Installation Guide

The installation can be done using conda or docker.

Using Docker

To install DASF using docker, you must in the go to the build/ directory and execute the command below directory according to your build type: cpu or gpu.

./build_docker.sh <cpu|gpu>

The dasf image will be created and be ready to use. Once it is ready, you can start a jupyter instance by executing the command:

./start_jupyter_server.sh

Using Conda

If you just want to create a base Conda environment for DASF, you need to create it, using the respective YAML file based on architecture: for CPUs or GPUs. The environment name is always dasf.

conda env create -f build/conda/{cpu,gpu}/environment.yml

Development version

To install this development version, all you need to do is run pip from the root project directory (the same where pyproject.toml lives).

python -m pip install -e .

Testing

If you have a working environment with DASF installed, you can execute the all the test set. Make sure you have all development packages installed such as pytest, parameterized and mock. To run, you need to execute pytest from the tests/ directory.

pytest tests/