Search Results for "training"
Sort By:
Showing 980 open source projects for "training"
View related business solutions-
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
One Monitoring Tool for IT, OT and Cloud | Free TrialModern infrastructure spans data centers, cloud platforms and factory floors, and every blind spot between them is a risk. PRTG supports SNMP, WMI, SSH and other standard protocols to monitor IT, OT and hybrid environments through one customizable dashboard. Build the views your team needs, from network health to application performance, without switching tools. Try PRTG free for 30 days now.
-
1
OpenVINO Training Extensions
Trainable models and NN optimization tools
OpenVINO™ Training Extensions provide a convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference. When ote_cli is installed in the virtual environment, you can use the ote command line interface to perform various actions for templates related to the chosen task type, such as running, training, evaluating, exporting, etc. ote train trains a model (a particular model template) on a dataset and saves results in two files. ote optimize optimizes a pre-trained model using NNCF or POT depending on the model format. ... -
2
SageMaker Training Toolkit
Train machine learning models within Docker containers
...Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. ... -
3
SageMaker TensorFlow Training Toolkit
Toolkit for running TensorFlow training scripts on SageMaker
Toolkit for running TensorFlow training scripts on SageMaker. SageMaker TensorFlow Training Toolkit is an open-source library for using TensorFlow to train models on Amazon SageMaker. To use your TensorFlow Serving model on SageMaker, you first need to create a SageMaker Model. After creating a SageMaker Model, you can use it to create SageMaker Batch Transform Jobs for offline inference, or create SageMaker Endpoints for real-time inference. -
4
Happy-LLM
Large Language Model Principles and Practice Tutorial from Scratch
...The project guides learners through the entire conceptual and practical pipeline of modern LLM development, starting with foundational natural language processing concepts and gradually progressing to advanced architectures and training techniques. It explains the Transformer architecture, pre-training paradigms, and model scaling strategies while also providing hands-on coding examples so readers can implement and experiment with their own models. The tutorial emphasizes practical understanding by walking users through building and training small language models, including tokenizer construction, pre-training workflows, and fine-tuning methods. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
5
MedicalGPT
MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training
MedicalGPT training medical GPT model with ChatGPT training pipeline, implementation of Pretraining, Supervised Finetuning, Reward Modeling and Reinforcement Learning. MedicalGPT trains large medical models, including secondary pre-training, supervised fine-tuning, reward modeling, and reinforcement learning training. -
6
DeepSeed
Deep learning optimization library making distributed training easy
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. ... -
7
Xtuner
A Next-Generation Training Engine Built for Ultra-Large MoE Models
Xtuner is a large-scale training engine designed for efficient training and fine-tuning of modern large language models, particularly mixture-of-experts architectures. The framework focuses on enabling scalable training for extremely large models while maintaining efficiency across distributed computing environments. Unlike traditional 3D parallel training strategies, XTuner introduces optimized parallelism techniques that simplify scaling and reduce system complexity when training massive models. ... -
8
Olmo-core
PyTorch building blocks for the OLMo ecosystem
OLMo-core is a Python library from AI2 that provides reusable PyTorch building blocks for training large language models at scale. It underpins the broader OLMo ecosystem and includes components for models, data loading, distributed execution, optimization, checkpointing, evaluation, and generation. Official scripts reproduce training configurations for released OLMo models. The library supports advanced attention backends, mixture-of-experts architectures, float8 training, and fused low-memory operations through optional dependencies. ... -
9
LLaMA-Factory
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
LLaMA-Factory is a fine-tuning and training framework for Meta's LLaMA language models. It enables researchers and developers to train and customize LLaMA models efficiently using advanced optimization techniques. -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
-
10
InsightFace
State-of-the-art 2D and 3D Face Analysis Project
...InsightFace efficiently implements a wide variety of state-of-the-art algorithms for face recognition, face detection, and face alignment, which are optimized for both training and deployment. Research institutes and industrial organizations can get benefits from InsightFace library. -
11
Unsloth Studio
Unified web UI for training and running open models locally
Unsloth Studio is a web-based interface for running and training AI models locally with a unified and user-friendly experience. It allows users to work with a wide range of models for text, audio, vision, embeddings, and more without relying heavily on cloud infrastructure. Built on top of the Unsloth framework, it focuses on high-performance training with reduced VRAM usage and faster speeds compared to traditional methods. -
12
SimpleTuner
A general fine-tuning kit geared toward image/video/audio diffusion
...The system includes configuration-driven training processes that allow users to define datasets, model paths, and training parameters with minimal setup. SimpleTuner also emphasizes experimentation and academic collaboration, encouraging contributions and iterative improvements from the open-source community. -
13
TNT
A lightweight library for PyTorch training tools and utilities
TNT is a lightweight training framework developed by Meta that simplifies the process of building and managing machine learning training loops using PyTorch. The project focuses on providing a flexible yet structured environment for implementing training pipelines without the complexity of large deep learning frameworks. It introduces modular abstractions that allow developers to organize training logic into reusable components such as trainers, evaluators, and callbacks. ... -
14
slime LLM
slime is an LLM post-training framework for RL Scaling
slime is an open-source large language model (LLM) post-training framework developed to support reinforcement learning (RL)-based scaling and high-performance training workflows for advanced LLMs, blending training and rollout modules into an extensible system. It offers a flexible architecture that connects high-throughput training (e.g., via Megatron-LM) with a customizable data generation pipeline, enabling researchers and engineers to iterate on new RL training paradigms effectively. ... -
