ams-ml-python-course

Machine Learning in Python for Environmental Science Problems AMS Short Course Material

Requirements

The modules for this short course require Python 3.6 and the following Python libraries:

• numpy
• scipy
• matplotlib
• xarray
• netcdf4
• pandas
• scikit-learn
• tensorflow-gpu or tensorflow
• keras
• shapely
• descartes
• jupyter
• ipython
• jupyterlab
• ipywidgets

The current pre-compiled version of tensorflow-gpu requires your machine to have an NVIDIA GPU, CUDA 9.0, CUDA Toolkit 9.0, and cuDNN 7. If you have different versions of CUDA available, you will have to build Tensorflow from source, which can take a few hours.

GPUs are recommended for modules 3 and 4 but are not needed for modules 1 and 2.

Data Access

The data for the course are stored online. The download_data.py script will download the data to the appropriate location and extract all files. The netCDF data is contained in a 2GB tar file, so make sure you have at least 4GB of storage available and a fast internet connection.

Course Videos

• Module 1: Data Analysis and Pre-Processing
• Module 2: Machine Learning with Scikit-Learn
• Module 3: Deep Learning with Keras
• Module 4: Model Interpretation

Setting up GPU-enabled short course Jupyter hub containers

Requirements for architecture

• Docker
• Google Compute Engine
• Google Kubernetes Engine
• NVIDIA CUDA docker images
• jupyter docker-stacks

Recipe

• Start a Google Compute Engine instance with an NVIDIA GPU and install CUDA and docker. See here.
• Clone the jupyter docker-stacks repository
• In the base-notebook Docker file, change the BASE_CONTAINER to "nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04"
• Build base notebook: >> docker build --rm -t username/base-notebook .
• Change to docker-stacks/minimal-notebook directory and change the FROM option to username/base-notebook.
• Build minimal notebook >> docker build --rm -t username/minimal-notebook .
• Change to directory containing short course docker file.
• Build the short course container >> docker build --rm -t username/ams-ml-short-course:gpu .
• Login to docker hub with >> docker login
• Push your container to Docker Hub.
• Start a Kubernetes cluster on Google Cloud with 1 CPU node and 1 GPU node. Use preemptible instances to save a lot of money.
• Log into a Kubernetes node and install CUDA here.
• Wait until the nvidia drivers have been completely installed. Check status by typing in kubectl get pods --all-namespaces and wait for everything to be running.
• Setup Jupyterhub on Google Cloud by following instructions here.