Transform 2020

The full repo of code and notebooks will be published ahead on the event here. You'll just need to check back and pull the latest before the event.

In the meantime there are a few things to read and a draft conda environment that you can get setup and maybe iron out any issues ahead of time.

What you'll need

This is the tutorial's maiden voyage. It was developed mostly on macos and then moved across to unbuntu with minimum fuss and has been test driven on those two platforms. It has not been tested in windows although it should work if you have a functioning anaconda environment.

What to expect during the tutorial

Ray and it's libraries are meant for implementing scalable ML, that means big problems, big dataset an/or big models or does it?!

Ray is beautiful in that is speeds up small things too, and let's you start small and scale later with minimal code change.

My overall aim for the tutorial is toget people excited about Ray and what it can do, and hopefully give you the feeling that using ray and following its patterns is a good way to start any ML project, and will give you superpowers as your project matures.

We have got 3 hours for the tutorial, so I have chosen small and simple problems. Logistics and limited experience in presenting this as a tutorial means that while everyone shold be able to trun parts of this locally, some parts of this, like multi-gpu training or launching remote clusters may be a matter of watching/following along.

In order to be prepared I suggest:

• Have a working anaconda environment, i.e. download and install anaconda
• Everyone have a go at environment setup, detailed in the next section
• If you have a nvidia gpu on your system make sure it shows up in nvidia-smi
• If you have an AWS account up and running:
  • make sure your local machine is setup with your access credentials link
  • identify an AWS region e.g. Ohio where you have the ability to run instances and make a note of it's code e.g. us-east-2
• If you don't have an AWS account, consider setting one up.
• During the tutorial I aim to run a small cluster of C5.xlarge instances which are $0.19/hr so AWS cost won't be huge
• UPDATE!! when experimenting I have seen that the cluster setup time fir time around is easily 10 mins, so if may be that you want to statr this off and then jsut watch alone
• I'll also be demonstrating multi-GPU on an instance in the cloud, and if you have access (check your service quotas) to p2.8xlarge on your AWS accounts you can try that out at $7-8/hr but I'll not be taking people though getting jupyter up and running from an EC2 instance. So you would be on your own, maybe just sit back, watch and run for yourself later.

Environment Setup

There is an extra step in configuring your local environment in order to get the Ray Dashboard setup. If you cannot get this working for whatever reason it is not the end of the world, but it is nice to have and use so let's try.

Do this, this before setting up the conda environment

1. First install Node.js either:

   1. using nvm link or v14.40.0
   2. by downloading the latest version and installing manually link

2. Install and build dashboard from source

   git clone https://github.com/ray-project/ray.git
   
   pushd ray/python/ray/dashboard/client
   npm ci
   npm run build
   popd
   

3. Build the conda environment:

   pushd transform-2020-ray
   conda env create -f environment.yml
   

If this doesn't work for you and you want to forgo the Dashboard install locally then just install the conda environment as per normal

cd transform-2020-ray
conda env create -f environment.yml

Gotchas

• if you are on a machine without a GPU, don't worry you'll still be able to run some of the notebooks. However, you might need to comment out the - cudatoolkit >= 10.0 line from environment.yml

Smoke Test

1. After you have your environment, activate it

   conda activate t20-fri-ray
   

2. Run the jupyter notebook (or lab if you prefer)

   jupyter notebook
   

3. In jupyter, open pinch_me.ipynb and run all cells. if all is ok everything should load.

Gotchas

• XGBoost Errors on run

  XGBoostError: XGBoost Library (libxgboost.dylib) could not be loaded.
   Likely causes:
     * OpenMP runtime is not installed (vcomp140.dll or libgomp-1.dll for Windows, libomp.dylib for Mac OSX, libgomp.so for Linux and other UNIX-like OSes). Mac OSX users: Run `brew install libomp` to install OpenMP runtime.
     * You are running 32-bit Python on a 64-bit OS
  

• install on macos brew install libomp

• install on Ubuntu https://askubuntu.com/questions/144352/how-can-i-install-openmp-in-ubuntu