Run

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A single run associated with an entity and project.

Run(
    client: "RetryingClient",
    entity: str,
    project: str,
    run_id: str,
    attrs: Optional[Mapping] = None,
    include_sweeps: bool = (True)
)
Python
Attributes

Methods

create

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@classmethod
create(
    api, run_id=None, project=None, entity=None
)
Python

Create a run for the given project.

delete

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delete(
    delete_artifacts=(False)
)
Python

Delete the given run from the wandb backend.

display

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

Display this object in jupyter.

file

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file(
    name
)
Python

Return the path of a file with a given name in the artifact.

Args
name (str): name of requested file.
Returns
A File matching the name argument.

files

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files(
    names=None, per_page=50
)
Python

Return a file path for each file named.

Args
names (list): names of the requested files, if empty returns all files per_page (int): number of results per page.
Returns
A Files object, which is an iterator over File objects.

history

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history(
    samples=500, keys=None, x_axis="_step", pandas=(True), stream="default"
)
Python

Return sampled history metrics for a run.

This is simpler and faster if you are ok with the history records being sampled.

Args
samples (int, optional) The number of samples to return
pandas (bool, optional) Return a pandas dataframe
keys (list, optional) Only return metrics for specific keys
x_axis (str, optional) Use this metric as the xAxis defaults to _step
stream (str, optional) “default” for metrics, “system” for machine metrics
Returns
pandas.DataFrame If pandas=True returns a pandas.DataFrame of history metrics. list of dicts: If pandas=False returns a list of dicts of history metrics.

load

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load(
    force=(False)
)
Python

log_artifact

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log_artifact(
    artifact: "wandb.Artifact",
    aliases: Optional[Collection[str]] = None,
    tags: Optional[Collection[str]] = None
)
Python

Declare an artifact as output of a run.

Args
artifact (Artifact): An artifact returned from wandb.Api().artifact(name). aliases (list, optional): Aliases to apply to this artifact.
tags (list, optional) Tags to apply to this artifact, if any.
Returns
A Artifact object.

logged_artifacts

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logged_artifacts(
    per_page: int = 100
) -> public.RunArtifacts
Python

Fetches all artifacts logged by this run.

Retrieves all output artifacts that were logged during the run. Returns a paginated result that can be iterated over or collected into a single list.

Args
per_page Number of artifacts to fetch per API request.
Returns
An iterable collection of all Artifact objects logged as outputs during this run.

Example:

>>> import wandb
>>> import tempfile
>>> with tempfile.NamedTemporaryFile(
...     mode="w", delete=False, suffix=".txt"
... ) as tmp:
...     tmp.write("This is a test artifact")
...     tmp_path = tmp.name
>>> run = wandb.init(project="artifact-example")
>>> artifact = wandb.Artifact("test_artifact", type="dataset")
>>> artifact.add_file(tmp_path)
>>> run.log_artifact(artifact)
>>> run.finish()
>>> api = wandb.Api()
>>> finished_run = api.run(f"{run.entity}/{run.project}/{run.id}")
>>> for logged_artifact in finished_run.logged_artifacts():
...     print(logged_artifact.name)
test_artifact

save

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save()
Python

scan_history

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scan_history(
    keys=None, page_size=1000, min_step=None, max_step=None
)
Python

Returns an iterable collection of all history records for a run.

Example:

Export all the loss values for an example run

run = api.run("l2k2/examples-numpy-boston/i0wt6xua")
history = run.scan_history(keys=["Loss"])
losses = [row["Loss"] for row in history]
Python
Args
keys ([str], optional): only fetch these keys, and only fetch rows that have all of keys defined. page_size (int, optional): size of pages to fetch from the api. min_step (int, optional): the minimum number of pages to scan at a time. max_step (int, optional): the maximum number of pages to scan at a time.
Returns
An iterable collection over history records (dict).

snake_to_camel

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snake_to_camel(
    string
)
Python

to_html

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to_html(
    height=420, hidden=(False)
)
Python

Generate HTML containing an iframe displaying this run.

update

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update()
Python

Persist changes to the run object to the wandb backend.

upload_file

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upload_file(
    path, root="."
)
Python

Upload a file.

Args
path (str): name of file to upload. root (str): the root path to save the file relative to. i.e. If you want to have the file saved in the run as “my_dir/file.txt” and you’re currently in “my_dir” you would set root to “../”.
Returns
A File matching the name argument.

use_artifact

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use_artifact(
    artifact, use_as=None
)
Python

Declare an artifact as an input to a run.

Args
artifact (Artifact): An artifact returned from wandb.Api().artifact(name) use_as (string, optional): A string identifying how the artifact is used in the script. Used to easily differentiate artifacts used in a run, when using the beta wandb launch feature’s artifact swapping functionality.
Returns
A Artifact object.

used_artifacts

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used_artifacts(
    per_page: int = 100
) -> public.RunArtifacts
Python

Fetches artifacts explicitly used by this run.

Retrieves only the input artifacts that were explicitly declared as used during the run, typically via run.use_artifact(). Returns a paginated result that can be iterated over or collected into a single list.

Args
per_page Number of artifacts to fetch per API request.
Returns
An iterable collection of Artifact objects explicitly used as inputs in this run.

Example:

>>> import wandb
>>> run = wandb.init(project="artifact-example")
>>> run.use_artifact("test_artifact:latest")
>>> run.finish()
>>> api = wandb.Api()
>>> finished_run = api.run(f"{run.entity}/{run.project}/{run.id}")
>>> for used_artifact in finished_run.used_artifacts():
...     print(used_artifact.name)
test_artifact

wait_until_finished

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wait_until_finished()
Python

Last modified March 24, 2025