Willump is unstable and under active development! Please report any bugs or unusual behavior.
Willump is an optimizer for machine learning inference. It speeds up ML inference pipelines written as Python functions whose performance is bottlenecked by feature computation. For a high-level summary of Willump, please see this blog post. For a full description, please see our paper, published at MLSys 2020.
Willump requires Python version 3.6 or later. These instructions were tested on a clean installation of Ubuntu 18.04 with Python 3.6.8.
First, install dependency packages:
sudo apt update sudo apt install build-essential curl python3-pip pip3 install setuptools
Then install the llvm-st branch of our Weld fork, weld-willump. Its repository and installation instructions are available here.
Copy the weld-willump libraries to /usr/lib so clang can find them:
sudo cp $WELD_HOME/target/release/libweld.so /usr/lib/libweld.so
Install the weld-willump Python libraries:
cd $WELD_HOME/python/pyweld sudo -E python3 setup.py install
Finally, clone Willump, set the WILLUMP_HOME environment variable to point at the package root, and include the package root in your PYTHONPATH:
git clone https://github.com/stanford-futuredata/Willump.git cd Willump pip3 install -r requirements.txt export WILLUMP_HOME=`pwd` python3 setup.py install --user
To confirm Willump works, run the Willump unit tests:
python3 setup.py test
For information on reproducing the experiments run in the Willump paper, please see our benchmarks guide.
You can also experiment with Willump using Docker with the Dockerfile we provide. To clone our repository and build our container, run:
git clone https://github.com/stanford-futuredata/Willump.git cd Willump export WILLUMP_HOME=`pwd` docker build -t willump .
Then, to verify the container built successfully, run our unit tests:
docker run -t willump python setup.py test