INSTALL.md

# Installing Faiss via conda

The recommended way to install Faiss is through [conda](https://docs.conda.io).
Stable releases are pushed regularly to the pytorch conda channel, as well as
pre-release nightly builds.

The CPU-only `faiss-cpu` conda package is currently available on Linux, OSX, and
Windows. The `faiss-gpu`, containing both CPU and GPU indices, is available on
Linux systems, for various versions of CUDA.

To install the latest stable release:

``` shell
# CPU-only version
$ conda install -c pytorch faiss-cpu

# GPU(+CPU) version
$ conda install -c pytorch faiss-gpu

# or for a specific CUDA version
$ conda install -c pytorch faiss-gpu cudatoolkit=10.2 # for CUDA 10.2
```

Nightly pre-release packages can be installed as follows:

``` shell
# CPU-only version
$ conda install -c pytorch/label/nightly faiss-cpu

# GPU(+CPU) version
$ conda install -c pytorch/label/nightly faiss-gpu
```

## Installing from conda-forge

Faiss is also being packaged by [conda-forge](https://conda-forge.org/), the
community-driven packaging ecosystem for conda. The packaging effort is
collaborating with the Faiss team to ensure high-quality package builds.

Due to the comprehensive infrastructure of conda-forge, it may even happen that
certain build combinations are supported in conda-forge that are not available
through the pytorch channel. To install, use

``` shell
# CPU version
$ conda install -c conda-forge faiss-cpu

# GPU version
$ conda install -c conda-forge faiss-gpu
```

You can tell which channel your conda packages come from by using `conda list`.
If you are having problems using a package built by conda-forge, please raise
an [issue](https://github.com/conda-forge/faiss-split-feedstock/issues) on the
conda-forge package "feedstock".

# Building from source

Faiss can be built from source using CMake.

Faiss is supported on x86_64 machines on Linux, OSX, and Windows. It has been
found to run on other platforms as well, see
[other platforms](https://github.com/facebookresearch/faiss/wiki/Related-projects#bindings-to-other-languages-and-porting-to-other-platforms).

The basic requirements are:
- a C++11 compiler (with support for OpenMP support version 2 or higher),
- a BLAS implementation (we strongly recommend using Intel MKL for best
performance).

The optional requirements are:
- for GPU indices:
  - nvcc,
  - the CUDA toolkit,
- for the python bindings:
  - python 3,
  - numpy,
  - and swig.

Indications for specific configurations are available in the [troubleshooting
section of the wiki](https://github.com/facebookresearch/faiss/wiki/Troubleshooting).

## Step 1: invoking CMake

``` shell
$ cmake -B build .
```

This generates the system-dependent configuration/build files in the `build/`
subdirectory.

Several options can be passed to CMake, among which:
- general options:
  - `-DFAISS_ENABLE_GPU=OFF` in order to disable building GPU indices (possible
  values are `ON` and `OFF`),
  - `-DFAISS_ENABLE_PYTHON=OFF` in order to disable building python bindings
  (possible values are `ON` and `OFF`),
  - `-DBUILD_TESTING=OFF` in order to disable building C++ tests,
  - `-DBUILD_SHARED_LIBS=ON` in order to build a shared library (possible values
  are `ON` and `OFF`),
- optimization-related options:
  - `-DCMAKE_BUILD_TYPE=Release` in order to enable generic compiler
  optimization options (enables `-O3` on gcc for instance),
  - `-DFAISS_OPT_LEVEL=avx2` in order to enable the required compiler flags to
  generate code using optimized SIMD instructions (possible values are `generic`,
  `sse4`, and `avx2`, by increasing order of optimization),
- BLAS-related options:
  - `-DBLA_VENDOR=Intel10_64_dyn -DMKL_LIBRARIES=/path/to/mkl/libs` to use the
  Intel MKL BLAS implementation, which is significantly faster than OpenBLAS
  (more information about the values for the `BLA_VENDOR` option can be found in
  the [CMake docs](https://cmake.org/cmake/help/latest/module/FindBLAS.html)),
- GPU-related options:
  - `-DCUDAToolkit_ROOT=/path/to/cuda-10.1` in order to hint to the path of
  the CUDA toolkit (for more information, see
  [CMake docs](https://cmake.org/cmake/help/latest/module/FindCUDAToolkit.html)),
  - `-DCMAKE_CUDA_ARCHITECTURES="75;72"` for specifying which GPU architectures
  to build against (see [CUDA docs](https://developer.nvidia.com/cuda-gpus) to
  determine which architecture(s) you should pick),
- python-related options:
  - `-DPython_EXECUTABLE=/path/to/python3.7` in order to build a python
  interface for a different python than the default one (see
  [CMake docs](https://cmake.org/cmake/help/latest/module/FindPython.html)).

