Run

A unit of computation logged by wandb. Typically, this is an ML experiment.

Run(
    settings: Settings,
    config: (dict[str, Any] | None) = None,
    sweep_config: (dict[str, Any] | None) = None,
    launch_config: (dict[str, Any] | None) = None
) -> None

Create a run with wandb.init():

import wandb

run = wandb.init()

There is only ever at most one active wandb.Run in any process, and it is accessible as wandb.run:

import wandb

assert wandb.run is None

wandb.init()

assert wandb.run is not None

anything you log with wandb.log will be sent to that run.

If you want to start more runs in the same script or notebook, you'll need to finish the run that is in-flight. Runs can be finished with wandb.finish or by using them in a with block:

import wandb

wandb.init()
wandb.finish()

assert wandb.run is None

with wandb.init() as run:
    pass  # log data here

assert wandb.run is None

See the documentation for wandb.init for more on creating runs, or check out our guide to wandb.init.

In distributed training, you can either create a single run in the rank 0 process and then log information only from that process, or you can create a run in each process, logging from each separately, and group the results together with the group argument to wandb.init. For more details on distributed training with W&B, check out our guide.

Currently, there is a parallel Run object in the wandb.Api. Eventually these two objects will be merged.

Attributes
summary	(Summary) Single values set for each wandb.log() key. By default, summary is set to the last value logged. You can manually set summary to the best value, like max accuracy, instead of the final value.
config	Config object associated with this run.
dir	The directory where files associated with the run are saved.
entity	The name of the W&B entity associated with the run. Entity can be a username or the name of a team or organization.
group	Name of the group associated with the run. Setting a group helps the W&B UI organize runs in a sensible way. If you are doing a distributed training you should give all of the runs in the training the same group. If you are doing cross-validation you should give all the cross-validation folds the same group.
id	Identifier for this run.
mode	For compatibility with 0.9.x and earlier, deprecate eventually.
name	Display name of the run. Display names are not guaranteed to be unique and may be descriptive. By default, they are randomly generated.
notes	Notes associated with the run, if there are any. Notes can be a multiline string and can also use markdown and latex equations inside $$, like $x + 3$.
path	Path to the run. Run paths include entity, project, and run ID, in the format entity/project/run_id.
project	Name of the W&B project associated with the run.
resumed	True if the run was resumed, False otherwise.
settings	A frozen copy of run's Settings object.
start_time	Unix timestamp (in seconds) of when the run started.
starting_step	The first step of the run.
step	Current value of the step. This counter is incremented by wandb.log.
sweep_id	ID of the sweep associated with the run, if there is one.
tags	Tags associated with the run, if there are any.
url	The W&B url associated with the run.

Methods

alert

View source

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

Launch an alert with the given title and text.

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

define_metric

View source

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.

detach

View source

detach() -> None

display

View source

display(
    height: int = 420,
    hidden: bool = (False)
) -> bool

Display this run in jupyter.

finish

View source

finish(
    exit_code: (int | None) = None,
    quiet: (bool | None) = None
) -> None

Mark a run as finished, and finish uploading all data.

This is used when creating multiple runs in the same process. We automatically call this method when your script exits or if you use the run context manager.

Args
exit_code	Set to something other than 0 to mark a run as failed
quiet	Deprecated, use wandb.Settings(quiet=...) to set this instead.

finish_artifact

View source

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

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

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

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

Returns
An Artifact object.

get_project_url

View source

get_project_url() -> (str | None)

Return the url for the W&B project associated with the run, if there is one.

Offline runs will not have a project url.

get_sweep_url

View source

get_sweep_url() -> (str | None)

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

get_url

View source

get_url() -> (str | None)

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

Offline runs will not have a url.

join

View source

join(
    exit_code: (int | None) = None
) -> None

Deprecated alias for finish() - use finish instead.

link_artifact

View source

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

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

The linked artifact will be visible in the UI for the specified portfolio.

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

Returns
None

link_model

View source

link_model(
    path: StrPath,
    registered_model_name: str,
    name: (str | None) = None,
    aliases: (list[str] | None) = None
) -> None

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

The linked model version will be visible in the UI for the specified registered model.

Steps:

• 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	(str) - 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	(str, optional) - 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	(List[str], optional) - alias(es) 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.

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"],
)

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
None

log

View source

log(
    data: dict[str, Any],
    step: (int | None) = None,
    commit: (bool | None) = None,
    sync: (bool | None) = None
) -> None

Upload run data.

Use log to log data from runs, such as scalars, images, video, histograms, plots, and tables.

See our guides to logging for live examples, code snippets, best practices, and more.

The most basic usage is run.log({"train-loss": 0.5, "accuracy": 0.9}). This will save the loss and accuracy to the run's history and update the summary values for these metrics.

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

Logged values don't have to be scalars. Logging any wandb object is supported. For example run.log({"example": wandb.Image("myimage.jpg")}) will log an example image which will be displayed nicely in the W&B UI. See the reference documentation for all of the different supported types or check out our guides to logging for examples, from 3D molecular structures and segmentation masks to PR curves and histograms. You can use wandb.Table to log structured data. See our guide to logging tables for details.

