What channel name should I use for template.json in SageMaker's estimator.fit()?

When including a template.json file in your SageMaker estimator.fit() method, you should use the channel name "config" for the template.json file. The channel names "train" and "validation" are typically used for training and validation datasets, respectively.

To modify the estimator.fit() call to include the template.json file, you would add another key-value pair to the inputs dictionary. Here's how you can modify the command:

estimator.fit(
    {
        "train": training_dataset_s3_path,
        "validation": validation_dataset_s3_path,
        "config": "s3://your-bucket/path/to/template.json"
    }
)

The choice of channel name for the template.json file may depend on the specific training container image being used. Some containers might expect configuration files in a channel named "config", while others might use a different name. It's important to consult the documentation or specifications of the particular container you're using to ensure you're using the correct channel name.

In general, the "config" channel is a common convention for configuration files like template.json, but always verify this with the specific requirements of your chosen algorithm or container. If you're using a custom container, you would need to ensure that your container is set up to read from the channel name you specify.
Sources
SageMaker Training and Inference Toolkits - Amazon SageMaker AI
create_training_job - Boto3 1.35.76 documentation
create_hyper_parameter_tuning_job - Boto3 1.35.76 documentation

Assuming you're referring to the prompt template JSON in instruction-based fine-tuning as mentioned here:

The training folder can also include a template JSON file (template.json) that describes the input and output formats of your data. If no template file is provided, the following template file is used...

...So I believe what you'd want is for training_dataset_s3_path to point at an S3 prefix containing both your JSON-Lines training data, and a single template.json file. I.e set training_dataset_s3_path = "s3://doc-example-bucket/my-folder"