Amazon SageMaker Automatic Model Tuning now allows you to specify environment variables for your tuning jobs
SageMaker Automatic Model Tuning allows you to find the most accurate version of your machine learning model by searching for the optimal set of hyperparameter configurations. Previously, you could only specify environment variables for your algorithm runtime in your SageMaker Training jobs, but not in your tuning jobs. Starting today, you have the flexibility to specify runtime environment variables for your scripts in your CreateTuningJob API.
SageMaker Automatic Model Tuning allows you to find the most accurate version of your machine learning model by searching for the optimal set of hyperparameter configurations. Previously, you could only specify environment variables for your algorithm runtime in your SageMaker Training jobs, but not in your tuning jobs. Starting today, you have the flexibility to specify runtime environment variables for your scripts in your CreateTuningJob API.
With this launch, you can specify different behaviour and configurations for your training jobs through the environment variables you pass into the CreateTuningJob request. This also makes it easier for you to re-use your training job definitions to start a tuning job. For example, you can benefit from more fine-grained logging for all your training jobs by setting up an environment variable at the tuning level, or you can specify the source of your data and customize the train/test split directly through your environment variables.
The ability to provide environment variables in SageMaker Automatic Model Tuning is now available in all commercial AWS regions and applicable to all tuning jobs. To learn more, please visit the API reference guide, the technical documentation, or SageMaker Automatic Model Tuning web page.