Return to AI-DL-ML-LLM GitHub, AI-DL-ML-LLM Focused Companies, Hugging Face AI-DL-ML-LLM Services, AWS AI-DL-ML-LLM Services, Azure AI-DL-ML-LLM Services, GCP AI-DL-ML-LLM Services, IBM Cloud AI-DL-ML-LLM Services, Oracle Cloud AI-DL-ML-LLM Services, OpenAI AI-DL-ML-LLM Services, NVIDIA AI-DL-ML-LLM Services, Intel AI-DL-ML-LLM Services, Kubernetes AI-DL-ML-LLM Services, Apple AI-DL-ML-LLM Services, Meta-Facebook AI-DL-ML-LLM Services, Cisco AI-DL-ML-LLM Services
For the top 15 GitHub repos, ask for 10 paragraphs. e.g. Amazon SageMaker Features, Amazon SageMaker Alternatives, Amazon SageMaker Security, , Amazon SageMaker DevOps
The Intel AI Reference Models repository offers pre-trained models, sample scripts, and tutorials optimized for Intel hardware. It assists developers in executing, training, and deploying deep learning models on platforms like Intel Xeon Scalable processors and Intel Data Center GPUs. The repository demonstrates AI capabilities on Intel platforms, providing an efficient software environment.
The DistML repository supports training large machine learning models on Apache Spark. It is compatible with Spark versions 1.2 and later, offering APIs like Model, Session, Matrix, and DataStore. Developers can expand existing algorithms using DistML, enabling scalable machine learning with minimal system overhead.
The Intel Transfer Learning Tool simplifies transfer learning workflows by leveraging public pretrained models, Intel-optimized frameworks, and custom datasets. It supports popular frameworks like PyTorch and TensorFlow while enabling features such as text classification, anomaly detection, and model quantization using the Intel Neural Compressor.
The Model Zoo for Intel Architecture repository provides pre-trained models, sample scripts, and best practices for AI developers. It supports machine learning frameworks optimized for Intel platforms, offering examples on executing, training, and deploying models in cloud or on-premises environments.
The OpenVINO Toolkit was introduced by Intel in 2018 to optimize and deploy deep learning models efficiently. It supports various workloads such as Computer Vision and Generative AI while being compatible with Intel hardware. OpenVINO is open-source and supports contributions for expanded device compatibility.
The BigDL framework, developed by Intel, enables distributed deep learning on Apache Spark. It supports the development of AI applications as standard Spark programs, offering scalable data processing and machine learning model training. BigDL accelerates AI workloads with efficient Spark-based computation.
The PlaidML project is a tensor compiler that generates code for multiple backends, including OpenCL and CUDA. It supports frameworks like Keras, ONNX, and nGraph, enabling deep learning on devices with limited hardware support. Its portable architecture makes it useful for custom AI deployments.
The Intel Extension for Transformers accelerates inference for transformer-based language models on CPUs. It uses Intel Deep Learning Boost for optimized sparse matrix multiplication, significantly improving performance. The extension facilitates large-scale NLP and language model applications with efficient resource use.
The Megatron-DeepSpeed repository, adapted for Intel Gaudi, supports training transformer-based models like LLaMA. It efficiently processes models with billions of parameters using advanced parallelization techniques. This project showcases Intel’s AI research capabilities for large-scale model training.
The LangChain framework supports the development of applications integrating large language models (LLMs). It facilitates tasks like document analysis, summarization, chatbots, and code analysis. Launched in 2022, LangChain provides modular tools for building advanced AI-driven systems.
https://github.com/langchain-ai/langchain
The Intel AI Open Source Portfolio encompasses a variety of tools and frameworks designed to assist developers in creating, training, and deploying AI solutions. This collection includes resources such as the Intel Neural Compressor for model compression and the Intel Extension for PyTorch to enhance performance on Intel hardware. These tools are optimized for platforms like Intel Xeon Scalable processors and Intel Data Center GPUs, facilitating efficient AI development.
The AI Playground is an open-source project that serves as a starter application for performing AI tasks such as image creation, image stylizing, and chatbot interactions on PCs powered by Intel Arc GPUs. It leverages libraries from GitHub and Hugging Face, providing users with an accessible platform to experiment with various AI capabilities.
The RAG-FiT (Retrieval-Augmented Generation Fine-Tuning) framework is an open-source tool introduced by Intel Labs to augment large language models (LLMs) for retrieval-augmented generation use cases. Available under an Apache 2.0 license, RAG-FiT integrates data creation, training, inference, and evaluation into a single workflow, enabling efficient experimentation with different retrieval-augmented generation techniques.
