Runs

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

class Runs

A lazy iterator of Run objects associated with a project and optional filter.

Runs are retrieved in pages from the W&B server as needed.

This is generally used indirectly using the Api.runs namespace.

method Runs.__init__

__init__(
    client: 'RetryingClient',
    entity: 'str',
    project: 'str',
    filters: 'dict[str, Any] | None' = None,
    order: 'str' = '+created_at',
    per_page: 'int' = 50,
    include_sweeps: 'bool' = True
)

Args:

• client: (wandb.apis.public.RetryingClient) The API client to use for requests.
• entity: (str) The entity (username or team) that owns the project.
• project: (str) The name of the project to fetch runs from.
• filters: (Optional[Dict[str, Any]]) A dictionary of filters to apply to the runs query.
• order: (str) Order can be created_at, heartbeat_at, config.*.value, or summary_metrics.*. If you prepend order with a + order is ascending (default). If you prepend order with a - order is descending. The default order is run.created_at from oldest to newest.
• per_page: (int) The number of runs to fetch per request (default is 50).
• include_sweeps: (bool) Whether to include sweep information in the runs. Defaults to True.

Examples:

from wandb.apis.public.runs import Runs
from wandb.apis.public import Api

# Get all runs from a project that satisfy the filters
filters = {"state": "finished", "config.optimizer": "adam"}

runs = Api().runs(
   client=api.client,
   entity="entity",
   project="project_name",
   filters=filters,
)

# Iterate over runs and print details
for run in runs:
   print(f"Run name: {run.name}")
   print(f"Run ID: {run.id}")
   print(f"Run URL: {run.url}")
   print(f"Run state: {run.state}")
   print(f"Run config: {run.config}")
   print(f"Run summary: {run.summary}")
   print(f"Run history (samples=5): {run.history(samples=5)}")
   print("----------")

# Get histories for all runs with specific metrics
histories_df = runs.histories(
   samples=100,  # Number of samples per run
   keys=["loss", "accuracy"],  # Metrics to fetch
   x_axis="_step",  # X-axis metric
   format="pandas",  # Return as pandas DataFrame
)

property Runs.length

method Runs.histories

histories(
    samples: 'int' = 500,
    keys: 'list[str] | None' = None,
    x_axis: 'str' = '_step',
    format: "Literal['default', 'pandas', 'polars']" = 'default',
    stream: "Literal['default', 'system']" = 'default'
)

Return sampled history metrics for all runs that fit the filters conditions.

Args:

• samples: The number of samples to return per run
• keys: Only return metrics for specific keys
• x_axis: Use this metric as the xAxis defaults to _step
• format: Format to return data in, options are “default”, “pandas”, “polars”
• stream: “default” for metrics, “system” for machine metrics

Returns:

• pandas.DataFrame: If format="pandas", returns a pandas.DataFrame of history metrics.
• polars.DataFrame: If format="polars", returns a polars.DataFrame of history metrics.
• list of dicts: If format="default", returns a list of dicts containing history metrics with a run_id key.