def _search(cls,
search_resource,
search_item_factory,
boto_next_token_name="NextToken",
sagemaker_boto_client=None,
**kwargs):
sagemaker_boto_client = sagemaker_boto_client or _utils.sagemaker_client(
)
next_token = None
try:
while True:
search_request_kwargs = _boto_functions.to_boto(
kwargs, cls._custom_boto_names, cls._custom_boto_types)
search_request_kwargs["Resource"] = search_resource
if next_token:
search_request_kwargs[boto_next_token_name] = next_token
search_method = getattr(sagemaker_boto_client, "search")
search_method_response = search_method(**search_request_kwargs)
search_items = search_method_response.get("Results", [])
next_token = search_method_response.get(boto_next_token_name)
for item in search_items:
if cls.__name__ in item:
yield search_item_factory(item[cls.__name__])
if not next_token:
break
except StopIteration:
return
def _list(cls,
boto_list_method,
list_item_factory,
boto_list_items_name,
boto_next_token_name="NextToken",
sagemaker_boto_client=None,
**kwargs):
sagemaker_boto_client = sagemaker_boto_client or _utils.sagemaker_client(
)
next_token = None
try:
while True:
list_request_kwargs = _boto_functions.to_boto(
kwargs, cls._custom_boto_names, cls._custom_boto_types)
if next_token:
list_request_kwargs[boto_next_token_name] = next_token
list_method = getattr(sagemaker_boto_client, boto_list_method)
list_method_response = list_method(**list_request_kwargs)
list_items = list_method_response.get(boto_list_items_name, [])
next_token = list_method_response.get(boto_next_token_name)
for item in list_items:
yield list_item_factory(item)
if not next_token:
break
except StopIteration:
return
def _construct(cls,
boto_method_name,
sagemaker_boto_client=None,
**kwargs):
sagemaker_boto_client = sagemaker_boto_client or _utils.sagemaker_client(
)
instance = cls(sagemaker_boto_client, **kwargs)
return instance._invoke_api(boto_method_name, kwargs)
def test_sagemaker_client_endpoint_env_set():
current = os.environ.get("SAGEMAKER_ENDPOINT")
os.environ["SAGEMAKER_ENDPOINT"] = "https://notexist.amazon.com"
client = _utils.sagemaker_client()
assert client._endpoint.host == "https://notexist.amazon.com"
if current is not None:
os.environ["SAGEMAKER_ENDPOINT"] = current
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 load(
cls,
trial_component_name=None,
artifact_bucket=None,
artifact_prefix=None,
boto3_session=None,
sagemaker_boto_client=None,
):
"""Create a new ``Tracker`` by loading an existing trial component.
Examples:
.. code-block:: python
from smexperiments import tracker
my_tracker = tracker.Tracker.load(trial_component_name='xgboost')
Args:
trial_component_name: (str, optional). The name of the trial component to track. If specified, this
trial component must exist in SageMaker. If you invoke this method in a running SageMaker training
or processing job, then trial_component_name can be left empty. In this case, the Tracker will
resolve the trial component automatically created for your SageMaker Job.
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 given trial component.
Raises:
ValueError: If the trial component failed to load.
"""
boto3_session = boto3_session or _utils.boto_session()
sagemaker_boto_client = sagemaker_boto_client or _utils.sagemaker_client(
)
tce = _environment.TrialComponentEnvironment.load()
# Resolve the trial component for this tracker to track: If a trial component name was passed in, then load
# and track that trial component. Otherwise, try to find a trial component given the current environment,
# failing if we're unable to load one.
if trial_component_name:
tc = trial_component.TrialComponent.load(
trial_component_name=trial_component_name,
sagemaker_boto_client=sagemaker_boto_client)
elif tce:
tc = tce.get_trial_component(sagemaker_boto_client)
else:
raise ValueError(
'Could not load TrialComponent. Specify a trial_component_name or invoke "create"'
)
# if running in a SageMaker context write metrics to file
if not trial_component_name and tce.environment_type == _environment.EnvironmentType.SageMakerTrainingJob:
metrics_writer = metrics.SageMakerFileMetricsWriter()
else:
metrics_writer = None
tracker = cls(
tc,
metrics_writer,
_ArtifactUploader(tc.trial_component_name, artifact_bucket,
artifact_prefix, boto3_session),
)
tracker._in_sagemaker_job = True if tce else False
return tracker