![]() Docker is the most reproducible way to use and deploy code. You have both standard and multi-animal installed. Next, head over to the Docs to decide which mode to use DeepLabCut in. Great, that’s it! DeepLabCut is installed! NOTE: no need to run pip install deeplabcut, as it is already installed!!! :) Now you should see ( nameofenv) on the left of your terminal screen, i.e. on your Mac: conda activate DEEPLABCUT or conda activate DEEPLABCUT_M1) Ubuntu/MacOS: source/conda activate nameoftheenv (i.e. You can now use this environment from anywhere on your comptuer (i.e., no need to go back into the conda- folder). Now, in the terminal run (Windows/Linux/MacBook Intel chip): 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. To get the location right, a cool trick is to drag the folder and drop it into Terminal. If you cloned the repo onto your Desktop, the command may look like:Ĭd C:\Users\YourUserName\Desktop\DeepLabCut\conda-environments Now, in Terminal (or Anaconda Command Prompt for Windows users), go to the DeepLabCut folder. Windows users: Be sure to open the program terminal/cmd/anaconda prompt with a RIGHT-click, “open as admin” Step 2: please use our supplied conda environment # Note, DeepLabCut is up to date with the latest CUDA and tensorflow versions!Īpple M1/M2 GPU? Be sure to install miniconda3, and your GPU will be used by default. So, please check “GPU Support” below carefully. Please note, which CUDA you install depends on what version of tensorflow you want to use. NVIDIA 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, CUDA, and cuDNN installed. You 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!).ĬPU? Great, jump to the next section below! ![]() With Anaconda you create all the dependencies in an environment on your machine.īash ~/miniconda.sh -b -p $HOME/miniconda One can of course also use other Python distributions than Anaconda, but Anaconda is the easiest route.ĬONDA: The installation process is as easy as this figure! –> # Step 1: You need to have Python installed # Install anaconda or use miniconda3 (ideal for MacOS users)! #Īnaconda is an easy way to install Python and additional packages across various operating systems. Please note, there are several modes of installation, and the user should decide to either use a system-wide (see note below), conda environment based installation ( recommended), or the supplied Docker container (recommended for Ubuntu advanced users). If you want to use the SuperAnimal models, then please use pip install 'deeplabcut'. PIP: #Įverything you need to build custom models within DeepLabCut (i.e., use our source code and our dependencies) can be installed with pip install 'deeplabcut' (for GUI support w/tensorflow) or without the gui: pip install 'deeplabcut'. We recommend using our supplied CONDA environment. Improving network performance on unbalanced data via augmentation ?ĭeepLabCut can be run on Windows, Linux, or MacOS (see also technical considerations and if you run into issues also check out the Installation Tips page).Automate training and video analysis: Batch Processing.Input/output manipulations with DeepLabCut.How to convert a pre-2.2 project for use with DeepLabCut 2.2.Multi-animal pose estimation with DeepLabCut: A 5-minute tutorial. ![]()
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