My articles

Welcome To My Blog

This is the first post in this blog.

Krishnatheja Vanka

PyTorch Lightning: A Comprehensive Guide

PyTorch Lightning is a lightweight wrapper for PyTorch that helps organize code and reduce boilerplate while adding powerful features for research and production. This guide…

Krishnatheja Vanka

From Pandas to Polars

As datasets grow in size and complexity, performance and efficiency become critical in data processing. While Pandas has long been the go-to library for data manipulation in…

Krishnatheja Vanka

Python Data Visualization: Matplotlib vs Seaborn vs Altair

This guide compares three popular Python data visualization libraries: Matplotlib, Seaborn, and Altair (Vega-Altair). Each library has its own strengths, weaknesses, and…

Krishnatheja Vanka

Active Learning Influence Selection: A Comprehensive Guide

Active learning is a machine learning paradigm where the algorithm can interactively query an oracle (typically a human annotator) to label new data points. The key idea is…

Krishnatheja Vanka

Vision Transformer (ViT) Implementation Guide

Vision Transformers (ViT) represent a significant paradigm shift in computer vision, applying the transformer architecture initially developed for NLP to image processing…

Krishnatheja Vanka

DINOv2: Comprehensive Implementation Guide

DINOv2 is a state-of-the-art self-supervised vision model developed by Meta AI Research that builds upon the original DINO (Self-Distillation with No Labels) framework. This…

Krishnatheja Vanka

DINO: Emerging Properties in Self-Supervised Vision Transformers

In 2021, Facebook AI Research (now Meta AI) introduced DINO (Self-Distillation with No Labels), a groundbreaking approach to self-supervised learning in computer vision.…

Krishnatheja Vanka

DINOv2: A Deep Dive into Architecture and Training

In 2023, Meta AI Research unveiled DINOv2 (Self-Distillation with No Labels v2), a breakthrough in self-supervised visual learning that produces remarkably versatile and…

Krishnatheja Vanka

Python 3.14: The Next Evolution in Python Development

Python continues its steady march forward with the anticipated release of Python 3.14, marking another significant milestone in the language’s evolution. As the Python…

Krishnatheja Vanka

Vision Transformers (ViT): A Simple Guide

Vision Transformers (ViTs) represent a paradigm shift in computer vision, adapting the transformer architecture that revolutionized natural language processing for image…

Krishnatheja Vanka

Python 3.14: Key Improvements and New Features

Python 3.14 introduces several significant improvements focused on performance, developer experience, and language capabilities. This guide covers the most important changes…

Krishnatheja Vanka

PyTorch to PyTorch Lightning Migration Guide

PyTorch Lightning is a lightweight wrapper around PyTorch that eliminates boilerplate code while maintaining full control over your models. It provides a structured approach…

Krishnatheja Vanka

MobileNetV2 PyTorch Docker Deployment Guide

This guide walks you through deploying a pre-trained MobileNetV2 model using PyTorch and Docker, creating a REST API for image classification.

Krishnatheja Vanka

Albumentations vs TorchVision Transforms: Complete Code Guide

This guide compares two popular image augmentation libraries for PyTorch:

Krishnatheja Vanka

LitServe Code Guide

LitServe is a high-performance, flexible AI model serving framework designed to deploy machine learning models with minimal code. It provides automatic batching, GPU…

Krishnatheja Vanka

LitServe with MobileNetV2 - Complete Code Guide

This guide demonstrates how to deploy a MobileNetV2 image classification model using LitServe for efficient, scalable inference.

Krishnatheja Vanka

Student-Teacher Network Training Guide in PyTorch

Student-teacher networks, also known as knowledge distillation, involve training a smaller “student” model to mimic the behavior of a larger, pre-trained “teacher” model.…

Krishnatheja Vanka

DINOv2 Student-Teacher Network Training Guide

This guide provides a complete implementation for training a DINOv2 (DINO version 2) student-teacher network from scratch using PyTorch. DINOv2 is a self-supervised learning…

Krishnatheja Vanka

CLIP Code Guide: Complete Implementation and Usage

CLIP (Contrastive Language-Image Pre-training) is a neural network architecture developed by OpenAI that learns visual concepts from natural language supervision. It can…

Krishnatheja Vanka

Self-Supervised Learning: Training AI Without Labels

Machine learning has traditionally relied on vast amounts of labeled data to train models effectively. However, acquiring high-quality labeled datasets is expensive…

Krishnatheja Vanka

MLflow for PyTorch - Complete Guide

MLflow is an open-source platform for managing the machine learning lifecycle, including experimentation, reproducibility, deployment, and model registry. This guide covers…

Krishnatheja Vanka

Kubeflow Deep Learning Guide with PyTorch

Kubeflow is a machine learning toolkit for Kubernetes that makes deployments of ML workflows on Kubernetes simple, portable, and scalable. This guide focuses on using…

Krishnatheja Vanka

Kubeflow: A Comprehensive Guide to Machine Learning on Kubernetes

Kubeflow is an open-source machine learning platform designed to make deployments of machine learning workflows on Kubernetes simple, portable, and scalable. Originally…

Krishnatheja Vanka

PyTorch Training and Inference Optimization Guide

The guide includes practical code examples you can directly use in your projects, along with best practices and common pitfalls to avoid. Each section builds upon the…

Krishnatheja Vanka

Why I Choose PyTorch for Deep Learning

When it comes to deep learning frameworks, the landscape offers several compelling options. TensorFlow, JAX, and PyTorch each have their strengths, but after working…

Krishnatheja Vanka

PyTorch Collate Function Speed-Up Guide

The collate function in PyTorch is a crucial component for optimizing data loading performance. It determines how individual samples are combined into batches, and custom…

Krishnatheja Vanka

Hugging Face Accelerate vs PyTorch Lightning Fabric: A Deep Dive Comparison

When you’re working with deep learning models that need to scale across multiple GPUs or even multiple machines, you’ll quickly encounter the complexity of distributed…

Krishnatheja Vanka

Hugging Face Accelerate Code Guide

I’ve created a comprehensive code guide for Hugging Face Accelerate that covers everything from basic setup to advanced features like DeepSpeed integration.

Krishnatheja Vanka

PyTorch Lightning Fabric Code Guide

I’ve created a comprehensive code guide for PyTorch Lightning Fabric that covers everything from basic setup to advanced distributed training features

Krishnatheja Vanka