`airflow.providers.amazon.aws.hooks.sagemaker`¶

Module Contents¶

Classes¶

`LogState`	Enum-style class holding all possible states of CloudWatch log streams.
`SageMakerHook`	Interact with Amazon SageMaker.

Functions¶

`argmin`(arr, f)	Return the index, i, in arr that minimizes f(arr[i])
`secondary_training_status_changed`(...)	Returns true if training job's secondary status message has changed.
`secondary_training_status_message`(job_description, ...)	Returns a string contains start time and the secondary training job status message.

Attributes¶

Position

class airflow.providers.amazon.aws.hooks.sagemaker.LogState[source]¶

Enum-style class holding all possible states of CloudWatch log streams. https://sagemaker.readthedocs.io/en/stable/session.html#sagemaker.session.LogState

STARTING = 1[source]¶

WAIT_IN_PROGRESS = 2[source]¶

TAILING = 3[source]¶

JOB_COMPLETE = 4[source]¶

COMPLETE = 5[source]¶

airflow.providers.amazon.aws.hooks.sagemaker.Position[source]¶

airflow.providers.amazon.aws.hooks.sagemaker.argmin(arr, f)[source]¶

Return the index, i, in arr that minimizes f(arr[i])

airflow.providers.amazon.aws.hooks.sagemaker.secondary_training_status_changed(current_job_description, prev_job_description)[source]¶

Returns true if training job’s secondary status message has changed.

Parameters

current_job_description (dict) – Current job description, returned from DescribeTrainingJob call.
prev_job_description (dict) – Previous job description, returned from DescribeTrainingJob call.

Returns

Whether the secondary status message of a training job changed or not.

Return type

bool

airflow.providers.amazon.aws.hooks.sagemaker.secondary_training_status_message(job_description, prev_description)[source]¶

Returns a string contains start time and the secondary training job status message.

Parameters

job_description (dict[str, list[Any]]) – Returned response from DescribeTrainingJob call
prev_description (dict | None) – Previous job description from DescribeTrainingJob call

Returns

Job status string to be printed.

Return type

str

class airflow.providers.amazon.aws.hooks.sagemaker.SageMakerHook(*args, **kwargs)[source]¶

Bases: airflow.providers.amazon.aws.hooks.base_aws.AwsBaseHook

Interact with Amazon SageMaker.

Additional arguments (such as aws_conn_id) may be specified and are passed down to the underlying AwsBaseHook.

See also

AwsBaseHook

non_terminal_states[source]¶

endpoint_non_terminal_states[source]¶

pipeline_non_terminal_states[source]¶

failed_states[source]¶

tar_and_s3_upload(path, key, bucket)[source]¶

Tar the local file or directory and upload to s3

Parameters

path (str) – local file or directory
key (str) – s3 key
bucket (str) – s3 bucket

Returns

None

Return type

None

configure_s3_resources(config)[source]¶

Extract the S3 operations from the configuration and execute them.

Parameters: config (dict) – config of SageMaker operation

check_s3_url(s3url)[source]¶

Check if an S3 URL exists

Parameters: s3url (str) – S3 url

check_training_config(training_config)[source]¶

Check if a training configuration is valid

Parameters: training_config (dict) – training_config
Returns: None
Return type: None

check_tuning_config(tuning_config)[source]¶

Check if a tuning configuration is valid

Parameters: tuning_config (dict) – tuning_config
Returns: None
Return type: None

multi_stream_iter(log_group, streams, positions=None)[source]¶

Iterate over the available events coming from a set of log streams in a single log group interleaving the events from each stream so they’re yielded in timestamp order.

Parameters

log_group (str) – The name of the log group.
streams (list) – A list of the log stream names. The position of the stream in this list is the stream number.
positions – A list of pairs of (timestamp, skip) which represents the last record read from each stream.

Returns

A tuple of (stream number, cloudwatch log event).

Return type

Generator

create_training_job(config, wait_for_completion=True, print_log=True, check_interval=30, max_ingestion_time=None)[source]¶

Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

Parameters

config (dict) – the config for training
wait_for_completion (bool) – if the program should keep running until job finishes
check_interval (int) – the time interval in seconds which the operator will check the status of any SageMaker job
max_ingestion_time (int | None) – the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job.

Returns

A response to training job creation

create_tuning_job(config, wait_for_completion=True, check_interval=30, max_ingestion_time=None)[source]¶

Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.

Parameters

config (dict) – the config for tuning
wait_for_completion (bool) – if the program should keep running until job finishes
check_interval (int) – the time interval in seconds which the operator will check the status of any SageMaker job
max_ingestion_time (int | None) – the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job.

Returns

A response to tuning job creation

create_transform_job(config, wait_for_completion=True, check_interval=30, max_ingestion_time=None)[source]¶

Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.

Parameters

config (dict) – the config for transform job
wait_for_completion (bool) – if the program should keep running until job finishes
check_interval (int) – the time interval in seconds which the operator will check the status of any SageMaker job
max_ingestion_time (int | None) – the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job.

