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[submodule "NVIDIA/benchmarks/transformer/implementations/pytorch/cutlass/tools/external/googletest"]
path = NVIDIA/benchmarks/transformer/implementations/pytorch/cutlass/tools/external/googletest
url = https://github.com/google/googletest.git
1 change: 1 addition & 0 deletions Alibaba/README.md
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Alibaba Sinian ResNet50 submission
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# Contributing guidelines

## Pull Request Checklist

Before sending your pull requests, make sure you followed this list.

- Read [contributing guidelines](CONTRIBUTING.md)
- Ensure you have signed the [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
- (Note: additional technical details TBD by community.)

## How to become a contributor and submit your own code

### Contributor License Agreements

We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles.

Please fill out either the individual or corporate Contributor License Agreement (CLA).

* If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](https://code.google.com/legal/individual-cla-v1.0.html).
* If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](https://code.google.com/legal/corporate-cla-v1.0.html).

Follow either of the two links above to access the appropriate CLA and instructions for how to sign and return it. Once we receive it, we'll be able to accept your pull requests.

***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository. (Note: we need to modify this to allow third party code under Apache2 or MIT license with additional review.)

### Contributing code

If you have improvements to MLPerf, send us your pull requests! For those
just getting started, Github has a [howto](https://help.github.com/articles/using-pull-requests/).

### Contribution guidelines and standards

(Note: Technical details TBD by community.)

#### General guidelines and philosophy for contribution

(Note: Technical details TBD by community.)

#### License

Include a license at the top of new files.

* [C/C++ license example](https://github.com/mlperf/policies/blob/master/license_example_DRAFT.cpp)
* [Python license example](https://github.com/mlperf/policies/blob/master/license_example_DRAFT.py)

#### C++ coding style

(Note: Technical details TBD by community.)

#### Python coding style

(Note: Technical details TBD by community.)

#### Running sanity check

(Note: Technical details TBD by community.)

#### Running unit tests

(Note: Technical details TBD by community.)
201 changes: 201 additions & 0 deletions Alibaba/code/LICENSE.md
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Apache License
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# MLPerf Alibaba-Optimized Implementations

This is a repository of Alibaba software and hardware co-optimized implementations for the MLPerf benchmark, along with reference implementations, forked from https://github.com/mlperf/training.git .

# Preliminary release (v0.6)

This release is very much an "alpha" release -- it could be improved in many ways. The benchmark suite is still being developed and refined, see the Suggestions section below to learn how to contribute.

# Contents

We provide reference implementations for each of the 7 benchmarks in the MLPerf suite.

* resnet - Resnet-50 v1.5 applied to Imagenet.

# Running Benchmarks

These benchmarks have been tested on the following machine configuration:

* Alibaba AI computing cluster
* AliOS7

# Suggestions
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# 1. Problem
Which benchmark is implemented, e.g. image classification
# 2. Directions
### Steps to configure machine
Ideally, a list of command lines
### Steps to download and verify data
Ideally, a list of command lines
### Steps to run and time
Ideally, a list of command lines
# 3. Dataset/Environment
### Publication/Attribution
Cite paper describing dataset plus any additional attribution requested by dataset authors
### Data preprocessing
What preprocessing is done to the the dataset?
### Training and test data separation
How is the test set extracted?
### Training data order
In what order is the training data traversed?
### Test data order
In what order is the test data traversed?
### Simulation environment (RL models only)
Describe simulation environment briefly, if applicable.
# 4. Model
### Publication/Attribution
Cite paper describing model plus any additional attribution requested by code authors
### List of layers
Brief summary of structure of model
### Weight and bias initialization
How are weights and biases initialized
### Loss function
Name/description of loss function used
### Optimizer
Name of optimizer used
# 5. Quality
### Quality metric
What is the target quality metric
### Quality target
What is the numeric quality target
### Evaluation frequency
How many training items between quality evaluations (typically all, evaluated every epoch)
### Evaluation thoroughness
How many test items per quality evaluation (typically all)
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The following table shows reference results, to be used to normalized benchmark results.
**These results are NOT for optimized code and do NOT measure framework or hardware performance.**
Individual seed results are provided only to show variance and should not be used for normalization.

Benchmark|Reference Result|Seed 1|Seed 2|Seed 3|Seed 4|Seed 5
---|---|---|---|---|---|---
Image classification|529877|530571|529438|530373|523480|529877
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