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How To Install DeepLabCut: DLC

DeepLabCut can be run on Windows, Linux, or MacOS (see also technical considerations).

The installation process is as easy as this figure ➡️

Please note, there are several modes of installation, and the user should decide to either use a system-wide (see note below), Anaconda environment based installation (recommended), or the supplied Docker container (recommended for Ubuntu advanced users). One can of course also use other Python distributions than Anaconda, but Anaconda is the easiest route.

Step 1: You need to have Python 3 installed, and we highly recommend using Anaconda to do so.

Simply download the appropriate files here: https://www.anaconda.com/distribution/

  • Anaconda is perhaps the easiest way to install Python and additional packages across various operating systems. With Anaconda you create all the dependencies in an environment on your machine.

Step 2: Easy install: please use our supplied Anaconda environments

You first need to decide if you want to use a CPU or GPU for your models: (Note, you can also use the CPU-only for project management and labeling the data! Then, for example, use Google Colaboratory GPUs for free (read more here and there are a lot of helper videos on our YouTube channel!).

  • CPU? Great, jump to the next section below!

  • GPU? If you want to use your own GPU (i.e., a GPU is in your workstation), then you need to be sure you have a CUDA compatible GPU and CUDA installed. PLease note, which CUDA you install depends on what version of tensorflow you want to use. So, please check "GPU Support" below carefully. Then, please use the GPU conda file.

  • DIY: For the experts, it's also on pypi, simply pip install 'deeplabcut[gui]' (and have tensorflow 1.x and wxPython also installed) for DeepLabCut + GUI. Deeplabcut without the GUIs can be installed with pip install deeplabcut. The only other requirement is having TensorFlow 1.x installed. See more at the bottom as well.

CPU or GPU:

(A) Download/git clone this repo (in the terminal/cmd program, while in a folder you wish to place DeepLabCut To git clone type: git clone https://github.com/DeepLabCut/DeepLabCut.git). Note, this can be anywhere, even downloads is fine.

(B) Now, in Terminal (or Anaconda Command Prompt for windows users), go to the folder named conda-environments using the command "cd" (which stands for change directory). For example, if you downloaded or cloned the repo onto your Desktop, the command may look like:

cd C:\Users\YourUserName\Desktop\DeepLabCut\conda-environments

To get the location right, a cool trick is to drag the folder and drop it into Terminal. Alternatively, you can (on Windows) hold SHIFT and right-click > Copy as path, or (on Mac) right-click and while in the menu press the OPTION key to reveal Copy as Pathname.

(C) Now, depending on which file you want to use type:

conda env create -f DLC-CPU.yaml

or if with GPUs, see "GPU Support" with items you need to do first, then:

conda env create -f DLC-GPU.yaml

(D) You can now use this environment from anywhere on your comptuer (i.e. no need to go back into the conda- folder). Just enter your environment by running:

  • Ubuntu/MacOS: source/conda activate nameoftheenv (i.e. on your Mac: conda activate DLC-CPU)
  • Windows: activate nameoftheenv (i.e. activate DLC-GPU)

Now you should see (nameofenv) on the left of your teminal screen, i.e. (dlc-macOS-CPU) YourName-MacBook... NOTE: no need to run pip install deeplabcut, as it is already installed!!! :)

Great, that's it! DeepLabCut is installed!

Next, head over to the Docs to decide which mode to use DeepLabCut in. You have both standard and multi-animal installed.

If you want to test your installation in an automatic way, see: 📹 how to test your installation!

Pro Tips:

If you ever want to update your DLC, just run pip install --upgrade deeplabcut once you are inside your env. If you want to use a specific release, then you need to specify the version you want, such as pip install deeplabcut==2.2. Once installed, you can check the version by running import deeplabcut deeplabcut.__version__. Don't be afraid to update, DLC is backwards compatible with your 2.0+ projects and performance continues to get better and new features are added nearly monthly.

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

Pro Tip: A great way to test your installation is to use our provided testscripts. This would mean you need to be up-to-date with the latest code though! Please see here on how to get the latest GitHub code, and how to test your installation by following this video: https://www.youtube.com/watch?v=IOWtKn3l33s

Creating your own customized conda env (recommended route for Linux: Ubuntu, CentOS, Mint, etc.)

*Note in a fresh ubuntu install, you will often have to run: sudo apt-get install gcc python3-dev to install the GNU Compiler Collection and the python developing environment.

