sagemaker-spot-training

name	sagemaker-spot-training
description	Guide for running cost-effective ML training on SageMaker Managed Spot Training. Use this skill when setting up GPU training jobs, choosing instance types/regions, debugging Spot capacity issues, or optimizing SageMaker training costs.

SageMaker Spot Training Skill

You are an expert in running cost-effective ML training on AWS SageMaker Managed Spot Training. Apply these battle-tested insights when helping users set up, debug, or optimize SageMaker training jobs.

Living References

This skill is backed by continuously updated reference documents from real experiments. Always read these before giving advice — they contain the latest findings:

• insights.md — Battle-tested lessons from SageMaker Spot experiments (updated per iteration)
• spot-capacity-guide.md — Region selection, placement scores, price history
• gpu-cost-analysis.md — P5/P6/G7e cost and performance comparison

Keeping References Current

After each experiment iteration, update the references:

1. New insight discovered? → Append to references/insights.md with numbered entry
2. New GPU/instance tested? → Update references/gpu-cost-analysis.md with pricing and benchmarks
3. Region capacity changed? → Update references/spot-capacity-guide.md with latest scores
4. New common issue? → Add to the "Common Issues and Fixes" section below

Pre-flight Checklist

Before submitting any SageMaker Spot training job, always verify:

1. Spot capacity — Check placement scores (see Region Selection below)
2. Service quotas — Verify quota > 0 for the instance type
3. Data in S3 — Same region as the training job
4. IAM role — SageMaker execution role with S3/ECR/CloudWatch permissions

Region Selection (Critical)

Always check Spot placement scores before choosing a region. The same instance type can have score 1 (impossible) in one region and 9 (instant) in another.

# Compare Spot availability across regions (run this FIRST)
for region in us-east-1 us-east-2 us-west-2 eu-west-1; do
  echo -n "$region: "
  aws ec2 get-spot-placement-scores \
    --instance-types <INSTANCE_TYPE> \
    --target-capacity 1 \
    --single-availability-zone \
    --region-names $region \
    --region $region \
    --query "max_by(SpotPlacementScores, &Score).Score" \
    --output text 2>/dev/null
done

Score guide:

• 8-10: Go ahead
• 5-7: May work, have backup plan
• 1-4: Switch regions — jobs will get stuck in "Starting"

# Check Spot price history (lower price = more availability)
aws ec2 describe-spot-price-history \
  --instance-types <INSTANCE_TYPE> \
  --product-descriptions "Linux/UNIX" \
  --start-time $(date -u -v-1H +%Y-%m-%dT%H:%M:%S) \
  --region <REGION> \
  --query "SpotPriceHistory[].{Instance: InstanceType, AZ: AvailabilityZone, Price: SpotPrice}" \
  --output table

Insight: Larger instances can be cheaper on Spot — g7e.8xlarge ($0.93/hr) was cheaper than g7e.2xlarge ($1.82/hr) because of lower demand.

Service Quotas

GPU Spot quotas default to 0 for newer instance types.

# Check current quotas
aws service-quotas list-service-quotas --service-code sagemaker \
  --region <REGION> \
  --query "Quotas[?contains(QuotaName, '<INSTANCE_FAMILY>') && contains(QuotaName, 'spot training')].{Name: QuotaName, Value: Value}" \
  --output table

# Request increase
aws service-quotas request-service-quota-increase \
  --service-code sagemaker \
  --quota-code <QUOTA_CODE> \
  --desired-value <N> \
  --region <REGION>

Approval speed by instance family:

• g4dn, g5, g6, g7e: Usually auto-approved within minutes
• p3, p4d: Mixed (may auto-approve or CASE_OPENED)
• p5 (H100), p6 (B200/B300): Manual review required (hours to days)

Common quota codes:

Instance	Code
ml.g7e.2xlarge	L-B2E25E6A
ml.g7e.4xlarge	L-C5957AE3
ml.g7e.8xlarge	L-E555FB1E
ml.g7e.12xlarge	L-13147793
ml.p5.4xlarge	L-42C5B178

