Best AI tools for< Optimize Training >

GymBuddy.ai is an AI workout planner that leverages artificial intelligence to create personalized workout plans tailored to individual fitness levels, equipment access, and target areas of the body. The platform offers a wide range of exercises and features to help users achieve their fitness goals effectively. With advanced analytics and full workout customization, GymBuddy.ai aims to revolutionize the way people approach their fitness journey.

Comet ML is an extensible, fully customizable machine learning platform that aims to move ML forward by supporting productivity, reproducibility, and collaboration. It integrates with existing infrastructure and tools to manage, visualize, and optimize models from training runs to production monitoring. Users can track and compare training runs, create a model registry, and monitor models in production all in one platform. Comet's platform can be run on any infrastructure, enabling users to reshape their ML workflow and bring their existing software and data stack.

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.

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.

Macgence is an AI training data services platform that offers high-quality off-the-shelf structured training data for organizations to build effective AI systems at scale. They provide services such as custom data sourcing, data annotation, data validation, content moderation, and localization. Macgence combines global linguistic, cultural, and technological expertise to create high-quality datasets for AI models, enabling faster time-to-market across the entire model value chain. With more than 5 years of experience, they support and scale AI initiatives of leading global innovators by designing custom data collection programs. Macgence specializes in handling AI training data for text, speech, image, and video data, offering cognitive annotation services to unlock the potential of unstructured textual data.

Bibit AI is a real estate marketing AI designed to enhance the efficiency and effectiveness of real estate marketing and sales. It can help create listings, descriptions, and property content, and offers a host of other features. Bibit AI is the world's first AI for Real Estate. We are transforming the real estate industry by boosting efficiency and simplifying tasks like listing creation and content generation.

Wedo AI is an all-in-one AI-powered platform designed to help businesses attract customers, convert leads, and manage various aspects of online marketing, sales, and delivery. It offers a range of tools such as AI ads, chat bots, social media planner, websites, ecommerce store, memberships, CRM, email marketing, analytics, and more. Wedo AI aims to streamline processes, increase efficiency, and drive revenue growth for entrepreneurs, startups, influencers, non-profits, coaches, contractors, freelancers, and consultants. The platform provides features for managing finances, automating billing, creating funnels, building websites, selling products, engaging with customers, and analyzing data to make informed decisions.

Granica AI is a Training Data Platform designed to make data safe for use with AI while keeping it cost-efficient. It offers state-of-the-art accuracy, cost-efficient data optimization, data visibility insights, and cloud cost savings. The platform helps in protecting data privacy, optimizing data costs, and gaining data visibility for AI teams to achieve big results while minimizing privacy risk.

BuildAi is an AI tool designed to provide the lowest cost GPU cloud for AI training on the market. The platform is powered with renewable energy, enabling companies to train AI models at a significantly reduced cost. BuildAi offers interruptible pricing, short term reserved capacity, and high uptime pricing options. The application focuses on optimizing infrastructure for training and fine-tuning machine learning models, not inference, and aims to decrease the impact of computing on the planet. With features like data transfer support, SSH access, and monitoring tools, BuildAi offers a comprehensive solution for ML teams.

SymTrain is an AI-powered platform that automates training and coaching for contact center agents. By utilizing simulations and AI technology, SymTrain offers a cost-effective solution that enhances agent performance, reduces training time, and improves overall customer satisfaction. The platform provides automated role-play scenarios, consistent feedback, and data-driven coaching to help organizations streamline their training processes and achieve better results. SymTrain revolutionizes how companies train and coach their agents, leading to increased efficiency, revenue, and customer satisfaction.

FluidStack is a leading GPU cloud platform designed for AI and LLM (Large Language Model) training. It offers unlimited scale for AI training and inference, allowing users to access thousands of fully-interconnected GPUs on demand. Trusted by top AI startups, FluidStack aggregates GPU capacity from data centers worldwide, providing access to over 50,000 GPUs for accelerating training and inference. With 1000+ data centers across 50+ countries, FluidStack ensures reliable and efficient GPU cloud services at competitive prices.

