@@ -84,7 +84,7 @@ def remote(
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):
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"""Decorator for running the annotated function as a SageMaker training job.
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- This decorator wraps the annotated code and runs it is a new SageMaker job synchronously
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+ This decorator wraps the annotated code and runs it as a new SageMaker job synchronously
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with the provided runtime settings.
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Unless mentioned otherwise, the decorator first looks up the value from the SageMaker
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instance_count (int): The number of instance to use. Defaults to 1.
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instance_type (str): The Amazon Elastic Compute Cloud (EC2) instance type to use to run
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- the SageMaker job. Valid values include ml.c4.xlarge. If not provided, ValueError is
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- thrown.
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+ the SageMaker job. e.g. ml.c4.xlarge. If not provided, ValueError is thrown.
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job_conda_env (str): The name of the conda environment to activate during job's runtime.
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Defaults to ``None``.
@@ -204,12 +203,12 @@ def remote(
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latency than over using SageMaker managed warm pools alone by caching the package source
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downloaded in the previous runs.
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- max_retry_attempts (int): The max number of times the job is retried after an on
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- ```InternalServerFailure``` Error. Defaults to 1.
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+ max_retry_attempts (int): The max number of times the job is retried on
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+ ```InternalServerFailure``` Error from SageMaker service . Defaults to 1.
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max_runtime_in_seconds (int): The upper limit in seconds to be used for training. After
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this specified amount of time, SageMaker terminates the job regardless of its current
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- status. The max_run_duration time defaults to 1 day or (86400 seconds).
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+ status. Defaults to 1 day or (86400 seconds).
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role (str): The IAM role (either name or full ARN) used to run your SageMaker training
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job. Defaults to:
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s3_kms_key (str): The key used to encrypt the input and output data. Default to ``None``.
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- s3_root_uri (str): The root S3 folder to which where the code archives and data are
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+ s3_root_uri (str): The root S3 folder to which the code archives and data are
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uploaded to. Defaults to ``s3://<sagemaker-default-bucket>``.
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sagemaker_session (sagemaker.session.Session): The underlying SageMaker session to which
@@ -577,8 +576,7 @@ def __init__(
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instance_count (int): The number of instance to use. Defaults to 1.
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instance_type (str): The Amazon Elastic Compute Cloud (EC2) instance type to use to run
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- the SageMaker job. Valid values include ml.c4.xlarge. If not provided, ValueError is
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- thrown.
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+ the SageMaker job. e.g. ml.c4.xlarge. If not provided, ValueError is thrown.
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job_conda_env (str): The name of the conda environment to activate during job's runtime.
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Defaults to ``None``.
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max_parallel_jobs (int): Maximum number of jobs that run in parallel. Defaults to 1.
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- max_retry_attempts (int): The max number of times the job is retried after an on
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- ```InternalServerFailure``` Error. Defaults to 1.
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+ max_retry_attempts (int): The max number of times the job is retried on
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+ ```InternalServerFailure``` Error from SageMaker service . Defaults to 1.
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max_runtime_in_seconds (int): The upper limit in seconds to be used for training. After
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this specified amount of time, SageMaker terminates the job regardless of its current
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- status. The max_run_duration time defaults to 1 day or (86400 seconds).
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+ status. Defaults to 1 day or (86400 seconds).
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role (str): The IAM role (either name or full ARN) used to run your SageMaker training
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job. Defaults to:
@@ -613,7 +611,7 @@ def __init__(
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s3_kms_key (str): The key used to encrypt the input and output data.
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Default to ``None``.
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- s3_root_uri (str): The root S3 folder to which where the code archives and data are
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+ s3_root_uri (str): The root S3 folder to which the code archives and data are
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uploaded to. Defaults to ``s3://<sagemaker-default-bucket>``.
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sagemaker_session (sagemaker.session.Session): The underlying SageMaker session to which
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will block until the job is completed.
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Args:
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- timeout (int): Timeout in seconds to wait for until the job is to completed before it is
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+ timeout (int): Timeout in seconds to wait for until the job is completed before it is
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stopped. Defaults to ``None``.
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Returns: None
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