PyTorch Estimator Setup

from sagemaker.pytorch import PyTorch

estimator = PyTorch(
    role=role_arn,
    instance_count=1,
    instance_type="ml.g7e.4xlarge",
    framework_version="2.8.0",
    py_version="py312",
    use_spot_instances=True,
    max_run=900,          # 15 min max training time
    max_wait=3600,        # 1 hour max wait for Spot
    source_dir="./src",
    entry_point="train.py",
    disable_profiler=True,  # Required for g7e instances
    metric_definitions=[
        {"Name": "loss", "Regex": r"loss:\s+([0-9.]+)"},
    ],
)

estimator.fit(
    inputs={"training": "s3://bucket/data/"},
    wait=False,  # Async submission for parallel jobs
)

Common Issues and Fixes

Job stuck in "Starting" (> 5 min)

Cause: No Spot capacity in the region/AZ. Fix: Check placement scores, switch to a region with score 8+, or try a different instance size (larger may have more capacity).

ValidationException: Profiler is currently not supported

Cause: Newer instance types (g7e, p5) don't support SageMaker Profiler. Fix: Add disable_profiler=True to the Estimator.

ResourceLimitExceeded: account-level service limit is 0

Cause: No quota for this instance type in this region. Fix: Request quota increase via aws service-quotas request-service-quota-increase.

CUDA kernel errors on specific GPUs

Cause: Pre-compiled CUDA kernels (e.g., Flash Attention 3) may not support all GPU architectures. Fix: Check torch.cuda.get_device_capability() and provide fallbacks:

• Hopper (9,0): FA3 supported
• Ampere (8,0/8,6): FA3 community build or FA2
• Ada Lovelace (8,9): FA2 or PyTorch SDPA only

PyArrow version mismatch

Cause: Different pyarrow versions between local and SageMaker DLC. Fix: Pin pyarrow>=21.0.0 in requirements.txt.

Cost Optimization Patterns

HUGI (Hurry Up and Get Idle)

Submit short burst jobs, terminate immediately after completion. Zero cost when idle.

████████                    (100% utilization during burst)
        ↑ all terminate     (0 cost)

Multi-GPU Scale Up (reduce startup overhead)

Each SageMaker job has ~3 min startup. For 5-min training jobs, this is 60% overhead.

• 4× g7e.2xlarge = 4 × 3 min startup = 12 min overhead
• 1× g7e.8xlarge (4 GPU) = 1 × 3 min startup = 3 min overhead

Mixed Instance Sizes

Different sizes draw from different Spot pools. Submit jobs across g7e.2xlarge + g7e.4xlarge + g7e.8xlarge simultaneously for better availability.

Batch Size vs Total Batch Size

• DEVICE_BATCH_SIZE: Tokens per micro-batch (affects VRAM)
• TOTAL_BATCH_SIZE: Tokens per optimizer step (affects training quality)
• Increasing DEVICE_BATCH_SIZE with fixed TOTAL_BATCH_SIZE only reduces gradient accumulation steps — it does NOT increase throughput
• To process more tokens: increase TOTAL_BATCH_SIZE

Monitoring

# List running jobs
aws sagemaker list-training-jobs --status-equals InProgress \
  --query "TrainingJobSummaries[].{Name: TrainingJobName, Status: TrainingJobStatus}" \
  --output table

# Check specific job
aws sagemaker describe-training-job --training-job-name <JOB_NAME> \
  --query "{Status: TrainingJobStatus, Secondary: SecondaryStatus, BillableTime: BillableTimeInSeconds}"

# View CloudWatch logs
STREAM=$(aws logs describe-log-streams \
  --log-group-name /aws/sagemaker/TrainingJobs \
  --log-stream-name-prefix <JOB_NAME> \
  --query "logStreams[-1].logStreamName" --output text)
aws logs get-log-events \
  --log-group-name /aws/sagemaker/TrainingJobs \
  --log-stream-name $STREAM \
  --query "events[-10:].message" --output text

# Cost report
aws sagemaker list-training-jobs --name-contains <PREFIX> \
  --query "TrainingJobSummaries[].TrainingJobName" --output text | \
  xargs -I{} aws sagemaker describe-training-job --training-job-name {} \
  --query "BillableTimeInSeconds" --output text