Skip to content
This repository has been archived by the owner on Mar 21, 2024. It is now read-only.

Latest commit

 

History

History
171 lines (130 loc) · 8.95 KB

environment.md

File metadata and controls

171 lines (130 loc) · 8.95 KB
Raw

Setting up your environment

Prerequisites

In order to work with the solution, your OS environment will need git and git lfs installed. Depending on the OS that you are running the installation instructions may vary. Please refer to respective documentation sections on the tools' websites for detailed instructions.

We recommend using PyCharm or VSCode as the Python editor.

You have two options for working with our codebase:

  • You can fork the InnerEye-DeepLearning repository, and work off that. We recommend that because it is easiest to set up.
  • Or you can create your project that uses the InnerEye-DeepLearning code, and include InnerEye-DeepLearning as a git submodule. We only recommended that if you are very handy with Python. More details about this option are here.

Windows Subsystem for Linux Setup

When developing on a Windows machine, we recommend using the Windows Subsystem for Linux, WSL2. That's because PyTorch has better support for Linux. If you want to use WSL2, please follow these instructions , that correspond to the manual installation in the official docs.

Installing Conda or Miniconda

You can skip this step if you have installed WSL as per the previous item.

Download a Conda or Miniconda installer for your platform and run it.

Creating a Conda environment

Note that in order to create the Conda environment you will need to have build tools installed on your machine. If you are running Windows, they should be already installed with Conda distribution.

You can install build tools on Ubuntu (and Debian-based distributions) by running
sudo apt-get install build-essential
If you are running CentOS/RHEL distributions, you can install the build tools by running
yum install gcc gcc-c++ kernel-devel make

Start the conda prompt for your platform. In that prompt, navigate to your repository root and run conda env create --file environment.yml

Using GPU locally

It is possible to run the training process on a local machine. It will not be as performant as using a GPU cluster that Azure ML offers and you will not be able to take advantage of other Azure ML features such as comparing run results, creating snapshots for repeatable machine learning experiments or keeping history of experiment runs. At the same time it could be useful to experiment with code or troubleshoot things locally.

The SDK uses PyTorch to compose and run DNN computations. PyTorch can leverage the underlying GPU via NVidia CUDA technology, which accelerates computations dramatically.

In order to enable PyTorch to use CUDA, you need to make sure that you have

  1. Compatible graphics card with CUDA compute capability of at least 3.0 (at the moment of writing). You can check compatibility list here: https://developer.nvidia.com/cuda-gpus
  2. Recent NVidia drivers installed

A quick way to check if PyTorch can use the underlying GPU for computation is to run the following line from your conda environment with all InnerEye packages installed:
python -c 'import torch; print(torch.cuda.is_available())'
It will output True if CUDA computation is available and False if it's not.

Some tips for installing NVidia drivers below:

Windows

You can download NVidia drivers for your graphics card from https://www.nvidia.com/download/index.aspx as a Windows .exe file and install them this way.

WSL

Microsoft provides GPU support via WSL starting WSL 2.0.

You can find more details on WSL in our separate WSL section.

Linux

The exact instructions for driver installation will differ depending on the Linux distribution. Generally, you should first run the nvidia-smi tool to see if you have NVidia drivers installed. This tool is installed together with NVidia drivers and if your system can not find it, it may mean that the drivers are not installed. A sample output of NVidia SMI tool may look like this:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.06    Driver Version: 450.51.06    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla K80           Off  | 0000027F:00:00.0 Off |                    0 |
| N/A   50C    P0    60W / 149W |      0MiB / 11441MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

In this case we can see that the system has access to a Tesla K80 GPU and is running driver version 450.51.06

If the driver is not available, you can try the following to install:

Ubuntu

  1. Run
    ubuntu-drivers devices
    to see what drivers are available (you may need to install the tool via sudo apt-get install ubuntu-drivers-common and update the package database via sudo apt update). You should see an output like this:
...
vendor   : NVIDIA Corporation
model    : GK210GL [Tesla K80]
driver   : nvidia-driver-450-server - distro non-free recommended
driver   : nvidia-driver-418-server - distro non-free
driver   : nvidia-driver-440-server - distro non-free
driver   : nvidia-driver-435 - distro non-free
driver   : nvidia-driver-450 - distro non-free
driver   : nvidia-driver-390 - distro non-free
driver   : xserver-xorg-video-nouveau - distro free builtin
  1. Run
    sudo apt install nvidia-driver-450-server
    (or whichever is the recommended in your case)
  2. Reboot your system

At this point you should be able to run the nvidia-smi tool and PyTorch should be able to communicate with the GPU

CentOS/RHEL

  1. Add NVidia repository to your config manager
    sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo (if you are running RHEL8, otherwise you can get the URL for your repo from here: https://developer.download.nvidia.com/compute/cuda/repos/)
  2. Clean repository cache via
    sudo dnf clean all
  3. Install drivers
    sudo dnf -y module install nvidia-driver:latest-dkms
  4. Reboot your system

At this point you should be able to run the nvidia-smi tool and PyTorch should be able to communicate with the GPU

You can find instructions for other Linux distributions on NVidia website: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html

More Details for Tool Setup

The following steps describe how to set up specific tools. You can execute most of those at a later point, if you want to dig deeper into the code.

VSCode

(VSCode for example)

Conda

  • conda env create -f environment.yml

Conda updates

In order to update the Conda environment, you can go down two routes:

  1. You can manually edit the existing environment.yml file to force specific (newer) versions of an existing package. You can do this, for example, to force an update of the azureml-sdk and all its contained packages, or pytorch
  2. Or you can manually add and update packages, and later export the updated environment to a yml file.

If you want to take the second route:

  1. Use conda env update -f environment.yml --prune to refresh if you make changes in environment.yml
  2. To update packages use conda update --all and pip-review --local --interactive

Using the hi-ml package

To work on hi-ml package at the same time as InnerEye-DeepLearning, it can help to add the hi-ml package as a submodule, rather than a package from pypi. Any change to the package will require a full new docker image build, and that costs 20min per run.

  • In the repository root, run git submodule add https://github.com/microsoft/hi-ml
  • In PyCharm's project browser, mark the folders hi-ml/hi-ml/src and hi-ml/hi-ml-azure/src as Sources Root
  • Remove the entry for the hi-ml and hi-ml-azure packages from environment.yml
  • There is already code in InnerEye.Common.fixed_paths.add_submodules_to_path that will pick up the submodules and add them to sys.path.

Once you are done testing your changes:

  • Remove the entry for hi-ml from .gitmodules
  • Execute these steps from the repository root:
git submodule deinit -f hi-ml
rm -rf hi-ml
rm -rf .git/modules/hi-ml

Alternatively, you can consume a developer version of hi-ml from test.pypi:

  • Remove the entry for the hi-ml package from environment.yml
  • Add a section like this to environment.yml, to point pip to test.pypi, and a specific version of th package:
  ...
  - pip:
      - --extra-index-url https://test.pypi.org/simple/
      - hi-ml==0.1.0.post236
      ...