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Add results table #24

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Add results table
Dec 7, 2020
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Update with latest results
Feb 25, 2021
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4 changes: 3 additions & 1 deletion _layouts/page.html
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---

<div class="page">
<h1 class="page-title">{{ page.title }}</h1>
{% if page.notitle != true %}
<h1 class="page-title">{{ page.title }}</h1>
{% endif %}
{{ content }}
</div>
7 changes: 4 additions & 3 deletions index.md
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<h1>MLBench: Distributed Machine Learning Benchmark</h1>

<hr>
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<a style="display: inline-block" href="https://travis-ci.com/mlbench/mlbench-benchmarks"><img src="https://travis-ci.com/mlbench/mlbench-benchmarks.svg?branch=develop"></a>
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7 changes: 7 additions & 0 deletions public/css/hyde.css
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Expand Up @@ -248,3 +248,10 @@ a.sidebar-nav-item:focus {
.theme-base-0f .related-posts li a:hover {
color: #8f5536;
}


table.wide {
max-width: 200%;
width: 200%;
font-size: 15px;
}
39 changes: 39 additions & 0 deletions results/index.md
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---
layout: page
notitle: true
title: Results
datatable: true
---

# MLBench Official Training Results
---

<p>
Here we present the official training results obtained by running the benchmark tasks.
It provides a reference and comparison point for different implementations.
</p>


<p>
The table below provides a comparison table for results. Times are in seconds. The baseline represents the 1 worker case, and we
only present the results for the best obtained speedups.

For more detailed results for each run, please check the documentation.

</p>

---

|Task | Dataset | Model | Aggregation scheme | Metric Name | Metric Goal | Framework | Baseline | Best Speedup | Best Speedup Workers | Description | Implementation | Results |
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instead of best speedup, let's only put the speedup at the max (or reasonable, fixed) number of workers, so 16?
then you can have 2 columns, one for total speedup (most important), and if you want a second one for compute only speedup. (and a column to say that #workers = 16)

and Bleu scores can have a decimal for clarity. 24.0 and 25.0 (minor nitpick :))

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if other experiments have a different max # workers that's ok too

|:-------------------:|:--------:|:---------------------:|:------------------:|:----------------------:|:-----------:|:------------:|:--------------:|:--------------:|:--------------------:|:-----------:|:--------------:|:-------:|
| Image Recognition | CIFAR10 | ResNet20 | All-Reduce | Validation Accuracy | 80% | Torch 1.7.0 | 171.15s | 6.6 (total), 11.9 (compute) | 16 | [details](https://mlbench.readthedocs.io/en/latest/benchmark-tasks.html#task-1-image-classification)| [code](https://github.com/mlbench/mlbench-benchmarks/tree/develop/pytorch/imagerecognition/cifar10-resnet20-all-reduce) | [results](https://mlbench.readthedocs.io/en/latest/benchmark-tasks.html#id17) |
| | | | PyTorch DDP | | | Torch 1.7.0 | 182.81s | 4.4 | 8 | [details](https://mlbench.readthedocs.io/en/latest/benchmark-tasks.html#task-1-image-classification)| [code](https://github.com/mlbench/mlbench-benchmarks/tree/develop/pytorch/imagerecognition/cifar10-resnet20-distributed-data-parallel) | |
|---------------------+----------+-----------------------+--------------------+------------------------+-------------+--------------+----------------+----------------+----------------------+-------------+---------|
| Language Modelling | Wikitext2| AWD-LSTM | All-Reduce | Validation Perplexity | 70 | Torch 1.7.0 | 87,401.33s | | | [details](https://mlbench.readthedocs.io/en/latest/benchmark-tasks.html#task-3-language-modelling) | [code]() | |
| Language Modelling | | BERT
|---------------------+----------+-----------------------+--------------------+------------------------+-------------+--------------+----------------+----------------+----------------------+----------------+---------|
| Machine Translation | WMT16 | LSTM (GNMT) | All-Reduce | Validation BLEU-score | 24 | Torch 1.7.0 | 65,206.62s | 2.8 (total), 15.8 (compute) | 16 | [details](https://mlbench.readthedocs.io/en/latest/benchmark-tasks.html#a-lstm-wmt16-en-de) | [code](https://github.com/mlbench/mlbench-benchmarks/tree/develop/pytorch/nlp/translation/wmt16-gnmt-all-reduce) | [results](https://mlbench.readthedocs.io/en/latest/benchmark-tasks.html#id30) |
|---------------------+----------+-----------------------+--------------------+------------------------+-------------+--------------+----------------+----------------+----------------------+----------------+---------|
| Machine Translation | WMT17 | Transformer | All-Reduce | Validation BLEU-score | 25 | Torch 1.7.0 | 37,594.21s | 3.0 (total), 18.3 (compute) | 16 | [details](https://mlbench.readthedocs.io/en/latest/benchmark-tasks.html#b-transformer-wmt17-en-de) | [code](https://github.com/mlbench/mlbench-benchmarks/tree/develop/pytorch/nlp/translation/wmt17-transformer-all-reduce) | [results](https://mlbench.readthedocs.io/en/latest/benchmark-tasks.html#id32) |
{:.wide}