Returns

A response to transform job creation

create_processing_job(config, wait_for_completion=True, check_interval=30, max_ingestion_time=None)[source]¶

Use Amazon SageMaker Processing to analyze data and evaluate machine learning models on Amazon SageMaker. With Processing, you can use a simplified, managed experience on SageMaker to run your data processing workloads, such as feature engineering, data validation, model evaluation, and model interpretation.

Parameters

config (dict) – the config for processing job
wait_for_completion (bool) – if the program should keep running until job finishes
check_interval (int) – the time interval in seconds which the operator will check the status of any SageMaker job
max_ingestion_time (int | None) – the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job.

Returns

A response to transform job creation

create_model(config)[source]¶

Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.

Parameters: config (dict) – the config for model
Returns: A response to model creation

create_endpoint_config(config)[source]¶

Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision.

See also

create_model create_endpoint

Parameters: config (dict) – the config for endpoint-config
Returns: A response to endpoint config creation

create_endpoint(config, wait_for_completion=True, check_interval=30, max_ingestion_time=None)[source]¶

When you create a serverless endpoint, SageMaker provisions and manages the compute resources for you. Then, you can make inference requests to the endpoint and receive model predictions in response. SageMaker scales the compute resources up and down as needed to handle your request traffic.

Requires an Endpoint Config.: See also

create_endpoint_config

Parameters

config (dict) – the config for endpoint
wait_for_completion (bool) – if the program should keep running until job finishes
check_interval (int) – the time interval in seconds which the operator will check the status of any SageMaker job
max_ingestion_time (int | None) – the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job.

Returns

A response to endpoint creation

update_endpoint(config, wait_for_completion=True, check_interval=30, max_ingestion_time=None)[source]¶

Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).

Parameters

config (dict) – the config for endpoint
wait_for_completion (bool) – if the program should keep running until job finishes
check_interval (int) – the time interval in seconds which the operator will check the status of any SageMaker job
max_ingestion_time (int | None) – the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job.

Returns

A response to endpoint update

describe_training_job(name)[source]¶

Return the training job info associated with the name

Parameters: name (str) – the name of the training job
Returns: A dict contains all the training job info

describe_training_job_with_log(job_name, positions, stream_names, instance_count, state, last_description, last_describe_job_call)[source]¶

Return the training job info associated with job_name and print CloudWatch logs

describe_tuning_job(name)[source]¶

Return the tuning job info associated with the name

Parameters: name (str) – the name of the tuning job
Returns: A dict contains all the tuning job info
Return type: dict

describe_model(name)[source]¶

Return the SageMaker model info associated with the name

Parameters: name (str) – the name of the SageMaker model
Returns: A dict contains all the model info
Return type: dict

describe_transform_job(name)[source]¶

Return the transform job info associated with the name

Parameters: name (str) – the name of the transform job
Returns: A dict contains all the transform job info
Return type: dict

describe_processing_job(name)[source]¶

Return the processing job info associated with the name

Parameters: name (str) – the name of the processing job
Returns: A dict contains all the processing job info
Return type: dict

describe_endpoint_config(name)[source]¶

Return the endpoint config info associated with the name

Parameters: name (str) – the name of the endpoint config
Returns: A dict contains all the endpoint config info
Return type: dict

describe_endpoint(name)[source]¶

Parameters: name (str) – the name of the endpoint
Returns: A dict contains all the endpoint info
Return type: dict

check_status(job_name, key, describe_function, check_interval, max_ingestion_time=None, non_terminal_states=None)[source]¶

Check status of a SageMaker resource

Parameters

job_name (str) – name of the resource to check status, can be a job but also pipeline for instance.
key (str) – the key of the response dict that points to the state
describe_function (Callable) – the function used to retrieve the status
args – the arguments for the function
check_interval (int) – the time interval in seconds which the operator will check the status of any SageMaker resource
max_ingestion_time (int | None) – the maximum ingestion time in seconds. Any SageMaker resources that run longer than this will fail. Setting this to None implies no timeout for any SageMaker resource.
non_terminal_states (set | None) – the set of nonterminal states

Returns

response of describe call after resource is done

Return type

dict

check_training_status_with_log(job_name, non_terminal_states, failed_states, wait_for_completion, check_interval, max_ingestion_time=None)[source]¶

Display the logs for a given training job, optionally tailing them until the job is complete.

Parameters

job_name (str) – name of the training job to check status and display logs for
non_terminal_states (set) – the set of non_terminal states
failed_states (set) – the set of failed states
wait_for_completion (bool) – Whether to keep looking for new log entries until the job completes
check_interval (int) – The interval in seconds between polling for new log entries and job completion
max_ingestion_time (int | None) – the maximum ingestion time in seconds. Any SageMaker jobs that run longer than this will fail. Setting this to None implies no timeout for any SageMaker job.

