Best AI tools for< Deploy Models On Embedded Systems >

Embedl is an AI tool that specializes in developing advanced solutions for efficient AI deployment in embedded systems. With a focus on deep learning optimization, Embedl offers a cost-effective solution that reduces energy consumption and accelerates product development cycles. The platform caters to industries such as automotive, aerospace, and IoT, providing cutting-edge AI products that drive innovation and competitive advantage.

Duckietown is a platform for delivering cutting-edge robotics and AI learning experiences. It offers teaching resources to instructors, hands-on activities to learners, an accessible research platform to researchers, and a state-of-the-art ecosystem for professional training. Duckietown's mission is to make robotics and AI education state-of-the-art, hands-on, and accessible to all.

Qualcomm AI Hub is a platform that allows users to run AI models on Snapdragon® 8 Elite devices. It provides a collaborative ecosystem for model makers, cloud providers, runtime, and SDK partners to deploy on-device AI solutions quickly and efficiently. Users can bring their own models, optimize for deployment, and access a variety of AI services and resources. The platform caters to various industries such as mobile, automotive, and IoT, offering a range of models and services for edge computing.

Passarel is an AI tool designed to simplify teammate onboarding by developing bespoke GPT-like models for employee interaction. It centralizes knowledge bases into a custom model, allowing new teammates to access information efficiently. Passarel leverages various integrations to tailor language models to team needs, handling contradictions and providing accurate information. The tool works by training models on chosen knowledge bases, learning from data and configurations provided, and deploying the model for team use.

Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images.

Mirage is a custom AI platform that builds custom LLMs to accelerate productivity. It is backed by Sequoia and offers a variety of features, including the ability to create custom AI models, train models on your own data, and deploy models to the cloud or on-premises.

Roboflow is an AI tool designed for computer vision tasks, offering a platform that allows users to annotate, train, deploy, and perform inference on models. It provides integrations, ecosystem support, and features like notebooks, autodistillation, and supervision. Roboflow caters to various industries such as aerospace, agriculture, healthcare, finance, and more, with a focus on simplifying the development and deployment of computer vision models.

Mystic.ai is an AI tool designed to deploy and scale Machine Learning models with ease. It offers a fully managed Kubernetes platform that runs in your own cloud, allowing users to deploy ML models in their own Azure/AWS/GCP account or in a shared GPU cluster. Mystic.ai provides cost optimizations, fast inference, simpler developer experience, and performance optimizations to ensure high-performance AI model serving. With features like pay-as-you-go API, cloud integration with AWS/Azure/GCP, and a beautiful dashboard, Mystic.ai simplifies the deployment and management of ML models for data scientists and AI engineers.

Contentable.ai is a platform for comparing multiple AI models, rapidly moving from prototyping to production, and management of your custom AI solutions across multiple vendors. It allows users to test multiple AI models in seconds, compare models side-by-side across top AI providers, collaborate on AI models with their team seamlessly, design complex AI workflows without coding, and pay as they go.

Groq is a fast AI inference tool that offers GroqCloud™ Platform and GroqRack™ Cluster for developers to build and deploy AI models with ultra-low-latency inference. It provides instant intelligence for openly-available models like Llama 3.1 and is known for its speed and compatibility with other AI providers. Groq powers leading openly-available AI models and has gained recognition in the AI chip industry. The tool has received significant funding and valuation, positioning itself as a strong challenger to established players like Nvidia.

OmniAI is an AI tool that allows teams to deploy AI applications on their existing infrastructure. It provides a unified API experience for building AI applications and offers a wide selection of industry-leading models. With tools like Llama 3, Claude 3, Mistral Large, and AWS Titan, OmniAI excels in tasks such as natural language understanding, generation, safety, ethical behavior, and context retention. It also enables users to deploy and query the latest AI models quickly and easily within their virtual private cloud environment.

Wallaroo.AI is an AI inference platform that offers production-grade AI inference microservices optimized on OpenVINO for cloud and Edge AI application deployments on CPUs and GPUs. It provides hassle-free AI inferencing for any model, any hardware, anywhere, with ultrafast turnkey inference microservices. The platform enables users to deploy, manage, observe, and scale AI models effortlessly, reducing deployment costs and time-to-value significantly.

Tune AI is an enterprise Gen AI stack that offers custom models to build competitive advantage. It provides a range of features such as accelerating coding, content creation, indexing patent documents, data audit, automatic speech recognition, and more. The application leverages generative AI to help users solve real-world problems and create custom models on top of industry-leading open source models. With enterprise-grade security and flexible infrastructure, Tune AI caters to developers and enterprises looking to harness the power of AI.

