Pinned Repositories
accelerate
🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision
ai-containers
This repository contains Dockerfiles, scripts, yaml files, Helm charts, etc. used to scale out AI containers with versions of TensorFlow and PyTorch that have been optimized for Intel platforms. Scaling is done with python, Docker, kubernetes, kubeflow, cnvrg.io, Helm, and other container orchestration frameworks for use in the cloud and on-premise
atk
analytics tool kit
benchmarks
Benchmark code
daal-tk
unet_distributed
Distributed TensorFlow UNet
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
ai-reference-models
Intel® AI Reference Models: contains Intel optimizations for running deep learning workloads on Intel® Xeon® Scalable processors and Intel® Data Center GPUs
intel-xai-tools
Explainable AI Tooling (XAI). XAI is used to discover and explain a model's prediction in a way that is interpretable to the user. Relevant information in the dataset, feature-set, and model's algorithms are exposed.
transfer-learning
Libraries and tools to support Transfer Learning
dmsuehir's Repositories
dmsuehir/unet_distributed
Distributed TensorFlow UNet
dmsuehir/accelerate
🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision
dmsuehir/ai-containers
This repository contains Dockerfiles, scripts, yaml files, Helm charts, etc. used to scale out AI containers with versions of TensorFlow and PyTorch that have been optimized for Intel platforms. Scaling is done with python, Docker, kubernetes, kubeflow, cnvrg.io, Helm, and other container orchestration frameworks for use in the cloud and on-premise
dmsuehir/benchmarks
Benchmark code
dmsuehir/draft
A tool for developers to create cloud-native applications on Kubernetes.
dmsuehir/examples
A repository to host extended examples and tutorials
dmsuehir/DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
dmsuehir/experimental-kvc
Kubernetes Volume Controller
dmsuehir/experiments
Experiments API for Experiment Tracking on Kubernetes
dmsuehir/Gaudi-tutorials
Tutorials for running models on First-gen Gaudi and Gaudi2 for Training and Inference. The source files for the tutorials on https://developer.habana.ai/
dmsuehir/GenAIExamples
Generative AI Examples is a collection of GenAI examples such as ChatQnA, Copilot, which illustrate the pipeline capabilities of the Open Platform for Enterprise AI (OPEA) project.
dmsuehir/gitflow-poc
POC for testing the Git Flow process
dmsuehir/helm
The Kubernetes Package Manager
dmsuehir/intel-extension-for-pytorch
A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
dmsuehir/kubeflow
Machine Learning Toolkit for Kubernetes
dmsuehir/lpot
Intel Low Precision Optimization Tool, targeting to provide a unified low precision inference interface cross different deep learning frameworks, and support auto-tune with specified accuracy criterion to find out best quantized model.
dmsuehir/mlt
ML Container toolkit
dmsuehir/mlt-1
ML Container Templates
dmsuehir/mlt-draft-packs
MLT template draft packs
dmsuehir/models
Model Zoo for Intel® Architecture: contains Intel optimizations for running deep learning workloads on Intel® Xeon® Scalable processors
dmsuehir/ngraph
nGraph - open source C++ library, compiler and runtime for Deep Learning
dmsuehir/ngraph-bridge
TensorFlow-nGraph bridge
dmsuehir/optimum-habana
Easy and lightning fast training of 🤗 Transformers on Habana Gaudi processor (HPU)
dmsuehir/optimum-intel
🤗 Optimum Intel: Accelerate inference with Intel optimization tools
dmsuehir/pipelines
Machine Learning Pipelines for Kubeflow
dmsuehir/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
dmsuehir/tensorflow
Computation using data flow graphs for scalable machine learning
dmsuehir/test
testing, will delete later
dmsuehir/transfer-learning
Libraries and tools to support Transfer Learning
dmsuehir/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.