Returns

None

list_training_jobs(name_contains=None, max_results=None, **kwargs)[source]¶

This method wraps boto3’s list_training_jobs. The training job name and max results are configurable via arguments. Other arguments are not, and should be provided via kwargs. Note boto3 expects these in CamelCase format, for example:

list_training_jobs(name_contains="myjob", StatusEquals="Failed")

Parameters

name_contains (str | None) – (optional) partial name to match
max_results (int | None) – (optional) maximum number of results to return. None returns infinite results
kwargs – (optional) kwargs to boto3’s list_training_jobs method

Returns

results of the list_training_jobs request

Return type

list[dict]

list_transform_jobs(name_contains=None, max_results=None, **kwargs)[source]¶

This method wraps boto3’s list_transform_jobs. The transform job name and max results are configurable via arguments. Other arguments are not, and should be provided via kwargs. Note boto3 expects these in CamelCase format, for example:

list_transform_jobs(name_contains="myjob", StatusEquals="Failed")

Parameters

name_contains (str | None) – (optional) partial name to match
max_results (int | None) – (optional) maximum number of results to return. None returns infinite results
kwargs – (optional) kwargs to boto3’s list_transform_jobs method

Returns

results of the list_transform_jobs request

Return type

list[dict]

list_processing_jobs(**kwargs)[source]¶

This method wraps boto3’s list_processing_jobs. All arguments should be provided via kwargs. Note boto3 expects these in CamelCase format, for example:

list_processing_jobs(NameContains="myjob", StatusEquals="Failed")

Parameters: kwargs – (optional) kwargs to boto3’s list_training_jobs method
Returns: results of the list_processing_jobs request
Return type: list[dict]

find_processing_job_by_name(processing_job_name)[source]¶

Query processing job by name

This method is deprecated. Please use airflow.providers.amazon.aws.hooks.sagemaker.count_processing_jobs_by_name.

count_processing_jobs_by_name(processing_job_name, job_name_suffix=None, throttle_retry_delay=2, retries=3)[source]¶

Returns the number of processing jobs found with the provided name prefix. :param processing_job_name: The prefix to look for. :param job_name_suffix: The optional suffix which may be appended to deduplicate an existing job name. :param throttle_retry_delay: Seconds to wait if a ThrottlingException is hit. :param retries: The max number of times to retry. :returns: The number of processing jobs that start with the provided prefix.

delete_model(model_name)[source]¶

Delete SageMaker model

Parameters: model_name (str) – name of the model

describe_pipeline_exec(pipeline_exec_arn, verbose=False)[source]¶

Get info about a SageMaker pipeline execution

Parameters

pipeline_exec_arn (str) – arn of the pipeline execution
verbose (bool) – Whether to log details about the steps status in the pipeline execution

start_pipeline(pipeline_name, display_name='airflow-triggered-execution', pipeline_params=None, wait_for_completion=False, check_interval=30, verbose=True)[source]¶

Start a new execution for a SageMaker pipeline

Parameters

pipeline_name (str) – Name of the pipeline to start (this is _not_ the ARN).
display_name (str) – The name this pipeline execution will have in the UI. Doesn’t need to be unique.
pipeline_params (dict | None) – Optional parameters for the pipeline. All parameters supplied need to already be present in the pipeline definition.
wait_for_completion (bool) – Will only return once the pipeline is complete if true.
check_interval (int) – How long to wait between checks for pipeline status when waiting for completion.
verbose (bool) – Whether to print steps details when waiting for completion. Defaults to true, consider turning off for pipelines that have thousands of steps.

Returns

the ARN of the pipeline execution launched.

Return type

str

stop_pipeline(pipeline_exec_arn, wait_for_completion=False, check_interval=10, verbose=True, fail_if_not_running=False)[source]¶

Stop SageMaker pipeline execution

Parameters

pipeline_exec_arn (str) – Amazon Resource Name (ARN) of the pipeline execution. It’s the ARN of the pipeline itself followed by “/execution/” and an id.
wait_for_completion (bool) – Whether to wait for the pipeline to reach a final state. (i.e. either ‘Stopped’ or ‘Failed’)
check_interval (int) – How long to wait between checks for pipeline status when waiting for completion.
verbose (bool) – Whether to print steps details when waiting for completion. Defaults to true, consider turning off for pipelines that have thousands of steps.
fail_if_not_running (bool) – This method will raise an exception if the pipeline we’re trying to stop is not in an “Executing” state when the call is sent (which would mean that the pipeline is already either stopping or stopped). Note that setting this to True will raise an error if the pipeline finished successfully before it was stopped.

Returns

Status of the pipeline execution after the operation. One of ‘Executing’|’Stopping’|’Stopped’|’Failed’|’Succeeded’.

Return type

str

create_model_package_group(package_group_name, package_group_desc='')[source]¶

Creates a Model Package Group if it does not already exist

Parameters

package_group_name (str) – Name of the model package group to create if not already present.
package_group_desc (str) – Description of the model package group, if it was to be created (optional).

Returns

True if the model package group was created, False if it already existed.

Return type

bool

airflow.providers.amazon.aws.hooks.sagemaker¶

Module Contents¶

Classes¶

Functions¶

Attributes¶

`airflow.providers.amazon.aws.hooks.sagemaker`¶