlib.pyplot as plt

# Generate an image for demonstration purposes
path_to_img = "/path/to/cafe.png"
im = plt.imread(path_to_img)

# Initialize a new run
with wandb.init(project="awesome-project") as run:

    # Log the image
    run.log({"img": [wandb.Image(im, caption="Cafe")]})

Using a Table Data Type to log a table with mixed text and labels:

import wandb

# Initialize a new run
with wandb.init(project="visualize-predictions", name="tables") as run:

    # Create tabular data, using a list of lists
    data = [["Cat", "1", "1"],["Dog", "0", "-1"]]
    run.log({"Table 1": wandb.Table(data=data, columns=["Text", "Predicted Label", "True Label"])})

    # Create tabular data, using `wandb.Table.add_data()` method
    table = wandb.Table(columns=["Text", "Predicted Label", "True Label"])
    table.add_data("Cat", "1", "1")
    table.add_data("Dog", "0", "-1")
    run.log({"Table 2": table})

2.1 - Audio

class Audio

W&B class for audio clips.

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.

Args:

• data_or_path: A path to an audio file or a NumPy array of audio data.
• sample_rate: Sample rate, required when passing in raw NumPy array of audio data.
• caption: Caption to display with audio.

classmethod Audio.durations

durations(audio_list)

Calculate the duration of the audio files.

classmethod Audio.sample_rates

sample_rates(audio_list)

Get sample rates of the audio files.

2.2 - box3d()

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.

2.3 - Html

class Html

W&B class for logging HTML content to W&B.

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.

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.

Examples: 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>")})

2.4 - Image

class Image

A class for logging images to W&B.

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

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})

property Image.image

2.5 - Molecule

class Molecule

W&B class for 3D Molecular data.

method Molecule.__init__

__init__(
    data_or_path: Union[str, pathlib.Path, ForwardRef('TextIO')],
    caption: Optional[str] = None,
    **kwargs: str
) → None

Initialize a Molecule object.

Args:

• data_or_path: Molecule can be initialized from a file name or an io object.
• caption: Caption associated with the molecule for display.

2.6 - Object3D

class Object3D

W&B class for 3D point clouds.

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

Creates a W&B Object3D object.

Args:

• data_or_path: Object3D can be initialized from a file or a numpy array.
• caption: Caption associated with the object for display.

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

2.7 - Plotly

class Plotly

W&B class for Plotly plots.

method Plotly.__init__

__init__(
    val: Union[ForwardRef('plotly.Figure'), ForwardRef('matplotlib.artist.Artist')]
)

Initialize a Plotly object.

Args:

• val: Matplotlib or Plotly figure.

2.8 - Table

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 W&B Tables https://docs.wandb.ai/guides/models/tables/.

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.
• rows: (List[List[any]]) 2D row-oriented array of values.
• 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 Table.add_data method 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.

2.9 - Video

class Video

A class for logging videos to W&B.

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

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)})

3 - Artifact

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 run.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.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"],
    )

property Artifact.id

The artifact’s ID.

property 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.

property 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.

property Artifact.manifest

The artifact’s manifest.

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

property Artifact.metadata

User-defined artifact metadata.

Structured data associated with the artifact.

property 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.

property 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.

property 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.

property Artifact.size

The total size of the artifact in bytes.

Includes any references tracked by this artifact.

property 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.

property Artifact.source_collection

The artifact’s source collection.

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

property Artifact.source_entity

The name of the entity of the source artifact.

property 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.

property Artifact.source_project

The name of the project of the source artifact.

property Artifact.source_qualified_name

The source_entity/source_project/source_name of the source artifact.

property Artifact.source_version

The source artifact’s version.

A string with the format v{number}.

property Artifact.state

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

property Artifact.tags

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

property 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.

property Artifact.type

The artifact’s type. Common types include dataset or model.

property Artifact.updated_at

The time when the artifact was last updated.

property Artifact.url

Constructs the URL of the artifact.

