aws-bootstrap-g4dn 0.3.0

Bootstrap AWS EC2 GPU instances for hybrid local-remote development

aws-bootstrap-g4dn

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One command to go from zero to a fully configured GPU dev box on AWS — with CUDA-matched PyTorch, Jupyter, SSH aliases, and a GPU benchmark ready to run.

aws-bootstrap launch          # Spot g4dn.xlarge in ~3 minutes
ssh aws-gpu1                  # You're in, venv activated, PyTorch works

✨ Key Features

	Feature	Details
🚀	One-command launch	Spot (default) or on-demand, with automatic fallback on capacity errors
🔑	Auto SSH config	Adds aws-gpu1 alias to ~/.ssh/config — no IP juggling. Cleaned up on terminate
🐍	CUDA-aware PyTorch	Detects the installed CUDA toolkit (nvcc) and installs PyTorch from the matching wheel index — no more torch.version.cuda mismatches
✅	PyTorch smoke test	Runs a quick torch.cuda matmul after setup to verify the GPU stack works end-to-end
📊	GPU benchmark included	CNN (MNIST) + Transformer benchmarks with FP16/FP32/BF16 precision and tqdm progress
📓	Jupyter ready	Lab server auto-starts as a systemd service on port 8888 — just SSH tunnel and open
🖥️	status --gpu	Shows CUDA toolkit version, driver max, GPU architecture, spot pricing, uptime, and estimated cost
🗑️	Clean terminate	Stops instances, removes SSH aliases, shows shutting-down state until fully gone

🎯 Target Workflows

1. Jupyter server-client — Jupyter runs on the instance, connect from your local browser
2. VSCode Remote SSH — ssh aws-gpu1 just works with the Remote SSH extension
3. NVIDIA Nsight remote debugging — GPU debugging over SSH

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Requirements

1. AWS profile configured with relevant permissions (profile name can be passed via --profile or read from AWS_PROFILE env var)
2. AWS CLI v2 — see here
3. Python 3.12+ and uv
4. An SSH key pair (see below)

Installation

From PyPI

pip install aws-bootstrap-g4dn

With uvx (no install needed)

uvx runs the CLI directly in a temporary environment — no global install required:

uvx --from aws-bootstrap-g4dn aws-bootstrap launch
uvx --from aws-bootstrap-g4dn aws-bootstrap status
uvx --from aws-bootstrap-g4dn aws-bootstrap terminate

From source (development)

git clone https://github.com/promptromp/aws-bootstrap-g4dn.git
cd aws-bootstrap-g4dn
uv venv
uv sync

All methods install the aws-bootstrap CLI.

SSH Key Setup

The CLI expects an Ed25519 SSH public key at ~/.ssh/id_ed25519.pub by default. If you don't have one, generate it:

ssh-keygen -t ed25519

Accept the default path (~/.ssh/id_ed25519) and optionally set a passphrase. The key pair will be imported into AWS automatically on first launch.

To use a different key, pass --key-path:

aws-bootstrap launch --key-path ~/.ssh/my_other_key.pub

Usage

🚀 Launching an Instance

# Show available commands
aws-bootstrap --help

# Dry run — validates AMI lookup, key import, and security group without launching
aws-bootstrap launch --dry-run

# Launch a spot g4dn.xlarge (default)
aws-bootstrap launch

# Launch on-demand in a specific region with a custom instance type
aws-bootstrap launch --on-demand --instance-type g5.xlarge --region us-east-1

# Launch without running the remote setup script
aws-bootstrap launch --no-setup

# Use a specific Python version in the remote venv
aws-bootstrap launch --python-version 3.13

# Use a non-default SSH port
aws-bootstrap launch --ssh-port 2222

# Use a specific AWS profile
aws-bootstrap launch --profile my-aws-profile

After launch, the CLI:

1. Adds an SSH alias (e.g. aws-gpu1) to ~/.ssh/config
2. Runs remote setup — installs utilities, creates a Python venv, installs CUDA-matched PyTorch, sets up Jupyter
3. Runs a CUDA smoke test — verifies torch.cuda.is_available() and runs a quick GPU matmul
4. Prints connection commands — SSH, Jupyter tunnel, GPU benchmark, and terminate

ssh aws-gpu1                  # venv auto-activates on login

🔧 What Remote Setup Does

The setup script runs automatically on the instance after SSH becomes available:

Step	What
GPU verify	Confirms nvidia-smi and nvcc are working
Utilities	Installs htop, tmux, tree, jq
Python venv	Creates ~/venv with uv, auto-activates in ~/.bashrc. Use --python-version to pin a specific Python (e.g. 3.13)
CUDA-aware PyTorch	Detects CUDA toolkit version → installs PyTorch from the matching cu{TAG} wheel index
CUDA smoke test	Runs torch.cuda.is_available() + GPU matmul to verify the stack
GPU benchmark	Copies gpu_benchmark.py to ~/gpu_benchmark.py
GPU smoke test notebook	Copies gpu_smoke_test.ipynb to ~/gpu_smoke_test.ipynb (open in JupyterLab)
Jupyter	Configures and starts JupyterLab as a systemd service on port 8888
SSH keepalive	Configures server-side keepalive to prevent idle disconnects

📊 GPU Benchmark

A GPU throughput benchmark is pre-installed at ~/gpu_benchmark.py on every instance:

# Run both CNN and Transformer benchmarks (default)
ssh aws-gpu1 'python ~/gpu_benchmark.py'

# CNN only, quick run
ssh aws-gpu1 'python ~/gpu_benchmark.py --mode cnn --benchmark-batches 20'

# Transformer only with custom batch size
ssh aws-gpu1 'python ~/gpu_benchmark.py --mode transformer --transformer-batch-size 16'

# Run CUDA diagnostics first (tests FP16/FP32 matmul, autocast, etc.)
ssh aws-gpu1 'python ~/gpu_benchmark.py --diagnose'

# Force FP32 precision (if FP16 has issues on your GPU)
ssh aws-gpu1 'python ~/gpu_benchmark.py --precision fp32'

Reports: iterations/sec, samples/sec, peak GPU memory, and avg batch time for each model.