FuriosaAI is an AI application that offers Hardware RNGD for LLM and Multimodality, as well as WARBOY for Computer Vision. It provides a comprehensive developer experience through the Furiosa SDK, Model Zoo, and Dev Support. The application focuses on efficient AI inference, high-performance LLM and multimodal deployment capabilities, and sustainable mass adoption of AI. FuriosaAI features the Tensor Contraction Processor architecture, software for streamlined LLM deployment, and a robust ecosystem support. It aims to deliver powerful and efficient deep learning acceleration while ensuring future-proof programmability and efficiency.

RunPod is a cloud platform specifically designed for AI development and deployment. It offers a range of features to streamline the process of developing, training, and scaling AI models, including a library of pre-built templates, efficient training pipelines, and scalable deployment options. RunPod also provides access to a wide selection of GPUs, allowing users to choose the optimal hardware for their specific AI workloads.

Kubeflow is an open-source machine learning (ML) toolkit that makes deploying ML workflows on Kubernetes simple, portable, and scalable. It provides a unified interface for model training, serving, and hyperparameter tuning, and supports a variety of popular ML frameworks including PyTorch, TensorFlow, and XGBoost. Kubeflow is designed to be used with Kubernetes, a container orchestration system that automates the deployment, management, and scaling of containerized applications.

Lobe is a machine learning tool that helps users train machine learning models and ship them to any platform of their choice. It provides a user-friendly interface for creating and managing machine learning projects, making it easy for both beginners and experienced users to work with AI models.

ClearML is an open-source, end-to-end platform for continuous machine learning (ML). It provides a unified platform for data management, experiment tracking, model training, deployment, and monitoring. ClearML is designed to make it easy for teams to collaborate on ML projects and to ensure that models are deployed and maintained in a reliable and scalable way.

JFrog ML is an AI platform designed to streamline AI development from prototype to production. It offers a unified MLOps platform to build, train, deploy, and manage AI workflows at scale. With features like Feature Store, LLMOps, and model monitoring, JFrog ML empowers AI teams to collaborate efficiently and optimize AI & ML models in production.

Comet ML is a machine learning platform that integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring.

AutoGPTQ is an easy-to-use LLM quantization package with user-friendly APIs, based on GPTQ algorithm (weight-only quantization). It provides a simple and efficient way to quantize large language models (LLMs) to reduce their size and computational cost while maintaining their performance. AutoGPTQ supports a wide range of LLM models, including GPT-2, GPT-J, OPT, and BLOOM. It also supports various evaluation tasks, such as language modeling, sequence classification, and text summarization. With AutoGPTQ, users can easily quantize their LLM models and deploy them on resource-constrained devices, such as mobile phones and embedded systems.

ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of PyTorch models to edge devices. Key value propositions of ExecuTorch are: * **Portability:** Compatibility with a wide variety of computing platforms, from high-end mobile phones to highly constrained embedded systems and microcontrollers. * **Productivity:** Enabling developers to use the same toolchains and SDK from PyTorch model authoring and conversion, to debugging and deployment to a wide variety of platforms. * **Performance:** Providing end users with a seamless and high-performance experience due to a lightweight runtime and utilizing full hardware capabilities such as CPUs, NPUs, and DSPs.

Awesome-Embedded is a curated list of resources for embedded systems enthusiasts. It covers a wide range of topics including MCU programming, RTOS, Linux kernel development, assembly programming, machine learning & AI on MCU, utilities, tips & tricks, and more. The repository provides valuable information, tutorials, and tools for individuals interested in embedded systems development.

RKLLM software stack is a toolkit designed to help users quickly deploy AI models to Rockchip chips. It consists of RKLLM-Toolkit for model conversion and quantization, RKLLM Runtime for deploying models on Rockchip NPU platform, and RKNPU kernel driver for hardware interaction. The toolkit supports RK3588 and RK3576 series chips and various models like TinyLLAMA, Qwen, Phi, ChatGLM3, Gemma, InternLM2, and MiniCPM. Users can download packages, docker images, examples, and docs from RKLLM_SDK. Additionally, RKNN-Toolkit2 SDK is available for deploying additional AI models.

RK3588 is a flagship 8K SoC chip by Rockchip, integrating Cortex-A76 and Cortex-A55 cores with NEON coprocessor for 8K video codec. This repository curates resources for developing with RK3588, including official resources, RKNN models, projects, development boards, documentation, tools, and sample code.

MNN is a highly efficient and lightweight deep learning framework that supports inference and training of deep learning models. It has industry-leading performance for on-device inference and training. MNN has been integrated into various Alibaba Inc. apps and is used in scenarios like live broadcast, short video capture, search recommendation, and product searching by image. It is also utilized on embedded devices such as IoT. MNN-LLM and MNN-Diffusion are specific runtime solutions developed based on the MNN engine for deploying language models and diffusion models locally on different platforms. The framework is optimized for devices, supports various neural networks, and offers high performance with optimized assembly code and GPU support. MNN is versatile, easy to use, and supports hybrid computing on multiple devices.

