/AWS1/CL_SGMMDELPACKAGECONTA00¶
Describes the Docker container for the model package.
CONSTRUCTOR¶
IMPORTING¶
Optional arguments:¶
iv_containerhostname TYPE /AWS1/SGMCONTAINERHOSTNAME /AWS1/SGMCONTAINERHOSTNAME¶
The DNS host name for the Docker container.
iv_image TYPE /AWS1/SGMCONTAINERIMAGE /AWS1/SGMCONTAINERIMAGE¶
The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]andregistry/repository[@digest]image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
iv_imagedigest TYPE /AWS1/SGMIMAGEDIGEST /AWS1/SGMIMAGEDIGEST¶
An MD5 hash of the training algorithm that identifies the Docker image used for training.
iv_modeldataurl TYPE /AWS1/SGMURL /AWS1/SGMURL¶
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzipcompressed tar archive (.tar.gzsuffix).The model artifacts must be in an S3 bucket that is in the same region as the model package.
io_modeldatasource TYPE REF TO /AWS1/CL_SGMMODELDATASOURCE /AWS1/CL_SGMMODELDATASOURCE¶
Specifies the location of ML model data to deploy during endpoint creation.
iv_productid TYPE /AWS1/SGMPRODUCTID /AWS1/SGMPRODUCTID¶
The Amazon Web Services Marketplace product ID of the model package.
it_environment TYPE /AWS1/CL_SGMENVIRONMENTMAP_W=>TT_ENVIRONMENTMAP TT_ENVIRONMENTMAP¶
The environment variables to set in the Docker container. Each key and value in the
Environmentstring to string map can have length of up to 1024. We support up to 16 entries in the map.
io_modelinput TYPE REF TO /AWS1/CL_SGMMODELINPUT /AWS1/CL_SGMMODELINPUT¶
A structure with Model Input details.
iv_framework TYPE /AWS1/SGMSTRING /AWS1/SGMSTRING¶
The machine learning framework of the model package container image.
iv_frameworkversion TYPE /AWS1/SGMMDELPACKAGEFRAMEWOR00 /AWS1/SGMMDELPACKAGEFRAMEWOR00¶
The framework version of the Model Package Container Image.
iv_nearestmodelname TYPE /AWS1/SGMSTRING /AWS1/SGMSTRING¶
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata.
io_additionals3datasource TYPE REF TO /AWS1/CL_SGMADDLS3DATASOURCE /AWS1/CL_SGMADDLS3DATASOURCE¶
The additional data source that is used during inference in the Docker container for your model package.
iv_modeldataetag TYPE /AWS1/SGMSTRING /AWS1/SGMSTRING¶
The ETag associated with Model Data URL.
Queryable Attributes¶
ContainerHostname¶
The DNS host name for the Docker container.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_CONTAINERHOSTNAME() |
Getter for CONTAINERHOSTNAME, with configurable default |
ASK_CONTAINERHOSTNAME() |
Getter for CONTAINERHOSTNAME w/ exceptions if field has no v |
HAS_CONTAINERHOSTNAME() |
Determine if CONTAINERHOSTNAME has a value |
Image¶
The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]andregistry/repository[@digest]image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_IMAGE() |
Getter for IMAGE, with configurable default |
ASK_IMAGE() |
Getter for IMAGE w/ exceptions if field has no value |
HAS_IMAGE() |
Determine if IMAGE has a value |
ImageDigest¶
An MD5 hash of the training algorithm that identifies the Docker image used for training.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_IMAGEDIGEST() |
Getter for IMAGEDIGEST, with configurable default |
ASK_IMAGEDIGEST() |
Getter for IMAGEDIGEST w/ exceptions if field has no value |
HAS_IMAGEDIGEST() |
Determine if IMAGEDIGEST has a value |
ModelDataUrl¶
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzipcompressed tar archive (.tar.gzsuffix).The model artifacts must be in an S3 bucket that is in the same region as the model package.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_MODELDATAURL() |
Getter for MODELDATAURL, with configurable default |
ASK_MODELDATAURL() |
Getter for MODELDATAURL w/ exceptions if field has no value |
HAS_MODELDATAURL() |
Determine if MODELDATAURL has a value |
ModelDataSource¶
Specifies the location of ML model data to deploy during endpoint creation.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_MODELDATASOURCE() |
Getter for MODELDATASOURCE |
ProductId¶
The Amazon Web Services Marketplace product ID of the model package.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_PRODUCTID() |
Getter for PRODUCTID, with configurable default |
ASK_PRODUCTID() |
Getter for PRODUCTID w/ exceptions if field has no value |
HAS_PRODUCTID() |
Determine if PRODUCTID has a value |
Environment¶
The environment variables to set in the Docker container. Each key and value in the
Environmentstring to string map can have length of up to 1024. We support up to 16 entries in the map.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_ENVIRONMENT() |
Getter for ENVIRONMENT, with configurable default |
ASK_ENVIRONMENT() |
Getter for ENVIRONMENT w/ exceptions if field has no value |
HAS_ENVIRONMENT() |
Determine if ENVIRONMENT has a value |
ModelInput¶
A structure with Model Input details.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_MODELINPUT() |
Getter for MODELINPUT |
Framework¶
The machine learning framework of the model package container image.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_FRAMEWORK() |
Getter for FRAMEWORK, with configurable default |
ASK_FRAMEWORK() |
Getter for FRAMEWORK w/ exceptions if field has no value |
HAS_FRAMEWORK() |
Determine if FRAMEWORK has a value |
FrameworkVersion¶
The framework version of the Model Package Container Image.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_FRAMEWORKVERSION() |
Getter for FRAMEWORKVERSION, with configurable default |
ASK_FRAMEWORKVERSION() |
Getter for FRAMEWORKVERSION w/ exceptions if field has no va |
HAS_FRAMEWORKVERSION() |
Determine if FRAMEWORKVERSION has a value |
NearestModelName¶
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_NEARESTMODELNAME() |
Getter for NEARESTMODELNAME, with configurable default |
ASK_NEARESTMODELNAME() |
Getter for NEARESTMODELNAME w/ exceptions if field has no va |
HAS_NEARESTMODELNAME() |
Determine if NEARESTMODELNAME has a value |
AdditionalS3DataSource¶
The additional data source that is used during inference in the Docker container for your model package.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_ADDITIONALS3DATASOURCE() |
Getter for ADDITIONALS3DATASOURCE |
ModelDataETag¶
The ETag associated with Model Data URL.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_MODELDATAETAG() |
Getter for MODELDATAETAG, with configurable default |
ASK_MODELDATAETAG() |
Getter for MODELDATAETAG w/ exceptions if field has no value |
HAS_MODELDATAETAG() |
Determine if MODELDATAETAG has a value |
Public Local Types In This Class¶
Internal table types, representing arrays and maps of this class, are defined as local types:
TT_MDELPACKAGECONTAINERDEFNLST¶
TYPES TT_MDELPACKAGECONTAINERDEFNLST TYPE STANDARD TABLE OF REF TO /AWS1/CL_SGMMDELPACKAGECONTA00 WITH DEFAULT KEY
.