## Step 2: Invoking Make

``` shell
$ make -C build -j faiss
```

This builds the C++ library (`libfaiss.a` by default, and `libfaiss.so` if
`-DBUILD_SHARED_LIBS=ON` was passed to CMake).

The `-j` option enables parallel compilation of multiple units, leading to a
faster build, but increasing the chances of running out of memory, in which case
it is recommended to set the `-j` option to a fixed value (such as `-j4`).

## Step 3: Building the python bindings (optional)

``` shell
$ make -C build -j swigfaiss
$ (cd build/faiss/python && python setup.py install)
```

The first command builds the python bindings for Faiss, while the second one
generates and installs the python package.

## Step 4: Installing the C++ library and headers (optional)

``` shell
$ make -C build install
```

This will make the compiled library (either `libfaiss.a` or `libfaiss.so` on
Linux) available system-wide, as well as the C++ headers. This step is not
needed to install the python package only.


## Step 5: Testing (optional)

### Running the C++ test suite

To run the whole test suite, make sure that `cmake` was invoked with
`-DBUILD_TESTING=ON`, and run:

``` shell
$ make -C build test
```

### Running the python test suite

``` shell
$ (cd build/faiss/python && python setup.py build)
$ PYTHONPATH="$(ls -d ./build/faiss/python/build/lib*/)" pytest tests/test_*.py
```

### Basic example

A basic usage example is available in
[`demos/demo_ivfpq_indexing.cpp`](https://github.com/facebookresearch/faiss/blob/main/demos/demo_ivfpq_indexing.cpp).

It creates a small index, stores it and performs some searches. A normal runtime
is around 20s. With a fast machine and Intel MKL's BLAS it runs in 2.5s.

It can be built with
``` shell
$ make -C build demo_ivfpq_indexing
```
and subsequently ran with
``` shell
$ ./build/demos/demo_ivfpq_indexing
```

### Basic GPU example

``` shell
$ make -C build demo_ivfpq_indexing_gpu
$ ./build/demos/demo_ivfpq_indexing_gpu
```

This produce the GPU code equivalent to the CPU `demo_ivfpq_indexing`. It also
shows how to translate indexes from/to a GPU.

### A real-life benchmark

A longer example runs and evaluates Faiss on the SIFT1M dataset. To run it,
please download the ANN_SIFT1M dataset from http://corpus-texmex.irisa.fr/
and unzip it to the subdirectory `sift1M` at the root of the source
directory for this repository.

Then compile and run the following (after ensuring you have installed faiss):

``` shell
$ make -C build demo_sift1M
$ ./build/demos/demo_sift1M
```

This is a demonstration of the high-level auto-tuning API. You can try
setting a different index_key to find the indexing structure that
gives the best performance.

### Real-life test

The following script extends the demo_sift1M test to several types of
indexes. This must be run from the root of the source directory for this
repository:

``` shell
$ mkdir tmp  # graphs of the output will be written here
$ python demos/demo_auto_tune.py
```

It will cycle through a few types of indexes and find optimal
operating points. You can play around with the types of indexes.

### Real-life test on GPU

The example above also runs on GPU. Edit `demos/demo_auto_tune.py` at line 100
with the values

``` python
keys_to_test = keys_gpu
use_gpu = True
```

and you can run
``` shell
$ python demos/demo_auto_tune.py
```
to test the GPU code.