At this point in the course, we have seen how neural networks are trained and evaluated from an algorithmic perspective. In this lab, we will see how these algorithms are optimized for performance on CPUs. Our goal is to get a more complete understanding of how software interacts with the CPU at an architectural level and then optimize it so that it is cache-friendly.
Please start the Docker and the Jupyter server like in Lab 0. Please pull
the docker first and then start with docker compose up.
cd <your-repository-directory>
export DOCKER_ARCH=amd64
# If you are using arm CPU (Apple M1/M2),
# export DOCKER_ARCH=arm64
docker compose pull
docker compose up
# Complete the lab then run the following from within the docker container
# make submit
After finishing all of the provided notebooks, please run make submit to
submit your code. Check your submission on the GitHub website and ensure that
all notebooks have all cells run and all outputs visible. Additionally, ensure
that the answers.yaml file in the website matches the answers you have in your
notebooks. If either the notebooks or the answers.yaml file are not up to
date, you may lose points or receive a zero for the assignment.
FAILURE TO FOLLOW THESE INSTRUCTIONS WILL RESULT IN YOU RECEIVING A ZERO FOR THE ASSIGNMENT.