Nebius AI is an AI-centric cloud platform designed to handle intensive workloads efficiently. It offers a range of advanced features to support various AI applications and projects. The platform ensures high performance and security for users, enabling them to leverage AI technology effectively in their work. With Nebius AI, users can access cutting-edge AI tools and resources to enhance their projects and streamline their workflows.

Qualint is an AI-driven platform revolutionizing call center quality assurance. It empowers call centers with superior analytics, comprehensive call analysis, and streamlined operations. The platform provides real-time insights, tailored quality standards, and enhances collaboration to ensure every customer interaction is understood. Qualint offers core features such as real-time call uploads & analysis, efficiency improvement, information accessibility, and team communication. With different pricing plans, Qualint caters to call centers of all sizes, from small to large, helping them optimize agent performance, navigate regulatory challenges, and elevate customer service.

ONNX Runtime is a production-grade AI engine designed to accelerate machine learning training and inferencing in various technology stacks. It supports multiple languages and platforms, optimizing performance for CPU, GPU, and NPU hardware. ONNX Runtime powers AI in Microsoft products and is widely used in cloud, edge, web, and mobile applications. It also enables large model training and on-device training, offering state-of-the-art models for tasks like image synthesis and text generation.

Jyotax.ai is an AI-powered tax solution that revolutionizes tax compliance by simplifying the tax process with advanced AI solutions. It offers comprehensive bookkeeping, payroll processing, worldwide tax returns and filing automation, profit recovery, contract compliance, and financial modeling and budgeting services. The platform ensures accurate reporting, real-time compliance monitoring, global tax solutions, customizable tax tools, and seamless data integration. Jyotax.ai optimizes tax workflows, ensures compliance with precise AI tax calculations, and simplifies global tax operations through innovative AI solutions.

Athletica AI is an AI-powered athletic training and personalized fitness application that offers tailored coaching and training plans for various sports like cycling, running, duathlon, triathlon, and rowing. It adapts to individual fitness levels, abilities, and availability, providing daily step-by-step training plans and comprehensive session analyses. Athletica AI integrates seamlessly with workout data from platforms like Garmin, Strava, and Concept 2 to craft personalized training plans and workouts. The application aims to help athletes train smarter, not harder, by leveraging the power of AI to optimize performance and achieve fitness goals.

The OR Society is a professional membership body that supports the development of people working in operational research, data science, and analytics. The society provides a range of services to its members, including access to world-class journals, events and conferences, training courses, and pro bono opportunities. The OR Society also works to promote the use of operational research in all areas of industry, business, government, the community, and the third sector.

Cerebras is an AI tool that offers products and services related to AI supercomputers, cloud system processors, and applications for various industries. It provides high-performance computing solutions, including large language models, and caters to sectors such as health, energy, government, scientific computing, and financial services. Cerebras specializes in AI model services, offering state-of-the-art models and training services for tasks like multi-lingual chatbots and DNA sequence prediction. The platform also features the Cerebras Model Zoo, an open-source repository of AI models for developers and researchers.

Hidden Layers AI is a consultancy and training company specializing in Generative AI for businesses. They offer services such as AI training, business assessments, and implementation to help organizations harness the power of AI efficiently. The company stands out for its expertise in seamlessly integrating GenAI and LLM technologies, empowering workforce with GenAI capabilities, and crafting efficient GenAI workflows. Hidden Layers AI provides customized training programs for all levels and departments, ensuring practical AI skills development. They also offer a range of AI solutions from basic implementation to full-scale transformation, with a focus on user adoption, risk management, and ongoing support.

