def create(cls,
experiment_name,
trial_name=None,
sagemaker_boto_client=None,
trial_components=None,
tags=None):
"""Create a new trial and return a ``Trial`` object.
Args:
experiment_name: (str): Name of the experiment to create this trial in.
trial_name: (str, optional): Name of the Trial. If not specified, an auto-generated name will be used.
sagemaker_boto_client (SageMaker.Client, optional): Boto3 client for SageMaker.
If not supplied, a default boto3 client will be created and used.
trial_components (list): A list of trial component names, trial components, or trial component trackers.
tags (List[dict[str, str]]): A list of tags to associate with the trial.
Returns:
smexperiments.trial.Trial: A SageMaker ``Trial`` object
"""
trial_name = trial_name or _utils.name("Trial")
trial = super(Trial, cls)._construct(
cls._boto_create_method,
trial_name=trial_name,
experiment_name=experiment_name,
tags=tags,
sagemaker_boto_client=sagemaker_boto_client,
)
if trial_components:
for tc in trial_components:
trial.add_trial_component(tc)
return trial
def create_trial(self,
trial_name=None,
trial_name_prefix="SageMakerTrial"):
"""Create a trial in this experiment.
Since trial names are expected to be unique in an account, ``trial_name_prefix`` can be provided
instead of ``trial_name``. In this case a unique name will be generated that begins with the specified
prefix.
Args:
trial_name (str): Name of the trial.
trial_name_prefix (str): Prefix for the trial name if you want SageMaker to
auto-generate the trial name.
Returns:
sagemaker.experiments.trial.Trial : A SageMaker ``Trial`` object representing the
created trial.
"""
if not trial_name:
trial_name = _utils.name(trial_name_prefix)
return trial.Trial.create(
trial_name=trial_name,
experiment_name=self.experiment_name,
sagemaker_boto_client=self.sagemaker_boto_client,
)
def test_create_default_bucket(boto3_session):
bucket_name_prefix=_utils.name('sm-test')
bucket = _utils.get_or_create_default_bucket(boto3_session, default_bucket_prefix=bucket_name_prefix)
s3_client = boto3_session.client('s3')
try:
s3_client.head_bucket(Bucket=bucket)
finally:
s3_client.delete_bucket(Bucket=bucket)
def create(
cls,
display_name=None,
artifact_bucket=None,
artifact_prefix=None,
boto3_session=None,
sagemaker_boto_client=None,
):
"""Create a new ``Tracker`` by creating a new trial component.
Examples
.. code-block:: python
from smexperiments import tracker
my_tracker = tracker.Tracker.create()
Args:
display_name: (str, optional). The display name of the trial component to track.
artifact_bucket: (str, optional) The name of the S3 bucket to store artifacts to.
artifact_prefix: (str, optional) The prefix to write artifacts to within ``artifact_bucket``
boto3_session: (boto3.Session, optional) The boto3.Session to use to interact with AWS services.
If not specified a new default boto3 session will be created.
sagemaker_boto_client: (boto3.Client, optional) The SageMaker AWS service client to use. If not
specified a new client will be created from the specified ``boto3_session`` or default
boto3.Session.
Returns:
Tracker: The tracker for the new trial component.
"""
boto3_session = boto3_session or _utils.boto_session()
sagemaker_boto_client = sagemaker_boto_client or _utils.sagemaker_client(
)
tc = trial_component.TrialComponent.create(
trial_component_name=_utils.name("TrialComponent"),
display_name=display_name,
sagemaker_boto_client=sagemaker_boto_client,
)
metrics_writer = metrics.SageMakerFileMetricsWriter()
return cls(
tc,
metrics_writer,
_ArtifactUploader(tc.trial_component_name, artifact_bucket,
artifact_prefix, boto3_session),
)
def _resolve_artifact_name(file_path):
_, filename = os.path.split(file_path)
if filename:
return filename
else:
return _utils.name("artifact")