OpenCV is an open-source computer vision and machine learning software library originally developed by Intel. It provides a comprehensive set of tools for real-time computer vision applications and is widely used in AI projects for tasks such as object detection, facial recognition, and image processing.
PlaidML is a portable tensor compiler that enables deep learning on devices with limited hardware support. It generates code for various backends, including OpenCL and CUDA, and supports machine learning libraries like Keras and ONNX. PlaidML facilitates AI development across diverse hardware platforms.
BigDL 2.0 is an open-source distributed deep learning library developed by Intel to seamlessly scale AI pipelines from laptops to distributed clusters. It allows users to build conventional Python notebooks that can be transparently accelerated on a single node and scaled out to large clusters, facilitating efficient AI development and deployment.
IntelCaffe is an optimized version of the Caffe deep learning framework, enhanced by Intel for efficient 8-bit low precision inference of convolutional neural networks on Intel Xeon Scalable processors. It provides significant improvements in inference throughput and latency, making it suitable for deploying deep learning applications.
The OpenVINO Toolkit is an open-source toolkit developed by Intel for optimizing and deploying deep learning models. It enables developers to create scalable and efficient AI solutions with minimal code, supporting various model formats and categories, including large language models, computer vision, and generative AI.
The Intel AI Analytics Toolkit is a suite of AI development tools optimized for Intel architecture. It includes components like the Intel Distribution of Modin for accelerated data processing and the Intel Optimization for TensorFlow for enhanced deep learning performance, facilitating efficient AI model development and deployment.
https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html
The Intel Optimization for Horovod enhances the performance of the Horovod distributed deep learning framework on Intel hardware. It provides optimizations that improve training efficiency and scalability, enabling faster development of AI models in distributed computing environments.
https://github.com/intel/intel-optimization-for-horovod
The Intel AI Open Source Portfolio encompasses a variety of tools and frameworks designed to assist developers in creating, deploying, and optimizing AI solutions. This collection includes resources such as the Intel Neural Compressor for model compression and the Intel Extension for PyTorch to enhance performance on Intel hardware. These tools are optimized for platforms like Intel Xeon Scalable processors and Intel Data Center GPUs, facilitating efficient AI development.
The AI Playground is an open-source project that serves as a starter application for performing AI tasks such as image creation, image stylizing, and chatbot interactions on PCs powered by Intel Arc GPUs. It leverages libraries from GitHub and Hugging Face, providing users with an accessible platform to experiment with various AI capabilities.
The RAG-FiT (Retrieval-Augmented Generation Fine-Tuning) framework is an open-source tool introduced by Intel Labs to augment large language models (LLMs) for retrieval-augmented generation use cases. Available under an Apache 2.0 license, RAG-FiT integrates data creation, training, inference, and evaluation into a single workflow, enabling efficient experimentation with different retrieval-augmented generation techniques.
OpenCV is an open-source computer vision and machine learning software library originally developed by Intel. It provides a comprehensive set of tools for real-time computer vision applications and is widely used in AI projects for tasks such as object detection, facial recognition, and image processing.
BigDL 2.0 is an open-source distributed deep learning library developed by Intel to seamlessly scale AI pipelines from laptops to distributed clusters. It allows users to build conventional Python notebooks that can be transparently accelerated on a single node and scaled out to large clusters, facilitating efficient AI development and deployment.
IntelCaffe is an optimized version of the Caffe deep learning framework, enhanced by Intel for efficient 8-bit low precision inference of convolutional neural networks on Intel Xeon Scalable processors. It provides significant improvements in inference throughput and latency, making it suitable for deploying deep learning applications.
The Intel AI Analytics Toolkit is a suite of AI development tools optimized for Intel architecture. It includes components like the Intel Distribution of Modin for accelerated data processing and the Intel Optimization for TensorFlow for enhanced deep learning performance, facilitating efficient AI model development and deployment.
https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html
The Intel Optimization for Horovod enhances the performance of the Horovod distributed deep learning framework on Intel hardware. It provides optimizations that improve training efficiency and scalability, enabling faster development of AI models in distributed computing environments.
The Intel Extension for DeepSpeed enhances the DeepSpeed deep learning optimization library to better utilize Intel hardware. It provides performance optimizations and features that improve the efficiency of training large-scale AI models on Intel platforms.
The Intel Extension for OpenXLA enhances the OpenXLA compiler ecosystem to better support Intel hardware. It provides optimizations that improve the performance of machine learning models compiled with OpenXLA on Intel platforms.