Returns:

• str: The URL of the artifact.

property Artifact.use_as

Deprecated.

property 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.

method Artifact.add

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 run.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.

method Artifact.link

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

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.

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.

method Artifact.unlink

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 - Automations

The W&B Automations API enables programmatic creation and management of automated workflows that respond to events in your ML pipeline. Configure actions to trigger when specific conditions are met, such as model performance thresholds or artifact creation.

Overview

Automations in W&B (wandb.automations) provide event-driven workflow automation for ML operations. Define triggers based on run metrics, artifact events, or other conditions, and specify actions such as sending notifications or webhooks. Automations execute automatically when their trigger conditions are satisfied, enabling responsive ML pipelines without manual intervention.

Available Components

Core Classes

Events (Triggers)

Actions

Filters

Common Use Cases

Model Performance Monitoring

• Alert when model accuracy drops below a threshold
• Notify team when training loss plateaus
• Trigger retraining pipelines based on performance metrics

Artifact Management

• Send notifications when new model versions are created
• Trigger deployment workflows when artifacts are tagged
• Automate downstream processing when datasets are updated

Experiment Tracking

• Alert on failed or crashed runs
• Notify when long-running experiments complete
• Send daily summaries of experiment metrics

Integration Workflows

• Update external tracking systems via webhooks
• Sync model registry with deployment platforms
• Trigger CI/CD pipelines based on W&B events

Configuration

Setting Up Integrations

# Configure Slack integration
from wandb.automations import SlackIntegration

slack = SlackIntegration(
    webhook_url="https://hooks.slack.com/services/..."
)

# Use in notification action
notification = SendNotification.from_integration(
    integration=slack,
    title="ML Alert",
    text="Training completed",
    level="INFO"
)

Filter Operators

Metric filters support standard comparison operators:

• ">": Greater than
• "<": Less than
• ">=": Greater than or equal
• "<=": Less than or equal
• "==": Equal to
• "!=": Not equal to

Aggregation Options

For metric filters with windows:

• "min": Minimum value in window
• "max": Maximum value in window
• "mean": Average value in window
• "sum": Sum of values in window

Usage Notes

• Automations require appropriate permissions in the target project or organization
• Rate limits apply to action executions (notifications, webhooks)
• Filters are evaluated on the W&B backend, not locally
• Disabled automations remain saved but do not trigger
• Test automations with DoNothing action before deploying

Example Usage

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

# Initialize W&B
wandb.login()

# Create an automation that alerts when accuracy exceeds 0.95
automation = OnRunMetric(
    filter=MetricThresholdFilter(
        name="accuracy",
        cmp=">",
        threshold=0.95
    ),
    scope="entity/project"
).then(
    SendNotification(
        title="High Accuracy Achieved",
        message="Model accuracy exceeded 95%",
        severity="INFO"
    )
)

# Save the automation
automation.save(name="accuracy-alert", enabled=True)

# Create an automation for artifact creation
artifact_automation = OnCreateArtifact(
    scope="entity/project/artifact-collection"
).then(
    SendWebhook.from_integration(
        integration=webhook_integration,
        payload={"event": "new_artifact", "collection": "models"}
    )
)

# Save with description
artifact_automation.save(
    name="model-webhook",
    description="Notify external service on new model creation",
    enabled=True
)

# Query existing automations
from wandb.apis.public import Api
api = Api()
automations = api.project("entity/project").automations()

for auto in automations:
    print(f"Automation: {auto.name}")
    print(f"Enabled: {auto.enabled}")
    print(f"Event: {auto.event}")

4.1 - Automation

A local instance of a saved W&B automation.

Attributes:

• action (Union): The action that will execute when this automation is triggered.
• description (Optional): An optional description of this automation.
• enabled (bool): Whether this automation is enabled. Only enabled automations will trigger.
• event (SavedEvent): The event that will trigger this automation.
• name (str): The name of this automation.
• scope (Union): The scope in which the triggering event must occur.

4.2 - DoNothing

Defines an automation action that intentionally does nothing.

