## Installation

### Requirements

- Linux, CUDA >= 11.7, Python >= 3.8, PyTorch >= 2.0.0 (our setup below is based on CUDA 11.8, Python 3.10, PyTorch 2.0.1; more recent versions should work too, but no guarantees)
- Conda (anaconda / miniconda work well)
- visual studio (2019) build tools (Just for windows)

### Setup

#### 1. Create conda environment

```bash
conda create --name ssvp_slt python=3.10 cmake
conda activate ssvp_slt
python -m pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118
conda install av -c conda-forge
```

#### 2. (Optional) Video dataloader GPU decoding backend support

If you want to use our video dataloader with GPU decoding backend, you need to reinstall torchvision by building it from scratch following the steps at [https://github.com/pytorch/vision/tree/main/torchvision/csrc/io/decoder/gpu](https://github.com/pytorch/vision/tree/main/torchvision/csrc/io/decoder/gpu). We found that this does not work with ffmpeg 6.1, so we recommend running `conda install 'ffmpeg<6.0'`. If you get a warning that torchvision is built without GPU decoding support due to `bsf.h` missing, we recommend manually downloading `bsf.h` from the `ffmpeg` source code ([https://github.com/FFmpeg/FFmpeg/blob/master/libavcodec/bsf.h](https://github.com/FFmpeg/FFmpeg/blob/master/libavcodec/bsf.h), **make sure it matches your ffmpeg version!**) and placing it under `$(path-to-your-conda)/ssvp_slt/include/libavcodec`.

#### 3. Pip Installs

Install the remaining dependencies and an egg of our ssvp-slt package:

```bash
pip install git+https://github.com/facebookresearch/stopes.git
pip install -r requirements.txt

# Move into fairseq folder and install egg
cd fairseq-sl
pip install -e .
cd ..

# Install ssvp_slt egg from the repo's root
pip install -e .
```

#### 4. Install dlib

- Install CUDA and cuDNN with

```bash
conda install cuda cudnn -c nvidia
```

- Install dlib from the source (write your VS versions)

```bash
git clone https://github.com/davisking/dlib.git
cd dlib
mkdir build
cd build
cmake .. -DDLIB_USE_CUDA=1 -DUSE_AVX_INSTRUCTIONS=1 -DCUDAToolkit_ROOT=/path/to/your/conda/envs/dlib/bin/ -G "Visual Studio 17 2022" -A x64 --verbose
cmake --build .
cd ..
python setup.py install --set DLIB_USE_CUDA=1
```

#### 5. BLEURT

If you want to compute BLEURT scores as part of the translation evals (via `common.compute_bleurt=true`), you need to install BLEURT:

```bash
pip install git+https://github.com/google-research/bleurt.git
```

#### 6. Set environment variables

You will also likely need to set some environment variables before running the translation code:

```bash
export XLA_FLAGS=--xla_gpu_cuda_data_dir=$(path-to-your-cuda-11.8)
export LD_LIBRARY_PATH=$(path-to-your-cuda-11.8)/lib64:${LD_LIBRARY_PATH}
```

#### 7. Weights and Biases

If you want to use [Weights and Biases](https://wandb.ai) to track your training runs (via `cfg.wandb.enabled=true`), you need to ensure your `WAND_API_KEY` environment variable is set correctly.

#### 8. Test your env

you can execute `test_env.py` python script to check if the dependencies are installed

```bash
python tests/test_env.py
```

The output (values could be quite different)

```bash
CUDA is available!
2.2.0+cu118
11.8
Device count: 1
Current device: 0
Device name: NVIDIA A100-SXM4-40GB
dlib CUDA is available!
Number of dlib CUDA devices: 1
```