Run
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)
)
| Attributes |
|---|
Methods
create
@classmethod
create(
api, run_id=None, project=None, entity=None
)
Create a run for the given project.
delete
delete(
delete_artifacts=(False)
)
Delete the given run from the wandb backend.
display
display(
height=420, hidden=(False)
) -> bool
Display this object in jupyter.
file
file(
name
)
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
files(
names=None, per_page=50
)
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
history(
samples=500, keys=None, x_axis="_step", pandas=(True), stream="default"
)
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
load(
force=(False)
)
log_artifact
log_artifact(
artifact: "wandb.Artifact",
aliases: Optional[Collection[str]] = None,
tags: Optional[Collection[str]] = None
)
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
logged_artifacts(
per_page: int = 100
) -> public.RunArtifacts
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
save()
scan_history
scan_history(
keys=None, page_size=1000, min_step=None, max_step=None
)
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]
| 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
snake_to_camel(
string
)
to_html
to_html(
height=420, hidden=(False)
)
Generate HTML containing an iframe displaying this run.
update
update()
Persist changes to the run object to the wandb backend.
upload_file
upload_file(
path, root="."
)
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
use_artifact(
artifact, use_as=None
)
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
used_artifacts(
per_page: int = 100
) -> public.RunArtifacts
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
wait_until_finished()