Docker Registry Paths and Example Code
The following topics list the Docker registry path and other parameters for each of the Amazon SageMaker provided algorithms and Deep Learning Containers (DLC).
Use the path as follows:
-
To create a training job (create_training_job
), specify the Docker registry path ( TrainingImage
) and the training input mode (TrainingInputMode
) for the training image. You create a training job to train a model using a specific dataset. -
To create a model (create_model
), specify the Docker registry path ( Image
) for the inference image (PrimaryContainer Image
). SageMaker launches machine learning compute instances that are based on the endpoint configuration and deploys the model, which includes the artifacts (the result of model training).
For the registry path, use the :1
version tag to ensure that you
are using a stable version of the algorithm/DLC. You can reliably host a model
trained using an image with the :1
tag on an inference image that
has the :1
tag. Using the :latest
tag in the registry
path provides you with the most up-to-date version of the algorithm/DLC, but
might cause problems with backward compatibility. Avoid using the
:latest
tag for production purposes.
For XGBoost, do not use :latest
or :1
. Use the
specific version you require, such as :0.90-1-cpu-py3
,
:0.90-2-cpu-py3
, :1.0-1-cpu-py3
, or
:1.2-1
.
To find the registry path, choose the Amazon Region, then choose the algorithm or DLC.
Topics
- US East (Ohio)
- US East (N. Virginia)
- US West (N. California)
- US West (Oregon)
- Africa (Cape Town)
- Asia Pacific (Hong Kong)
- Asia Pacific (Mumbai)
- Asia Pacific (Osaka)
- Asia Pacific (Seoul)
- Asia Pacific (Singapore)
- Asia Pacific (Sydney)
- Asia Pacific (Tokyo)
- Canada (Central)
- China (Beijing)
- China (Ningxia)
- Europe (Frankfurt)
- Europe (Ireland)
- Europe (London)
- Europe (Paris)
- Europe (Stockholm)
- Europe (Milan)
- Middle East (Bahrain)
- South America (São Paulo)
- Amazon GovCloud (US-West)