Best AI tools for< Training >

SC Training, formerly known as EdApp, is a mobile learning management system that offers a wide range of features to enhance the training experience for both administrators and learners. The platform provides tools for creating, managing, and tracking training courses, with a strong focus on microlearning and gamification. SC Training aims to deliver bite-sized, engaging content that can be accessed anytime, anywhere, on any device. The application also incorporates AI technology to streamline course creation and improve the learning experience. With a diverse course library, practical assessments, and group training capabilities, SC Training is designed to help organizations deliver effective and efficient training programs.

SC Training, formerly known as EdApp, is a mobile learning management system that offers a comprehensive platform for creating, delivering, and tracking training courses. The application provides features such as admin control, content creation tools, analytics tracking, AI course generation, microlearning courses, gamification elements, and support for various industries. SC Training aims to deliver efficient and engaging training experiences to users, with a focus on bite-sized learning and accessibility across devices. The platform also offers course libraries, practical assessments, rapid course refresh, and group training options. Users can customize courses, integrate with existing tools, and access a range of resources through the help center and blog.

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.

Endurance is a platform designed for runners, swimmers, and cyclists to engage in group training activities with friends or local communities. Users can create or join teams, share structured workouts, and benefit from collective motivation and accountability. The platform aims to make training fun and effective by leveraging the power of group workouts and social connections.

LEAi is an AI-powered tool designed for training course content authoring. It enables users to quickly create, update, and repurpose training courses by leveraging artificial intelligence to streamline the course creation process. LEAi eliminates manual tasks, provides real-time guidance on course structure and content writing, and ensures optimal learning outcomes by applying best practices in course development. The tool is ideal for companies looking to save time and resources in developing high-quality training content.

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.

Lingio is an AI-powered employee training software designed for frontline workers, offering gamified learning experiences and mobile-based training solutions. The platform combines gamification and AI to enhance course completion rates and improve learning outcomes for deskless industries such as hospitality, cleaning, transportation, elderly care, and facility management.

Weekly Workout is an AI-powered fitness platform that provides personalized workout plans and tracks your progress. With Weekly Workout, you can get four days of invigorating exercise routines every week, tailored to your fitness level and goals. The platform also offers a weekly newsletter with tips and advice from fitness experts.

Zenfit AI is a revolutionary personalized training application that leverages artificial intelligence to transform fitness journeys. It offers tailored exercises, real-time workout generation, performance tracking, and automatic program adjustments. Users can create personalized training programs by providing their details. Zenfit addresses the challenges of achieving fitness goals efficiently by providing personalized training and coaching through AI, benefiting both fitness enthusiasts and personal trainers. The application is still in its early stages with potential features in the pipeline for fitness enthusiasts and personal trainers. The mission of Zenfit is to make fitness training more accessible, personalized, and effective for everyone involved.

Grasply.ai is an AI-powered personalized training solution that transforms documents into impactful learning resources using multi-agent AI training assistants. It enhances productivity, improves skill transfer, and empowers teams to succeed by creating customized learning resources for training and assessment. Grasply allows users to upload documents, define learning goals, customize the learning experience, build tailored micro-courses with AI, share personalized courses, and track learner progress. It offers different pricing plans with varying features to cater to different user needs.

Atlanta Voiceover Studio is a professional voiceover training and recording studio based in Atlanta, GA. They offer a wide range of workshops and classes for voiceover artists of all levels, from beginners to experienced professionals. The studio provides training in various aspects of voiceover work, including animation, commercial voiceover, audiobook narration, and more. In addition to training, they also offer services such as auditions, demos, and business coaching to help voiceover artists succeed in the industry.

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.

Solidroad is an AI-first training and feedback platform that turns company knowledge-base into immersive training programs. It offers personalized coaching, realistic simulations, and real-time feedback to improve team performance. The platform aims to make training programs easier to manage and more engaging for employees.

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.

Impakt is an AI fitness coaching application that offers personalized workout plans, real-time feedback, community support, and tailored workouts to help users reach their fitness goals. The app allows users to track workouts, analyze performance, share progress, and receive insights to improve their fitness journey. With a focus on community engagement and athlete endorsements, Impakt aims to provide a comprehensive fitness experience for users of all levels.

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.

SkillsTeq is an interactive e-learning and training platform powered by AI technology. It revolutionizes learning and training by offering interactive courses, sales and customer experience training, assessments, and feedback through a unique chat-based learning environment. The platform leverages AI to deliver personalized training tailored to company data, engaging users in dynamic conversations and immersive experiences. SkillsTeq provides efficient AI e-learning, immersive roleplays, intelligent assessments, roleplay training, sales coaching, and customer experience training, all aimed at enhancing knowledge retention and preparing users for real-world scenarios.

