| Name | pylzy JSON |
| Version |
1.14.0
JSON |
| download |
| home_page | |
| Summary | |
| upload_time | 2023-09-20 14:05:52 |
| maintainer | |
| docs_url | None |
| author | ʎzy developers |
| requires_python | >=3.8 |
| license | Apache-2.0 |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://pypi.org/project/pylzy/)
[](https://github.com/lambda-zy/lzy/actions/workflows/pull-tests.yaml)
[]()
[](https://github.com/lambdazy/lzy/tree/master/pylzy/tests)
[](https://pypi.org/project/pylzy/)
[](https://t.me/+ad3OA-J96b9jYWJi)
# ʎzy
ʎzy is a platform for a hybrid execution of ML workflows that transparently integrates local and remote runtimes
with the following properties:
- Python-native SDK
- Automatic env (pip/conda) sync
- K8s-native runtime
- Resources allocation on-demand
- Env-independent results storage
## Quick start
ʎzy allows running any python functions on a cluster by annotating them with `@op` decorator:
```python
@op(gpu_count=1, gpu_type=GpuType.V100.name)
def train(data_set: Bunch) -> CatBoostClassifier:
cb_model = CatBoostClassifier(iterations=1000, task_type="GPU", devices='0:1', train_dir='/tmp/catboost')
cb_model.fit(data_set.data, data_set.target, verbose=True)
return cb_model
# local python function call
model = train(data_set)
# remote call on a cluster
lzy = Lzy()
with lzy.workflow("training"):
model = train(data_set)
```
Please read the [tutorial](https://github.com/lambdazy/lzy/tree/master/docs/tutorials/0-contents.md) for details.
## Runtime
Check out our [key concepts](https://github.com/lambdazy/lzy/tree/master/docs/arch/key-concepts.md) and [architecture intro](https://github.com/lambdazy/lzy/tree/master/docs/arch/intro_en.md).
## Community
Join our chat [on telegram](https://t.me/+ad3OA-J96b9jYWJi)!
## Development
Development [guide](https://github.com/lambdazy/lzy/tree/master/docs/development.md).
## Deployment
Deployment guide.
* [YCloud](https://github.com/lambdazy/lzy/tree/master/docs/deployment_ycloud.md)
Raw data
{
"_id": null,
"home_page": "",
"name": "pylzy",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "",
"keywords": "",
"author": "\u028ezy developers",
"author_email": "",
"download_url": "https://files.pythonhosted.org/packages/02/ed/073501627c6e0162217792ce86182bdb71a8493ba16e80c43c60f89e5283/pylzy-1.14.0.tar.gz",
"platform": null,
"description": "[](https://pypi.org/project/pylzy/)\n[](https://github.com/lambda-zy/lzy/actions/workflows/pull-tests.yaml)\n[]()\n[](https://github.com/lambdazy/lzy/tree/master/pylzy/tests)\n[](https://pypi.org/project/pylzy/)\n[](https://t.me/+ad3OA-J96b9jYWJi)\n\n# \u028ezy\n\n\u028ezy is a platform for a hybrid execution of ML workflows that transparently integrates local and remote runtimes\nwith the following properties:\n\n- Python-native SDK\n- Automatic env (pip/conda) sync\n- K8s-native runtime\n- Resources allocation on-demand\n- Env-independent results storage\n\n## Quick start\n\n\u028ezy allows running any python functions on a cluster by annotating them with `@op` decorator:\n\n```python\n@op(gpu_count=1, gpu_type=GpuType.V100.name)\ndef train(data_set: Bunch) -> CatBoostClassifier:\n cb_model = CatBoostClassifier(iterations=1000, task_type=\"GPU\", devices='0:1', train_dir='/tmp/catboost')\n cb_model.fit(data_set.data, data_set.target, verbose=True)\n return cb_model\n\n\n# local python function call\nmodel = train(data_set)\n\n# remote call on a cluster\nlzy = Lzy()\nwith lzy.workflow(\"training\"):\n model = train(data_set)\n```\n\nPlease read the [tutorial](https://github.com/lambdazy/lzy/tree/master/docs/tutorials/0-contents.md) for details.\n\n## Runtime\n\nCheck out our [key concepts](https://github.com/lambdazy/lzy/tree/master/docs/arch/key-concepts.md) and [architecture intro](https://github.com/lambdazy/lzy/tree/master/docs/arch/intro_en.md).\n\n## Community\n\nJoin our chat [on telegram](https://t.me/+ad3OA-J96b9jYWJi)!\n\n## Development\n\nDevelopment [guide](https://github.com/lambdazy/lzy/tree/master/docs/development.md).\n\n## Deployment\n\nDeployment guide.\n\n* [YCloud](https://github.com/lambdazy/lzy/tree/master/docs/deployment_ycloud.md)\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "",
"version": "1.14.0",
"project_urls": null,
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "85bddc0704f146f95cb3c9c80b906ebb8f0d9284ffe956ac591e591404451da8",
"md5": "49ccc65bc6f137503241fa5d6867a6f5",
"sha256": "439a17e0cc4d1fa82f8593ca1e7ed1e747f1cde12fcd88b110fe0e9dad1233da"
},
"downloads": -1,
"filename": "pylzy-1.14.0-py2.py3-none-any.whl",
"has_sig": false,
"md5_digest": "49ccc65bc6f137503241fa5d6867a6f5",
"packagetype": "bdist_wheel",
"python_version": "py2.py3",
"requires_python": ">=3.8",
"size": 121171,
"upload_time": "2023-09-20T14:05:49",
"upload_time_iso_8601": "2023-09-20T14:05:49.781947Z",
"url": "https://files.pythonhosted.org/packages/85/bd/dc0704f146f95cb3c9c80b906ebb8f0d9284ffe956ac591e591404451da8/pylzy-1.14.0-py2.py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "02ed073501627c6e0162217792ce86182bdb71a8493ba16e80c43c60f89e5283",
"md5": "ffdb9ee4610dfa2e5cbb1860e672ae78",
"sha256": "4664b08ac785c668bb68d15e612b79f343fbe23d22f36786d52229c2b023348f"
},
"downloads": -1,
"filename": "pylzy-1.14.0.tar.gz",
"has_sig": false,
"md5_digest": "ffdb9ee4610dfa2e5cbb1860e672ae78",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 84898,
"upload_time": "2023-09-20T14:05:52",
"upload_time_iso_8601": "2023-09-20T14:05:52.088386Z",
"url": "https://files.pythonhosted.org/packages/02/ed/073501627c6e0162217792ce86182bdb71a8493ba16e80c43c60f89e5283/pylzy-1.14.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-09-20 14:05:52",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "pylzy"
}
