Integrate with SageMaker¶
Many ML practioners use AWS SageMaker in combination with Comet.
Comet is best used for experiment management, artifact management, production monitoring, and as the go-to UI and reporting tool for data science teams. Sagemaker provides a complementary set of tools for infrastructure, resource management and compute (training, orchestration, deployment).
Sagemaker Model Training¶
Training job with a custom script and container¶
Training job with Built-in algorithms¶
If you trained a model with one of Sagemaker Built-in Algorithm and you cannot change the container or training script, you can still import your Sagemaker Training jobs as Comet Experiment.
The recommended way is to use
comet_ml.integration.sagemaker.log_sagemaker_training_job_v1 if you have access to the Estimator object. This will import the latest training job that was scheduled using that Estimator object.
You can also use
comet_ml.integration.sagemaker.log_sagemaker_training_job_by_name_v1 if you have the name of the Sagemaker Training job that you want to import.
Lastly, you can use
comet_ml.integration.sagemaker.log_last_sagemaker_training_job_v1 to import the last sagemaker training job, you should only use this function if you are the only person using this AWS account.
When a Sagemaker training job is imported as a new Comet experiment, the following metadata are logged:
- All Hyper-Parameters.
- All metrics defined in the Algortihm Definition.
- Pip packages from the environment where
- If you are calling
comet_ml.integration.sagemaker.log_*from an Ipython environment (like Sagemaker Studio or Sagemaker hosted notebook), the source code of the notebook.
- Tags as Experiment tags.
- The following Sagemaker metadata fields as Comet Other fields:
- All metadata for "ModelArtifacts"
- All metadata for "OutputDataConfig"
- All metadata for "ResourceConfig"
- All metadata for "InputDataConfig"