jchen8000/pytorch_gpu_jupyterlab

About This Image

This image provides a GPU-accelerated PyTorch environment and JupyterLab preinstalled.

This image is designed for the project:
https://github.com/jchen8000/DemystifyingLLMs.git

It is ideal for deep learning research, model prototyping, NLP workloads, and general Python development with NVIDIA GPU support.

Running the Container

Create a .env file to store your API keys and secrets.

Example .env file:

# Hugging Face API token
HF_TOKEN=huggingface_token

# GROQ API key
GROQ_API_KEY=groq_api_key

To start the container with GPU support:

bash
docker run -it --gpus all -p 8888:8888 \
  --env-file .env \
  -v $(pwd):/workspace \
  jchen8000/pytorch_gpu_jupyterlab:latest

To start the container with CPU only:

bash
docker run -it -p 8888:8888 \
  --env-file .env \
  -v $(pwd):/workspace \
  jchen8000/pytorch_gpu_jupyterlab:latest

Verify the environment (optional)

GPU availability

Inside a notebook:

python
import torch
print(torch.__version__)
print(torch.cuda.is_available())
print(torch.cuda.get_device_name(0) if torch.cuda.is_available() else "CPU only")

Access .env file

Inside a notebook:

python
import os
hf_token = os.environ.get("HF_TOKEN")
groq_key = os.environ.get("GROQ_API_KEY")

print(hf_token[:4] + "...")
print(groq_key[:4] + "...")