micado/optimizer repository overview

⁠MiCADO - Scaling Optimizer with Machine Learning Support

⁠Don't forget to set environment

From project root run source activate optimizer

⁠Test program

From project root run python pure.py

⁠Test flask

From project root run python helloMTA.py

⁠Start program

From project root run python optimizer.py --cfg path/to_config_file

python optimizer.py --cfg config/config.yaml

python optimizer.py --cfg config/config.yaml --host=192.168.0.60

python optimizer.py --cfg config/config.yaml --host 0.0.0.0 --port 5000

⁠Test REST API

GET /optimizer/hello Test REST API. curl -X GET http://193.224.59.115:5000/optimizer/hello

curl -X GET http://193.224.59.115:5000/

POST /optimizer/init Initialize optimizer with the neccessary constants. curl -X POST http://127.0.0.1:5000/optimizer/init --data-binary @test_files/optimizer_constants.yaml

curl -X POST http://193.224.59.115:5000/optimizer/init --data-binary @test_files/optimizer_constants.yaml

POST /optimizer/sample Send a new training sample. curl -X POST http://127.0.0.1:5000/optimizer/sample --data-binary @test_files/metrics_sample_example.yaml

curl -X POST http://193.224.59.115:5000/optimizer/sample --data-binary @test_files/metrics_sample_example.yaml

curl -X POST http://193.224.59.115:5000/optimizer/sample --data-binary @test_files/metrics_sample_example_up.yaml

curl -X POST http://193.224.59.115:5000/optimizer/sample --data-binary @test_files/metrics_sample_example_down.yaml

GET /optimizer/advice Get scaling advice. curl -X GET http://127.0.0.1:5000/optimizer/advice

curl -X GET http://193.224.59.115:5000/optimizer/advice

curl -X GET http://193.224.59.115:5000/optimizer/advice?last=False

GET /optimizer/training_data Download zipped training data that contains both neural network and linear regression data. curl -X GET http://127.0.0.1:5000/optimizer/training_data

⁠TEST CSV

cd csv/csv_to_optimizer source env/csv_to_optimizer/bin/activate python csv_to_optimizer.py