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benchmark_readme_template.md

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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)