AITreasureBox is a comprehensive collection of AI tools and resources designed to simplify and accelerate the development of AI projects. It provides a wide range of pre-trained models, datasets, and utilities that can be easily integrated into various AI applications. With AITreasureBox, developers can quickly prototype, test, and deploy AI solutions without having to build everything from scratch. Whether you are working on computer vision, natural language processing, or reinforcement learning projects, AITreasureBox has something to offer for everyone. The repository is regularly updated with new tools and resources to keep up with the latest advancements in the field of artificial intelligence.

The STM32 AI model zoo is a collection of reference machine learning models optimized to run on STM32 microcontrollers. It provides a large collection of application-oriented models ready for re-training, scripts for easy retraining from user datasets, pre-trained models on reference datasets, and application code examples generated from user AI models. The project offers training scripts for transfer learning or training custom models from scratch. It includes performances on reference STM32 MCU and MPU for float and quantized models. The project is organized by application, providing step-by-step guides for training and deploying models.

uTensor is an extremely light-weight machine learning inference framework built on Tensorflow and optimized for Arm targets. It consists of a runtime library and an offline tool that handles most of the model translation work. The core runtime is only ~2KB. The workflow involves constructing and training a model in Tensorflow, then using uTensor to produce C++ code for inferencing. The runtime ensures system safety, guarantees RAM usage, and focuses on clear, concise, and debuggable code. The high-level API simplifies tensor handling and operator execution for embedded systems.

Awesome Mobile LLMs is a curated list of Large Language Models (LLMs) and related studies focused on mobile and embedded hardware. The repository includes information on various LLM models, deployment frameworks, benchmarking efforts, applications, multimodal LLMs, surveys on efficient LLMs, training LLMs on device, mobile-related use-cases, industry announcements, and related repositories. It aims to be a valuable resource for researchers, engineers, and practitioners interested in mobile LLMs.

Efficient Foundation Model Survey is a comprehensive analysis of resource-efficient large language models (LLMs) and multimodal foundation models. The survey covers algorithmic and systemic innovations to support the growth of large models in a scalable and environmentally sustainable way. It explores cutting-edge model architectures, training/serving algorithms, and practical system designs. The goal is to provide insights on tackling resource challenges posed by large foundation models and inspire future breakthroughs in the field.

This course is designed to teach you how to harness the power of AI on the Raspberry Pi, with a focus on using an AI kit for computer vision tasks. Learn to integrate AI into IoT applications, from object detection to visual recognition. Suitable for hobbyists, students, and professionals to bring AI-driven solutions to life on resource-constrained devices like the Raspberry Pi.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

Okay, here is a JSON object with the requested information about the awesome-cuda-tensorrt-fpga repository:

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

QMedia is an open-source multimedia AI content search engine designed specifically for content creators. It provides rich information extraction methods for text, image, and short video content. The tool integrates unstructured text, image, and short video information to build a multimodal RAG content Q&A system. Users can efficiently search for image/text and short video materials, analyze content, provide content sources, and generate customized search results based on user interests and needs. QMedia supports local deployment for offline content search and Q&A for private data. The tool offers features like content cards display, multimodal content RAG search, and pure local multimodal models deployment. Users can deploy different types of models locally, manage language models, feature embedding models, image models, and video models. QMedia aims to spark new ideas for content creation and share AI content creation concepts in an open-source manner.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

大型语言模型的学习和训练指导专家

A witty yet succinct guide for HuggingFace, offering technical assistance on using the platform - based on their Learning Hub

Help Beginners Get Started With AI

A detailed expert trained on all 3,018 pages of Apple CoreML, offering complete coding solutions. Saving time? https://www.buymeacoffee.com/parkerrex ☕️❤️

A tech guide for software engineers, focusing on the latest tools and foundational knowledge.

Agent specialist on building and deploying OpenAI Assistant API's

Guides in data engineering tasks with a focus on practical solutions.

Friendly Azure guide for beginners

协助撰写《基于CNN网络的手写汉字识别》的毕业论文

Formador para la certificación de ML Engineer en GCP, con respuestas y explicaciones detalladas.

Your expert in libtorch and C++ programming.

Expert in Hugging Face platform tools and resources

I'm an expert in TensorFlow, providing detailed, accurate guidance for all skill levels.

NeurIPS 2023 Gateway

I'm a Python and PyTorch expert with knowledge of ML infrastructure requirements ready to help you build and scale your ML projects.

Adapts explanations to user's TensorFlow skill level

Expert in PyTorch, adept at simplifying complex concepts.

I'm your Machine Learning and Python mentor, ready to teach and guide you.

An expert MLOps engineer assisting in DevOps and pipeline optimization.

Evolves with user interactions in data science.