Pinned Repositories
api-gateway-kubernetes-eks-service-mesh
Microservices architecture deployed via API Gateway, AWS EKS, AWS AppMesh, and Docker, Kubernetes, and Flask
ask-fsdl
Document Q&A over The Full Stack's Corpus
course
The Hugging Face course on Transformers
cs249r_book
Collaborative book for CS249r: Tiny Machine Learning
GPU-Accelerated-Data-Augmentation
Accelerating Data Augmentation with CUDA and OpenCL
interactive_lottery_game
Find winners in a lottery game
Made-With-ML
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Multi-Label-Text-Classification-of-PubMed-Articles
Multi-Label Text Classfication of PubMed Articles
RGB-to-Gray-CUDA-OpenCL
Parallel Computing for Converting an RGB Image to Grayscale and Matrix Transpose Operation
Wind-Turbines-Grid-Monitoring-System
Provide a web app that simulates wind turbines emitting metrics, breaking, and repairing, in real-time
DrewAfromsky's Repositories
DrewAfromsky/RGB-to-Gray-CUDA-OpenCL
Parallel Computing for Converting an RGB Image to Grayscale and Matrix Transpose Operation
DrewAfromsky/Wind-Turbines-Grid-Monitoring-System
Provide a web app that simulates wind turbines emitting metrics, breaking, and repairing, in real-time
DrewAfromsky/api-gateway-kubernetes-eks-service-mesh
Microservices architecture deployed via API Gateway, AWS EKS, AWS AppMesh, and Docker, Kubernetes, and Flask
DrewAfromsky/ask-fsdl
Document Q&A over The Full Stack's Corpus
DrewAfromsky/course
The Hugging Face course on Transformers
DrewAfromsky/cs249r_book
Collaborative book for CS249r: Tiny Machine Learning
DrewAfromsky/GPU-Accelerated-Data-Augmentation
Accelerating Data Augmentation with CUDA and OpenCL
DrewAfromsky/interactive_lottery_game
Find winners in a lottery game
DrewAfromsky/Made-With-ML
Learn how to design, develop, deploy and iterate on production-grade ML applications.
DrewAfromsky/Multi-Label-Text-Classification-of-PubMed-Articles
Multi-Label Text Classfication of PubMed Articles
DrewAfromsky/Non-Square-Matrix-Multiplication-CUDA-OpenCL
Optimized and Naive Non-Square Matrix Multiplication With CUDA and OpenCL
DrewAfromsky/Office-Add-in-samples
Code samples for Office Add-in development on the Microsoft 365 platform.
DrewAfromsky/notebooks
Notebooks using the Hugging Face libraries 🤗
DrewAfromsky/spring-boot-microservices
This repository contains the latest source code of th spring-boot-microservices tutorial
DrewAfromsky/Tiled-Naive-2D-Convolution-1-D-Histogram-CUDA-OpenCL
2D convolution and 1D histogram calculation was performed in both CUDA and OpenCL. 2D convolution was implemented, taking advantage of both shared memory/tiles and global memory (naive methods). Tiled 2D convolution was performed in CUDA only. For naive 2D convolution, the input to the algorithm is an [M X N] matrix and a [K X K] kernel of odd dimension sizes. The output after convolution remained the same size as the input; zero padding was performed to take into account halo/ghost cells, when the kernel was ”acting” on ”non-existent” pixels/matrix elements. For the tiled 2D convolution, the kernel size was fixed: [5 x 5] to avoid dealing with dynamic memory allocation. For both methods, a serial implementation of 2D convolution was performed using scipy function (signal.convolve2D). Execution times for 2D convolution CUDA naive, 2D convolution CUDA tiled, and 2D convolution serial were recorded and plotted for comparison. Execution times for 2D convolution in OpenCL were compared to 2D convolution in serial and plotted as well. Matrices for both CUDA and OpenCL were initialized and iteratively increased in size in both dimensions by the same factor. Similarly, for 1D histogram calculation, execution times were recorded in both OpenCL and CUDA, and serial code, and then plotted and compared. One plot was created for CUDA versus serial. A second plot was created for OpenCL versus serial.
DrewAfromsky/zenml
ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.