![]() For Windows and MacOS, you just run pip install -U wxPython<4.1.0 but for linux you might need the specific wheel ( ). Pre-version2.3 (Dec 2022): The only thing you then need to add to the env is deeplabcut ( pip install deeplabcut) or pip install 'deeplabcut' which has wxPython for GUI support. Here is an example.Ĭurrent version: The only thing you then need to add to the env is deeplabcut ( pip install deeplabcut) or pip install 'deeplabcut' which has a pyside/napari based GUI. Some users might want to create their own customize env. *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. Creating your own customized conda env (recommended route for Linux: Ubuntu, CentOS, Mint, etc.) # This would mean you need to be up-to-date with the latest GitHub-based code though! Please see here on how to get the latest GitHub code, and how to test your installation by following this video. Pro Tip: If you want to modify code and then test it, you can use our provided testscripts. Here are some conda environment management tips: 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. Once installed, you can check the version by running import deeplabcut deeplabcut._version_. ![]() If you want to use a specific release, then you need to specify the version you want, such as pip install deeplabcut=2.2. If you ever want to update your DLC, just run pip install -upgrade deeplabcut once you are inside your env. More installation ProTips are also available. Please see our dedicated docker package and page here. Docker is the most reproducible way to use and deploy code. 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! ![]() If you want to use the SuperAnimal models, then please use pip install 'deeplabcut'. You have both standard and multi-animal installed! 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'. □ 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 computer (i.e., no need to go back into the conda- folder). Now, in the terminal run (Windows/Linux/MacBook Intel chip): 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. If you cloned the repo onto your Desktop, the command may look like:Ĭd C:\Users\YourUserName\Desktop\DeepLabCut\conda-environments Publishing Notebooks into the Main DLC Cookbook.Improving network performance on unbalanced data via augmentation □.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.Using ModelZoo models on your own datasets.Clustering in the napari-DeepLabCut GUI.Helper & Advanced Optional Function Documentation.DeepLabCut User Guide (for single animal projects).□ Get started with DeepLabCut: our key recommendations.
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