With a sagemaker.session.Session instance, you can describe training jobs:
import sagemaker
sagemaker_session = sagemaker.session.Session()
sagemaker_session.describe_training_job("Job...")
AWS
docs.aws.amazon.com › amazon sagemaker › developer guide › model training › distributed training in amazon sagemaker ai › sagemaker model parallelism library v2 › (archived) sagemaker model parallelism library v1.x › run a sagemaker distributed training job with model parallelism › step 2: launch a training job using the sagemaker python sdk
Step 2: Launch a Training Job Using the SageMaker Python SDK - Amazon SageMaker AI
The SageMaker Python SDK supports managed training of models with ML frameworks such as TensorFlow and PyTorch. To launch a training job using one of these frameworks, you define a SageMaker TensorFlow estimator
SageMaker
sagemaker.readthedocs.io › en › stable › overview.html
Using the SageMaker Python SDK — sagemaker 2.254.1 documentation
The VPC should be the same as that ... jobs. The usage is the same as above. ... The SageMaker Python SDK allows you to specify a name and a regular expression for metrics you want to track for training....
AWS
docs.aws.amazon.com › amazon sagemaker › amazon sagemaker api reference › actions › amazon sagemaker service › createtrainingjob
CreateTrainingJob - Amazon SageMaker
You must grant this role the necessary permissions so that SageMaker can successfully complete model training. StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.
Hugging Face
huggingface.co › docs › sagemaker › en › train
Run training on Amazon SageMaker
The Hugging Face extension for the SageMaker Python SDK means we can benefit from fully-managed EC2 spot instances. This can help you save up to 90% of training costs! Note: Unless your training job completes quickly, we recommend you use checkpointing with managed spot training.
AWS
docs.aws.amazon.com › amazon sagemaker › developer guide › model training › run your local code as a sagemaker training job
Run your local code as a SageMaker training job - Amazon SageMaker AI
@remote(**settings) def divide(x, y): return x / y · The SageMaker Python SDK will automatically translate your existing workspace environment and any associated data processing code and datasets into a SageMaker training job that runs on the SageMaker training platform.
GitHub
github.com › aws › sagemaker-training-toolkit
GitHub - aws/sagemaker-training-toolkit: Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
Use the Docker image to start a training job using the SageMaker Python SDK.
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SageMaker
sagemaker.readthedocs.io › en › stable › api › training › smd_data_parallel_use_sm_pysdk.html
Launch a Distributed Training Job Using the SageMaker Python SDK — sagemaker 2.251.0 documentation
Your input data must be in an S3 bucket or in FSx in the AWS region that you will use to launch your training job. If you use the Jupyter notebooks provided, create a SageMaker notebook instance in the same region as the bucket that contains your input data. For more information about storing your training data, refer to the SageMaker Python SDK data inputs documentation.
AWS
docs.aws.amazon.com › amazon sagemaker › developer guide › reserve training plans for your training jobs or hyperpod clusters › training plans utilization for sagemaker training jobs › create a training job using the api, aws cli, sagemaker sdk
Create a training job using the API, AWS CLI, SageMaker SDK - Amazon SageMaker AI
Run a training job on a plan using the CLIRun a training job on a plan using the SageMaker AI Python SDK
AWS
docs.aws.amazon.com › amazon sagemaker › developer guide › machine learning environments offered by amazon sagemaker ai › amazon sagemaker notebook instances › tutorial for building models with notebook instances › train a model
Train a Model - Amazon SageMaker AI
In this step, you choose a training algorithm and run a training job for the model. The Amazon SageMaker Python SDK
AWS
docs.aws.amazon.com › amazon sagemaker › developer guide › algorithms and packages in the aws marketplace › custom algorithms and models with the aws marketplace › usage of algorithm and model package resources › use an algorithm to run a training job
Use an Algorithm to Run a Training Job - Amazon SageMaker AI
You can create use an algorithm resource to create a training job by using the Amazon SageMaker AI console, the low-level Amazon SageMaker API, or the Amazon SageMaker Python SDK .
Mission Control
missioncloud.com › blog › remote-training-amazon-sagemaker
Taking the next step: A data scientists introduction to remote training with Amazon Sagemaker
As you author your notebook, you’ll utilize the SageMaker SDK to define and manage your training job. It’s worth noting that your training notebook typically doesn’t contain the model and training code itself. Instead, you specify various parameters for your training job, such as the location of your training data, the instance types to be used, and the training container.
Amazon Web Services
boto3.amazonaws.com › v1 › documentation › api › latest › reference › services › sagemaker › client › create_training_job.html
create_training_job - Boto3 1.42.10 documentation
You must grant this role the necessary permissions so that SageMaker can successfully complete model training. StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.
SageMaker
sagemaker.readthedocs.io › en › v2.23.0 › overview.html
Using the SageMaker Python SDK — sagemaker 2.23.0 documentation
The VPC should be the same as that ... jobs. The usage is the same as above. ... The SageMaker Python SDK allows you to specify a name and a regular expression for metrics you want to track for training....
SageMaker
sagemaker.readthedocs.io › en › v2.31.1 › overview.html
Using the SageMaker Python SDK — sagemaker 2.31.1 documentation
The VPC should be the same as that ... jobs. The usage is the same as above. ... The SageMaker Python SDK allows you to specify a name and a regular expression for metrics you want to track for training....
AWS
sdk.amazonaws.com › java › api › latest › software › amazon › awssdk › services › sagemaker › model › TrainingJob.html
TrainingJob (AWS SDK for Java - 2.38.4)
If the service returns an enum ...NKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from secondaryStatusAsString(). ... Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition. SageMaker provides primary ...
Medium
ymwdalex.medium.com › trigger-pre-built-framework-training-job-via-amazon-sagemaker-api-b6e49984e707
Trigger Pre-built Framework Training Job via Amazon SageMaker API | by Zhe Sun | Medium
December 16, 2021 - HyperParameters.sagemaker_program: the name of the entry point file · AlgorithmSpecification.TrainingImage: the Amazon ECR registry path of the pre-built framework container images. You can find the images URL here. Below is an example to trigger the same training job by using Python boto3 SDK.
GitHub
github.com › aws › sagemaker-python-sdk
GitHub - aws/sagemaker-python-sdk: A library for training and deploying machine learning models on Amazon SageMaker
A library for training and deploying machine learning models on Amazon SageMaker - aws/sagemaker-python-sdk
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AWS
awscli.amazonaws.com › v2 › documentation › api › latest › reference › sagemaker › create-training-job.html
create-training-job — AWS CLI 2.31.18 Command Reference
RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError . For more information about SageMaker, see How It Works . ... create-training-job --training-job-name <value> [--hyper-parameters <value>] --algorithm-specification <value> --role-arn <value> [--input-data-config <value>] --output-data-config <value> --resource-config <value> [--vpc-config <value>] --stopping-condition <value> [--tags <value>] [--enable-network-isolation | --no-enable-network-isolation] [--enable-inter-container-traffic-encryption | --no-enable-inter-container-traffic-encrypt