README.md

NeuroFlow MLOps

Overview

• Model Serving Platform for seamless demonstration of models from multiple ML projects.

Technologies Used

• Nvidia Triton Inference Server - GPU Inference
  • For more details, visit the Nvidia Triton Documentation.
• FastAPI - API serving
• MongoDB - Advanced Data/Log Management

Expected Effects

• A model serving platform that can be utilized in any project.
• Enables ML engineers without MLOps knowledge to deploy ML models immediately.
• Based on Docker, enabling easy deployment of models from TensorFlow, PyTorch, ONNX, etc.

How to Run

Set Up Triton Config (.pbtxt)

name: "wine_prediction_model"
platform: "pytorch_libtorch"
input [
  {
    name: "wine_data__0"
    data_type: TYPE_FP32
    dims: [ -1, 77 ]
  },
  {
    name: "climate_data__1"
    data_type: TYPE_FP32
    dims: [ -1, 7, 22 ]
  }
]
output [
  {
    name: "output__0"
    data_type: TYPE_FP32
    dims: [ 1 ]  
  }
]

Pull Docker and Run

docker pull nvcr.io/nvidia/tritonserver:21.10-py3

docker run --gpus all --rm \
-p8000:8000 -p8001:8001 -p8002:8002 \
-v /home/ubuntu/playground/neuroflow_models:/models\ 
nvcr.io/nvidia/tritonserver:20.08-py3 \
tritonserver --model-repository=/models

Development Notes

• Export Model using torch.jit.trace
• Adhere to the naming convention - NAME__INDEX format
• Beware of Input/Ouput Dimentions writing .pbtxt configs.