ivotron / mlperf-workflows

Popperized MLPerf benchmark workflows

mlperf-benchmarks

Steps to configure the machine

1. Clone the repository.

git clone https://github.com/blkswanio/mlperf-benchmarks

2. Install docker, cuda-runtime and nvidia-docker on the machine.

cd mlperf-benchmarks/
./install_cuda_docker.sh

3. Install the popper tool.

pip install popper

You can also install it in a virtualenv.

Running the benchmarks

Object Detection

cd object_detection/
popper run -f main.yml -c settings.py

Sentiment Analysis

cd sentiment_analysis/
popper run -f main.yml -c settings.py

Single Stage Detector

cd single_stage_detector/
popper run -f main.yml -c settings.py

RNN translator

cd rnn_translator/
popper run -f main.yml -c settings.py

Translation

cd translation/
popper run -f main.yml -c settings.py

Benchmark output

results/
|----- ssd/
|      |--result_1.txt
|      |--result_2.txt
|      |--result_3.txt
|      |--result_4.txt
|      |--result_5.txt
|----- maskrcnn/
|----- gnmt/
|----- transformer/
|----- systems/
       |--system_details.json

On running the benchmarks, a directory structure like the one above would be generated in the root of the repository.