Install Lambda Stack in one command
wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
sudo reboot
Always updated AI software stack. Usable everywhere.
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Keep your AI software up-to-date with one command
Run this command and all of your AI software, from PyTorch® to CUDA, will be updated. Like Magic.
sudo apt-get update && sudo apt-get dist-upgrade
Compatible with your Docker and NGC containers
If you're already using GPU docker images or NGC containers, rest assured that Lambda Stack can run them.
After you've installed Lambda Stack, you can install a version of GPU accelerated Docker with this command:
sudo apt-get install docker.io nvidia-container-toolkit
We've written open source Lambda Stack GPU Dockerfiles
Lambda Stack supports air gapped / behind the firewall installations
Everyone loves Lambda Stack — used by the F500, research labs, and the DOD
Lambda Stack includes a system-wide package, a Dockerfile, and a Docker image
Lambda Stack is not only a system-wide installation of all of your favorite frameworks and drivers, but also a convenient "everything included" deep learning Docker image. Now you'll have your team up and running with GPU-accelerated Docker images in minutes instead of weeks. To learn more about how to set up Lambda Stack GPU Dockerfiles check out our tutorial:
https://lambdalabs.com/blog/set-up-a-tensorflow-gpu-docker-container-using-lambda-stack-dockerfile/
Create an Ubuntu 22.04 Docker image with PyTorch® & TensorFlow support
# Build a Docker image for Ubuntu 22.04 (jammy). You can substitute jammy for focal or noble to change the ubuntu version.
sudo docker build -t lambda-stack:22.04 -f Dockerfile.focal git://github.com/lambdal/lambda-stack-dockerfiles.git
Using Lambda Stack with python virtual environments
We're often asked how to best use Lambda Stack with a python virtual environment. You have two choices: use Lambda Stack as a way to install CUDA, CuDNN, and NVIDIA drivers; or, use Lambda Stack as a way to manage TensorFlow and PyTorch® as well as CUDA, CuDNN, NVIDIA drivers. Here's how to do that:
python3 -m venv lambda-stack-with-tensorflow-pytorch --system-site-packages
source lambda-stack-with-tensorflow-pytorch/bin/activate
Here's how to do it where the TensorFlow version is managed within the virtual environment:
python3 -m venv lambda-stack-without-tensorflow
source lambda-stack-without-tensorflow/bin/activate
# Note, we need to install libcudnn8 separately.
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/libcudnn8_8.1.1.33-1+cuda11.2_amd64.deb
https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/libcudnn8-dev_8.1.1.33-1+cuda11.2_amd64.deb
sudo dpkg -i libcudnn8_8.1.1.33-1+cuda11.2_amd64.deb
sudo dpkg -i libcudnn8-dev_8.1.1.33-1+cuda11.2_amd64.deb
sudo apt-get install -f # resolve dependency errors you saw earlier
pip install tensorflow-gpu
Install Lambda Stack on Ubuntu 22.04 and Ubuntu 24.04 servers
This headless installation will work for servers running Ubuntu 22.04 and Ubuntu 24.04 without a GUI (i.e. Ubuntu 22.04 server edition and Ubuntu 24.04 server edition).
wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | I_AGREE_TO_THE_CUDNN_LICENSE=1 sh -
Use Lambda Stack in a shell script, Dockerfile, Ansible file, etc.
If you want to integrate Lambda Stack installation into a script, you'll likely want to avoid all user input prompts. To use Lambda Stack in this way, you must have read and agreed to the CUDNN license.
wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | I_AGREE_TO_THE_CUDNN_LICENSE=1 sh -
How to update / upgrade to the latest Lambda Stack
Do this if a new version of PyTorch®, TensorFlow (or any other framework) is released and you want to upgrade.
sudo apt-get update && sudo apt-get dist-upgrade
This will upgrade all packages, including dependencies such as CUDA, cuDNN, and NVIDIA drivers.