Whale is an AI-powered software designed to help businesses document their standard operating procedures, policies, and internal company knowledge. It streamlines the process of onboarding, training, and growing teams by leveraging AI technology to assist in creating and organizing documentation. Whale offers features such as AI-assisted SOP and process documentation, automated training flows, a single source of truth for knowledge management, and an AI assistant named Alice to help with various tasks. The platform aims to systemize and scale businesses by providing a user-friendly interface and dedicated support services.

PocketCoach is an online AI-powered learning platform designed for athletes worldwide who aim to enhance their sports skills. The platform utilizes cutting-edge AI and computer vision technology to provide gamified drills, comprehensive analytics, and real-time feedback to help athletes break through plateaus and achieve faster results. PocketCoach also offers an innovative computer vision app called Playi, which transforms practice sessions by offering real-time analysis and personalized feedback on technique using a smartphone's camera.

MIT Sloan Teaching & Learning Technologies connects MIT Sloan to research-driven best practices, resources, and training in instructional technology and design. They help the community make an impact in the classroom and beyond. They offer various services such as trainings, practice sessions, how-to guides, consultations, and a teaching studio. Their latest news and announcements include supporting learning with AI-generated images, providing students with access to Microsoft Copilot, and making Microsoft Copilot available for faculty and staff.

Kubeflow Training Operator is a Kubernetes-native project for fine-tuning and scalable distributed training of machine learning (ML) models created with various ML frameworks such as PyTorch, Tensorflow, XGBoost, MPI, Paddle and others. Training Operator allows you to use Kubernetes workloads to effectively train your large models via Kubernetes Custom Resources APIs or using Training Operator Python SDK. > Note: Before v1.2 release, Kubeflow Training Operator only supports TFJob on Kubernetes. * For a complete reference of the custom resource definitions, please refer to the API Definition. * TensorFlow API Definition * PyTorch API Definition * Apache MXNet API Definition * XGBoost API Definition * MPI API Definition * PaddlePaddle API Definition * For details of all-in-one operator design, please refer to the All-in-one Kubeflow Training Operator * For details on its observability, please refer to the monitoring design doc.

This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).

SimpleAICV_pytorch_training_examples is a repository that provides simple training and testing examples for various computer vision tasks such as image classification, object detection, semantic segmentation, instance segmentation, knowledge distillation, contrastive learning, masked image modeling, OCR text detection, OCR text recognition, human matting, salient object detection, interactive segmentation, image inpainting, and diffusion model tasks. The repository includes support for multiple datasets and networks, along with instructions on how to prepare datasets, train and test models, and use gradio demos. It also offers pretrained models and experiment records for download from huggingface or Baidu-Netdisk. The repository requires specific environments and package installations to run effectively.

This repository provides resources and notebooks for building text-based applications using the ChatGPT API and Langchain. It includes guides on prompt engineering, fine-tuning ChatGPT, using LangChain, and creating applications like a quiz generator and notes summarizer. The repository aims to help users understand and implement various natural language processing tasks with pre-trained language models.

This repository contains a student training series focusing on AI-driven science on supercomputers. It covers topics such as ALCF systems overview, AI on supercomputers, neural networks, LLMs, and parallel training techniques. The content is organized into subdirectories with prefixed indexes for easy navigation. The series aims to provide hands-on experience and knowledge in utilizing AI on supercomputers for scientific research.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

LLM training in simple, pure C/CUDA. There is no need for 245MB of PyTorch or 107MB of cPython. For example, training GPT-2 (CPU, fp32) is ~1,000 lines of clean code in a single file. It compiles and runs instantly, and exactly matches the PyTorch reference implementation. I chose GPT-2 as the first working example because it is the grand-daddy of LLMs, the first time the modern stack was put together.

Aim is an open-source, self-hosted ML experiment tracking tool designed to handle 10,000s of training runs. Aim provides a performant and beautiful UI for exploring and comparing training runs. Additionally, its SDK enables programmatic access to tracked metadata — perfect for automations and Jupyter Notebook analysis. **Aim's mission is to democratize AI dev tools 🎯**

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.

The fsdp_qlora repository provides a script for training Large Language Models (LLMs) with Quantized LoRA and Fully Sharded Data Parallelism (FSDP). It integrates FSDP+QLoRA into the Axolotl platform and offers installation instructions for dependencies like llama-recipes, fastcore, and PyTorch. Users can finetune Llama-2 70B on Dual 24GB GPUs using the provided command. The script supports various training options including full params fine-tuning, LoRA fine-tuning, custom LoRA fine-tuning, quantized LoRA fine-tuning, and more. It also discusses low memory loading, mixed precision training, and comparisons to existing trainers. The repository addresses limitations and provides examples for training with different configurations, including BnB QLoRA and HQQ QLoRA. Additionally, it offers SLURM training support and instructions for adding support for a new model.