📓 Jupyter (via SSH Tunnel)

ssh -NL 8888:localhost:8888 aws-gpu1
# Then open: http://localhost:8888

Or with explicit key/IP:

ssh -i ~/.ssh/id_ed25519 -NL 8888:localhost:8888 ubuntu@<public-ip>

A GPU smoke test notebook (~/gpu_smoke_test.ipynb) is pre-installed on every instance. Open it in JupyterLab to interactively verify the CUDA stack, run FP32/FP16 matmuls, train a small CNN on MNIST, and visualise training loss and GPU memory usage.

📋 Listing Resources

# List all g4dn instance types (default)
aws-bootstrap list instance-types

# List a different instance family
aws-bootstrap list instance-types --prefix p3

# List Deep Learning AMIs (default filter)
aws-bootstrap list amis

# List AMIs with a custom filter
aws-bootstrap list amis --filter "ubuntu/images/hvm-ssd-gp3/ubuntu-noble*"

# Use a specific region
aws-bootstrap list instance-types --region us-east-1
aws-bootstrap list amis --region us-east-1

🖥️ Managing Instances

# Show all aws-bootstrap instances (including shutting-down)
aws-bootstrap status

# Include GPU info (CUDA toolkit + driver version, GPU name, architecture) via SSH
aws-bootstrap status --gpu

# Hide connection commands (shown by default for each running instance)
aws-bootstrap status --no-instructions

# List instances in a specific region
aws-bootstrap status --region us-east-1

# Terminate all aws-bootstrap instances (with confirmation prompt)
aws-bootstrap terminate

# Terminate specific instances
aws-bootstrap terminate i-abc123 i-def456

# Skip confirmation prompt
aws-bootstrap terminate --yes

status --gpu reports both the installed CUDA toolkit version (from nvcc) and the maximum CUDA version supported by the driver (from nvidia-smi), so you can see at a glance whether they match:

CUDA: 12.8 (driver supports up to 13.0)

SSH aliases are managed automatically — they're created on launch, shown in status, and cleaned up on terminate. Aliases use sequential numbering (aws-gpu1, aws-gpu2, etc.) and never reuse numbers from previous instances.

EC2 vCPU Quotas

AWS accounts have service quotas that limit how many vCPUs you can run per instance family. New or lightly-used accounts often have a default quota of 0 vCPUs for GPU instance families (G and VT), which will cause errors on launch:

• Spot: MaxSpotInstanceCountExceeded
• On-Demand: VcpuLimitExceeded

Check your current quotas (g4dn.xlarge requires at least 4 vCPUs):

# Spot G/VT quota
aws service-quotas get-service-quota \
  --service-code ec2 \
  --quota-code L-3819A6DF \
  --region us-west-2

# On-Demand G/VT quota
aws service-quotas get-service-quota \
  --service-code ec2 \
  --quota-code L-DB2BBE81 \
  --region us-west-2

Request increases:

# Spot — increase to 4 vCPUs
aws service-quotas request-service-quota-increase \
  --service-code ec2 \
  --quota-code L-3819A6DF \
  --desired-value 4 \
  --region us-west-2

# On-Demand — increase to 4 vCPUs
aws service-quotas request-service-quota-increase \
  --service-code ec2 \
  --quota-code L-DB2BBE81 \
  --desired-value 4 \
  --region us-west-2

Quota codes may vary by region or account type. To list the actual codes in your region:

# List all G/VT-related quotas
aws service-quotas list-service-quotas \
  --service-code ec2 \
  --region us-west-2 \
  --query "Quotas[?contains(QuotaName, 'G and VT')].[QuotaCode,QuotaName,Value]" \
  --output table

Common quota codes:

• L-3819A6DF — All G and VT Spot Instance Requests
• L-DB2BBE81 — Running On-Demand G and VT instances

Small increases (4-8 vCPUs) are typically auto-approved within minutes. You can also request increases via the Service Quotas console. While waiting, you can test the full launch/poll/SSH flow with a non-GPU instance type:

aws-bootstrap launch --instance-type t3.medium --ami-filter "ubuntu/images/hvm-ssd-gp3/ubuntu-noble-24.04-amd64-server-*"

Additional Resources

Topic	Link
GPU instance pricing	instances.vantage.sh
Spot instance quotas	AWS docs
Deep Learning AMIs	AWS docs
Nvidia Nsight remote debugging	Nvidia docs

Tutorials on setting up a CUDA environment on EC2 GPU instances:

• Provision an EC2 GPU Host on AWS (DoltHub, 2025)
• AWS EC2 Setup for GPU/CUDA Programming (TechForTalk, 2025)