How do I set up a short-lived GPU environment for experiments?

A good pattern is to treat the GPU environment as ephemeral: create it, run experiments, save results, then tear it down. The exact setup depends on where you’re running, but here are the common options.

1) Easiest: use a container on a GPU machine

If you have access to a GPU server or cloud VM:

Steps

1. Provision a GPU machine

   • Cloud: AWS/GCP/Azure or managed platforms like RunPod, Lambda Labs, Paperspace
   • Local: a workstation with NVIDIA GPU

2. Install NVIDIA drivers + container runtime

   • On the host, install:
     • NVIDIA driver
     • Docker
     • NVIDIA Container Toolkit

3. Run a GPU-enabled container Example:

   docker run --rm -it --gpus all \
     -v $PWD:/workspace \
     -w /workspace \
     pytorch/pytorch:2.4.0-cuda12.1-cudnn9-runtime
   

4. Install experiment dependencies inside the container

   pip install -r requirements.txt
   

5. Run your code and save outputs

   • Mount a host directory or cloud volume for logs/checkpoints.

6. Destroy the environment

   • Exit the container and terminate the VM if it was temporary.

2) Use a temporary Python environment on a GPU machine

If you already have a GPU machine and just want a throwaway env:

With venv

python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

When done:

deactivate
rm -rf .venv

With Conda

conda create -n exp-gpu python=3.11
conda activate exp-gpu
conda install pytorch pytorch-cuda=12.1 -c pytorch -c nvidia

Remove later:

conda deactivate
conda env remove -n exp-gpu

3) Best for repeatability: use Docker + a pinned image

Create a Dockerfile so you can recreate the exact environment.

Example:

FROM pytorch/pytorch:2.4.0-cuda12.1-cudnn9-runtime

WORKDIR /workspace
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "train.py"]

Build and run:

docker build -t my-gpu-exp .
docker run --rm --gpus all -v $PWD:/workspace my-gpu-exp

4) If you only need it briefly, use a preemptible/spot GPU instance

For cloud costs, create a spot/preemptible GPU VM:

• cheaper
• may be interrupted
• good for short experiments

To make this practical:

• write checkpoints frequently
• store logs/results on persistent storage
• make runs resumable

5) Practical checklist for short-lived experiments

• Pin versions (requirements.txt, environment.yml, or Docker image tag)
• Save outputs to a mounted directory or object storage
• Log config + git commit hash
• Set random seeds
• Use checkpoints if runs are long
• Clean up resources automatically when done

Recommended setup

If you want the simplest robust approach:

• Use Docker
• Run on a temporary GPU VM
• Mount a persistent folder for results

That gives you:

• reproducibility
• easy teardown
• minimal dependency drift

If you tell me your platform (local Linux, Windows, AWS, GCP, RunPod, etc.), I can give you exact commands.