| Name | leptonai JSON |
| Version |
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| Summary | Lepton AI Platform |
| upload_time | 2025-02-13 01:31:59 |
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| requires_python | <3.13,>=3.8 |
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<img src="https://raw.githubusercontent.com/leptonai/leptonai/main/assets/logo.svg" height=100>
# Lepton AI
**A Pythonic framework to simplify AI service building**
<a href="https://lepton.ai/">Homepage</a> •
<a href="https://dashboard.lepton.ai/playground">API Playground</a> •
<a href="https://github.com/leptonai/examples">Examples</a> •
<a href="https://lepton.ai/docs/">Documentation</a> •
<a href="https://lepton.ai/references">CLI References</a> •
<a href="https://twitter.com/leptonai">Twitter</a> •
<a href="https://leptonai.medium.com/">Blog</a>
The LeptonAI Python library allows you to build an AI service from Python code with ease. Key features include:
- A Pythonic abstraction `Photon`, allowing you to convert research and modeling code into a service with a few lines of code.
- Simple abstractions to launch models like those on [HuggingFace](https://huggingface.co) in few lines of code.
- Prebuilt examples for common models such as Llama, SDXL, Whisper, and others.
- AI tailored batteries included such as autobatching, background jobs, etc.
- A client to automatically call your service like native Python functions.
- Pythonic configuration specs to be readily shipped in a cloud environment.
## Getting started with one-liner
Install the library with:
```shell
pip install -U leptonai
```
This installs the `leptonai` Python library, as well as the commandline interface `lep`. You can then launch a HuggingFace model, say `gpt2`, in one line of code:
```python
lep photon runlocal --name gpt2 --model hf:gpt2
```
If you have access to the Llama2 model ([apply for access here](https://huggingface.co/meta-llama/Llama-2-7b)) and you have a reasonably sized GPU, you can launch it with:
```python
# hint: you can also write `-n` and `-m` for short
lep photon runlocal -n llama2 -m hf:meta-llama/Llama-2-7b-chat-hf
```
(Be sure to use the `-hf` version for Llama2, which is compatible with huggingface pipelines.)
You can then access the service with:
```python
from leptonai.client import Client, local
c = Client(local(port=8080))
# Use the following to print the doc
print(c.run.__doc__)
print(c.run(inputs="I enjoy walking with my cute dog"))
```
Fully managed Llama2 models and CodeLlama models can be found in the [playground](https://dashboard.lepton.ai/playground).
Many standard HuggingFace pipelines are supported - find out more details in the [documentation](https://www.lepton.ai/docs/advanced/prebuilt_photons#hugging-face-photons). Not all HuggingFace models are supported though, as many of them contain custom code and are not standard pipelines. If you find a popular model you would like to support, please [open an issue or a PR](https://github.com/leptonai/leptonai/issues/new).
## Checking out more examples
You can find out more examples from the [examples repository](https://github.com/leptonai/examples). For example, launch the Stable Diffusion XL model with:
```shell
git clone git@github.com:leptonai/examples.git
cd examples
```
```python
lep photon runlocal -n sdxl -m advanced/sdxl/sdxl.py
```
Once the service is running, you can access it with:
```python
from leptonai.client import Client, local
c = Client(local(port=8080))
img_content = c.run(prompt="a cat launching rocket", seed=1234)
with open("cat.png", "wb") as fid:
fid.write(img_content)
```
or access the mounted Gradio UI at [http://localhost:8080/ui](http://localhost:8080/ui). Check the [README file](https://github.com/leptonai/examples/blob/main/advanced/sdxl/README.md) for more details.
A fully managed SDXL is hosted at [https://dashboard.lepton.ai/playground/sdxl](https://dashboard.lepton.ai/playground/sdxl) with API access.
## Writing your own photons
Writing your own photon is simple: write a Python Photon class and decorate functions with `@Photon.handler`. As long as your input and output are JSON serializable, you are good to go. For example, the following code launches a simple echo service:
```python
# my_photon.py
from leptonai.photon import Photon
class Echo(Photon):
@Photon.handler
def echo(self, inputs: str) -> str:
"""
A simple example to return the original input.
"""
return inputs
```
You can then launch the service with:
```shell
lep photon runlocal -n echo -m my_photon.py
```
Then, you can use your service as follows:
```python
from leptonai.client import Client, local
c = Client(local(port=8080))
# will print available paths
print(c.paths())
# will print the doc for c.echo. You can also use `c.echo?` in Jupyter.
print(c.echo.__doc__)
# will actually call echo.
c.echo(inputs="hello world")
```
For more details, checkout the [documentation](https://lepton.ai/docs/) and the [examples](https://github.com/leptonai/examples).
## Contributing
Contributions and collaborations are welcome and highly appreciated. Please check out the [contributor guide](https://github.com/leptonai/leptonai/blob/main/CONTRIBUTING.md) for how to get involved.
## License
The Lepton AI Python library is released under the Apache 2.0 license.
Developer Note: early development of LeptonAI was in a separate mono-repo, which is why you may see commits from the `leptonai/lepton` repo. We intend to use this open source repo as the source of truth going forward.
