Building thousands of models with timeseries data to monitor systems.
Gordo fulfills the role of inhaling config files and supplying components to the pipeline of:
- Fetching data
- Training model
- Serving model
See our example notebooks for how to develop with gordo locally.
pip install --upgrade gordo
Bleeding edge:
pip install git+https://github.com/equinor/gordo.git
pip uninstall gordo
This section will explain how to start development of Gordo.
- install requirements
# create and activate virtualenv. Note: you should use python3.7 (project's tensorflow version is not compatible with python3.8)
# then:
pip install --upgrade pip
pip install --upgrade pip-tools
pip install -r requirements/full_requirements.txt
pip install -r requirements/test_requirements.txtTo run tests it's required for your system to has (note: commands might differ from your OS):
- running docker process;
- available 5432 port for postgres container.
List of commands to run tests can be found here.
Running of tests takes some time, so it's faster to run tests in parallel:
# example
pytest tests/gordo/client/test_client.py --ignore benchmarks --cov-report= --no-cov -n auto -m 'not dockertest'
# or if you have multiple python versions and they're not resolved properly:
python3.7 -m pytest ... Note: this example is for Pycharm IDE to use breakpoints in the code of the tests.
On the configuration setup for test running add to Additional arguments: in pytest section following string: --ignore benchmarks --cov-report= --no-cov