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The RAI client allows one to interact with a cluster of machine to submit and evaluate code. RAI is a scalable job submission system designed for diverse workloads. RAI’s design addresses challenges of scalability, configurability, security, and cost in delivering a flexible programming environments.
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README.md

RAI Client Travis Build Status AppVeyor Build status

The RAI client is an executable downloaded by the users and runs on the user' machines. The executable requires no library dependencies and works on all the main operating systems and CPU architectures. Both features reduce the likelihood that users will have technical difficulties running the client. Users use the RAI client to interact with a distributed elastic system to submit jobs.

Download Binaries

The code is continuously built and published. The client can be downloaded from the following URLs (depending on your OS and Architecture). There are two types of binaries one for classes and one for general use. If you are using RAI for a class, then you need to use the class binary.

Classes Binaries

Operating System Architecture Stable Version Link
Linux amd64 URL
Linux armv5 URL
Linux armv6 URL
Linux armv7 URL
Linux arm64 URL
Linux ppc64 URL
Linux ppc64le URL
OSX/Darwin amd64 URL
Windows amd64 URL

General Use Binaries

Operating System Architecture Stable Version Link
Linux amd64 URL
Linux armv5 URL
Linux armv6 URL
Linux armv7 URL
Linux arm64 URL
Linux ppc64 URL
Linux ppc64le URL
OSX/Darwin amd64 URL
Windows amd64 URL

Building From Source

This is not recommended unless you are interested in developing and/or deploying rai on your personal cluster. To build from source simple run

go get -u github.com/rai-project/rai

You will need an extra secret key if you build from source.

  • Create a .rai_config.yml in the rai directory. You can copy the existing rai_config.yml as a starting point.
  • Run rai with go run -tags develop main.go -d -v -s <app-secret> -p <project-folder>

Alternatively, you can place the app secret in ~/.rai_secret and just do

go run -tags debug main.go -d -v -p <project-folder>

The -tags debug casuses rai to read the local configuration instead of using an embedded one.

Usage

To run the client, use

rai -p <project folder>

From a user's point a view when the client runs, the local directory specified by -p gets uploaded to the server and extracted into the /src directory on the server. The server then executes the build commands from the rai_build.yml specification within the /build directory. Once the commands have been run, or there is an error, a zipped version of that /build directory is available from the server for download.

The server limits the task time to be an hour with a maximum of 8GB of memory being used within a session. The output /build directory is only available to be downloaded from the server for a short amount of time. Networking is also disabled on the execution server. Contact the teaching assistants if this is an issue.

Other Options

  -c, --color         Toggle color output.
  -d, --debug         Toggle debug mode.
  -p, --path string   Path to the directory you wish to submit. Defaults to the current working directory. (default "current working directory")
  -v, --verbose       Toggle verbose mode.

On Windows, it might be useful to disable the colored output. You can do that by using the -c=false option

Setting your Profile

Each student will be contacted by a TA and given a secret key to use this service. Do not share your key with other users. The secret key is used to authenticate you with the server.

The RAI_SECRET_KEY, RAI_TEAM_NAME, and RAI_ACCESS_KEY should be specified in your ~/.rai_profile (Linux/OSX) or %HOME%/.rai_profile (Windows -- for me this is C:\Users\abduld\.rai_profile) in the following way.

profile:
  firstname: Abdul
  lastname: Dakkak
  username: abduld
  email: dakkak@illinois.edu
  access_key: XXXXXXXXXXXXXXXXXXX
  secret_key: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

Project Build Specification

The rai_build.yml must exist in your project directory. In some cases, you may not be able to execute certain builtin bash commands, in this scenario the current workaround is to create a bash file and insert the commands you need to run. You can then execute the bash script within rai_build.yml.

The rai_build.yml is written as a Yaml (Spec) file and has the following structure.

rai:
  version: 0.2 # this is required
  image:
    nimbix/ubuntu-cuda-ppc64le:latest # nimbix/ubuntu-cuda-ppc64le:latest is a docker image
    # You can specify any image found on dockerhub
resources:
  cpu:
    architecture: ppc64le
  gpu:
    architecture: pascal
    count: 1 # tell the system that you're using a gpu
  network: false
commands:
  build:
    - echo "Building project"
    # Use CMake to generate the build files. Remember that your directory gets uploaded to /src
    - cmake /src
    # Run the make file to compile the project.
    - make
    # here we break the long command into multiple lines. The Yaml
    # format supports this using a block-strip command. See
    # http://stackoverflow.com/a/21699210/3543720 for info
    - >-
      ./mybinary -i input1,input2 -o output

Syntax errors will be reported, and the job will not be executed. You can check if your file is in a valid yaml format by using tools such as Yaml Validator.

