RAI Client

The RAI client is an executable downloaded by the students and runs on the students' machines. The executable requires no library dependencies and works on all the main operating systems and CPU architectures. Both features reduce the likelihood that students will have technical difficulties running the client. Students 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):

Operating System	Architecture	Stable Version Link
Linux	i386	URL
Linux	amd64	URL
Linux	armv5	URL
Linux	armv6	URL
Linux	armv7	URL
Linux	arm64	URL
Linux	ppc64	URL
Linux	ppc64le	URL
OSX/Darwin	i386	URL
OSX/Darwin	amd64	URL
Windows	i386	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.

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: nvidia/cuda:8.0-devel-ubuntu16.04 # nvidia/cuda:8.0-devel-ubuntu16.04 is a docker image 
                                  # and can be viewed at https://hub.docker.com/r/nvidia/cuda/
                         				  # You can specify any image found on dockerhub
resources:
  gpus: 1 # tell the system that you're using a gpu
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.

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.

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

License

NCSA/UIUC © Abdul Dakkak