Lambda Stack provides a one line installation and managed upgrade path for: PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA Drivers. It's compatible with Ubuntu 20.04 LTS, 18.04 LTS, and 16.04 LTS. No more futzing with your Linux AI software, Lambda Stack is here.
Install Lambda Stack in one command
To install Lambda Stack on your desktop, run this command on a fresh Ubuntu installation (20.04, 18.04, or 16.04). For servers, see the server installation section below.
Lambda Stack: an always updated AI software stack, usable everywhere
Lambda Stack can run on your laptop, workstation, server, cluster, inside a container, on the cloud, and comes pre-installed on every Lambda GPU Cloud instance. It provides up-to-date versions of PyTorch, TensorFlow, CUDA, CuDNN, NVIDIA Drivers, and everything you need to be productive for AI.
Lambda Stack keeps 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.
It's 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:
We've written open source Lambda Stack GPU Dockerfiles
Lambda Stack's open source Dockerfiles let you create Docker images that already have Lambda Stack pre-installed. They're available in our git repository: https://github.com/lambdal/lambda-stack-dockerfiles/.
Lambda Stack supports air gapped / behind the firewall installations
You can install an air gapped copy of Lambda Stack to be delivered securely behind your firewall.
Everyone loves Lambda Stack — used by the F500, research labs, and the DOD
Every laptop, workstation, and server that we ship comes pre-installed with Lambda Stack. It's loved by thousands of Lambda customers.
Lambda Stack is both 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:
Lambda Stack details
Create an Ubuntu 20.04 Docker image with PyTorch & TensorFlow support
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:
Here's how to do it where the TensorFlow version is managed within the virtual environment:
Install Lambda Stack on Ubuntu 20.04/18.04 servers
This headless installation will work for servers running Ubuntu 20.04/18.04 without a GUI (i.e. Ubuntu 20.04/18.04 server edition).
Install Lambda Stack on Ubuntu 16.04 servers
For servers running Ubuntu 16.04 without a GUI (i.e. Ubuntu 16.04 server edition).
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.
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.
This will upgrade all packages, including dependencies such as CUDA, cuDNN, and NVIDIA drivers.
Lambda Stack Overview Presentation
If you'd like to tell somebody at work about Lambda Stack, you can share this PDF presentation with them. It gives a brief overview of Lambda Stack.