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INSTALL.md

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Installing Prerequisites

This codebase currently only supports Python3 with CUDA. Make sure you have an NVIDIA GPU with corresponding drivers installed.

There are 2 ways to install dependencies. We recommend using nvidia-docker so that you run with specific GCC and CUDA versions verified to work with the arithmetic coder library torchac. However, this is not required to install torchac.

Note that torchac is not needed to train models or evaluate theoretical bitrates, but it needs to be installed to compress/decompress images. In theory, you should be able to train/evaluate models without using the exact versions of libraries we used.

Option 1: Using nvidia-docker

nvidia-docker allows you to run docker images with specific versions of CUDA and GCC. This is the recommended way to install torchac.

Set up nvidia-docker

If you don't have docker installed, you can install docker using instructions here.

Install nvidia-docker using instructions here.

Once you have nvidia-docker installed, you should register an account on ngc.nvidia.com and pull the following PyTorch image:

docker pull nvcr.io/nvidia/pytorch:19.06-py3

Installing dependencies

First clone the repo. Then run the docker image:

docker run -it --runtime=nvidia --rm -v SReC:/SReC/ nvcr.io/nvidia/pytorch:19.06-py3 bash

Note that -v [directory on disk]:[directory in container] mounts SReC directory as /SReC inside the container. Running cd /SReC in container should get you inside SReC directory.

Now, you cann install all the dependencies besides torchac.

pip install -r requirements.txt

Installing torchac

Installing torchac requires specific versions of NVCC and GCC. Because we use nvidia-docker, you should see the following versions of gcc and nvcc:

  • GCC 5.4
  • NVCC 10.1 You can run gcc --version and nvcc -V to obtain gcc and nvcc versions.

Run the following to install torchac:

cd torchac
COMPILE_CUDA=force python3 setup.py install

This installs a package called torchac-backend-gpu in your pip.

To test if it works, you can do

cd torchac
python3 -c "import torchac"

It should not print any error messages.

Option 2: Installing without nvidia-docker

Installing dependencies

First clone the repo. Once inside the repo, you can install all the dependencies besides torchac using pip.

pip install -r requirements.txt torch==1.2 torchvision==0.2.1

Installing torchac

Installing torchac requires specific versions of NVCC and GCC. Note that we used different versions of NVCC and GCC than the L3C authors. We used:

  • GCC 5.4
  • NVCC 10.1

See L3C for other combinations of NVCC and GCC versions that are supported.

To install torchac, run the following:

cd torchac
COMPILE_CUDA=force python3 setup.py install

This installs a package called torchac-backend-gpu in your pip.

To test if it works, you can do

cd torchac
python3 -c "import torchac"

It should not print any error messages.