RISE Camp was held remotely on October 29-30, 2020.
RISE Camp is a bootcamp organized by the UC Berkeley RISELab where you can get exposure to research and hands-on experience with systems and technologies for emerging AI applications including reinforcement learning, prediction serving, agile ML development, context management, and AI security.
Videos
Agenda
DAY 1 – THURSDAY, OCTOBER 29th 2020 (All times in Pacific Daylight Time)
- 9:00 – 10:15 AM:
- 10:15 – 10:30 AM: Break
- 10:30 – 11:45 AM:
- 11:45 – 12:45 PM: Lunch with Office Hours (Prof. Ion Stoica)
- 12:45 – 2:00 PM:
- Autopandas (Rohan Bavishi): Attendees will learn how a rich specification that goes beyond input-output examples can be both convenient and easier to provide, and still benefit synthesis of table transformations. The tutorial will include a demonstration of our special UI to support this specification and some example tasks we can solve using the UI. Slides available here.
- 2:00 – 2:15 PM: Break
- 2:15 – 3:30 PM:
DAY 2 – FRIDAY, OCTOBER 30th 2020 (All times in Pacific Daylight Time)
- 9:00 – 10:15 AM:
- 10:15 – 10:30 AM: Break
- 10:30 – 11:45 AM:
- ERDOS (Sukrit Kalra): Attendees will learn how to develop a planning component for an autonomous vehicle and drive it in simulation. Further, the tutorial will also show how the ability of ERDOS to dynamically adapt to the environment allows an autonomous vehicle to prevent accidents. Slides available here.
- 11:45 – 12:45 PM: Lunch with Office Hours (MC2)
- 12:45 – 2:00 PM:
- AdaHessian and PyHessian (Amir Gholami and Zhewei Yao):Attendees will learn advanced analysis of neural networks by learning to compute second-order derivatives. The tutorial would then show how this could be used to (i) analyze the loss landscape of the model, and (ii) how it could be used to achieve high accuracy training of different neural networks. Slides available here.
- AdaHessian and PyHessian (Amir Gholami and Zhewei Yao):Attendees will learn advanced analysis of neural networks by learning to compute second-order derivatives. The tutorial would then show how this could be used to (i) analyze the loss landscape of the model, and (ii) how it could be used to achieve high accuracy training of different neural networks. Slides available here.
- 2:00 – 2:15 PM: Break
- 2:15 – 3:30 PM:
FAQ
What are the prerequisites for attendees of the RISE Camp?
- “Modern” browser (Firefox, Chrome, Safari etc.)
- Zoom account. PLEASE NOTE: All participants and hosts are now required to sign into a Zoom account prior to joining meetings hosted by UC Berkeley. Participants who do not have a UC Berkeley-provided Zoom account can use a Zoom account provided by their institution or create a free, consumer Zoom account (at https://zoom.us/freesignup/). If your company policies do not allow downloading Zoom on your laptop, please choose “Join from your browser” option when prompted. For best results, we recommend using Chrome with Zoom add-on
- Experience programming in Python in notebook environment
- Basic understanding of AI/ML concepts (e.g., training, validation, linear models)
Tutorials
Ray:
You can find the code for the tutorials at this GitHub repo.
Also, here are some instructions for how to run the tutorials locally using Docker. You can also run the code directly from the GitHub repo included above, but the Docker image already has the Python and data dependencies pre-packaged, so we recommend running with Docker.
- Download the Docker image from Docker Hub (make sure you have Docker installed first):
$ docker pull swangster/rise-camp-tutorial:v0 - Start a container. This command includes a flag to forward the default port for Jupyter.
$ docker run -it -p 8888:8888 swangster/rise-camp-tutorial:v0
- Inside the container, navigate to the directory with the Jupyter notebooks and start Jupyter lab:
$ cd /root/rise-camp-tutorial && jupyter lab –ip=0.0.0.0 –allow-root
- The last command should have printed a link that you can click on or copy-paste into a web browser to navigate to the Jupyter Lab session.
Modin:
To run the tutorial, do the following:
git clone https://github.com/modin-project/modin.git
cd modin
pip install virtualenv
virtualenv modin-tutorial
source ./modin-tutorial/bin/activate
cd examples/tutorial/
pip install -r requirements.txt
jupyter lab
Autopandas:
Link to repo : https://github.com/rbavishi/gauss-rise-camp
NBSafety:
The NBSafety tutorial + instructions are available at this GitHub repo.
MC2 :
You can find the tutorial with instructions here: https://github.com/mc2-project/risecamp.
ERDOS
To run the tutorial on your own machine, we require Docker and recommend a good GPU in order to run the simulations. Follow the following instructions to run the image:
docker pull erdosproject/pylot:risecamp
Nvidia-docker run -itd –name pylot_risecamp_20 -p 80:8080 erdosproject/pylot:risecamp /bin/bash
And then navigate to localhost to see the jupyter notebook!
AdaHessian and PyHessian:
You can find the tutorial with instructions here: https://github.com/yaozhewei/PyHessian_AdaHessian_RiseCamp
Lux:
You can find the tutorial instructions here: https://github.com/lux-org/lux-binder/
The Github repo contains instructions on how to set up the tutorial locally using Docker.
You can also try out the tutorial by accessing a live notebook at this Binder link: https://mybinder.org/v2/gh/lux-org/lux-binder/master?urlpath=tree/exercise