SageMaker Training Job

Submit ML training jobs to AWS SageMaker — package code, upload to S3, launch on GPU/CPU instances, poll status, download artifacts. Use when training machin...

This skill appears to do exactly what it claims. Before installing or running it: 1) Be prepared to provide AWS credentials (prefer an EC2 instance profile or a scoped IAM user) and create two IAM roles with the least privilege needed for S3 and SageMaker as described; 2) Review the source packaging/dry-run output to avoid unintentionally uploading secrets (don’t point --source-dir at your home directory); 3) Run the smoke test in a controlled account/bucket to verify behavior and cost (it will submit a real SageMaker job and incur charges); 4) Ensure the Caller role is tightly scoped to your S3 bucket and the PassRole action is limited to the specific SageMaker execution role ARN. If you want stricter metadata, ask the maintainer to declare AWS credential env vars explicitly in requires.env so the platform makes credential needs clearer.

Type: OpenClaw Skill Name: sagemaker-training-job Version: 1.0.2 The skill bundle provides a comprehensive set of tools for managing AWS SageMaker training jobs, including scripts for submission (sagemaker_train.py), listing (sagemaker_list.py), and cost estimation (sagemaker_cost.py). The code uses standard AWS SDKs (boto3) and follows security best practices, such as implementing a robust exclusion list (SOURCE_EXCLUDE_PATTERNS) to prevent sensitive files like .env or .git from being packaged and uploaded to S3. No evidence of malicious intent, data exfiltration, or unauthorized remote execution was found.