15
mosaicml composer
Supercharge Your Model Training
composer is a deep learning training framework built on PyTorch and designed to make large-scale model training more efficient, scalable, and customizable. At the center of the project is a highly optimized Trainer abstraction that simplifies the management of training loops, parallelization, metrics, logging, and data loading. The framework is intended for modern workloads that may span anything from a single GPU to very large distributed training environments, which makes it suitable for both experimentation and production-scale development. ... -
16
nanoGPT
The simplest, fastest repository for training/finetuning models
...It distills the GPT architecture into a few hundred lines of Python code, making it far easier to understand than large, production-scale implementations. The repo is organized with a training pipeline (dataset preprocessing, model definition, optimizer, training loop) and inference script so you can train a small GPT on text datasets like Shakespeare or custom corpora. It emphasizes readability and clarity: the training loop is cleanly written, and the code avoids heavy abstractions, letting students follow the architecture step by step. ... -
17
Reef
Continual learning infra for self-improving agents
...The system can improve either underlying model weights or the surrounding agent harness, including prompts, skills, and rules. Its core loop follows four stages: serve requests, observe feedback, grow candidate improvements, and commit accepted updates. Weight-training workflows can integrate with systems such as Slime and SGLang, while harness optimization can run without local training GPUs. Versioned artifacts let deployments stay operational while new candidates are evaluated and released. Reef also supports test-time learning scenarios where measurable task outcomes can drive iterative improvement. -
18
AI Infra
Understanding of AI Infra: Quantitative Analysis and System Design
AI Infra Book is an open-source technical book and companion repository focused on the infrastructure behind modern large language models. It approaches inference and training through quantitative analysis of hardware limits, data movement, model architecture, and distributed systems. The book contains twelve chapters supported by formulas, diagrams, experiments, and case studies. Companion tools help readers reproduce resource calculations and inspect the assumptions behind system-design decisions. Additional material covers accelerators, networking, KV caches, inference serving, training systems, mixture-of-experts models, and performance engineering. ... -
19
DeepSeek-V3
Powerful AI language model (MoE) optimized for efficiency/performance
...It employs Multi-head Latent Attention (MLA) and the DeepSeekMoE architecture to enhance computational efficiency. The model introduces an auxiliary-loss-free load balancing strategy and a multi-token prediction training objective to boost performance. Trained on 14.8 trillion diverse, high-quality tokens, DeepSeek-V3 underwent supervised fine-tuning and reinforcement learning to fully realize its capabilities. Evaluations indicate that it outperforms other open-source models and rivals leading closed-source models, achieving this with a training duration of 55 days on 2,048 Nvidia H800 GPUs, costing approximately $5.58 million. -
20
Opacus
Training PyTorch models with differential privacy
Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment. Vectorized per-sample gradient computation that is 10x faster than micro batching. Supports most types of PyTorch models and can be used with minimal modification to the original neural network. ... -
21
PaddleX
PaddlePaddle End-to-End Development Toolkit
...Image classification and labeling is the most basic and simplest labeling task. Users only need to put pictures belonging to the same category in the same folder. When the model is trained, we need to divide the training set, the validation set and the test set. Therefore, we need to divide the above data. Using the paddlex command, the data set can be randomly divided into 70% training set, 20% validation set and 10% test set. If you use the PaddleX visualization client for model training, the data set division function is integrated in the client, and you do not need to use command division by yourself. -
22
Train LLM From Scratch
A straightforward method for training your LLM
Train LLM From Scratch is an educational PyTorch project that shows how to build and train a transformer-based language model from the ground up. It is based on the architecture described in Attention Is All You Need and is designed to make the training pipeline understandable rather than hidden behind a large framework. The repository walks through the process from downloading data to generating text with a trained model. It supports training smaller or larger models, including million- and billion-parameter configurations depending on available hardware. A major goal is accessibility, since the author frames it as possible to train models using a single GPU. ... -
23
Megatron-LM
Ongoing research training transformer models at scale
Megatron-LM is a GPU-optimized deep learning framework from NVIDIA designed to train extremely large transformer-based language models efficiently at scale. The repository provides both a reference training implementation and Megatron Core, a composable library of high-performance building blocks for custom large-model pipelines. It supports advanced parallelism strategies including tensor, pipeline, data, expert, and context parallelism, enabling training across massive multi-GPU and multi-node clusters. The framework includes mixed-precision training options such as FP16, BF16, FP8, and FP4 to maximize performance and memory efficiency on modern hardware. ... -
24
DINOv3
Reference PyTorch implementation and models for DINOv3
...It continues the paradigm of learning strong image representations without labels using teacher–student distillation, but introduces a simplified and more scalable training recipe that performs well across datasets and architectures. DINOv3 removes the need for complex augmentations or momentum encoders, streamlining the pipeline while maintaining or improving feature quality. The model supports multiple backbone architectures, including Vision Transformers (ViT), and can handle larger image resolutions with improved stability during training. -
25
TensorBoardX
tensorboard for pytorch (and chainer, mxnet, numpy, etc.)
The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it. The class updates the file contents asynchronously. This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training. TensorboardX now supports logging directly to Comet. Comet is a free cloud based solution that allows you to automatically track, compare and explain your experiments. It adds a lot of functionality on top of tensorboard such as dataset management, diffing experiments, seeing the code that generated the results and more. ...
