This is the multi-page printable view of this section. Click here to print.

Return to the regular view of this page.

Legacy Functions

1: define_metric()
2: link_model()
3: log_artifact()
4: log_model()
5: log()
6: save()
7: unwatch()
8: use_artifact()
9: use_model()
10: watch()

1 - define_metric()

`function` `wandb.define_metric`

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

Customize metrics logged with wandb.log().

Args:

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

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

2 - link_model()

View the source code

`function` `wandb.link_model`

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

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

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

This method will:

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

Args:

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

Raises:

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

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

Examples:

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

Invalid usage

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

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

3 - log_artifact()

View the source code

`function` `wandb.log_artifact`

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

Declare an artifact as an output of a run.

Args:

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

Returns: An Artifact object.

4 - log_model()

View the source code

`function` `wandb.log_model`

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

Logs a model artifact as an output of this run.

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

Args:

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

Returns: None

Raises:

ValueError: if name has invalid special characters.

Examples:

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

Invalid usage

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

5 - log()

View the source code

`function` `wandb.log`

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

Upload run data.

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

Basic usage:

import wandb

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Args:

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

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

Basic usage

import wandb

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

Incremental logging

import wandb

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

Histogram

import numpy as np
import wandb

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

Image from NumPy

import numpy as np
import wandb

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

Image from PIL

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

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

Video from NumPy

import numpy as np
import wandb

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

Matplotlib plot

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

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

PR Curve

import wandb

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

3D Object

import wandb

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

Raises:

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

Examples:

# Basic usage
import wandb

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

# Incremental logging
import wandb

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

# Histogram
import numpy as np
import wandb

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

# Image from numpy
import numpy as np
import wandb

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

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

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

# Video from numpy
import numpy as np
import wandb

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

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

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

# PR Curve
import wandb

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

# 3D Object
import wandb

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

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

6 - save()

View the source code

`function` `wandb.save`

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

Sync one or more files to W&B.

Relative paths are relative to the current working directory.

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

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

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

Args:

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

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

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

import wandb

wandb.init()

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

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

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

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

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

7 - unwatch()

View the source code

`function` `wandb.unwatch`

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

Remove pytorch model topology, gradient and parameter hooks.

Args:

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

8 - use_artifact()

View the source code

`function` `wandb.use_artifact`

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

Declare an artifact as an input to a run.

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

Args:

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

Returns: An Artifact object.

Examples:

import wandb

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

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

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

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

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

9 - use_model()

View the source code

`function` `wandb.use_model`

wandb.use_model(name: 'str') → FilePathStr

Download the files logged in a model artifact name.

Args:

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

Raises:

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

Returns:

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

Examples:

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

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

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

Invalid usage

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

10 - watch()

View the source code

`function` `wandb.watch`

wandb.watch(
    models: 'torch.nn.Module | Sequence[torch.nn.Module]',
    criterion: 'torch.F | None' = None,
    log: "Literal['gradients', 'parameters', 'all'] | None" = 'gradients',
    log_freq: 'int' = 1000,
    idx: 'int | None' = None,
    log_graph: 'bool' = False
) → None

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

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

Args:

models: A single model or a sequence of models to be monitored.
criterion: The loss function being optimized (optional).
log: Specifies whether to log “gradients”, “parameters”, or “all”. Set to None to disable logging. (default=“gradients”).
log_freq: Frequency (in batches) to log gradients and parameters. (default=1000)
idx: Index used when tracking multiple models with wandb.watch. (default=None)
log_graph: Whether to log the model’s computational graph. (default=False)

Raises: ValueError: If wandb.init has not been called or if any of the models are not instances of torch.nn.Module.

Legacy Functions

1 - define_metric()

function wandb.define_metric

2 - link_model()

function wandb.link_model

3 - log_artifact()

function wandb.log_artifact

4 - log_model()

function wandb.log_model

5 - log()

function wandb.log

6 - save()

function wandb.save

7 - unwatch()

function wandb.unwatch

8 - use_artifact()

function wandb.use_artifact

9 - use_model()

function wandb.use_model

10 - watch()

function wandb.watch

`function` `wandb.define_metric`

`function` `wandb.link_model`

`function` `wandb.log_artifact`

`function` `wandb.log_model`

`function` `wandb.log`

`function` `wandb.save`

`function` `wandb.unwatch`

`function` `wandb.use_artifact`

`function` `wandb.use_model`

`function` `wandb.watch`