Skip to content

Latest commit

 

History

History
31 lines (22 loc) · 3.08 KB

Preparation.md

File metadata and controls

31 lines (22 loc) · 3.08 KB
Raw

Workshop setup - before you arrive

Clone the repository

Before you attend the workshop, you will need a copy of this repository on your personal laptop. Clone the repository into your chosen folder on your personal laptop with this terminal command:

git clone --recurse-submodules https://github.com/rvss-australia/RVSS_Need4Speed.git

Using Conda for package management

In this workshop, we will use Conda. Conda is a package manager for Windows, Mac and Linux - it allows you to install packages similar to apt, homebrew and vcpkg. Conda not only supports Python packages, but also packages in C/C++, FORTRAN, and much more.

Nothing that can be done with conda cannot be achieved otherwise. However, with conda it is usually easier and cross-platform.

Conda handles dependencies seamlessly and makes it easy to set up different environments with different versions of libraries.

Conda is also an environment manager (like virtualenv). Therefore, if an environment is ruined beyond repair, you can just remove it and start over with a clean one.

Guide for setting up Conda with conda-forge

  • We strongly recommend using the community-driven conda-forge channel, instead of the defaults channel that is maintained by Anaconda.
  • The easiest way to install conda with conda-forge as default channel is with Miniforge3. You can download the installers from here.
  • If you are on Linux/MacOS, simply type sh Miniforge3-*.sh and follow the instructions. On Windows, double click Miniforge3-Windows-x86_64.exe and follow the instructions.

Guide for setting up an RVSS conda environment

  • MacOS or Linux without GPU: Create a new environment called rvss with all required packages: mamba create -n rvss numpy scipy pytorch scikit-learn ipython scikit-image matplotlib tqdm roboticstoolbox-python git ipykernel mediapy py-opencv seaborn gym jupyter spatialmath-python machinevision-toolbox-python ipywidgets plotly torchvision conda-pack tensorboardx pynput click -c conda-forge.
  • Linux with GPU: Replace pytorch with pytorch-gpu above to enforce a GPU version of pytorch (this should happen automatically though).
  • Windows without GPU: mamba create -n rvss numpy scipy pytorch cpuonly scikit-learn ipython scikit-image matplotlib tqdm roboticstoolbox-python git ipykernel mediapy py-opencv seaborn gym jupyter spatialmath-python machinevision-toolbox-python ipywidgets plotly torchvision conda-pack tensorboardx pynput click -c conda-forge -c pytorch
  • Windows with GPU: mamba create -n rvss numpy scipy 'pytorch=*=*cuda*' scikit-learn ipython scikit-image matplotlib tqdm roboticstoolbox-python git ipykernel mediapy py-opencv seaborn gym jupyter spatialmath-python machinevision-toolbox-python ipywidgets plotly torchvision conda-pack tensorboardx pynput click pytorch-cuda=11.8 cuda-version=11.8 -c conda-forge -c pytorch -c nvidia

Working with the environment:

  • Activating the environment you just created: conda activate rvss
  • Deactivating: conda deactivate rvss
  • Deleting an environment: conda remove --name FAILED_ENVIRONMENT --all