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Nvidia - Tensorflow

  1. Nvidia Docs Tensorflow Image pull 
    docker pull nvcr.io/nvidia/tensorflow:23.06-tf2-py3
  2. Test succesful image pull on Docker >= 19.03: 
    docker run --gpus all -it --rm nvcr.io/nvidia/tensorflow:<xx.xx>-tf<x>-py<x>

Writing custom server to Pytorch infering

  1. Create server.py script and place endpoints of the API
    • /: displays a hello-world message
    • /version: displays current torch version
    from prediction import get_tf_version
from flask import Flask
app = Flask(__name__)

@app.route("/", methods=["GET"])
def home():
    return "Welcome to Cuda Tensorflow container!"

@app.route("/version", methods=["POST"])
def get_version():
    return f"{get_tf_version()}"

if __name__ == '__main__':
    app.run(host='0.0.0.0')
  2. Create prediction.py script that retrieves torch version 
    import tensorflow as tf

def get_tf_version():
    return f"Version de Tensorflow: {tf.__version__}. GPU name: {tf.config.experimental.get_device_details(tf.config.list_physical_devices('GPU')[0])['device_name']}"
  3. Create Dockerfile and configure the imagen 
    FROM nvcr.io/nvidia/tensorflow:21.04-tf2-py3

# Aditional dependencies if exists
# RUN apt-get update && apt-get install -y <packagename>

# Copy all files on these folder to /app folder on container
COPY . /app
# Change the workdir to /app
WORKDIR /app

# Install Python dependencies
# RUN pip install -r requirements.txt

# Expose Flask port 5000
EXPOSE 5000

# Command to launch the server 'python server.py'
CMD ["python", "server.py"]
  4. Build the image from the Dockerfile 
    docker build -t image-name .
    • -t specifies image label
    • . indicates place of the Dockerfile
  5. Run the container associated to the image 
    docker run -p 5000:5000 --gpus all -it image-name
    • -p mapping port 5000 of host machine to container port 5000
    • --gpus map gpu to the image
    • -it specifies name of the image

Docker Compose

  1. Install cmd:
    • sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
    • sudo chmod +x /usr/local/bin/docker-compose
    • docker-compose --version
  2. Create docker-compose.yml: 
    version: '3'
services:
    app:
        build:
            context: .
            dockerfile: dockerfile
        ports:
            - 5000:5000
        deploy:
            resources:
                reservations:
                devices:
                - capabilities: [gpu]
                    driver: nvidia
    • context maps to current directory
    • dockerfile maps to specified dockerfile
    • ports attach host port to container port
    • runtime: nvidia specifies Nvidia Container Toolkit GPU support
  3. Run the compose by docker-compose up
  4. Finish and delete containers by docker-compose down