Installation

This section provides detailed instructions on how to install gflownet. Ensure your system meets the following requirements before proceeding with the installation.

Requirements

Python Version: Python 3.10 is required.
CUDA Version: CUDA 11.8 is needed for GPU acceleration (optional for CPU-only installation).
Operating System: The setup is primarily supported on Ubuntu. It should also work on OSX, but you might need to handle the package dependencies manually.

Quick Installation

If you simply want to install everything on a GPU-enabled machine, clone the repo and run install.sh:

git clone git@github.com:alexhernandezgarcia/gflownet.git
cd gflownet
source install.sh

Note

This project requires Python 3.10 and CUDA 11.8.
It is also possible to install a CPU-only environment that supports most features (see below).
Setup is currently only supported on Ubuntu. It should also work on OSX, but you will need to handle the package dependencies.

Step by Step Installation

The following steps, as well as the script install.sh, assume the use of Python virtual environments for the installation.

Load Required Modules (if on a cluster):

Ensure that you have Python 3.10 and, if you want to install GPU-enabled PyTorch, CUDA 11.8. In a cluster that uses modules, you may be able to load Python and CUDA with:
```
module load python/3.10
module load cuda/11.8
```
Create and Activate Virtual Environment:

Create and activate a Python virtual environment with venv. For example:
```
python -m venv gflownet-env
source gflownet-env/bin/activate
```

Install PyTorch 2.5.1:

For a CUDA-enabled installation:

python -m pip install torch==2.5.1 --index-url https://download.pytorch.org/whl/cu118
python -m pip install torch-scatter -f https://data.pyg.org/whl/torch-2.5.1+cu118.html

For a CPU-only installation:

python -m pip install torch==2.5.1 --index-url https://download.pytorch.org/whl/cpu
python -m pip install torch-scatter -f https://data.pyg.org/whl/torch-2.5.1+cpu.html

Install the Rest of the Dependencies:
```
python -m pip install .
```
The above command will install the minimum set of dependencies to run the core features of the gflownet package. Specific features require the installation of extra dependencies.

Optional Packages (Extras)

Currently, these are the existing sets of extras:

dev: dependencies for development, such as linting and testing packages.
materials: dependencies for materials applications, such as the Crystal-GFN.
molecules: dependencies for molecular modelling and generation, such the Conformer-GFN.

Extras can be installed by specifying the tags in square brackets:

python -m pip install .[dev]

or

python -m pip install .[dev,materials]

Installing with `install.sh`

The script install.sh simplifies the installation of a Python environment with the necessary or desired dependencies.

By default, running source install.sh will create a Python environment in ./gflownet-env with CUDA-enabled PyTorch and all the dependencies (all extras). However, the script admits the following arguments to modify the configuration of the environment:

Available Arguments:

--cpu: Install CPU-only PyTorch (mutually exclusive with –cuda).
--cuda: Install CUDA-enabled PyTorch (default, and mutually exclusive with –cpu).
--envpath PATH: Path of the Python virtual environment to be installed. Default: ./gflownet-env
--extras LIST: Comma-separated list of extras to install. Default: all. Options:
- dev: dependencies for development, such as linting and testing packages.
- materials: dependencies for materials applications, such as the Crystal-GFN.
- molecules: dependencies for molecular modelling and generation, such the Conformer-GFN.
- all: all of the above
- minimal: none of the above, that is the minimal set of dependencies.
--dry-run: Print the summary of the configuration selected and exit.
--help: Show the help message and exit.

Example Usage:

For example, you may run:

source install.sh --cpu --envpath ~/myenvs/gflownet-env --extras dev,materials

to install an environment on ~/myenvs/gflownet-env, with a CPU-only PyTorch and the dev and materials extras.