okik 0.0.341

A Python package to serve python functions, classes, or .py files on a local server or cloud-based environment.

About

Okik is a command-line interface (CLI) that allows users to run various inference services such as LLM, RAG(WIP), or anything in between using various frameworks on any *cloud. With Okik, you can easily run these services directly on any cloud without the hassle of managing your own infra.

Installation

Using pip

pip install okik

Or To install Okik, follow these steps:

1. Clone the repository: git clone https://github.com/okikorg/okik.git
2. Navigate to the project directory: cd okik
3. Install Okik using pip: pip install .

Quick Start

To run Okik, simply execute the following command in your terminal: okik

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Simplify. Deploy. Scale.
Type 'okik --help' for more commands.

Initialise the project

okik init

Quick Example

Write this in your main.py file:

from okik.endpoints import service, endpoint, app
import asyncio
from typing import Any
from sentence_transformers import SentenceTransformer
import sentence_transformers
from torch.nn.functional import cosine_similarity as cosine
import torch
import random

# your service configuration
@service(
    replicas=1,
    resources={"accelerator": {"type": "A40", "device": "cuda", "count": 1, "memory": 4}},
    backend="okik" # <- provisioning backend is okik
)
class Embedder:
    def __init__(self):
        self.model = SentenceTransformer("paraphrase-MiniLM-L6-v2", cache_folder=".okik/cache")

    @endpoint()
    def embed(self, sentence: str):
        logits = self.model.encode(sentence)
        return logits

    @endpoint()
    def similarity(self, sentence1: str, sentence2: str):
        logits1 = self.model.encode(sentence1, convert_to_tensor=True)
        logits2 = self.model.encode(sentence2, convert_to_tensor=True)
        return cosine(logits1.unsqueeze(0), logits2.unsqueeze(0))

    @endpoint()
    def version(self):
        return sentence_transformers.__version__

    @endpoint(stream=True)
    async def stream_data(self) -> Any:
        async def data_generator():
            for i in range(10):
                yield f"data: {i}\n"
                await asyncio.sleep(1)
        return data_generator()

# Mock LLM Service Example
@service(replicas=1)
class MockLLM:
    def __init__(self):
        pass

    @endpoint(stream=True) # <- streaming response enabled for use cases like chatbot
    async def stream_random_words(self, prompt: str = "Hello"):
        async def word_generator():
            words = ["hello", "world", "fastapi", "stream", "test", "random", "words", "python", "async", "response"]
            for _ in range(10):
                word = random.choice(words)
                yield f"{word}\n"
                await asyncio.sleep(0.4)
        return word_generator()

Verify the routes

# run the okik routes to check all available routes
okik routes

# output should be similar to this
main.py Application Routes
├── <HOST>/health/
│   └── /health | GET
├── <HOST>/embedder/
│   ├── /embedder/embed | POST
│   ├── /embedder/similarity | POST
│   ├── /embedder/stream_data | POST
│   └── /embedder/version | POST
└── <HOST>/mockllm/
    └── /mockllm/stream_random_words | POST

Serving the app

# run the okik run to start the server in production mode
okik server
# or run in dev mode
okik server --dev --reload
#or
okik server -d -r

Test the app

curl -X POST http://0.0.0.0:3000/embedder/version
# or if you like to use httpie then
http POST 0.0.0.0:3000/embedder/version

# or test the stream endpoint
curl -X POST http://0.0.0.0:3000/mockllm/stream_random_words -d '{"prompt": "Hello"}'
# or if you like to use httpie then
http POST 0.0.0.0:3000/mockllm/stream_random_words prompt="hello" --stream

Build the app

okik build -a "your_awesome_app" -t latest

Deploy the app

okik deploy

Monitor the app

# similar to kubectl commands, infact you can use kubectl commands as well
okik get deployments # for deployments
okik get services # for services

Delete the app

okik delete deployment "your_awesome_app"

Status

Okik is currently in development so expect sharp edges and bugs. Feel free to contribute to the project by submitting a pull request.

Roadmap

• [] Add support for various inference engines such as vLLM, TGI, etc.
• [] Add support for various cloud providers such as AWS, GCP, Azure, etc.