The W&B UI 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.

The W&B step

With basic usage, each call to log creates a new "step". The step must always increase, and it is not possible to log to a previous step.

Note that you can use any metric as the X axis in charts. 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})

See also define_metric.

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

log_artifact

View source

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	(str or Artifact) A path to the contents of this artifact, can be in the following forms: - /local/directory - /local/directory/file.txt - s3://bucket/path You can also pass an Artifact object created by calling wandb.Artifact.
name	(str, optional) An artifact name. Valid names can be in the following forms: - name:version - name:alias - digest This will default to the basename of the path prepended with the current run id if not specified.
type	(str) The type of artifact to log, examples include dataset, model
aliases	(list, optional) Aliases to apply to this artifact, defaults to ["latest"]
tags	(list, optional) Tags to apply to this artifact, if any.

Returns
An Artifact object.

log_code

View source

log_code(
    root: (str | None) = ".",
    name: (str | None) = None,
    include_fn: (Callable[[str, str], bool] | Callable[[str], bool]) = _is_py_requirements_or_dockerfile,
    exclude_fn: (Callable[[str, str], bool] | Callable[[str], bool]) = filenames.exclude_wandb_fn
) -> (Artifact | None)

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

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

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

Examples:

Basic usage

run.log_code()

Advanced usage

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

Returns
An Artifact object if code was logged

log_model

View source

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

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

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
name	(str, optional) A name to assign to the model artifact that the file contents will be added to. The string must contain only the following alphanumeric characters: dashes, underscores, and dots. This will default to the basename of the path prepended with the current run id if not specified.
aliases	(list, optional) Aliases to apply to the created model artifact, defaults to ["latest"]

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"],
)

Raises
ValueError	if name has invalid special characters

Returns
None

mark_preempting

View source

mark_preempting() -> None

Mark this run as preempting.

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

plot_table

View source

@staticmethod
plot_table(
    vega_spec_name: str,
    data_table: Table,
    fields: dict[str, Any],
    string_fields: (dict[str, Any] | None) = None,
    split_table: bool = (False)
) -> CustomChart

Create a custom plot on a table.

Args
vega_spec_name	the name of the spec for the plot
data_table	a wandb.Table object containing the data to be used on the visualization
fields	a dict mapping from table keys to fields that the custom visualization needs
string_fields	a dict that provides values for any string constants the custom visualization needs
split_table	a boolean that indicates whether the table should be in a separate section in the UI

project_name

View source

project_name() -> str

restore

View source

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

Download the specified file from cloud storage.

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

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

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

Raises
wandb.CommError	if we can't connect to the wandb backend
ValueError	if the file is not found or can't find run_path

save

View source

save(
    glob_str: (str | os.PathLike | None) = None,
    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. It's best understood through examples:

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/".

Note: 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.

status

View source

status() -> RunStatus

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

to_html

View source

to_html(
    height: int = 420,
    hidden: bool = (False)
) -> str

Generate HTML containing an iframe displaying the current run.

unwatch

View source

unwatch(
    models: (torch.nn.Module | Sequence[torch.nn.Module] | None) = None
) -> None

Remove pytorch model topology, gradient and parameter hooks.

Args
models (torch.nn.Module	Sequence[torch.nn.Module]): Optional list of pytorch models that have had watch called on them

upsert_artifact

View source

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

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

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

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

Returns
An Artifact object.

use_artifact

View source

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	(str or Artifact) An artifact name. May be prefixed with project/ or entity/project/. If no entity is specified in the name, the Run or API setting's entity is used. Valid names can be in the following forms: - name:version - name:alias You can also pass an Artifact object created by calling wandb.Artifact
type	(str, optional) The type of artifact to use.
aliases	(list, optional) Aliases to apply to this artifact
use_as	(string, optional) Optional string indicating what purpose the artifact was used with. Will be shown in UI.

Returns
An Artifact object.

use_model

View source

use_model(
    name: str
) -> FilePathStr

Download the files logged in a model artifact 'name'.

Args
name	(str) 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

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",
)

Raises
AssertionError	if model artifact 'name' is of a type that does not contain the substring 'model'.

Returns
path	(str) path to downloaded model artifact file(s).

watch

View source

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

Hooks into the given PyTorch model(s) to monitor gradients and the model's computational graph.

This function can track parameters, gradients, or both during training. It should be extended to support arbitrary machine learning models in the future.

Args

models (Union[torch.nn.Module, Sequence[torch.nn.Module]]): A single model or a sequence of models to be monitored. criterion (Optional[torch.F]): The loss function being optimized (optional). log (Optional[Literal["gradients", "parameters", "all"]]): Specifies whether to log "gradients", "parameters", or "all". Set to None to disable logging. (default="gradients") log_freq (int): Frequency (in batches) to log gradients and parameters. (default=1000) idx (Optional[int]): Index used when tracking multiple models with wandb.watch. (default=None) log_graph (bool): 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.

__enter__

View source

__enter__() -> Run

__exit__

View source

__exit__(
    exc_type: type[BaseException],
    exc_val: BaseException,
    exc_tb: TracebackType
) -> bool