Train Model and Poll Job
A 2-step recipe with Amazon SageMaker. We can help you get access.
Recipe · 2 steps
Train Model and Poll Job
with Amazon SageMaker · Artificial Intelligence Management
Create a training job and poll it to a terminal status.
- Start a model training job using the supplied algorithm image, IAM role, input data, output location, and compute resources.
- Describe the training job to read its current status. Repeat this step while the status remains InProgress and continue once it reaches a terminal state.
What you'll need to have ready
- trainingJobName: A unique name for the training job.
- trainingImage: The registry path of the Docker image that contains the training algorithm.
- trainingInputMode: The input mode the algorithm supports (Pipe or File).
- roleArn: The ARN of the IAM role SageMaker assumes to perform training.
- inputDataConfig: The input data channels for the training job.
- s3OutputPath: The S3 path where SageMaker stores the model artifacts.
- instanceType: The ML compute instance type to use for training.
- instanceCount: The number of ML compute instances to use.
- volumeSizeInGB: The size of the ML storage volume attached to each instance, in GB.
- maxRuntimeInSeconds: The maximum length of time, in seconds, that the training job can run.
For your developer
Arazzo workflow amazon-sagemaker:train-and-poll-job, 2 operations: CreateTrainingJob → DescribeTrainingJob. Specs on APIs.io.