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MLPerf @ Backend.AI

A repo for the current MLperf implementation at Backend.AI. This repo is created for tracking the progress and issues related with implementing MLPerf on Backend.AI

Current implementations

  1. Object Detection - Mask R-CNN with a ResNet50 - IN PROGRESS
  2. Translation - GNMT - TO DO

Detailed information on each repo is for listed at each subdirectory.

Setup

  1. In the task directory, download the data using bash download_dataset.sh script. The time to download the dataset varies, depending on the task.
  2. Run bash verify_dataset.sh script if there is any to check for the dataset integrity (optional).
  3. Since running docker or nvidia-docker command is not possible in Backend.AI console due to concerns listed here. Therefore, you should create a separate Dockerfile featuring the requirements from the image listed in the problem's Dockerfile, and edit it so that it will be used as an image for Backend.AI platform. A good example would be to refer to Backend.AI kernels here. Please keep in mind that you should add labels specific to Backend.AI:
LABEL ai.backend.kernelspec="1" \
      ai.backend.envs.corecount="OPENBLAS_NUM_THREADS,OMP_NUM_THREADS,NPROC" \
      ai.backend.features="batch query uid-match user-input" \
      ai.backend.base-distro="ubuntu16.04" \
      ai.backend.resource.min.cpu="1" \
      ai.backend.resource.min.mem="1g" \
      ai.backend.runtime-type="python" \
      ai.backend.runtime-path="/usr/local/bin/python" \
      ai.backend.service-ports="ipython:pty:3000,jupyter:http:8080,jupyterlab:http:8090"

Currently, I have added a add_label.py python script to update labels in the Dockerfile, but it needs further testing and improvements.

The command to run the script is python add_label.py <name/path to current Dockerfile> <new Dockerfile>.

  1. Ideally, you should be able to run bash run_and_time.sh script, but most likely, the script contents need an overhaul as well. TO DO

Pushing the image to Backend.AI

  1. Build the image in docker using docker build command.
  2. Login through Docker to Harbor (https://beta.docker.backend.ai) using docker login -u username SERVER command.
  3. Tag the image as the beta.docker.backend.ai/mlperf/<your-image-name>.
  4. Push the image using docker push <image tag> command.

TO DOs

  1. Remake the Docker images to adapt them to Backend.AI platform - TOP PRIORITY
  2. Automate the conversion of Docker images to Backend.AI platform