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README.md

logo file

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AWS-foryou

AWS-foryou helps you decide which AWS instance to use for your machine learning project. Input your algorithm, dataset, and your choice of constraints, be it time or budget, and AWS-foryou will help you decide which instance best suits your needs.

Project Organization

AWS-foryou/
  |- README.md
  |- awsforyou/
    |- __init__.py
    |- algo_runner.py
    |- aws_metadata.py
    |- aws_pricing.py
    |- benchmark_runner.py
    |- recommender.py
    |- report_generator.py
    |- total_time_component.py
    | - ui/
      | - template.html
    |- tests/
      |- __init__.py
      |- test_algo_runner.py
      |- test_aws_metadata.py
      |- test_aws_pricing.py
      |- test_benchmark_runner.py
      |- test_keras_mnist.py
      |- test_reccomender.py
      |- test_report_generator.py
      |- test_total_time_compoment.py
  |- data/
    |- aws-scorecard.csv
  |- docs/
    |- component-specification.md
    |- functional-specification.md
  |-examples/
    |-demo,py
    |-examples.ipynb
    |-sklearn_diabetes.py
    |-x_diabetes.csv
    |-y_diabetes.csv
  |- setup.py
  |- requirements.txt
  |- LICENSE

Installation

Clone the repo and create a virtual environment in the root of the repo

python -m venv venv
source venv/bin/activate

If you're using Anaconda, create and activate a new conda environment. For conda run

conda create --name awsforyou
conda activate awsforyou

Install the dependencies from the requirements.txt file using

python -m pip install -r requirements.txt

If you don't have setuptools and wheel install them using

python -m pip install --upgrade setuptools wheel

Install the package using the following command

python setup.py sdist bdist_wheel

This will generate the pip installation package awsforyou-0.0.1-py3-none-any.whl in the dist/ directory. The package awsforyou can now be installed using

pip install awsforyou-0.0.1-py3-none-any.whl

Usage

To see how to use the package to get instance recommendation, refer to the example notebook


The Output

Below is an image of how the output of our recommendation looks like. sample_reccommendation

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