Deploy models and create custom handlers in Torchserve 🚀

_All the code used in this article is _here

Recently, PyTorch has introduced its new production framework to properly serve models, called torchserve.So, without further due, let’s present today’s roadmap:

1. Installation with Docker
2. Export your model
3. Define a handler
4. Serve our model

To showcase torchserve, we will serve a fully trained ResNet34 to perform image classification.

Installation with Docker

_Official doc _here

The best way to install torchserve is with docker. You just need to pull the image.

You can use the following command to save the latest image.

docker pull pytorch/torchserve:latest

All the tags are available here

More about docker and torchserve here

Handlers

_Official doc _here

Handlers are the ones responsible to make a prediction using your model from one or more HTTP requests.

Default handlers

Torchserve supports the following default handlers

1. image_classifier
2. object_detector
3. text_classifier
4. image_segmenter

But keep in mind that none of them supports batching requests!

Custom handlers

torchserve exposes a rich interface to do almost everything you want. An Handler is just a class that must have three functions

• preprocess
• inference
• postprocess

You can create your own class or just subclassBaseHandler . The main advantage of subclasssing BaseHandler is to have the model loaded accessible at self.model . The following snippet shows how to subclass BaseHandler

Subclassing BaseHandler to create your own handler

Going back to our image classification example. We need to

• get the images from each request and preprocess them
• get the prediction from the model
• send back a response

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What is GEEK

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Workshop Alert! Deep Learning Model Deployment & Management

The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.

Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.

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In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.

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Django admin full Customization step by step

Welcome to my blog , hey everyone in this article you learn how to customize the Django app and view in the article you will know how to register  and unregister  models from the admin view how to add filtering how to add a custom input field, and a button that triggers an action on all objects and even how to change the look of your app and page using the Django suit package let’s get started.

Database

Custom Titles of Django Admin

Exclude in Django Admin

Fields in Django Admin

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Laravel 8 Create Custom Helper Function Example

Today, We will see laravel 8 create custom helper function example, as we all know laravel provides many ready mate function in their framework, but many times we need to require our own customized function to use in our project that time we need to create custom helper function, So, here i am show you custom helper function example in laravel 8.

Laravel 8 Create Custom Helper Function Example

https://websolutionstuff.com/post/laravel-8-create-custom-helper-function-example

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Read Also : Cron Job Scheduling In Laravel

https://websolutionstuff.com/post/cron-job-scheduling-in-laravel

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Deploying Machine learning models using Flask on your website

Understanding of Machine Learning using Python (sklearn)
Basics of Flask
Basics of HTML,CSS

#machine-learning #deployment #ml-model-deployment #flask #deploying

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