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.

Only Train Once (OTO) is an automatic, architecture-agnostic DNN training and compression framework that allows users to train a general DNN from scratch or a pretrained checkpoint to achieve high performance and slimmer architecture simultaneously in a one-shot manner without fine-tuning. The framework includes features for automatic structured pruning and erasing operators, as well as hybrid structured sparse optimizers for efficient model compression. OTO provides tools for pruning zero-invariant group partitioning, constructing pruned models, and visualizing pruning and erasing dependency graphs. It supports the HESSO optimizer and offers a sanity check for compliance testing on various DNNs. The repository also includes publications, installation instructions, quick start guides, and a roadmap for future enhancements and collaborations.

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.

PARAM Benchmarks is a repository of communication and compute micro-benchmarks as well as full workloads for evaluating training and inference platforms. It complements commonly used benchmarks by focusing on AI training with PyTorch based collective benchmarks, GEMM, embedding lookup, linear layer, and DLRM communication patterns. The tool bridges the gap between stand-alone C++ benchmarks and PyTorch/Tensorflow based application benchmarks, providing deep insights into system architecture and framework-level overheads.

EasyDist is an automated parallelization system and infrastructure designed for multiple ecosystems. It offers usability by making parallelizing training or inference code effortless with just a single line of change. It ensures ecological compatibility by serving as a centralized source of truth for SPMD rules at the operator-level for various machine learning frameworks. EasyDist decouples auto-parallel algorithms from specific frameworks and IRs, allowing for the development and benchmarking of different auto-parallel algorithms in a flexible manner. The architecture includes MetaOp, MetaIR, and the ShardCombine Algorithm for SPMD sharding rules without manual annotations.

WebWhiz is an open-source tool that allows users to train ChatGPT on website data to build AI chatbots for customer queries. It offers easy integration, data-specific responses, regular data updates, no-code builder, chatbot customization, fine-tuning, and offline messaging. Users can create and train chatbots in a few simple steps by entering their website URL, automatically fetching and preparing training data, training ChatGPT, and embedding the chatbot on their website. WebWhiz can crawl websites monthly, collect text data and metadata, and process text data using tokens. Users can train custom data, but bringing custom open AI keys is not yet supported. The tool has no limitations on context size but may limit the number of pages based on the chosen plan. WebWhiz SDK is available on NPM, CDNs, and GitHub, and users can self-host it using Docker or manual setup involving MongoDB, Redis, Node, Python, and environment variables setup. For any issues, users can contact [email protected].

Steel-LLM is a project to pre-train a large Chinese language model from scratch using over 1T of data to achieve a parameter size of around 1B, similar to TinyLlama. The project aims to share the entire process including data collection, data processing, pre-training framework selection, model design, and open-source all the code. The goal is to enable reproducibility of the work even with limited resources. The name 'Steel' is inspired by a band '万能青年旅店' and signifies the desire to create a strong model despite limited conditions. The project involves continuous data collection of various cultural elements, trivia, lyrics, niche literature, and personal secrets to train the LLM. The ultimate aim is to fill the model with diverse data and leave room for individual input, fostering collaboration among users.

ReaLHF is a distributed system designed for efficient RLHF training with Large Language Models (LLMs). It introduces a novel approach called parameter reallocation to dynamically redistribute LLM parameters across the cluster, optimizing allocations and parallelism for each computation workload. ReaL minimizes redundant communication while maximizing GPU utilization, achieving significantly higher Proximal Policy Optimization (PPO) training throughput compared to other systems. It supports large-scale training with various parallelism strategies and enables memory-efficient training with parameter and optimizer offloading. The system seamlessly integrates with HuggingFace checkpoints and inference frameworks, allowing for easy launching of local or distributed experiments. ReaLHF offers flexibility through versatile configuration customization and supports various RLHF algorithms, including DPO, PPO, RAFT, and more, while allowing the addition of custom algorithms for high efficiency.

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.

Expert in engaging, interactive training methods and activities.

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This chatbot answers questions and provides guidance on how the Army trains to compete, fight, and win, ensuring individuals are masters of their craft.

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Guides technical skill development within an organization.

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Train to become a superhero or a supervillain. Master your powers, make pivotal choices. Each decision you make in this action-packed game not only shapes your abilities but also your moral alignment in the battle between good and evil. Another GPT Simulator by Dave Lalande

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