Building Docker Images

Most of the images on Docker Hub are compiled for X86 architectures. If you are using PPC64le, Power 8 architecture, e.g. Minsky, then you will have to build your Docker image from scratch. RAI has support for building Docker images on the host system.

  1. Create your Dockerfile we have created some example files that can be used as base and/or inspiration: CUDNN, OpenCV, CUMF, NCCL, ... Refer to Docker Syntax Reference to understand the Docker commands.

  2. Tell the RAI client that you want to build a Dockerfile. This can be done by modifying the .rai-build.yml file to include the following:

commands:
  build_image:
  image_name: your_user_name/your_image_name:your_image_version # example dakkak/cudnn:6.0
  dockerfile: "./Dockerfile" # the location of the Dockerfile on your local file system
  build: ...
  1. Run rai as if you are submitting the project. RAI will build and use the image you've specified.

A repository containing prebuilt Dockerfiles for PPC64le is available Here and Here and we accept contributions and/or fixes.

Disabling Caching

By default, rai will not rebuild a docker image if it has the same name as a preexisting image on the system. You can disable that by changing the nocache option to true in the rai_build.yml file.

rai:
  version: 0.2
resources:
  cpu:
    architecture: ppc64le
  network: false
commands:
  build_image:
    image_name: rai/cumf:8.0
    dockerfile: "./Dockerfile"
    no_cache: true

Publishing Docker Images

Docker images built using rai can be published on DockerHub. You will have to explicitly tell rai to push the image in the rai_build file

rai:
  version: 0.2
commands:
  build_image:
    image_name: c3sr/celery:4.0.2
    dockerfile: "./Dockerfile"
    push:
      push: true

Specifying DockerHub Credentials

There are two ways of specifying the DockerHub credentials. Through the ~/.rai_profile file (prefered) by adding a dockerhub section e.g.

profile:
  firstname: Abdul
  lastname: Dakkak
  ...
  dockerhub:
    username: dakkak
    password: ==AES32==PASS

or by placing it in the rai_build.yml file

rai:
  version: 0.2
commands:
  build_image:
    image_name: c3sr/celery:4.0.2
    dockerfile: "./Dockerfile"
    push:
      push: true
      credentials:
        username: dakkak
        password: ==AES32==PASS

The password can be encrypted using the rai encrypt command.

CUDA Profiling

Profiling can be performed using nvprof. Place the following build commands in your rai_build.yml file

- >-
  nvprof --cpu-profiling on --export-profile timeline.nvprof --
  ./mybinary -i input1,input2 -o output
- >-
  nvprof --cpu-profiling on --export-profile analysis.nvprof --analysis-metrics --
  ./mybinary -i input1,input2 -o output

You could change the input and test datasets. This will output two files timeline.nvprof and analysis.nvprof which can be viewed using the nvvp tool (by performing a file>import). You will have to install the nvvp viewer on your machine to view these files.

NOTE: nvvp will only show performance metrics for GPU invocations, so it may not show any analysis when you only have serial code.

Benchmark

export GOTRACEBACK=all
go build -tags=bench
./rai bench --concurrency_count=10 --iteration_count=100 -s <<SECRET>> -p ./_fixtures/cuda_runtime |& panicparse

Reporting Issues

Please use the Github issue manager to report any issues or suggestions.

Include the outputs of

rai version

as well as the output of

rai buildtime

In your bug report. You can also invoke the rai command with verbose and debug outputs using

rai --verbose --debug

Usage

Publications

Papers

Posters

  • GTC 2017

Presentations

GTC 2017

{::nomarkdown}

<iframe src="https://docs.google.com/presentation/d/1dnn0yJ_VJdJ108gv8TDMcuIoP6eUtoQMkGJ-zRvGZl0/embed?start=true&loop=false&delayms=3000" frameborder="0" width="960" height="569" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe>

{:/}

License

NCSA/UIUC © Abdul Dakkak

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