Human Driven AI is a leading AI consulting and optimization service provider that empowers businesses to leverage the power of AI for automation, innovation, and transformation. They offer custom team trainings, consulting services, and product development to help organizations stay ahead of the curve in today's competitive landscape. With a focus on ethical and responsible AI implementation, Human Driven AI ensures that businesses use Generative AI effectively to drive efficiency, creativity, and engagement. Their proven TDC Method guarantees responsible AI usage, while their tailored service levels cater to every stage of AI adoption.

llm-analysis is a tool designed for Latency and Memory Analysis of Transformer Models for Training and Inference. It automates the calculation of training or inference latency and memory usage for Large Language Models (LLMs) or Transformers based on specified model, GPU, data type, and parallelism configurations. The tool helps users to experiment with different setups theoretically, understand system performance, and optimize training/inference scenarios. It supports various parallelism schemes, communication methods, activation recomputation options, data types, and fine-tuning strategies. Users can integrate llm-analysis in their code using the `LLMAnalysis` class or use the provided entry point functions for command line interface. The tool provides lower-bound estimations of memory usage and latency, and aims to assist in achieving feasible and optimal setups for training or inference.

High Performance LLMs 2024 is a comprehensive course focused on building a high-performance Large Language Model (LLM) from scratch using Jax. The course covers various aspects such as training, inference, roofline analysis, compilation, sharding, profiling, and optimization techniques. Participants will gain a deep understanding of Jax and learn how to design high-performance computing systems that operate close to their physical limits.

Liger Kernel is a collection of Triton kernels designed for LLM training, increasing training throughput by 20% and reducing memory usage by 60%. It includes Hugging Face Compatible modules like RMSNorm, RoPE, SwiGLU, CrossEntropy, and FusedLinearCrossEntropy. The tool works with Flash Attention, PyTorch FSDP, and Microsoft DeepSpeed, aiming to enhance model efficiency and performance for researchers, ML practitioners, and curious novices.

Firefly is an open-source large model training project that supports pre-training, fine-tuning, and DPO of mainstream large models. It includes models like Llama3, Gemma, Qwen1.5, MiniCPM, Llama, InternLM, Baichuan, ChatGLM, Yi, Deepseek, Qwen, Orion, Ziya, Xverse, Mistral, Mixtral-8x7B, Zephyr, Vicuna, Bloom, etc. The project supports full-parameter training, LoRA, QLoRA efficient training, and various tasks such as pre-training, SFT, and DPO. Suitable for users with limited training resources, QLoRA is recommended for fine-tuning instructions. The project has achieved good results on the Open LLM Leaderboard with QLoRA training process validation. The latest version has significant updates and adaptations for different chat model templates.

Fira is a memory-efficient training framework for Large Language Models (LLMs) that enables full-rank training under low-rank constraint. It introduces a method for training with full-rank gradients of full-rank weights, achieved with just two lines of equations. The framework includes pre-training and fine-tuning functionalities, packaged as a Python library for easy use. Fira utilizes Adam optimizer by default and provides options for weight decay. It supports pre-training LLaMA models on the C4 dataset and fine-tuning LLaMA-7B models on commonsense reasoning tasks.

llm-finetuning is a repository that provides a serverless twist to the popular axolotl fine-tuning library using Modal's serverless infrastructure. It allows users to quickly fine-tune any LLM model with state-of-the-art optimizations like Deepspeed ZeRO, LoRA adapters, Flash attention, and Gradient checkpointing. The repository simplifies the fine-tuning process by not exposing all CLI arguments, instead allowing users to specify options in a config file. It supports efficient training and scaling across multiple GPUs, making it suitable for production-ready fine-tuning jobs.

A collection of available inference solutions for Large Language Models (LLMs) including high-throughput engines, optimization libraries, deployment toolkits, and deep learning frameworks for production environments.

