https://github.com/ivotron/mlperf-workflows
Popperized MLPerf benchmark workflows
https://github.com/ivotron/mlperf-workflows
benchmarks mlperf popper reproducible-workflows
Last synced: 6 months ago
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Popperized MLPerf benchmark workflows
- Host: GitHub
- URL: https://github.com/ivotron/mlperf-workflows
- Owner: ivotron
- Created: 2020-01-16T04:18:50.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T02:37:06.000Z (almost 3 years ago)
- Last Synced: 2025-02-11T12:19:02.075Z (8 months ago)
- Topics: benchmarks, mlperf, popper, reproducible-workflows
- Language: Python
- Homepage:
- Size: 2.26 MB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
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README
# 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](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/#creating-a-virtual-environment).
## 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.