🐳 Minimal Jupyter via SSH + Docker (Lambda Cloud)

This guide sets up Jupyter running inside a Docker container on your Lambda Cloud instance and forwards it securely to your local machine

✅ 1. SSH into your Lambda instance

On your local machine:

ssh -i <YOUR_SSH_KEY_PATH> ubuntu@<INSTANCE_IP>

Replace <YOUR_SSH_KEY_PATH> with your private key path and <INSTANCE_IP> with your instance's IP address.

✅ 2. Start your Docker container with Jupyter

Please replace the image URL with an image of your choosing. The BioNemo image has pytorch and datasets pre-installed as well as implementations of several Bio-FMs.

sudo docker run --gpus all --shm-size=64g -dit \
  --name bionemo \
  -p 8888:8888 \
  -v /home/ubuntu/bionemo_workspace:/workspace \
  nvcr.io/nvidia/clara/bionemo-framework:nightly

Enter the container:

sudo docker exec -it bionemo bash

✅ 3. Install Jupyter and other Python tools inside the container

Inside the container shell, replace the pip and git commands with the packages that you need.

pip install jupyter anndata
git clone https://huggingface.co/datasets/tahoebio/Tahoe-100M

Then launch Jupyter:

jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root

Note the access token printed in the terminal output (you’ll need it to log in).

✅ 4. Forward Jupyter port from remote to local

On your local machine, open a new terminal and run: Please note that you can do the same port-forwarding setup to use VScode if you prefer.

ssh -i <YOUR_SSH_KEY_PATH> -L 8888:127.0.0.1:8888 ubuntu@<INSTANCE_IP>

Now visit:

http://localhost:8888/?token=<TOKEN>

Paste in the token you copied from the container output.

✅ Optional cleanup

To stop and remove the container:

sudo docker stop bionemo
sudo docker rm bionemo