Experiments — sagemaker 2.199.0 documentation (original) (raw)

sagemaker

Run¶

class sagemaker.experiments. Run(experiment_name, run_name=None, experiment_display_name=None, run_display_name=None, tags=None, sagemaker_session=None, artifact_bucket=None, artifact_prefix=None)¶

A collection of parameters, metrics, and artifacts to create a ML model.

Construct a Run instance.

SageMaker Experiments automatically tracks the inputs, parameters, configurations, and results of your iterations as runs. You can assign, group, and organize these runs into experiments. You can also create, compare, and evaluate runs.

The code sample below shows how to initialize a run, log parameters to the Run object and invoke a training job under the context of this Run object, which automatically passes the run’s experiment_config (including the experiment name, run name etc.) to the training job.

Note

All log methods (e.g. log_parameter, log_metric, etc.) have to be called within the run context (i.e. the with statement). Otherwise, a RuntimeError is thrown.

with Run(experiment_name="my-exp", run_name="my-run", ...) as run: run.log_parameter(...) ... estimator.fit(job_name="my-job") # Create a training job

In order to reuse an existing run to log extra data, load_run is recommended. For example, instead of the Run constructor, the load_run is recommended to use in a job script to load the existing run created before the job launch. Otherwise, a new run may be created each time you launch a job.

The code snippet below displays how to load the run initialized above in a custom training job script, where no run_name or experiment_nameis presented as they are automatically retrieved from the experiment config in the job environment.

with load_run(sagemaker_session=sagemaker_session) as run: run.log_metric(...) ...

Parameters

• experiment_name (str) – The name of the experiment. The name must be unique within an account.
• run_name (str) – The name of the run. If it is not specified, one is auto generated.
• experiment_display_name (str) – Name of the experiment that will appear in UI, such as SageMaker Studio. (default: None). This display name is used in a create experiment call. If an experiment with the specified name already exists, this display name won’t take effect.
• run_display_name (str) – The display name of the run used in UI (default: None). This display name is used in a create run call. If a run with the specified name already exists, this display name won’t take effect.
• tags (List [_ _Dict_ _[_str,_ str] ]) – A list of tags to be used for all create calls, e.g. to create an experiment, a run group, etc. (default: None).
• sagemaker_session (sagemaker.session.Session) – Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one is created using the default AWS configuration chain.
• artifact_bucket (str) – The S3 bucket to upload the artifact to. If not specified, the default bucket defined in sagemaker_sessionwill be used.
• artifact_prefix (str) – The S3 key prefix used to generate the S3 path to upload the artifact to (default: “trial-component-artifacts”).

property experiment_config¶

Get experiment config from run attributes.

log_parameter(name, value)¶

Record a single parameter value for this run.

Overwrites any previous value recorded for the specified parameter name.

Parameters

• name (str) – The name of the parameter.
• value (str or int or float) – The value of the parameter.

log_parameters(parameters)¶

Record a collection of parameter values for this run.

Parameters

parameters (dict[_str,_ str or int or float]) – The parameters to record.

log_metric(name, value, timestamp=None, step=None)¶

Record a custom scalar metric value for this run.

Note

This method is for manual custom metrics, for automatic metrics see theenable_sagemaker_metrics parameter on the estimator class.

Parameters

• name (str) – The name of the metric.
• value (float) – The value of the metric.
• timestamp (datetime.datetime) – The timestamp of the metric. If not specified, the current UTC time will be used.
• step (int) – The integer iteration number of the metric value (default: None).

log_precision_recall(y_true, predicted_probabilities, positive_label=None, title=None, is_output=True, no_skill=None)¶

Create and log a precision recall graph artifact for Studio UI to render.

The artifact is stored in S3 and represented as a lineage artifact with an association with the run.

You can view the artifact in the UI. If your job is created by a pipeline execution you can view the artifact by selecting the corresponding step in the pipelines UI. See also SageMaker Pipelines

This method requires sklearn library.

Parameters

• y_true (list or array) – True labels. If labels are not binary then positive_label should be given.
• predicted_probabilities (list or array) – Estimated/predicted probabilities.
• positive_label (str or int) – Label of the positive class (default: None).
• title (str) – Title of the graph (default: None).
• is_output (bool) – Determines direction of association to the run. Defaults to True (output artifact). If set to False then represented as input association.
• no_skill (int) – The precision threshold under which the classifier cannot discriminate between the classes and would predict a random class or a constant class in all cases (default: None).

log_roc_curve(y_true, y_score, title=None, is_output=True)¶

Create and log a receiver operating characteristic (ROC curve) artifact.

