> For the complete documentation index, see [llms.txt](https://lwang010.gitbook.io/longw/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lwang010.gitbook.io/longw/mlops/chap-6.-team-infra/intro/set-up-dl-environment-and-versions-dependencies.md).

# Set up DL environment and versions/dependencies

When the server is available, the next step is to set up a compatible and robust environment that supports the DL platform. 

The most commonly used system is **Ubuntu 20.04 LTS, Ubuntu 18.04 LTS, and 16.04 LTS**. What's need to be managed include: **PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA drivers**. 

**Lambda stack**

The easiest way is to use lambda stack directly: <https://lambdalabs.com/lambda-stack-deep-learning-software#server-installation>

It includes the setup with python virtual environments, on ubuntu 20.04/18.04 servers, ubuntu 16.04, or some docker container.&#x20;

**Others:**

if you already have a system set up, and here is some information that might be helpful:

* conda takes care of the version compatibility, even on cudnn. preferred it over pip.
* be cautious about CUDA 10.1 and PyTorch.
*