Streamlining machine learning tracking for seamless experiment management.
Project description
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:
- install hatch globally, e.g. with pipx, i.e.
pipx install hatch
, - optionally run
hatch config set dirs.env.virtual .direnv
to let VS Code find your virtual environments, - make sure
pre-commit
is installed globally, e.g. withpipx install pre-commit
, - 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.
Project details
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