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"description": "<img src=\"https://raw.githubusercontent.com/leptonai/leptonai/main/assets/logo.svg\" height=100>\n\n# Lepton AI\n\n**A Pythonic framework to simplify AI service building**\n\n<a href=\"https://lepton.ai/\">Homepage</a> \u2022\n<a href=\"https://dashboard.lepton.ai/playground\">API Playground</a> \u2022\n<a href=\"https://github.com/leptonai/examples\">Examples</a> \u2022\n<a href=\"https://lepton.ai/docs/\">Documentation</a> \u2022\n<a href=\"https://lepton.ai/references\">CLI References</a> \u2022\n<a href=\"https://twitter.com/leptonai\">Twitter</a> \u2022\n<a href=\"https://leptonai.medium.com/\">Blog</a>\n\nThe LeptonAI Python library allows you to build an AI service from Python code with ease. Key features include:\n\n- A Pythonic abstraction `Photon`, allowing you to convert research and modeling code into a service with a few lines of code.\n- Simple abstractions to launch models like those on [HuggingFace](https://huggingface.co) in few lines of code.\n- Prebuilt examples for common models such as Llama, SDXL, Whisper, and others.\n- AI tailored batteries included such as autobatching, background jobs, etc.\n- A client to automatically call your service like native Python functions.\n- Pythonic configuration specs to be readily shipped in a cloud environment.\n\n## Getting started with one-liner\nInstall the library with:\n\n```shell\npip install -U leptonai\n```\nThis installs the `leptonai` Python library, as well as the commandline interface `lep`. You can then launch a HuggingFace model, say `gpt2`, in one line of code:\n\n```python\nlep photon runlocal --name gpt2 --model hf:gpt2\n```\n\nIf you have access to the Llama2 model ([apply for access here](https://huggingface.co/meta-llama/Llama-2-7b)) and you have a reasonably sized GPU, you can launch it with:\n\n```python\n# hint: you can also write `-n` and `-m` for short\nlep photon runlocal -n llama2 -m hf:meta-llama/Llama-2-7b-chat-hf\n```\n\n(Be sure to use the `-hf` version for Llama2, which is compatible with huggingface pipelines.)\n\nYou can then access the service with:\n\n```python\nfrom leptonai.client import Client, local\nc = Client(local(port=8080))\n# Use the following to print the doc\nprint(c.run.__doc__)\nprint(c.run(inputs=\"I enjoy walking with my cute dog\"))\n```\n\nFully managed Llama2 models and CodeLlama models can be found in the [playground](https://dashboard.lepton.ai/playground).\n\nMany standard HuggingFace pipelines are supported - find out more details in the [documentation](https://www.lepton.ai/docs/advanced/prebuilt_photons#hugging-face-photons). Not all HuggingFace models are supported though, as many of them contain custom code and are not standard pipelines. If you find a popular model you would like to support, please [open an issue or a PR](https://github.com/leptonai/leptonai/issues/new).\n\n## Checking out more examples\n\nYou can find out more examples from the [examples repository](https://github.com/leptonai/examples). For example, launch the Stable Diffusion XL model with:\n\n```shell\ngit clone git@github.com:leptonai/examples.git\ncd examples\n```\n\n```python\nlep photon runlocal -n sdxl -m advanced/sdxl/sdxl.py\n```\n\nOnce the service is running, you can access it with:\n \n```python\nfrom leptonai.client import Client, local\n\nc = Client(local(port=8080))\n\nimg_content = c.run(prompt=\"a cat launching rocket\", seed=1234)\nwith open(\"cat.png\", \"wb\") as fid:\n fid.write(img_content)\n```\n\nor access the mounted Gradio UI at [http://localhost:8080/ui](http://localhost:8080/ui). Check the [README file](https://github.com/leptonai/examples/blob/main/advanced/sdxl/README.md) for more details.\n\nA fully managed SDXL is hosted at [https://dashboard.lepton.ai/playground/sdxl](https://dashboard.lepton.ai/playground/sdxl) with API access.\n\n## Writing your own photons\n\nWriting your own photon is simple: write a Python Photon class and decorate functions with `@Photon.handler`. As long as your input and output are JSON serializable, you are good to go. For example, the following code launches a simple echo service:\n\n```python\n# my_photon.py\nfrom leptonai.photon import Photon\n\nclass Echo(Photon):\n @Photon.handler\n def echo(self, inputs: str) -> str:\n \"\"\"\n A simple example to return the original input.\n \"\"\"\n return inputs\n```\n\nYou can then launch the service with:\n\n```shell\nlep photon runlocal -n echo -m my_photon.py\n```\n\nThen, you can use your service as follows:\n```python\nfrom leptonai.client import Client, local\n\nc = Client(local(port=8080))\n\n# will print available paths\nprint(c.paths())\n# will print the doc for c.echo. You can also use `c.echo?` in Jupyter.\nprint(c.echo.__doc__)\n# will actually call echo.\nc.echo(inputs=\"hello world\")\n```\n\nFor more details, checkout the [documentation](https://lepton.ai/docs/) and the [examples](https://github.com/leptonai/examples).\n\n## Contributing\n\nContributions and collaborations are welcome and highly appreciated. Please check out the [contributor guide](https://github.com/leptonai/leptonai/blob/main/CONTRIBUTING.md) for how to get involved.\n\n## License\n\nThe Lepton AI Python library is released under the Apache 2.0 license.\n\nDeveloper Note: early development of LeptonAI was in a separate mono-repo, which is why you may see commits from the `leptonai/lepton` repo. We intend to use this open source repo as the source of truth going forward.\n",
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