Attributes:

• action_type (Literal): The kind of action to be triggered.
• no_op (bool): Placeholder field which exists only to satisfy backend schema requirements. There should never be a need to set this field explicitly, as its value is ignored.

4.3 - 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.

Attributes:

• agg (Optional): Aggregate operation, if any, to apply over the window size.
• change_dir (ChangeDir): No description provided.
• change_type (ChangeType): No description provided.
• name (str): Name of the observed metric.
• prior_window (int): Size of the prior window over which the metric is aggregated (ignored if agg is None). If omitted, defaults to the size of the current window.
• threshold (Union): Threshold value to compare against.
• window (int): Size of the window over which the metric is aggregated (ignored if agg is None).

4.4 - MetricThresholdFilter

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

Attributes:

• agg (Optional): Aggregate operation, if any, to apply over the window size.
• cmp (Literal): Comparison operator used to compare the metric value (left) vs. the threshold value (right).
• name (str): Name of the observed metric.
• threshold (Union): Threshold value to compare against.
• window (int): Size of the window over which the metric is aggregated (ignored if agg is None).

4.5 - NewAutomation

A new automation to be created.

Attributes:

• action (Optional): The action that will execute when this automation is triggered.
• description (Optional): An optional description of this automation.
• enabled (Optional): Whether this automation is enabled. Only enabled automations will trigger.
• event (Optional): The event that will trigger this automation.
• name (Optional): The name of this automation.

4.6 - OnAddArtifactAlias

A new alias is assigned to an artifact.

Attributes:

• event_type (Literal): No description provided.
• filter (Union): Additional condition(s), if any, that must be met for this event to trigger an automation.
• scope (Union): The scope of the event.

method then

then(self, action: 'InputAction') -> 'NewAutomation'

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

4.7 - OnCreateArtifact

A new artifact is created.

Attributes:

• event_type (Literal): No description provided.
• filter (Union): Additional condition(s), if any, that must be met for this event to trigger an automation.
• scope (Union): The scope of the event: only artifact collections are valid scopes for this event.

method then

then(self, action: 'InputAction') -> 'NewAutomation'

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

4.8 - OnLinkArtifact

A new artifact is linked to a collection.

Attributes:

• event_type (Literal): No description provided.
• filter (Union): Additional condition(s), if any, that must be met for this event to trigger an automation.
• scope (Union): The scope of the event.

method then

then(self, action: 'InputAction') -> 'NewAutomation'

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

4.9 - OnRunMetric

A run metric satisfies a user-defined condition.

Attributes:

• event_type (Literal): No description provided.
• filter (RunMetricFilter): Run and/or metric condition(s) that must be satisfied for this event to trigger an automation.
• scope (ProjectScope): The scope of the event: only projects are valid scopes for this event.

method then

then(self, action: 'InputAction') -> 'NewAutomation'

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

4.10 - SendNotification

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

Attributes:

• action_type (Literal): The kind of action to be triggered.
• message (str): The message body of the sent notification.
• severity (AlertSeverity): The severity (INFO, WARN, ERROR) of the sent notification.
• title (str): The title of the sent notification.

method from_integration

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

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

4.11 - SendWebhook

Defines an automation action that sends a webhook request.

Attributes:

• action_type (Literal): The kind of action to be triggered.
• request_payload (Optional): The payload, possibly with template variables, to send in the webhook request.

method from_integration

from_integration(cls, integration: 'WebhookIntegration', *, payload: 'Optional[SerializedToJson[dict[str, Any]]]' = None) -> 'Self'

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

5 - Custom Charts

The W&B Python SDK includes functions for creating specialized charts from your data. These functions generate interactive visualizations that integrate with the W&B UI.

Overview

Custom Charts in W&B (wandb.plot) are visualization functions that transform data into interactive charts. These functions handle common ML visualization requirements such as confusion matrices, ROC curves, and distribution plots. Custom charts can also be created using Vega-Lite specifications.

