dat1-cli 0.3.0

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dat1-cli

A command line interface for the dat1 platform.

Installation

pip install dat1-cli

Usage

Initialize with your API key:

dat1 login

To initialize a new model project, run in the root directory of your project:

dat1 init

This will create a dat1.yaml file in the root directory of your project. This file contains the configuration for your model:

model_name: <your model name>
exclude:
  - '**/.git/**'
  - '**/.idea/**'
  - '*.md'
  - '*.jpg'
  - .dat1.yaml
  - .DS_Store

Exclude uses glob patterns to exclude files from being uploaded to the platform.

To upload your model to the platform:

dat1 deploy

A good starting point for your model is using one of the example models.

Otherwise, the platform expects a handler.py file in the root directory of your project that contains a FastAPI app with two endpoints: GET / for healthchecks and POST /infer for inference. An example handler is shown below:

from fastapi import Request, FastAPI
from vllm import LLM, SamplingParams
import os

# Model initialization Code
# This code should be placed before the FastAPI app is initialized

llm = LLM(model=os.path.expanduser('./'), load_format="safetensors", enforce_eager=True)

app = FastAPI()

@app.get("/")
async def root():
    return "OK"

@app.post("/infer")
async def infer(request: Request):
    # Inference Code
    request = await request.json()
    prompts = request["prompt"]
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
    outputs = llm.generate(prompts, sampling_params)
    return { "response" : outputs[0].outputs[0].text }

Streaming Responses with Server-Sent Events

To stream responses to the client, you can use Server-Sent Events (SSE). To specify that the response should be streamed, you need to add response_type: sse to the model definition in the dat1.yaml file.

model_name: chat_completion
response_type: sse
exclude:
  - '**/.git/**'
  - '**/.idea/**'
  - '*.md'
  - '*.jpg'
  - .dat1.yaml

The handler code should be modified to return a generator that yields the responses:

from fastapi import Request, FastAPI
from sse_starlette.sse import EventSourceResponse
import json

app = FastAPI()

@app.get("/")
async def root():
    return "OK"

async def response_generator():
    for i in range(10):
        yield json.dumps({"response": f"Response {i}"})  

@app.post("/infer")
async def infer(request: Request):
    return EventSourceResponse(response_generator(), sep="\n")

Launching Locally

Pre-requisites

• Docker
• CUDA-compatible GPU
• NVIDIA Container Toolkit

To launch your model locally, run:

dat1 serve

License

MIT