Comet's SageMaker integration is available to Enterprise customers only. If you are interested in learning more about Comet Enterprise, or are in a trial period with Comet.ml and would like to evaluate the SageMaker integration, please email support@comet.ml and credentials can be shared to download the correct packages.
This repository contains examples of using Comet.ml with SageMaker built-in Algorithms Linear Learner and Random Cut Forests.
Full documentation and additional training examples are available on our website.
Please contact us for installation instructions.
The SageMaker integration is following the Comet.ml Python SDK configuration for configuring your Rest API Key, your workspace and project_name for created experiments. It's also following the Boto configuration to find your SageMaker training jobs.
Below find three different ways you can log your SageMaker jobs to Comet: with an existing regressor/estimator object, with a SageMaker Job Name, or with the last SageMaker job.
log_sagemaker_job(sagemaker_object, api_key, workspace, project_name)
Logs a Sagemaker job based on an estimator/regressor object
- estimator/regressor = Sagemaker estimator/regressor object
- api_key = your Comet REST API key
- workspace = your Comet workspace
- project_name = your Comet project_name
log_sagemaker_job_by_name(job_name, api_key, workspace, project_name)
Logs a specific Sagemaker training job based on the jobname from the Sagemaker SDK.
- job_name = Cloudwatch/Sagemaker training job name
- api_key = your Comet REST API key
- workspace = your Comet workspace
- project_name = your Comet project_name
log_last_sagemaker_job(api_key, workspace, project_name)
Will log the last started Sagemaker training job based on the current config.
- api_key = your Comet REST API key
- workspace = your Comet workspace
- project_name = your Comet project_name
Have questions? We have answers -
- Try checking our FAQ Page
- Email us at info@comet.ml
- For the fastest response, ping us on Slack
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