SageMaker Training

Submit ML training jobs to AWS SageMaker from the command line. Supports PyTorch,

TensorFlow, scikit-learn, and XGBoost with managed spot training for cost savings.

Prerequisites

• boto3 Python package installed (pip install boto3). sagemaker recommended.
• AWS credentials available — EC2 instance profile (recommended), or aws configure / env vars (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)
• S3 bucket for training artifacts
• Two IAM roles configured — see references/setup.md for exact policies:
• Role A (Caller): SageMaker job management + S3 access + ECR image pull
• Role B (Execution): S3 data access + CloudWatch logs + ECR images

Security Notes

• AWS credentials are never logged, embedded in scripts, or uploaded to S3.

boto3 resolves credentials from the standard chain (instance profile → env → config file).

• Source packaging excludes .git, .env, venv, __pycache__, and other

non-essential files. Use --source-dir to explicitly scope what gets packaged.

Always review --dry-run output before submitting to production.

• IAM scope: Both caller and execution role policies should be scoped to your

specific S3 bucket and SageMaker execution role ARN. See references/setup.md.

Quick Start

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

Key Options

Instance Selection

For tabular/Kaggle workloads:

• Gradient boosting (LightGBM/XGBoost): ml.m5.2xlarge (CPU, $0.54/hr)
• Small neural nets: ml.g4dn.xlarge (T4, $0.74/hr) — cheapest GPU
• Standard deep learning: ml.g5.xlarge (A10G, $1.41/hr) — best price/performance
• Heavy training: ml.p3.2xlarge (V100, $4.28/hr)

Always use --spot for non-urgent training — typical savings of 30-70%.

Workflow Integration

For autonomous agents running training jobs in a loop:

1. Prepare data locally or upload to S3
2. Write training script following the contract in references/training-scripts.md
3. Use --dry-run first to validate config
4. Submit with sagemaker_train.py — it blocks until completion by default
5. Results download automatically to --output-dir
6. Parse metrics from the output for experiment tracking

For parallel experiments, use --no-wait and poll with sagemaker_list.py.

Smoke Test

Verify the entire pipeline works end-to-end (~$0.01, takes ~3 min):

python3 scripts/sagemaker_smoke_test.py \
  --role arn:aws:iam::ACCOUNT:role/SageMakerTrainingExecutionRole \
  --bucket my-sagemaker-bucket

This runs a local pre-flight, submits a minimal job to SageMaker, verifies

the downloaded model artifact, and checks cost. Use --keep to preserve output files.