tpu
There are 206 repositories under tpu topic.
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
tensorflow/tensor2tensor
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
skypilot-org/skypilot
Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, 17+ clouds, or on-prem).
tensorflow/adanet
Fast and flexible AutoML with learning guarantees.
hollance/neural-engine
Everything we actually know about the Apple Neural Engine (ANE)
imcaspar/gpt2-ml
GPT2 for Multiple Languages, including pretrained models. GPT2 多语言支持, 15亿参数中文预训练模型
aphrodite-engine/aphrodite-engine
Large-scale LLM inference engine
sophgo/tpu-mlir
Machine learning compiler based on MLIR for Sophgo TPU.
LuxDL/Lux.jl
Elegant and Performant Deep Learning
ayaka14732/tpu-starter
Everything you want to know about Google Cloud TPU
jofrfu/tinyTPU
Implementation of a Tensor Processing Unit for embedded systems and the IoT.
chrisbutner/ChessCoach
Neural network-based chess engine capable of natural language commentary
tumaer/JAXFLUIDS
Differentiable Fluid Dynamics Package
AI-Hypercomputer/JetStream
JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome).
magic-blue-smoke/Dual-Edge-TPU-Adapter
Dual Edge TPU Adapter to use it on a system with single PCIe port on m.2 A/B/E/M slot
Kohulan/DECIMER-Image_Transformer
DECIMER Image Transformer is a deep-learning-based tool designed for automated recognition of chemical structure images. Leveraging transformer architectures, the model converts chemical images into SMILES strings, enabling the digitization of chemical data from scanned documents, literature, and patents.
embedeep/Free-TPU
Free TPU for FPGA with compiler supporting Pytorch/Caffe/Darknet/NCNN. An AI processor for using Xilinx FPGA to solve image classification, detection, and segmentation problem.
JuliaGPU/XLA.jl
Julia on TPUs
cameronshinn/tiny-tpu
Small-scale Tensor Processing Unit built on an FPGA
robotperf/benchmarks
Benchmarking suite to evaluate 🤖 robotics computing performance. Vendor-neutral. ⚪Grey-box and ⚫Black-box approaches.
cea-wind/SimpleTPU
A FPGA Based CNN accelerator, following Google's TPU V1.
embedeep/FREE-TPU-V3plus-for-FPGA
FREE TPU V3plus for FPGA is the free version of a commercial AI processor (EEP-TPU) for Deep Learning EDGE Inference
AI-Hypercomputer/xpk
xpk (Accelerated Processing Kit, pronounced x-p-k,) is a software tool to help Cloud developers to orchestrate training jobs on accelerators such as TPUs and GPUs on GKE.
hhk7734/tensorflow-yolov4
YOLOv4 Implemented in Tensorflow 2.
nyx-ai/stylegan2-flax-tpu
🖼 Training StyleGAN2 at scale on TPUs
HomebrewML/revlib
Simple and efficient RevNet-Library for PyTorch with XLA and DeepSpeed support and parameter offload
rwightman/efficientnet-jax
EfficientNet, MobileNetV3, MobileNetV2, MixNet, etc in JAX w/ Flax Linen and Objax
yapay-ogrenme/googlecodelabs
TPU ile Yapay Sinir Ağlarınızı Çok Daha Hızlı Eğitin
koshian2/OctConv-TFKeras
Unofficial implementation of Octave Convolutions (OctConv) in TensorFlow / Keras.
sayakpaul/FunMatch-Distillation
TF2 implementation of knowledge distillation using the "function matching" hypothesis from https://arxiv.org/abs/2106.05237.
wmcnally/evopose2d
EvoPose2D is a two-stage human pose estimation model that was designed using neuroevolution. It achieves state-of-the-art accuracy on COCO.
PINTO0309/TPU-MobilenetSSD
Edge TPU Accelerator / Multi-TPU + MobileNet-SSD v2 + Python + Async + LattePandaAlpha/RaspberryPi3/LaptopPC
rickiepark/deep-learning-with-python-2nd
<케라스 창시자에게 배우는 딥러닝 2판> 도서의 코드 저장소
AI-Hypercomputer/jetstream-pytorch
PyTorch/XLA integration with JetStream (https://github.com/google/JetStream) for LLM inference"
captain-pool/GSOC
Repository for Google Summer of Code 2019 https://summerofcode.withgoogle.com/projects/#4662790671826944
GoogleCloudPlatform/ml-testing-accelerators
Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)