Tutorial: ML-Ops

Administrative Information

Title	Serving Production Models
Duration	60 minutes
Module	B
Lesson Type	Tutorial
Focus	Practical - Organisational AI
Topic	Building production model API

Keywords

machine learning opertion,containers,

Learning Goals

• Getting familiar with containerisation
• Learning TFX Serving
• Ability to apply serving Locally and on Azure Container Instances

Expected Preparation

Lesson materials

• Video Tutorial (MP4)
• JN Notebook: (.ipynb)
• HTML Notebook: (HTML)
• Stanford dogs dataset complete dataset
• Jack Russel's Training Data
• Rhodesian Ridgeback's Training Data
• Jack Russel's Unseen Data
• Rhodesian Ridgeback's Unseen Data

Instructions for Teachers

Production Models using TFX Serving

• This tutorial will introduce students to take a developed trained model that was developed in a Jupyter Notebook (using Tensorflow 2.x and Keras) and saving the model in Tensorflow format. The tutorial will start by developing a basic CNN to identify the bread of a dog. We will then save the model as a Tensorflow model. The tutorial will then use the TFX (Tensorflow Extended https://www.tensorflow.org/tfx/serving/docker) approach for MLOps where we will focus in this tutorial on TRX serving component, that is building restful APIs to use/query in production environments. To do this we will build a Docker TFX serving image, and deploy this image:
  • Locally (localhost)
  • Via Azure Container Instances (ACI), where a public IP address can be queried

• There are prior installs needed, please see the obligatory preparations for students below.
• The dataset is the Stanford dogs dataset, in which we use two classes of dogs, Jack Russel's and Rhodesian Ridgeback's, the complete dataset can be found here, We have also provided the subset used in this tutorial in the dataset section below.
• We have also provided all of the Docker CLI commands at the bottom of this tutorial WIKI page.

Outline/time schedule

Duration (Min)	Description
20	Problem 1: Building a CNN model using a subset of the Stanford dogs dataset, saving this model as a Tensorflow model
10	Problem 2: Deploying the Tensorflow model to a Rest API locally (using Docker) and querying the model
20	Problem 3: Deploying the Tensorflow model to a Rest API using Azure Container Instances (ACI) (using Docker) and querying the model
10	Recap on the forward pass process

Docker CLI commands

The following command line interface commands are used in this tutorial to run the models.

Run locally

Step 1: Pull tensorflow

docker pull tensorflow/serving:latest-gpu

Step 2: Run the image

docker run --gpus all -p 8501:8501 --name tfserving_classifier --mount type=bind,source=c:\production\,target=/models/img_classifier -e MODEL_NAME=img_classifier -t tensorflow/serving:latest-gpu

or with no GPU

docker run -p 8501:8501 --name tfserving_classifier --mount type=bind,source=c:\production\,target=/models/img_classifier -e MODEL_NAME=img_classifier -t tensorflow/serving:latest-gpu

Run on Azure using ACI

Step 1: Modify the local image to have model included

docker run -d --name serving_base tensorflow/serving:latest-gpu
docker cp c:\production\ serving_base:/models/img_classifier
docker ps -a   # to get id
docker commit --change "ENV MODEL_NAME img_classifier" <id goes here> tensorflow_dogs_gpu
docker kill serving_base

Step 2: Deploy Image to Azure ACI

docker login azure
docker context create aci deleteme
docker context use deleteme
docker run -p 8501:8501 kquille/tensorflow_dogs_gpu:kq

Step3 Access the ACI logs, IP address, and then stop and remove the ACI service =====

docker logs jolly-ride
docker ps
docker stop jolly-ride
docker rm jolly-ride

Acknowledgements

Keith Quille (TU Dublin, Tallaght Campus)

The Human-Centered AI Masters programme was Co-Financed by the Connecting Europe Facility of the European Union Under Grant â„–CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.

Lesson plan on SURF

WikiWijs page