<commit_before># Installation

## Conda installation

Conda binaries are available for Python 3.6, 3.7 and 3.8 on CUDA 10.1 and 10.2. To install tsne-cuda with conda, run `conda install -c CannyLab -c pytorch tsnecuda`.

## Pip installation

Pip binaries are available for Python >=3.6 and CUDA 10.1, 10.2, 11.0, 11.1, 11.2 and 11.3 and FAISS version 1.6.5 and Intel MKL 2018.

To install tsne-cuda with pip, run `pip3 install tsnecuda`. This installs tsnecuda with a CUDA version of 10.2. For this to work, you MUST have both FAISS version 1.6.5
and intel MKL installed already on your machine - see installation instructions for FAISS: [here](https://github.com/facebookresearch/faiss/blob/v1.6.5/INSTALL.md).
and installation instructions for MKL on Ubuntu/debian [here](https://github.com/eddelbuettel/mkl4deb)

When installing FAISS from source, it is necessary to build and install both the C++ and the python requirements.

If you do not want to use 10.2, you can download using pip from our hosted sources:
```
# CUDA 11.0
pip3 install tsnecuda==3.0.0+cu110 -f https://tsnecuda.isx.ai/whl/tsnecuda_stable.html
# CUDA 11.1
pip3 install tsnecuda==3.0.0+cu111 -f https://tsnecuda.isx.ai/whl/tsnecuda_stable.html
# CUDA 11.2
pip3 install tsnecuda==3.0.0+cu112 -f https://tsnecuda.isx.ai/whl/tsnecuda_stable.html
# CUDA 11.3
pip3 install tsnecuda==3.0.0+cu113 -f https://tsnecuda.isx.ai/whl/tsnecuda_stable.html
# CUDA 10.1
pip3 install tsnecuda==3.0.0+cu101 -f https://tsnecuda.isx.ai/whl/tsnecuda_stable.html
```


## From source

A number of requirements are necessary for building our code from source.

- CUDA: You will need a working version of the CUDA toolkit which can be obtained from here. Our code has been tested compiling with CUDA versions 9.0, 9.1, 9.2, 10.0, 10.1, 10.2, 11.0 and 11.2. Other versions - may not be supported.
- CMAKE: Version >= 3.20.0 which can be obtained by running sudo apt install cmake on ubuntu/debian systems.
- MKL/OpenBLAS: If you're using MKL, install it using the intel provided installation scripts. If you're using OpenBLAS install it using sudo apt install libopenblas-dev on ubuntu/debian systems.<commit_msg>Fix bug with pip whl links<commit_after># Installation

## Conda installation

Conda binaries are available for Python 3.6, 3.7 and 3.8 on CUDA 10.1 and 10.2. To install tsne-cuda with conda, run `conda install -c CannyLab -c pytorch tsnecuda`.

## Pip installation

Pip binaries are available for Python >=3.6 and CUDA 10.1, 10.2, 11.0, 11.1, 11.2 and 11.3 and FAISS version 1.6.5 and Intel MKL 2018.

To install tsne-cuda with pip, run `pip3 install tsnecuda`. This installs tsnecuda with a CUDA version of 10.2. For this to work, you MUST have both FAISS version 1.6.5
and intel MKL installed already on your machine - see installation instructions for FAISS: [here](https://github.com/facebookresearch/faiss/blob/v1.6.5/INSTALL.md).
and installation instructions for MKL on Ubuntu/debian [here](https://github.com/eddelbuettel/mkl4deb)

When installing FAISS from source, it is necessary to build and install both the C++ and the python requirements.

If you do not want to use 10.2, you can download using pip from our hosted sources:
```
# CUDA 11.0
pip3 install tsnecuda==3.0.0+cu110 -f https://tsnecuda.isx.ai/tsnecuda_stable.html
# CUDA 11.1
pip3 install tsnecuda==3.0.0+cu111 -f https://tsnecuda.isx.ai/tsnecuda_stable.html
# CUDA 11.2
pip3 install tsnecuda==3.0.0+cu112 -f https://tsnecuda.isx.ai/tsnecuda_stable.html
# CUDA 11.3
pip3 install tsnecuda==3.0.0+cu113 -f https://tsnecuda.isx.ai/tsnecuda_stable.html
# CUDA 10.1
pip3 install tsnecuda==3.0.0+cu101 -f https://tsnecuda.isx.ai/tsnecuda_stable.html
```


## From source

A number of requirements are necessary for building our code from source.

- CUDA: You will need a working version of the CUDA toolkit which can be obtained from here. Our code has been tested compiling with CUDA versions 9.0, 9.1, 9.2, 10.0, 10.1, 10.2, 11.0 and 11.2. Other versions - may not be supported.
- CMAKE: Version >= 3.20.0 which can be obtained by running sudo apt install cmake on ubuntu/debian systems.
- MKL/OpenBLAS: If you're using MKL, install it using the intel provided installation scripts. If you're using OpenBLAS install it using sudo apt install libopenblas-dev on ubuntu/debian systems.
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