Efficient Multimodal Large Language Models: A Survey provides a comprehensive review of efficient and lightweight Multimodal Large Language Models (MLLMs), focusing on model size reduction and cost efficiency for edge computing scenarios. The survey covers the timeline of efficient MLLMs, research on efficient structures and strategies, and applications. It discusses current limitations and future directions in efficient MLLM research.

LLM-Travel is a repository dedicated to exploring the mysteries of Large Language Models (LLM). It provides in-depth technical explanations, practical code implementations, and a platform for discussions and questions related to LLM. Join the journey to explore the fascinating world of large language models with LLM-Travel.

XTuner is an efficient, flexible, and full-featured toolkit for fine-tuning large models. It supports various LLMs (InternLM, Mixtral-8x7B, Llama 2, ChatGLM, Qwen, Baichuan, ...), VLMs (LLaVA), and various training algorithms (QLoRA, LoRA, full-parameter fine-tune). XTuner also provides tools for chatting with pretrained / fine-tuned LLMs and deploying fine-tuned LLMs with any other framework, such as LMDeploy.

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

Trace is a new AutoDiff-like tool for training AI systems end-to-end with general feedback. It generalizes the back-propagation algorithm by capturing and propagating an AI system's execution trace. Implemented as a PyTorch-like Python library, users can write Python code directly and use Trace primitives to optimize certain parts, similar to training neural networks.

The Multipack sampler is a tool designed for padding-free distributed training of large language models. It optimizes batch processing efficiency using an approximate solution to the identical machine scheduling problem. The V2 update further enhances the packing algorithm complexity, achieving better throughput for a large number of nodes. It includes two variants for models with different attention types, aiming to balance sequence lengths and optimize packing efficiency. Users can refer to the provided benchmark for evaluating efficiency, utilization, and L^2 lag. The tool is compatible with PyTorch DataLoader and is released under the MIT license.

Prime is a framework for efficient, globally distributed training of AI models over the internet. It includes features such as fault-tolerant training with ElasticDeviceMesh, asynchronous distributed checkpointing, live checkpoint recovery, custom Int8 All-Reduce Kernel, maximizing bandwidth utilization, PyTorch FSDP2/DTensor ZeRO-3 implementation, and CPU off-loading. The framework aims to optimize communication, checkpointing, and bandwidth utilization for large-scale AI model training.

GLake is an acceleration library and utilities designed to optimize GPU memory management and IO transmission for AI large model training and inference. It addresses challenges such as GPU memory bottleneck and IO transmission bottleneck by providing efficient memory pooling, sharing, and tiering, as well as multi-path acceleration for CPU-GPU transmission. GLake is easy to use, open for extension, and focuses on improving training throughput, saving inference memory, and accelerating IO transmission. It offers features like memory fragmentation reduction, memory deduplication, and built-in security mechanisms for troubleshooting GPU memory issues.

The End-to-End LLM Bootcamp is a comprehensive training program that covers the entire process of developing and deploying large language models. Participants learn to preprocess datasets, train models, optimize performance using NVIDIA technologies, understand guardrail prompts, and deploy AI pipelines using Triton Inference Server. The bootcamp includes labs, challenges, and practical applications, with a total duration of approximately 7.5 hours. It is designed for individuals interested in working with advanced language models and AI technologies.

Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production. Users can accelerate Lightning training with the Torch ORT Callback to optimize ONNX graph for faster training & inference. Additionally, users can introduce sparsity with the SparseMLCallback to accelerate inference by leveraging the DeepSparse engine. Specific research implementations are encouraged, with contributions that help train SSL models and integrate with Lightning Flash for state-of-the-art models in applied research.

AIFoundation focuses on AI Foundation, large model systems. Large models optimize the performance of full-stack hardware and software based on AI clusters. The training process requires distributed parallelism, cluster communication algorithms, and continuous evolution in the field of large models such as intelligent agents. The course covers modules like AI chip principles, communication & storage, AI clusters, computing architecture, communication architecture, large model algorithms, training, inference, and analysis of hot technologies in the large model field.

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