HYPR.AI is a cloud-based deep learning and hyperparameter optimization platform. It contains a master service that serves this web-based UI and handles user submissions through a RESTful API. It also contains a worker service (which can be spawned multiple times) for training deep learning models on cloud-based services like AWS or Paperspace.

Ryan's running example is currently at hypr.umx.io.

• Use predefined JSON file to define deep learning network architetures
• Support modular models to reduce the complexity of building similar models
• Pretrained model can be loaded into extended network architectures
• Training instances (dockerized container) can be deployed anywhere
• In a "hypr task", the user specifies hyperparameters to explore, creating multiple trainings (currently using random search)

• Python 3
• Docker 17.03 or above
• AWS Access key with fullaccess of S3 and DynamoDB
• Nvidia GPU (training instance)

Two kinds of services need to be deployed:

A. RESTful service (only need one)
B. Training instances (not limited, the more the better)

It is highly recommended to run RESTful service on AWS which will have short latency on accessing DynamoDB and S3, but it still can be deployed to your local machine (make sure that machine can be accessed through internet otherwise the logs come from training instance will be lost).

The recommended EC2 instance is at least: t2.xlarge or m4.xlarge

1. Setup: Both the RESTful service and the training instance require Docker: Here is the tutorial for installing Docker on Ubuntu:
   https://docs.docker.com/engine/installation/linux/docker-ce/ubuntu/

2. Clone hypr-ai to where you want to deploy the RESTful service:

   git clone https://github.com/rreece/hypr-ai.git

3. Change the Monitor Service and Docker Image of Worker in hypr-ai/settings.py:

   DOCKER = {
       'IMAGE': 'insight/tworker',
       'VERSION': 'latest'
   }
   MONITOR = {
       'HOST': 'http://[YOUR IP or DOMAIN where running RESTful service]',
       'PATH': '/monitor'
   }

4. Build the service docker image

   docker build -t insight/kservice -f Dockerfile.service .
   

5. Start the service

   ./start_restful_docker_service.sh

The training instance requires docker, same as the machine with the RESTful service, above. The training instance can be depolyed to anywhere as long it has an Nvidia GPU, running Linux, and docker. It is not necessary to keep the training instance running all the time. You can add tasks to system first, then start one or more training instances to run these tasks.

1. Setup: The training instance requires docker, same as the : Nvidia driver, CUDA8.0 and nvidia-docker. The training instance require: Nvidia driver, CUDA8.0 and nvidia-docker.

   Example bash script for GPU ready (Ubuntu):

   # install nvidia driver and cuda
   sudo add-apt-repository ppa:graphics-drivers/ppa -y
   sudo apt update
   sudo apt install nvidia-375 -y
   wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb
   sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb
   sudo apt-get update
   sudo apt-get install cuda -y
   # Install nvidia-docker
   wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
   sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb
   # Test nvidia-docker
   sudo nvidia-docker run --rm nvidia/cuda nvidia-smi

2. Clone hypr-ai to where you want to deploy the training instance:

   git clone https://github.com/rreece/hypr-ai.git

3. Build the worker docker image:

   docker build -t insight/tworker -f Dockerfile.worker .
   

4. Export Environment variable in training instance (or add to your ~/.bashrc:

   export AWS_ACCESS_KEY_ID={ACCESS_KEY}
   export AWS_SECRET_ACCESS_KEY={SECRET_KEY}
   export AWS_DEFAULT_REGION={REGION}

5. Start the Agent service on the training instance:

   ./start_worker_docker_service.sh

Access the system through:

http://[YOUR IP or DOMAIN where running RESTful service]

Set the AWS credentials by clicking the setting on the left:

AWS regions:

Dockerfile.service      Docker file for RESTful service
Dockerfile.worker       Docker file for Training instance
cli.sh                  bash script redirect to cli.py
- package               Python package main folder
    - insight
        - __init__.py
        - agent.py
        - applications
            - AgentService.py       Agent service entrance
            - RESTservice.py        RESTfull service entrace
            - cli.py                A command-line based client
            - settings.py           general settings
            - worker.py             Worker entity run inside docker
        - builder.py                Model builder
        - optimizer.py              Generator for hyperparameter optimization
        - storage.py                Manipulate AWS DynamoDB and S3
        - web                       All Web related code
            REST_server.py          Back-end, RESTful service
            - static                Front-end
                css
                fonts
                images
                index.html          Main html
                js
    setup.py
    - test
        service_test.py
        unit_test.py
run_agentservice.sh               Shortcut to start agent service
run_restservice.sh                Shortcut to start RESTful service
run_worker.sh                     Docker.worker entry point
settings.py                       Setting file refer by docker container
start_restful_docker_service.sh   bash script of starting docker container to run RESTful service
start_worker_docker_service.sh    bash script of starting docker container to run worker service

• Ryan Reece ryan.reece@cern.ch
• Jiaming "Jimmy" Hu https://github.com/JMHOO/planet-insight