The artifact is stored in S3 and represented as a lineage artifact with an association with the run.

You can view the artifact in the UI. If your job is created by a pipeline execution you can view the artifact by selecting the corresponding step in the pipelines UI. See also SageMaker Pipelines

This method requires sklearn library.

Parameters

• y_true (list or array) – True labels. If labels are not binary then positive_label should be given.
• y_score (list or array) – Estimated/predicted probabilities.
• title (str) – Title of the graph (default: None).
• is_output (bool) – Determines direction of association to the run. Defaults to True (output artifact). If set to False then represented as input association.

log_confusion_matrix(y_true, y_pred, title=None, is_output=True)¶

Create and log a confusion matrix artifact.

The artifact is stored in S3 and represented as a lineage artifact with an association with the run.

You can view the artifact in the UI. If your job is created by a pipeline execution you can view the artifact by selecting the corresponding step in the pipelines UI. See also SageMaker PipelinesThis method requires sklearn library.

Parameters

• y_true (list or array) – True labels. If labels are not binary then positive_label should be given.
• y_pred (list or array) – Predicted labels.
• title (str) – Title of the graph (default: None).
• is_output (bool) – Determines direction of association to the run. Defaults to True (output artifact). If set to False then represented as input association.

log_artifact(name, value, media_type=None, is_output=True)¶

Record a single artifact for this run.

Overwrites any previous value recorded for the specified name.

Parameters

• name (str) – The name of the artifact.
• value (str) – The value.
• media_type (str) – The MediaType (MIME type) of the value (default: None).
• is_output (bool) – Determines direction of association to the run. Defaults to True (output artifact). If set to False then represented as input association.

log_file(file_path, name=None, media_type=None, is_output=True)¶

Upload a file to s3 and store it as an input/output artifact in this run.

Parameters

• file_path (str) – The path of the local file to upload.
• name (str) – The name of the artifact (default: None).
• media_type (str) – The MediaType (MIME type) of the file. If not specified, this library will attempt to infer the media type from the file extension of file_path.
• is_output (bool) – Determines direction of association to the run. Defaults to True (output artifact). If set to False then represented as input association.

close()¶

Persist any data saved locally.

run. load_run(experiment_name=None, sagemaker_session=None, artifact_bucket=None, artifact_prefix=None)¶

Load an existing run.

In order to reuse an existing run to log extra data, load_run is recommended. It can be used in several ways:

1. Use load_run by explicitly passing in run_name and experiment_name.

If run_name and experiment_name are passed in, they are honored over the default experiment config in the job environment or the run context (i.e. within the with block).

Note

Both run_name and experiment_name should be supplied to make this usage work. Otherwise, you may get a ValueError.

with load_run(experiment_name="my-exp", run_name="my-run") as run: run.log_metric(...) ...

1. Use the load_run in a job script without supplying run_name and experiment_name.

In this case, the default experiment config (specified when creating the job) is fetched from the job environment to load the run.

In a job script

with load_run() as run: run.log_metric(...) ...

3. Use the load_run in a notebook within a run context (i.e. the with block) but without supplying run_name and experiment_name.

Every time we call with Run(...) as run1:, the initialized run1 is tracked in the run context. Then when we call load_run() under this with statement, the run1in the context is loaded by default.

In a notebook

with Run(experiment_name="my-exp", run_name="my-run", ...) as run1: run1.log_parameter(...)

with load_run() as run2: # run2 is the same object as run1
    run2.log_metric(...)
    ...

Parameters

• run_name (str) – The name of the run to be loaded (default: None). If it is None, the RunName in the ExperimentConfig of the job will be fetched to load the run.
• experiment_name (str) – The name of the Experiment that the to be loaded run is associated with (default: None). Note: the experiment_name must be supplied along with a valid run_name. Otherwise, it will be ignored.
• sagemaker_session (sagemaker.session.Session) – Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one is created using the default AWS configuration chain.
• artifact_bucket (str) – The S3 bucket to upload the artifact to. If not specified, the default bucket defined in sagemaker_sessionwill be used.
• artifact_prefix (str) – The S3 key prefix used to generate the S3 path to upload the artifact to (default: “trial-component-artifacts”).

