Name | modelz-py JSON |
Version |
0.3.3
JSON |
| download |
home_page | |
Summary | machine learning models |
upload_time | 2023-07-14 05:42:17 |
maintainer | |
docs_url | None |
author | |
requires_python | >=3.7 |
license | Apache-2.0 |
keywords |
machine learning
deep learning
|
VCS |
|
bugtrack_url |
|
requirements |
No requirements were recorded.
|
Travis-CI |
No Travis.
|
coveralls test coverage |
No coveralls.
|
# Modelz Python SDK
```shell
pip install modelz-py
```
## CLI
```shell
modelz --help
```
### Stable Diffusion
```shell
echo "cute cat" | modelz inference $PROJECT --serde msgpack --write-file cat.jpg --read-stdin
```
## Gradio Client on Modelz Endpoints
We provide a lightweight Python library that makes it very easy to use any Gradio app served on modelz as an API. The functionalities of `GradioClient` are completely identical to `Client` in `gradio_client` library provided by Gradio. The only difference is that when initializing the client, you should enter your Modelz serving endpoint URL instead of a Hugging Face space.
### Example Usage
```python
from modelz import GradioClient as Client
# Parameter here is the endpoint of your Modelz deployment
# The format is like https://${DEPOLOYMENT_KEY}.modelz.io/
cli = Client("https://translator-th85ze61tj4n3klc.modelz.io/")
cli.view_api()
# >> Client.predict() Usage Info
# ---------------------------
# Named API endpoints: 1
# - predict(text, api_name="/predict") -> output
# Parameters:
# - [Textbox] text: str
# Returns:
# - [Textbox] output: str
cli.predict("hallo", api_name="/predict")
# >> "Bonjour."
```
Raw data
{
"_id": null,
"home_page": "",
"name": "modelz-py",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "machine learning,deep learning",
"author": "",
"author_email": "TensorChord <modelz-support@tensorchord.ai>",
"download_url": "https://files.pythonhosted.org/packages/10/25/1bf603c22c0bceeb03fdcebfeb5fe57db3ae2453edd41304b13ad8b44675/modelz-py-0.3.3.tar.gz",
"platform": null,
"description": "# Modelz Python SDK\n\n```shell\npip install modelz-py\n```\n\n## CLI\n\n```shell\nmodelz --help\n```\n\n### Stable Diffusion\n\n```shell\necho \"cute cat\" | modelz inference $PROJECT --serde msgpack --write-file cat.jpg --read-stdin\n```\n\n\n\n## Gradio Client on Modelz Endpoints\n\nWe provide a lightweight Python library that makes it very easy to use any Gradio app served on modelz as an API. The functionalities of `GradioClient` are completely identical to `Client` in `gradio_client` library provided by Gradio. The only difference is that when initializing the client, you should enter your Modelz serving endpoint URL instead of a Hugging Face space.\n\n### Example Usage\n\n```python\nfrom modelz import GradioClient as Client\n\n# Parameter here is the endpoint of your Modelz deployment\n# The format is like https://${DEPOLOYMENT_KEY}.modelz.io/\ncli = Client(\"https://translator-th85ze61tj4n3klc.modelz.io/\")\n\ncli.view_api() \n# >> Client.predict() Usage Info\n# ---------------------------\n# Named API endpoints: 1\n\n# - predict(text, api_name=\"/predict\") -> output\n# Parameters:\n# - [Textbox] text: str \n# Returns:\n# - [Textbox] output: str \n\n \ncli.predict(\"hallo\", api_name=\"/predict\")\n# >> \"Bonjour.\"\n\n\n```\n\n\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "machine learning models",
"version": "0.3.3",
"project_urls": null,
"split_keywords": [
"machine learning",
"deep learning"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "eb518ef0ffe40cc0c179602f818144880c92e25987b827cd8cf157116fef90c5",
"md5": "0eb4c0958762ef35df4a44607b7d731c",
"sha256": "b1b580a7bd6194f5ac0cef920b82cebad563aee289cc53663e889a05a27ac304"
},
"downloads": -1,
"filename": "modelz_py-0.3.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "0eb4c0958762ef35df4a44607b7d731c",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 11317,
"upload_time": "2023-07-14T05:42:16",
"upload_time_iso_8601": "2023-07-14T05:42:16.502799Z",
"url": "https://files.pythonhosted.org/packages/eb/51/8ef0ffe40cc0c179602f818144880c92e25987b827cd8cf157116fef90c5/modelz_py-0.3.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "10251bf603c22c0bceeb03fdcebfeb5fe57db3ae2453edd41304b13ad8b44675",
"md5": "69ccc51f18c5ccba19cf8f01a93ba2dc",
"sha256": "a8c7988e30af5b3c41543e3367e2c79662e75552de48844bf000d068bb0bd33d"
},
"downloads": -1,
"filename": "modelz-py-0.3.3.tar.gz",
"has_sig": false,
"md5_digest": "69ccc51f18c5ccba19cf8f01a93ba2dc",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 10239,
"upload_time": "2023-07-14T05:42:17",
"upload_time_iso_8601": "2023-07-14T05:42:17.919212Z",
"url": "https://files.pythonhosted.org/packages/10/25/1bf603c22c0bceeb03fdcebfeb5fe57db3ae2453edd41304b13ad8b44675/modelz-py-0.3.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-07-14 05:42:17",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "modelz-py"
}