FamousDirector / HowToDeploymentNotebooksForTMLS2022

This repository was created to add code examples to be used alongside the workshop "Bringing an AI system from proof of concept to deployment" presented at Toronto Machine Learning Summit 2022 by James Cameron.

Notebooks on how to Deploy AI/ML systems to production

This repository was created to add code examples to be used alongside the workshop "Bringing an AI system from proof of concept to deployment" presented at Toronto Machine Learning Summit 2022 by James Cameron. Slides and notes can be found here

Disclaimer

This repository is meant to serve as a starting point for new projects. Always to be used only as a reference.

System Requirements

• 4 core CPU (min 3.0 GHz)
• 8 GB System Memory
• Nvidia GPU w/ CUDA Compute Capability >=7.0
• 8GB Memory minimum

Installation

Setting up the environment

We need to install docker and docker-compose. Please follow the instructions from here (Docker) and here (Docker Compose).

Next we need to install the Nvidia Container Toolkit. Please follow the instructions from here.

Set the nvidia Docker runtime as default:

sudo tee /etc/docker/daemon.json <<EOF
{
  "default-runtime": "nvidia",
  "runtimes": {
    "nvidia": {
      "path": "/usr/bin/nvidia-container-runtime",
      "runtimeArgs": []
    }
  }
}
EOF

Restart Docker:

sudo service docker restart

Then test the installation:

docker run nvcr.io/nvidia/cuda:11.0.3-base-ubuntu20.04 nvidia-smi

You should see a similar output:

Tue Mar 29 12:38:23 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.81       Driver Version: 472.39       CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA RTX A3000    Off  | 00000000:01:00.0 Off |                  N/A |
| 25%   46C    P8    12W / 100W |    319MiB /  6144MiB |    18%       Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Running the project

To start the services, run the following command:

bash run.sh

Accessing the Notebooks

Navigate to http://localhost:8884/lab

Montioring the services

Prometheus and Grafana have been configured to monitor the services. Go to the dashboard to see some example GPU usage metrics.

References

• Grafana documentation
• Prometheus documentation
• Gradio documentation