Angela  Dickens

Angela Dickens


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


_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

Image for post

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

#pytorch #data-science #deep-learning #data analysis

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Buddha Community

Deploy models and create custom handlers in Torchserve 🚀
Michael  Hamill

Michael Hamill


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.

Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.

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.

#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop

Ahebwe  Oscar

Ahebwe Oscar


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.


Custom Titles of Django Admin

Exclude in Django Admin

Fields in Django Admin

#django #create super user django #customize django admin dashboard #django admin #django admin custom field display #django admin customization #django admin full customization #django admin interface #django admin register all models #django customization

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

Read Also : Cron Job Scheduling In Laravel

#laravel 8 create custom helper function example #laravel #custom helper function #how to create custom helper in laravel 8 #laravel helper functions #custom helper functions in laravel

Justice  Reilly

Justice Reilly


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

Fynzo Survey

Fynzo Survey


Fynzo Customer Feedback Software For Cafes, Hotels, Saloons, Spa!

Customer Feedback Tool | Fynzo online customer feedback comes with Android, iOS app. Collect feedback from your customers with tablets or send them feedback links.

Visit page for more information:


#customer feedback system #powerful customer feedback system #free customer feedback tools #automated customer feedback system #customer feedback tools #customer rating system