Returns

The loaded Run object.

Return type

Run

experiments. list_runs(created_before=None, created_after=None, sagemaker_session=None, max_results=None, next_token=None, sort_by=<SortByType.CREATION_TIME: 'CreationTime'>, sort_order=<SortOrderType.DESCENDING: 'Descending'>)¶

Return a list of Run objects matching the given criteria.

Parameters

• experiment_name (str) – Only Run objects related to the specified experiment are returned.
• created_before (datetime.datetime) – Return Run objects created before this instant (default: None).
• created_after (datetime.datetime) – Return Run objects created after this instant (default: None).
• sagemaker_session (sagemaker.session.Session) – Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one is created using the default AWS configuration chain.
• max_results (int) – Maximum number of Run objects to retrieve (default: None).
• next_token (str) – Token for next page of results (default: None).
• sort_by (SortByType) – The property to sort results by. One of NAME, CREATION_TIME (default: CREATION_TIME).
• sort_order (SortOrderType) – One of ASCENDING, or DESCENDING (default: DESCENDING).

Returns

A list of Run objects.

Return type

list

Experiment¶

class sagemaker.experiments. Experiment(sagemaker_session=None, **kwargs)¶

An Amazon SageMaker experiment, which is a collection of related trials.

New experiments are created by calling experiments.experiment.Experiment.create. Existing experiments can be reloaded by calling experiments.experiment.Experiment.load.

experiment_name¶

The name of the experiment. The name must be unique within an account.

Type

str

display_name¶

Name of the experiment that will appear in UI, such as SageMaker Studio.

Type

str

description¶

A description of the experiment.

Type

str

tags¶

A list of tags to associate with the experiment.

Type

List[Dict[str, str]]

Init Record.

save()¶

Save the state of this Experiment to SageMaker.

Returns

Update experiment API response.

Return type

dict

delete()¶

Delete this Experiment from SageMaker.

Deleting an Experiment does not delete associated Trials and their Trial Components. It requires that each Trial in the Experiment is first deleted.

Returns

Delete experiment API response.

Return type

dict

classmethod load(experiment_name, sagemaker_session=None)¶

Load an existing experiment and return an Experiment object representing it.

Parameters

• experiment_name – (str): Name of the experiment
• sagemaker_session (sagemaker.session.Session) – Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one is created using the default AWS configuration chain.

Returns

A SageMaker Experiment object

Return type

experiments.experiment.Experiment

classmethod create(experiment_name, display_name=None, description=None, tags=None, sagemaker_session=None)¶

Create a new experiment in SageMaker and return an Experiment object.

Parameters

• experiment_name – (str): Name of the experiment. Must be unique. Required.
• display_name – (str): Name of the experiment that will appear in UI, such as SageMaker Studio (default: None).
• description – (str): Description of the experiment (default: None).
• sagemaker_session (sagemaker.session.Session) – Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one is created using the default AWS configuration chain.
• tags (List [_ _Dict_ _[_str,_ str] ]) – A list of tags to associate with the experiment (default: None).

Returns

A SageMaker Experiment object

Return type

experiments.experiment.Experiment

list_trials(created_before=None, created_after=None, sort_by=None, sort_order=None)¶

List trials in this experiment matching the specified criteria.

Parameters

• created_before (datetime.datetime) – Return trials created before this instant (default: None).
• created_after (datetime.datetime) – Return trials created after this instant (default: None).
• sort_by (str) – Which property to sort results by. One of ‘Name’, ‘CreationTime’ (default: None).
• sort_order (str) – One of ‘Ascending’, or ‘Descending’ (default: None).

Returns

An iterator over trials matching the criteria.

Return type

collections.Iterator[experiments._api_types.TrialSummary]

Other¶

class sagemaker.experiments. SortByType(value)¶

The type of property by which to sort the list_runs results.

CREATION_TIME = 'CreationTime'¶

NAME = 'Name'¶

class sagemaker.experiments. SortOrderType(value)¶

The type of order to sort the list or search results.

ASCENDING = 'Ascending'¶

DESCENDING = 'Descending'¶