| Name | octoflow JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | Streamlining machine learning tracking for seamless experiment management. |
| upload_time | 2024-07-22 21:25:51 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
machine-learning
tracking
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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](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](pyproject.toml) for more commands as well as the package, build and tool configuration.
## Credits
This package was created with [The Hatchlor] project template.
[The Hatchlor]: https://github.com/florianwilhelm/the-hatchlor
[pipx]: https://pypa.github.io/pipx/
[hatch]: https://hatch.pypa.io/
[pre-commit]: https://pre-commit.com/
[VS Code]: https://code.visualstudio.com/docs/python/environments#_where-the-extension-looks-for-environments
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