Installation Local

YASS - Installation Instructions For Linux (Ubuntu 18.04)

The instructions below are for installing YASS on a local workstation or PC running Ubuntu operating system. Windows and Mac installations should be possible using similar instructions, please contact us if you run into installation issues.

Installing NVIDIA driver and Toolkit:

YASS was tested with NVIDIA 410+ drivers and Toolkit 10.0+, but should work with other versions. If you do not have experience installing installed NVIDIA video drivers, we recommend seeking assistance from IT support as upgrading video drivers may corrupt your Ubuntu installation.

1. Install NVIDIA 410+ from NVIDIA: https://www.nvidia.com/Download/index.aspx

2. Install Toolkit: https://developer.nvidia.com/cuda-downloads

3. [Optional]: some toolkit installations require additional referencing to the cuda-toolkit. These lines can be run at the command line with CUDA_TOOLKIT_NAME replaced by your cuda toolkit version or added to your ./bashrc file:

   export PATH=/usr/local/CUDA_TOOLKIT_NAME/bin:${PATH}

   export LD_LIBRARY_PATH=/usr/local/CUDA_TOOLKIT_NAME/lib64:$LD_LIBRARY_PATH

   export CUDA_HOME=/usr/local/CUDA_TOOLKIT_NAME

   Or adding them to your ~.bashrc file (this example uses cuda-10.1 toolkit):

echo 'export PATH=/usr/local/cuda-10.1/bin:${PATH}' >> ~/.bashrc

echo 'export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc

echo 'export CUDA_HOME=/usr/local/cuda-10.1' >> ~/.bashrc

Installing Anaconda and creating YASS environment:

1. [Optional] Download anaconda environment manager (recommended): https://www.anaconda.com/distribution/

2. [Optional] Create a conda environment to run yass using python 3.7 (recommended):

   conda create -n yass python=3.7

3. [Optional] Activate conda environment (recommended):

   source activate yass

Cloning and installing YASS python and cuda:

4. Clone YASS repository:

   git clone https://github.com/paninski-lab/yass

5. Install YASS dependencies:

   cd yass

   pip install -e .

6. Install pytorch master branch from conda:

   conda install pytorch

7. Compile cuda code using default gcc [Note updated CUDA code in Mar/2020]:

   cd src/gpu_bspline_interp

   setup.py install --force

   cd ..

   cd gpu_rowshift

   python setup.py install --force

Some installation issues may arise if different gcc versions were used for pytorch installation and for cuda code.

Running Default Test

Yass comes with a test dataset (60 second; 10 channels) for testing the install. To run this test:

1. Change directory to main directory of dataset:

   cd samples/10chan

2. Run test using default configuration:

   yass sort config.yaml

If yass runs successfully, several files will be generated in your root directory.

├── data.bin
├── config.yaml
├── geom.txt
├── tmp
│   ├── block_1
│   ├── block_2
│   ├── final_deconv
│   ├── ...
│   ├── spike_train.npy
│   └── yass.log

The spike_train.npy file is a 2-column python numpy array containing spiketimes (first column) and cluster/neuron ids (second column).