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conda-environments

README.md

Quick Anaconda Install for Windows 10, MacOS, Ubuntu 18.04!

Please use one (or more) of the supplied Anaconda environments for a fast and easy installation process.

(0) Be sure you have Anaconda 3 installed! https://www.anaconda.com/distribution/, and get familiar with using "cmd" or terminal!

(1) Either go to www.deeplabcut.org to download the correct environment file:

or download or git clone this repo (in the terminal/cmd program, while in a folder you wish to place DeepLabCut type git clone https://github.com/AlexEMG/DeepLabCut.git Now, "cd", i.e. go into, the folder named conda-environments

(2) Now, depending on which file you want to use (if with GPUs, see extra note below), open the program terminal or cmd where you placed the file (i.e. cd conda-environments) and then type:

conda env create -f dlc-macOS-CPU.yaml

or

conda env create -f dlc-windowsCPU.yaml

or

conda env create -f dlc-windowsGPU.yaml

or

conda env create -f dlc-ubuntu-GPU.yaml

(3) Enter your environment by running:

  • Ubuntu/MacOS: source activate nameoftheenv (i.e. source activate dlc-macOS-CPU)
  • Windows: activate nameoftheenv (i.e. activate dlc-windowsGPU)

Now you should see (nameofenv) on the left of your teminal screen, i.e. (dlc-macOS-CPU) YourName-MacBook... NOTE: DO NOT run pip install deeplabcut, etc! It is already installed!!! :)

Great, that's it!

Simply run ipython or pythonw (macOS only) to lauch the terminal, jupyter notebook to lauch a browser session, or ipython, import deeplabcut, deeplabcut.launch_dlc() to use our Project Manager GUI! Many more details here!

Now just follow the user guide, to get DeepLabCut up and running in no time!

Just as a reminder, you can exit the environment anytime and (later) come back! So the environments really allow you to manage multiple packages that you might want to install on your computer.

Here are some conda environment management tips: https://kapeli.com/cheat_sheets/Conda.docset/Contents/Resources/Documents/index

GPUs: The ONLY thing you need to do first if have an NVIDIA driver installed, and CUDA (currently, TensorFlow 1.13 is installed inside the env, so you can install CUDA 10 and an appropriate driver).