Better handling of nans and expanded docs

README.md

@@ -16,99 +16,6 @@ Motion Sequencing (MoSeq) is an unsupervised machine learning method for animal
 
 - [MoSeq homepage](https://dattalab.github.io/moseq2-website/index.html)
 
-## Installation
-
-- The first import of `keypoint_moseq` after installation can take a few minutes. 
-- To install on a Mac with an M1 chip, we recommend option 2 (conda environment installation).
-
-### Option 1: Install using pip
-
-1. If you plan to use a GPU (recommended), install the appropriate driver and CUDA version. CUDA ≥11.1 and cuDNN ≥8.2 are required. [This section of the DeepLabCut docs](https://deeplabcut.github.io/DeepLabCut/docs/installation.html#gpu-support) may be helpful.
-
-
-2. Install [Anaconda](https://docs.anaconda.com/anaconda/install/index.html) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html). Create and activate an environment called `keypoint_moseq` with python 3.9:
-```
-conda create -n keypoint_moseq python=3.9
-conda activate keypoint_moseq
-
-# Include the following line if installing on Windows
-# conda install -c conda-forge pytables
-```
-
-3. Install jax
-```
-# MacOS and Linux (CPU-only)
-pip install "jax[cpu]"
-
-# MacOS and Linux (GPU)
-pip install "jax[cuda11_cudnn82]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
-
-# Windows (CPU-only)
-pip install jax==0.3.22 https://whls.blob.core.windows.net/unstable/cpu/jaxlib-0.3.22-cp39-cp39-win_amd64.whl
-
-# Windows (GPU)
-pip install jax==0.3.22 https://whls.blob.core.windows.net/unstable/cuda111/jaxlib-0.3.22+cuda11.cudnn82-cp39-cp39-win_amd64.whl
-```
-
-4. Install keypoint-moseq
-```
-pip install keypoint-moseq
-```
-
-5. Make the new environment accessible in jupyter 
-```
-python -m ipykernel install --user --name=keypoint_moseq
-```
-
-## Option 2: Conda environment installation
-As an alternative, you can install directly from conda environment files. This will automatically install the appropriate GPU drivers and other dependencies.
-
-1. Clone the repository:
-```
-git clone https://github.com/dattalab/keypoint-moseq && cd keypoint-moseq
-```
-
-2. Install the appropriate conda environment for your platform:
-
-```
-# Windows (CPU-only)
-conda env create -f conda_envs/environment.win64_cpu.yml
-
-# Windows (GPU)
-conda env create -f conda_envs/environment.win64_gpu.yml
-
-# Linux (CPU-only)
-conda env create -f conda_envs/environment.linux_cpu.yml
-
-# Linux (GPU)
-conda env create -f conda_envs/environment.linux_gpu.yml
-
-# Mac (CPU-only)
-conda env create -f conda_envs/environment.mac_cpu.yml
-```
-
-See [this issue](https://github.com/dattalab/keypoint-moseq/issues/5) for updates regarding Apple Silicon (M1/M2) support. For now, you can use the Mac (CPU) version on newer Macs.
-
-3. Activate the new environment:
-```
-conda activate keypoint_moseq
-```
-
-
-### Troubleshooting
-#### `UNKNOWN: no kernel image is available for execution on the device`
-If you're running into issues when trying to use the GPU-accelerated version, you might see this error message:
-```
-jaxlib.xla_extension.XlaRuntimeError: UNKNOWN: no kernel image is available for execution on the device
-```
-First, check if Jax can detect your GPU:
-```
-(keypoint_moseq) λ python -c "import jax; print(jax.default_backend())"
-gpu
-```
-If it can't, then you might not be using the right version of `cudatoolkit` or `cudnn`. If you installed these via `conda`, you can check by doing a `conda list | grep cud`.
-
-If you are on the right versions, try [updating your GPU driver to the latest version](https://nvidia.com/drivers).
 
 # License
 MoSeq is freely available for academic use under a license provided by Harvard University. Please refer to the license file for details. If you are interested in using MoSeq for commercial purposes please contact Bob Datta directly at srdatta@hms.harvard.edu, who will put you in touch with the appropriate people in the Harvard Technology Transfer office.

conda_envs/environment.win64_cpu.yml

@@ -6,7 +6,7 @@ channels:
 
 dependencies:
   - python=3.9
-  - pytables
+  - conda-forge::pytables
   - pip
   - pip:
     - jax==0.3.22

conda_envs/environment.win64_gpu.yml

@@ -7,7 +7,7 @@ channels:
 
 dependencies:
   - python=3.9
-  - pytables
+  - conda-forge::pytables
   - cudatoolkit=11.1
   - cudnn=8.2
   - cuda-nvcc