Technical (Hardware) Considerations#
Quick summary:#
On our install page we highlight that for GPU computing through standard installation you need a NVIDIA GPU, with at least 8 GB of memory. If you have an Intel or AMD GPU, and are on windows, there is an alternative method of installation available which is shown on the installation tips page under “How to install Deeplabcut for Intel and AMD GPUs”. Note, some info is repeated here, and will be updated as systems and hardware changes.
Computer:#
For reference, we use e.g. Dell workstations (79xx series) with Ubuntu 16.04 LTS, 18.04 LTS, or 20.04 LTS and run a Docker container that has TensorFlow, etc. installed (DeepLabCut/Docker4DeepLabCut2.0).
Computer Hardware:#
Ideally, you will use a strong GPU with at least 8GB memory such as the NVIDIA GeForce 1080 Ti, 2080 Ti, or 3090. A GPU is not strictly necessary, but on a CPU the (training and evaluation) code is considerably slower (10x) for ResNets, but MobileNets and EfficientNets are slightly faster. Still, a GPU will give you a massive speed boost. You might also consider using cloud computing services like Google cloud/amazon web services or Google Colaboratory.
Camera Hardware:#
The software is very robust to track data from any camera (cell phone cameras, grayscale, color; captured under infrared light, different manufacturers, etc.). See demos on our website.
Software:#
Operating System: Linux (Ubuntu), MacOS* (Mojave), or Windows 10. However, the authors strongly recommend Ubuntu! *MacOS does not support NVIDIA GPUs (easily), so we only suggest this option for CPU use or a case where the user wants to label data, refine data, etc and then push the project to a cloud resource for GPU computing steps, or use MobileNets.
Anaconda/Python3: Anaconda: a free and open source distribution of the Python programming language (download from https://www.anaconda.com/). DeepLabCut is written in Python 3 (https://www.python.org/) and not compatible with Python 2.
TensorFlow You will need TensorFlow (we used version 1.0 in the paper, later versions also work with the provided code (we tested TensorFlow versions 1.0 to 1.15, and 2.0 to 2.5; we recommend TF2.5 now) for Python 3.7, 3.8, or 3.9 with GPU support.
To note, is it possible to run DeepLabCut on your CPU, but it will be VERY slow (see: Mathis & Warren). However, this is the preferred path if you want to test DeepLabCut on your own computer/data before purchasing a GPU, with the added benefit of a straightforward installation! Otherwise, use our COLAB notebooks for GPU access for testing.
Docker: We highly recommend advaced users use the supplied Docker container. NOTE: this container does not work on windows hosts!