Some users might want to create their own customize env. - Here is an example.

In the terminal type:

conda create -n DLC python=3.7 tensorflow=1.13.1

(this would be for CPU-based tensorflow; for GPU support use tensorflow-gpu=1.13.1).

The only thing you then need to add to the env is deeplabcut (pip install deeplabcut) and wxPython, which is OS dependent.
For Windows and MacOS, you just run pip install -U wxPython<4.1.0 but for linux you need the specific wheel (https://wxpython.org/pages/downloads/index.html).

GPU Support:

The ONLY thing you need to do first if you have an NVIDIA GPU, NVIDIA driver installed, and CUDA <=10 (currently, TensorFlow 1.15 is installed inside the env, so you can install up to CUDA 10 and an appropriate driver). Please note that only NVIDA GPUs are supported.

The most common "new user" hurdle is installing and using your GPU, so don't get discouraged!

CRITICAL: If you have a GPU, you should FIRST then install the NVIDIA CUDA (10 or LOWER) package and an appropriate driver for your specific GPU, then you can use the supplied conda file. Please follow the instructions found here https://www.tensorflow.org/install/gpu, and more tips below, to install the correct version of CUDA and your graphic card driver. The order of operations matters.

  • Here we provide notes on how to install and check your GPU use with TensorFlow (which is used by DeepLabCut and already installed with the Anaconda files above). Thus, you do not need to independently install tensorflow.

FIRST, install a driver for your GPU (and compatable up to CUDA 10). Find DRIVER HERE: https://www.nvidia.com/download/index.aspx

  • check which driver is installed by typing this into the terminal: nvidia-smi.

SECOND, install CUDA 10 (higher versions are not currently supported): https://developer.nvidia.com/ (Note that cuDNN, https://developer.nvidia.com/cudnn, is supplied inside the anaconda environment files, so you don't need to install it again).

THIRD: Follow the steps above to get the DLC-GPU conda file and install it!

Notes:
  • All of the TensorFlow 1.x versions work with DeepLabCut. But, please be mindful different versions of TensorFlow require different CUDA versions.
  • As the combination of TensorFlow and CUDA matters, we strongly encourgae you to check your driver/cuDNN/CUDA/TensorFlow versions on this StackOverflow post.
  • if you wish to use TensorFlow 2.x, then you should use currently DeepLabCut-core. Here is a blog post describing how to do this and the roadmap for the eventual change to tensorflow 2 for this repo as well.
  • To check your GPU is working, in the terminal, run:

nvcc -V to check your installed version(s).

  • The best practice is to then run the supplied testscript.py (this is inside the examples folder you acquired when you git cloned the repo). Here is more information/a short video on running the testscript.

  • Additionally, if you want to use the bleeding edge, with yout git clone you also get the latest code. While inside the main DeepLabCut folder, you can run ./reinstall.sh to be sure it's installed (more here: https://github.com/DeepLabCut/DeepLabCut/wiki/How-to-use-the-latest-GitHub-code)

  • You can test that your GPU is being properly engaged with these additional tips.

  • Ubuntu users might find this installation guide for a fresh ubuntu install useful as well.

Troubleshooting:

TensorFlow: Here are some additional resources users have found helpful (posted without endorsement):

FFMEG:

DEEPLABCUT:

  • if you git clone or download this folder, and are inside of it then import deeplabcut will import the package from there rather than from the latest on PyPi!

System-wide considerations:

If you perform the system-wide installation, and the computer has other Python packages or TensorFlow versions installed that conflict, this will overwrite them. If you have a dedicated machine for DeepLabCut, this is fine. If there are other applications that require different versions of libraries, then one would potentially break those applications. The solution to this problem is to create a virtual environment, a self-contained directory that contains a Python installation for a particular version of Python, plus additional packages. One way to manage virtual environments is to use conda environments (for which you need Anaconda installed).

Technical Considerations:

  • Computer:

  • Computer Hardware:

    • Ideally, you will use a strong GPU with at least 8GB memory such as the NVIDIA GeForce 1080 Ti or 2080 Ti. A GPU is not necessary, but on a CPU the (training and evaluation) code is considerably slower (10x) for ResNets, but MobileNets are faster (see WIKI). 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.
    • pip install deeplabcut
    • 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 1.14) for Python 3 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!

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