Magicpoint is an open source model packaging tool developed by 1019. You can use Magicpoint to package and deploy any model to 1019, from open-source models on Hugging Face 🤗 and Github to your own proprietary models. Magicpoint python package has 3 main functionality:
  • Initialize
  • Task
  • Background Task
If you need more information about Magicpoint, read the documentation.

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Initalize your Application

Initialize runs once when pod is ready. Init process is required for handling the inference or training applications. 
from magicpoint import Magicpoint, Request, Response
import transformers 
app = Magicpoint("MyCoolApp 😎")


@app.init
def init():
    model = transformers.AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
    context = {
        model: model
    }

    return context

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Define your Task

Task is an endpoint that you can run via HTTP request or Magicpoint Client SDK. There is an difference between task and background task: Task: It’s syncronous process that your http request can wait until the end. Background Task: It’s asyncronous process that these type of endpoints returns an background task ID to track details. 
@app.background_task("/generate_async")
def generate_image_async(context, request):
    prompt = request.json["prompt"]
    torch.backends.cuda.matmul.allow_tf32 = True
    with torch.inference_mode():
        with torch.autocast("cuda"):
            image = context.get('model')(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

    print(f"Saved Image: {image}")
    image.save("output.png")
Task: 
@app.task("/generate")
def generate_image(context, request):
    prompt = request.json["prompt"]
    torch.backends.cuda.matmul.allow_tf32 = True
    with torch.inference_mode():
        with torch.autocast("cuda"):
            image = context.get('model')(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

    print(f"Saved Image: {image}")
    image.save("output.png")
    return {
        'output': image
    }