Towards Data Science’s Post

Learn all about the inner workings of denoising diffusion probabilistic models (DDPM) by following along Nicholas DiSalvo's debut TDS article, which includes a detailed PyTorch implementation of the model.

Quantization is one of those key points that any AI/ML engineer should be aware of and be able to implement when needed, especially these days with the new LLM models. This short course from DeepLearning.AI (with Hugging Face) is a great starting point for anyone interested https://lnkd.in/e7tM6tG9

Here is a good guide, found on YouTube, on how to create a diffusion model in PyTorch:

Checkout my implementation of ResNet50 from scratch using PyTorch 👇 https://lnkd.in/gCmhMaxs

Beyond the Basics: While SGD and Adam are staples in PyTorch, advanced algorithms like SLSQP and CMA-ES can tackle tough optimization problems. Learn how these gradient-free methods can enhance your models in Benjamin Bodner's latest article. #PyTorch #DataScience

Solving the XOR Problem: A Comparative Approach Using Perceptrons, Sklearn, and TensorFlow/Keras

How I built my own custom 8-bit Quantizer from scratch: a step-by-step guide using PyTorch. Check out my article. https://lnkd.in/eqjy2XRB

Building Your First Generative Model with TensorFlow and Keras: A Step-by-Step Guide

🚀 Optimizing Vertex Pipelines for Faster Inference 🚀 I recently embarked on optimizing our inference process using Vertex Batch Predict, and the results have been remarkable. Our existing system took around 19 hours for inference, but by leveraging Vertex Batch Predict, I managed to slash the inference time by approximately 60%. After some fine-tuning and research, I made further optimizations that cut the time down to just 55 minutes—a 90% reduction! Here are the key variables I optimized in Vertex Batch Prediction settings: Machine Type Starting Replica Count Max Replica Count Batch Size Insights for Optimization: Batch Size: Ensure your TensorFlow Serving process is configured to utilize multiple threads (equal to the number of CPU cores). If the default settings are used, it will process fewer instances in parallel, underutilizing the VM's CPU cores. A too-large batch size can lead to RAM bottlenecks. Machine Type: A massive machine type might not be fully utilized by your TensorFlow serving process. A medium VM, like n1-highmem-8, is typically sufficient and cost-effective. Replica Count: Set starting_replica_count equal to max_replica_count to avoid the delay in starting new replicas. If your TensorFlow serving isn’t optimized for multi-threading per VM, opt for many small to medium-sized VMs instead. Model Configuration: Consider these variables on the TensorFlow threading side for optimization: https://lnkd.in/gkMxxhng After implementing these optimizations, I achieved a dramatic reduction in inference time from 19 hours to just 55 minutes, achieving a ~90% improvement. Additionally, the cost has decreased as Vertex Batch Predict scales resources on demand. Optimizing your systems not only saves time but also reduces costs. I hope these insights help others in their development journey! #MachineLearning #DataScience #AI #VertexAI #Optimization #TensorFlow #TechTips #batchpredict #vertex_batch_predict

Just finished the course “Transfer Learning for Images Using PyTorch: Essential Training” by Jonathan Fernandes! Check it out: https://lnkd.in/gR6BTPG3 #machinelearning #transferlearning #pytorch.