There are 8 repositories under tpu topic.
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.
Fast and flexible AutoML with learning guarantees.
Everything we actually know about the Apple Neural Engine (ANE)
Everything you want to know about Google Cloud TPU
Neural network-based chess engine capable of natural language commentary
Dual Edge TPU Adapter to use it on a system with single PCIe port on m.2 A/B/E/M slot
DECIMER: Deep Learning for Chemical Image Recognition using Efficient-Net V2 + Transformer
🖼 Training StyleGAN2 on TPUs in JAX
Benchmarking suite to evaluate 🤖 robotics computing performance. Vendor-neutral. ⚪Grey-box and ⚫Black-box approaches.
EfficientNet, MobileNetV3, MobileNetV2, MixNet, etc in JAX w/ Flax Linen and Objax
TPU ile Yapay Sinir Ağlarınızı Çok Daha Hızlı Eğitin
Simple and efficient RevNet-Library for PyTorch with XLA and DeepSpeed support and parameter offload
Small-scale Tensor Processing Unit built on an FPGA
Unofficial implementation of Octave Convolutions (OctConv) in TensorFlow / Keras.
Edge TPU Accelerator / Multi-TPU + MobileNet-SSD v2 + Python + Async + LattePandaAlpha/RaspberryPi3/LaptopPC
TF2 implementation of knowledge distillation using the "function matching" hypothesis from https://arxiv.org/abs/2106.05237.
Repository for Google Summer of Code 2019 https://summerofcode.withgoogle.com/projects/#4662790671826944
FREE TPU V3plus for FPGA is the free version of a commercial AI processor (EEP-TPU) for Deep Learning EDGE Inference
Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)
Tutorial to pretrain & fine-tune a 🤗 Flax T5 model on a TPUv3-8 with GCP
<케라스 창시자에게 배우는 딥러닝 2판> 도서의 코드 저장소
:dart: Accumulated Gradients for TensorFlow 2
🪐 The Sebulba architecture to scale reinforcement learning on Cloud TPUs in JAX
Edge TPU Accelerator / Multi-TPU / Multi-Model + Posenet/DeeplabV3/MobileNet-SSD + Python + Sync / Async + LaptopPC / RaspberryPi