Administrative information

Title	Serving Production Models
Duration	60 min
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

Learning Events to be Completed Before

None.

Obligatory for Students

Install WSL 2 prior to installing Docker
- You can use the Windows Hyper-V option, but this does not support GPU's
- You will need to install Ubuntu (or other suitable variants), with the WSL CLI command wsl --install -d ubuntu
- For some machines the WSL will also need to be updated with the command wsl --update (More information on this update)
- You will need to have the latest NVIDIA/CUDA drivers installed
Install Docker for Windows: https://docs.docker.com/desktop/install/windows-install/
Create an Azure account with access to create Azure Container Instances (ACI)

Optional for Students

References and background for students:

Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

Recommended for Teachers

Do the tasks listed as obligatory and optional for the students.

Lesson Materials

The materials of this learning event are available under CC BY-NC-SA 4.0.

https://www.tensorflow.org/tfx/guide

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

More information

Click here for an overview of all lesson plans of the master human centred AI

Please visit the home page of the consortium HCAIM

Acknowledgements

The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.

The materials of this learning event are available under CC BY-NC-SA 4.0

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities

Colofon

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Auteur: HCAIM Consortium
Laatst gewijzigd: 2024-05-15 11:21:56
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Eindgebruiker: leerling/student
Moeilijkheidsgraad: gemiddeld
Studiebelasting: 4 uur en 0 minuten

Gebruikte Wikiwijs Arrangementen

HCAIM Consortium. (z.d.).

Acknowledgement

https://maken.wikiwijs.nl/198386/Acknowledgement

HCAIM Consortium. (z.d.).

Tutorial: Data architecture

https://maken.wikiwijs.nl/200242/Tutorial__Data_architecture

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