| Name | octoflow JSON |
| Version |
0.0.45
JSON |
| download |
| home_page | None |
| Summary | Streamlining machine learning tracking for seamless experiment management. |
| upload_time | 2024-04-30 15:58:29 |
| 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
Raw data
{
"_id": null,
"home_page": null,
"name": "octoflow",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "machine-learning, tracking",
"author": null,
"author_email": "Yasas Senarath <email@example.com>",
"download_url": "https://files.pythonhosted.org/packages/27/f7/81acb35eccca2076ac6c3debcd967e1cf40030a1a1a285628f960f1381f7/octoflow-0.0.45.tar.gz",
"platform": null,
"description": "# OctoFlow\n\nStreamlining machine learning tracking for seamless experiment management.\n\n## Features\n\n* Feature 1\n* Feature 2\n* ...\n\n## Development\n\nTo set up [hatch] and [pre-commit] for the first time:\n\n1. install [hatch] globally, e.g. with [pipx], i.e. `pipx install hatch`,\n2. optionally run `hatch config set dirs.env.virtual .direnv` to let [VS Code] find your virtual environments,\n3. make sure `pre-commit` is installed globally, e.g. with `pipx install pre-commit`,\n4. run `pre-commit install` to install [pre-commit].\n\nA special feature that makes hatch very different from other familiar tools is that you almost never\nactivate, or enter, an environment. Instead, you use `hatch run env_name:command` and the `default` environment\nis assumed for a command if there is no colon found. Thus you must always define your environment in a declarative\nway and hatch makes sure that the environment reflects your declaration by updating it whenever you issue\na `hatch run ...`. This helps with reproducability and avoids forgetting to specify dependencies since the\nhatch workflow is to specify everything directly in [pyproject.toml](pyproject.toml). Only in rare cases, you\nwill use `hatch shell` to enter the `default` environment, which is similar to what you may know from other tools.\n\nTo get you started, use `hatch run cov` or `hatch run no-cov` to run the unitest with or without coverage reports,\nrespectively. Use `hatch run lint:all` to run all kinds of typing and linting checks. Try to automatically fix linting\nproblems with `hatch run lint:fix` and use `hatch run docs:serve` to build and serve your documentation.\nYou can also easily define your own environments and commands. Check out the environment setup of hatch\nin [pyproject.toml](pyproject.toml) for more commands as well as the package, build and tool configuration.\n\n## Credits\n\nThis package was created with [The Hatchlor] project template.\n\n[The Hatchlor]: https://github.com/florianwilhelm/the-hatchlor\n[pipx]: https://pypa.github.io/pipx/\n[hatch]: https://hatch.pypa.io/\n[pre-commit]: https://pre-commit.com/\n[VS Code]: https://code.visualstudio.com/docs/python/environments#_where-the-extension-looks-for-environments\n",
"bugtrack_url": null,
"license": null,
"summary": "Streamlining machine learning tracking for seamless experiment management.",
"version": "0.0.45",
"project_urls": {
"Documentation": "https://github.com/ysenarath/octoflow",
"Source": "https://github.com/ysenarath/octoflow"
},
"split_keywords": [
"machine-learning",
" tracking"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "73b00f9e9d3b00e8cf0ac9360ffbe22599b4ebfe72ecdb5e307e17a6165e2400",
"md5": "0fcf23ead44f5cddcd4f62e970ec419a",
"sha256": "37070a7005e49c2d6d4053fed969ec22162ef6c8ee1c5e390a144eb8b65d200b"
},
"downloads": -1,
"filename": "octoflow-0.0.45-py3-none-any.whl",
"has_sig": false,
"md5_digest": "0fcf23ead44f5cddcd4f62e970ec419a",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 41068,
"upload_time": "2024-04-30T15:58:27",
"upload_time_iso_8601": "2024-04-30T15:58:27.509651Z",
"url": "https://files.pythonhosted.org/packages/73/b0/0f9e9d3b00e8cf0ac9360ffbe22599b4ebfe72ecdb5e307e17a6165e2400/octoflow-0.0.45-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "27f781acb35eccca2076ac6c3debcd967e1cf40030a1a1a285628f960f1381f7",
"md5": "8cb32dbcb0baf78b57144fa85380c1af",
"sha256": "5dac9b1f6e6de487b6b5199f5bdc321e7c012bb364f3c6d5dd8d3d1ce1540cc8"
},
"downloads": -1,
"filename": "octoflow-0.0.45.tar.gz",
"has_sig": false,
"md5_digest": "8cb32dbcb0baf78b57144fa85380c1af",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 33385,
"upload_time": "2024-04-30T15:58:29",
"upload_time_iso_8601": "2024-04-30T15:58:29.452815Z",
"url": "https://files.pythonhosted.org/packages/27/f7/81acb35eccca2076ac6c3debcd967e1cf40030a1a1a285628f960f1381f7/octoflow-0.0.45.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-30 15:58:29",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "ysenarath",
"github_project": "octoflow",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "octoflow"
}