Available Chart Functions

Common Use Cases

Model Evaluation

• Classification: confusion_matrix(), roc_curve(), and pr_curve() for classifier evaluation
• Regression: scatter() for prediction vs. actual plots and histogram() for residual analysis

Training Monitoring

• Learning Curves: line() or line_series() for tracking metrics over epochs
• Hyperparameter Comparison: bar() charts for comparing configurations

Data Analysis

• Distribution Analysis: histogram() for feature distributions
• Correlation Analysis: scatter() plots for variable relationships

Custom Visualizations

• Vega-Lite Charts: plot_table() for domain-specific visualizations
• Multi-Chart Dashboards: Combination of multiple chart types in a single run

Example: Scatter plot

import wandb
import numpy as np
from sklearn.metrics import roc_curve as sklearn_roc_curve

# Initialize a run
wandb.init(project="custom-charts-demo")

# Example 1: Log a confusion matrix
y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 2, 0, 1, 1]
class_names = ["class_0", "class_1", "class_2"]

wandb.log({
    "conf_mat": wandb.plot.confusion_matrix(
        y_true=y_true, 
        preds=y_pred,
        class_names=class_names
    )
})

# Example 2: Create an ROC curve for binary classification
y_true_binary = np.array([0, 0, 1, 1, 0, 1])
y_scores = np.array([0.1, 0.4, 0.35, 0.8, 0.2, 0.9])

wandb.log({
    "roc": wandb.plot.roc_curve(
        y_true=y_true_binary, 
        y_probas=y_scores,
        labels=["negative", "positive"]
    )
})

# Example 3: Create a custom line chart from a table
table = wandb.Table(columns=["epoch", "accuracy", "loss"])
for epoch in range(10):
    table.add_data(epoch, 0.8 + 0.02 * epoch, 1.0 - 0.1 * epoch)

wandb.log({
    "training_progress": wandb.plot.line(
        table, x="epoch", y="accuracy",
        title="Model Accuracy Over Time"
    )
})

# Example 4: Build a scatter plot for feature analysis
data_table = wandb.Table(columns=["feature_1", "feature_2", "label"])
for _ in range(100):
    data_table.add_data(
        np.random.randn(), 
        np.random.randn(), 
        np.random.choice(["A", "B"])
    )

wandb.log({
    "feature_scatter": wandb.plot.scatter(
        data_table, x="feature_1", y="feature_2",
        title="Feature Distribution"
    )
})

wandb.finish()

5.1 - bar()

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})

5.2 - confusion_matrix()

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.

5.3 - histogram()

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})

5.4 - line_series()

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”.

5.5 - line()

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})

5.6 - plot_table()

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.Run.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 the chart object as argument to wandb.Run.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

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"}

with wandb.init() as run:
   # Training code goes here

   # 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"},
   )

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

5.7 - pr_curve()

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})

5.8 - roc_curve()

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})

5.9 - scatter()

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})

6 - Public API

The W&B Public API provides programmatic access to query, export, and update data stored in W&B. Use this API for post-hoc analysis, data export, and programmatic management of runs, artifacts, and sweeps.

Training and fine-tuning models is done elsewhere in the W&B Python SDK, not the Public API.

Overview

The Public API (wandb.apis.public) is designed for querying and managing data after it has been logged to W&B. While the main SDK handles real-time logging during training, the Public API enables you to retrieve historical data, update metadata, manage artifacts, and perform analysis on completed experiments. Access is provided through the main Api class which serves as the entry point to all functionality.

