sagemaker.train.container_drivers.scripts.environment

This module is used to define the environment variables for the training job container.

Functions

deserialize_hyperparameters(hyperparameters)	Deserialize hyperparameters from string to their original types.
log_env_variables(env_vars_dict)	Log Environment Variables from the environment and an env_vars_dict.
log_key_value(key, value)	Log a key-value pair, masking sensitive values if necessary.
main()	Main function to set the environment variables for the training job container.
mask_sensitive_info(data)	Recursively mask sensitive information in a dictionary.
num_cpus()	Return the number of CPUs available in the current container.
num_gpus()	Return the number of GPUs available in the current container.
num_neurons()	Return the number of neuron cores available in the current container.
set_env(resource_config, input_data_config, ...)	Set environment variables for the training job container.

sagemaker.train.container_drivers.scripts.environment.deserialize_hyperparameters(hyperparameters: Dict[str, str]) → Dict[str, Any]

Deserialize hyperparameters from string to their original types.

Parameters:

hyperparameters (Dict[str, str]) – Hyperparameters as strings.

Returns:

Hyperparameters as their original types.

Return type:

Dict[str, Any]

sagemaker.train.container_drivers.scripts.environment.log_env_variables(env_vars_dict: Dict[str, Any])

Log Environment Variables from the environment and an env_vars_dict.

sagemaker.train.container_drivers.scripts.environment.log_key_value(key: str, value: str)

Log a key-value pair, masking sensitive values if necessary.

sagemaker.train.container_drivers.scripts.environment.main()

Main function to set the environment variables for the training job container.

sagemaker.train.container_drivers.scripts.environment.mask_sensitive_info(data)

Recursively mask sensitive information in a dictionary.

sagemaker.train.container_drivers.scripts.environment.num_cpus() → int

Return the number of CPUs available in the current container.

Returns:

Number of CPUs available in the current container.

Return type:

int

sagemaker.train.container_drivers.scripts.environment.num_gpus() → int

Return the number of GPUs available in the current container.

Returns:

Number of GPUs available in the current container.

Return type:

int

sagemaker.train.container_drivers.scripts.environment.num_neurons() → int

Return the number of neuron cores available in the current container.

Returns:

Number of Neuron Cores available in the current container.

Return type:

int

sagemaker.train.container_drivers.scripts.environment.set_env(resource_config: Dict[str, Any], input_data_config: Dict[str, Any], hyperparameters_config: Dict[str, Any], output_file: str = '/opt/ml/input/sm_training.env')

Set environment variables for the training job container.

Parameters:

• resource_config (Dict[str, Any]) – Resource configuration for the training job.

• input_data_config (Dict[str, Any]) – Input data configuration for the training job.

• hyperparameters_config (Dict[str, Any]) – Hyperparameters configuration for the training job.

• output_file (str) – Output file to write the environment variables.