goes16ci 0.1.3

Machine learning benchmark for lightning prediction with GOES16

GOES 16 Lightning Count Prediction Benchmark

The GOES 16 Lightning Count Prediction benchmark is a deep learning benchmark for HPC systems used for atmospheric science problems.

Contributors

• David John Gagne
• Bill Anderson
• Gunther Wallach

Requirements

The code is designed to run on Python 3.6 and 3.7. It requires the following Python libraries:

• numpy
• scipy
• pandas
• xarray
• tensorflow>=2.0.0
• scikit-learn
• pyproj
• dask distributed (for data processing)
• ipython
• jupyter (for interactive visualization of neural networks)

Setup from Scratch

• Install Python 3.7 on your machine. I recommend the Miniconda Python installer available here.

• Create a benchmark environment: conda create -n goes16 python=3.7

• Once the environment is installed activate it on your machine: source activate goes16

• Install the needed Python libraries from conda

conda install -c conda-forge --yes \
    pip \
    ipython \
    jupyter \
    numpy \
    scipy \
    matplotlib \
    xarray \
    netcdf4 \
    pandas \
    pyyaml \
    dask \
    distributed \
    scikit-learn \
    pyproj

• Make sure the CUDA kernel and CUDA toolkit are installed on your system and know the path and versions.

• Install the tensorflow-gpu binary (if installing tensorflow 1.15) or tensorflow binary (if tensorflow 2). For more detailed installation instructions visit the tensorflow website.

# If you plan to use tensorflow 2
pip install tensorflow

Run Benchmark Script

• Clone the goes16ci git repository to your home directory.

cd ~
git clone https://github.com/NCAR/goes16ci.git
cd goes16ci

• Install the goes16ci library

pip install .

• Download the GOES16 patch files. You will need about 8 GB free to download and untar the data.

python download_data.py

• Run the benchmark script. The script will output trained neural networks and a yaml file with the timing information for each step.

python goes16_deep_learning_benchmark.py

• If you want to modify the neural network or other properties of the script, you can make a copy of benchmark_config_default.yml and modify it. To run the script with the new config file:

python goes16_deep_learning_benchmark.py -c benchmark_config_default.yml

Setup on Cheyenne/Casper

• Clone the git repo to your home directory

cd ~
git clone https://github.com/NCAR/goes16ci.git
cd goes16ci

• Create a link to the patch data on GLADE

ln -s /glade/p/cisl/aiml/dgagne/goes16_nc/ABI_patches_20190315 data

• Modify the goes16_benchmark_casper.sh script with your account number.

• Submit the benchmark script to the casper queue: sbatch goes16_benchmark_casper.sh