1. Installation

1.1. Requirements

jDAS depends on the following Python libraries:

  • TensorFlow (>= 2.2.0): while training and inference is much faster on a GPU, the CPU version of TensorFlow is sufficient in case problems arise installing the CUDA dependencies.

  • NumPy and SciPy for numerical manipulations.

  • (Optional) Matplotlib for visualisation.

  • (Optional) h5py for IO.

  • (Optional) Jupyter notebook or lab to run the examples

The optional dependencies are required to run the examples. All of these can be installed with Anaconda:

conda install -c conda-forge "tensorflow-gpu>=2.2.0" numpy scipy matplotlib h5py notebook

Or through PyPI:

pip install "tensorflow-gpu>=2.2.0" numpy scipy matplotlib h5py notebook

1.2. Setting-up jDAS

To obtain the jDAS source code, you can pull it directly from the GitHub repository:

git clone https://github.com/martijnende/jDAS.git

No additional building is required. To test the installation, try running one of the examples Jupyter notebooks in the examples directory.

Please open a ticket under the tab “Issues” on the GitHub repository if you have trouble setting-up jDAS.