/AWS1/CL_CRLCONTAINERCONFIG¶
Provides configuration information for the dockerized container where the model algorithm is stored.
CONSTRUCTOR¶
IMPORTING¶
Required arguments:¶
iv_imageuri TYPE /AWS1/CRLALGORITHMIMAGE /AWS1/CRLALGORITHMIMAGE¶
The registry path of the docker image that contains the algorithm. Clean Rooms ML currently only supports the
registry/repository[:tag]image path format. For more information about using images in Clean Rooms ML, see the Sagemaker API reference.
Optional arguments:¶
it_entrypoint TYPE /AWS1/CL_CRLCONTAINERENTRPT_W=>TT_CONTAINERENTRYPOINT TT_CONTAINERENTRYPOINT¶
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for additional information. For more information, see How Sagemaker runs your training image.
it_arguments TYPE /AWS1/CL_CRLCONTAINERARGUMEN00=>TT_CONTAINERARGUMENTS TT_CONTAINERARGUMENTS¶
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information. For more information, see How Sagemaker runs your training image.
it_metricdefinitions TYPE /AWS1/CL_CRLMETRICDEFINITION=>TT_METRICDEFINITIONLIST TT_METRICDEFINITIONLIST¶
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon Web Services Clean Rooms ML publishes each metric to all members' Amazon CloudWatch using IAM role configured in PutMLConfiguration.
Queryable Attributes¶
imageUri¶
The registry path of the docker image that contains the algorithm. Clean Rooms ML currently only supports the
registry/repository[:tag]image path format. For more information about using images in Clean Rooms ML, see the Sagemaker API reference.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_IMAGEURI() |
Getter for IMAGEURI, with configurable default |
ASK_IMAGEURI() |
Getter for IMAGEURI w/ exceptions if field has no value |
HAS_IMAGEURI() |
Determine if IMAGEURI has a value |
entrypoint¶
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for additional information. For more information, see How Sagemaker runs your training image.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_ENTRYPOINT() |
Getter for ENTRYPOINT, with configurable default |
ASK_ENTRYPOINT() |
Getter for ENTRYPOINT w/ exceptions if field has no value |
HAS_ENTRYPOINT() |
Determine if ENTRYPOINT has a value |
arguments¶
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information. For more information, see How Sagemaker runs your training image.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_ARGUMENTS() |
Getter for ARGUMENTS, with configurable default |
ASK_ARGUMENTS() |
Getter for ARGUMENTS w/ exceptions if field has no value |
HAS_ARGUMENTS() |
Determine if ARGUMENTS has a value |
metricDefinitions¶
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon Web Services Clean Rooms ML publishes each metric to all members' Amazon CloudWatch using IAM role configured in PutMLConfiguration.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_METRICDEFINITIONS() |
Getter for METRICDEFINITIONS, with configurable default |
ASK_METRICDEFINITIONS() |
Getter for METRICDEFINITIONS w/ exceptions if field has no v |
HAS_METRICDEFINITIONS() |
Determine if METRICDEFINITIONS has a value |