SageMaker Training Job

1. Write a training script

Follow the SageMaker training script contract: read data from SM_CHANNEL_TRAIN, save model to SM_MODEL_DIR. See references/training-scripts.md for templates.

2. Submit a training job

python3 scripts/sagemaker_train.py \
  --job-name my-experiment-001 \
  --script ./train.py \
  --role arn:aws:iam::ACCOUNT:role/SageMakerRole \
  --bucket my-sagemaker-bucket \
  --instance-type ml.g5.xlarge \
  --spot \
  --framework pytorch \
  --input-data s3://my-bucket/data/train/ \
  --hyperparameters '{"epochs":"50","lr":"0.001"}' \
  --output-dir ./results

The script packages your code, uploads to S3, submits the job, polls until complete, and downloads model artifacts to --output-dir.

3. Check cost

# Estimate before running
python3 scripts/sagemaker_cost.py --instance-type ml.g5.xlarge --duration 3600 --spot
Check actual cost after job completes
python3 scripts/sagemaker_cost.py --job-name my-experiment-001

4. List recent jobs

python3 scripts/sagemaker_list.py --max 5
python3 scripts/sagemaker_list.py --status Failed