"Magenta is distributed as an open source Python library, powered by TensorFlow. This library includes utilities for manipulating source data (primarily music and images), using this data to train machine learning models, and finally generating new content from these models."
Magenta on HPRC clusters
Ideally we would rebuild everything needed for Magenta to optimize for our clusters but this is a significant and less than straightforward task. So for now we recommend the Python solution below.
Magenta via an Anaconda virtual environment
A lot of what is described below is probably easily adapted to Anaconda, but we are going to use a plain Python for now
Magenta via a Python virtual environment
In order to get the most out of Magenta you'll need the following modules:
- cuDNN/7.0.5-CUDA-9.0.176 - the version of Magenta installed by pip strictly requires CUDA 9.0 and cuDNN 7.0
- an "oldish" Python. Python 3.6 is too new. At the moment we recommend a Python 3.5.2 from the 2017A toolchain.
Verify the needed sound library development files are installed (currently only on ada2 and terra2):
rpm -q alsa-lib-devel
Using the steps here, and replacing 'venv' with '$SCRATCH/mymagenta' we can create a virtual environment to install magenta-gpu (we want the GPU version given our TensorFlow benchmarks. We'll start with the 2017A, GCC-based, 'foss' toolchain to keep things simple.
ml purge # clean module environment ml Python/3.5.2-foss-2017A # load an older Python ml cuDNN/7.0.5-CUDA-9.0.176 # load the needed CUDA/cuDNN ml # list modules
Those steps will be required everytime you wish to access Magenta. If you load other modules, make sure they all comply with foss-2017A toolchain.
A quick test is:
- run the 'ml' commands above to load the needed modules
- run: python -c 'import magenta; magenta.__version__'