Available Components

Common Use Cases

Data Export and Analysis

• Export run history as DataFrames for analysis in Jupyter notebooks
• Download metrics for custom visualization or reporting
• Aggregate results across multiple experiments

Post-Hoc Updates

• Update run metadata after completion
• Add tags or notes to completed experiments
• Modify run configurations or summaries

Artifact Management

• Query artifacts by version or alias
• Download model checkpoints programmatically
• Track artifact lineage and dependencies

Sweep Analysis

• Access sweep results and best performing runs
• Export hyperparameter search results
• Analyze parameter importance

Usage Notes

• Read-Only vs. Write Operations: Most API operations are read-only; write operations are limited to metadata updates
• Pagination: Large result sets are automatically paginated for efficient data retrieval
• Filtering: Use MongoDB-style query filters for precise data selection
• Lazy Loading: Data is fetched on-demand to minimize API calls and memory usage
• Authentication: Uses the same authentication as the main W&B SDK

Authentication

The Public API uses the same authentication mechanism as the W&B SDK:

# Option 1: Set environment variable
# export WANDB_API_KEY=your_api_key

# Option 2: Pass API key directly
api = Api(api_key="your_api_key")

# Option 3: Use wandb login
import wandb
wandb.login()
api = Api()

Example Usage

from wandb.apis.public import Api

# Initialize the API client
api = Api()

# Query runs with filters
runs = api.runs(
    path="entity/project",
    filters={"state": "finished", "config.learning_rate": {"$gte": 0.001}}
)

# Analyze run metrics
for run in runs:
    print(f"Run: {run.name}")
    print(f"Final accuracy: {run.summary.get('accuracy')}")
    
    # Get detailed history
    history = run.history(keys=["loss", "accuracy"])
    
    # Update run metadata
    run.tags.append("reviewed")
    run.update()

# Access artifacts
artifact = api.artifact("entity/project/model:v1")
artifact_dir = artifact.download()

# Query sweep results
sweep = api.sweep("entity/project/sweep_id")
best_run = sweep.best_run()
print(f"Best parameters: {best_run.config}")

# Export data as DataFrame
import pandas as pd
runs_df = pd.DataFrame([
    {**run.config, **run.summary} 
    for run in runs
])

6.1 - Api

Training and fine-tuning models is done elsewhere in the W&B Python SDK, not the Public API.

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 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: 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: HTTP timeout in seconds for API requests. If not specified, the default timeout will be used.
• api_key: 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.

Raises:

• ValueError: If viewer data is not able to be fetched from W&B.
• requests.RequestException: If an error occurs while making the graphql request.

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”:

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

method Api.automations

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")

method Api.create_automation

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.",
)

method Api.create_custom_chart

create_custom_chart(
    entity: str,
    name: str,
    display_name: str,
    spec_type: Literal['vega2'],
    access: Literal['private', 'public'],
    spec: Union[str, dict]
) → str

Create a custom chart preset and return its id.

Args:

• entity: The entity (user or team) that owns the chart
• name: Unique identifier for the chart preset
• display_name: Human-readable name shown in the UI
• spec_type: Type of specification. Must be “vega2” for Vega-Lite v2 specifications.
• access: Access level for the chart: - “private”: Chart is only accessible to the entity that created it - “public”: Chart is publicly accessible
• spec: The Vega/Vega-Lite specification as a dictionary or JSON string

Returns: The ID of the created chart preset in the format “entity/name”

Raises:

• wandb.Error: If chart creation fails
• UnsupportedError: If the server doesn’t support custom charts

Example:

   import wandb

   api = wandb.Api()

   # Define a simple bar chart specification
   vega_spec = {
        "$schema": "https://vega.github.io/schema/vega-lite/v6.json",
        "mark": "bar",
        "data": {"name": "wandb"},
        "encoding": {
            "x": {"field": "${field:x}", "type": "ordinal"},
            "y": {"field": "${field:y}", "type": "quantitative"},
        },
   }

   # Create the custom chart
   chart_id = api.create_custom_chart(
        entity="my-team",
        name="my-bar-chart",
        display_name="My Custom Bar Chart",
        spec_type="vega2",
        access="private",
        spec=vega_spec,
   )

   # Use with wandb.plot_table()
   chart = wandb.plot_table(
        vega_spec_name=chart_id,
        data_table=my_table,
        fields={"x": "category", "y": "value"},
   )
   ``` 

---

### <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"],
)

method Api.create_run

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()