Popular GitHub Repositories for Batch Size

Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.

Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 15+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

This is a Phi Family of SLMs book for getting started with Phi Models. Phi a family of open sourced AI models developed by Microsoft. Phi models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks

Lightning-fast serving engine for any AI model of any size. Flexible. Easy. Enterprise-scale.

Conquer Any Code in VSCode: One-Click Comments, Conversions, UI-to-Code, and AI Batch Processing of Files! 在 VSCode 中征服任何代码：一键注释、转换、UI 图生成代码、AI 批量处理文件！?

???YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

A batch scheduler of kubernetes for high performance workload, e.g. AI/ML, BigData, HPC

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:oncoming_automobile: "MORE THAN VEHICLE COUNTING!" This project provides prediction for speed, color and size of the vehicles with TensorFlow Object Counting API.

JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.

Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning

convert mmdetection model to tensorrt, support fp16, int8, batch input, dynamic shape etc.

⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x.

A deep learning-powered visual navigation engine to enables autonomous navigation of pocket-size quadrotor - running on PULP

BharatMLStack is an open-source, end-to-end machine learning infrastructure stack built at Meesho to support real-time and batch ML workloads at Bharat scale

Implementation of Vision Mamba from the paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model" It's 2.8x faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on high-res images

ML pipeline orchestration and model deployments on Kubernetes.