octoflow 0.1.6

Streamlining machine learning tracking for seamless experiment management.

OctoFlow

Streamlining machine learning tracking for seamless experiment management.

Features

• Feature 1
• Feature 2
• ...

Development

To set up hatch and pre-commit for the first time:

1. install hatch globally, e.g. with pipx, i.e. pipx install hatch,
2. optionally run hatch config set dirs.env.virtual .direnv to let VS Code find your virtual environments,
3. make sure pre-commit is installed globally, e.g. with pipx install pre-commit,
4. run pre-commit install to install pre-commit.

A special feature that makes hatch very different from other familiar tools is that you almost never activate, or enter, an environment. Instead, you use hatch run env_name:command and the default environment is assumed for a command if there is no colon found. Thus you must always define your environment in a declarative way and hatch makes sure that the environment reflects your declaration by updating it whenever you issue a hatch run .... This helps with reproducability and avoids forgetting to specify dependencies since the hatch workflow is to specify everything directly in pyproject.toml. Only in rare cases, you will use hatch shell to enter the default environment, which is similar to what you may know from other tools.

To get you started, use hatch run cov or hatch run no-cov to run the unitest with or without coverage reports, respectively. Use hatch run lint:all to run all kinds of typing and linting checks. Try to automatically fix linting problems with hatch run lint:fix and use hatch run docs:serve to build and serve your documentation. You can also easily define your own environments and commands. Check out the environment setup of hatch in pyproject.toml for more commands as well as the package, build and tool configuration.

Credits

This package was created with The Hatchlor project template.