How to: Model Training and Orchestration
* [AutoTrain Advanced](https://github.com/huggingface/autotrain-advanced) ![](https://img.shields.io/github/stars/huggingface/autotrain-advanced.svg?cacheSeconds=86400) - AutoTrain Advanced is a no-code solution that allows you to train machine learning models in just a few clicks.
* [Avalanche](https://github.com/ContinualAI/avalanche) ![](https://img.shields.io/github/stars/ContinualAI/avalanche.svg?cacheSeconds=86400) - Avalanche is an end-to-end Continual Learning library to provide a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducible evaluation of continual learning algorithms.
* [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) ![](https://img.shields.io/github/stars/axolotl-ai-cloud/axolotl.svg?cacheSeconds=86400) - Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.
* [BindsNET](https://github.com/BindsNET/bindsnet) ![](https://img.shields.io/github/stars/BindsNET/bindsnet.svg?cacheSeconds=86400) - BindsNET is a spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning.
* [CML](https://github.com/iterative/cml) ![](https://img.shields.io/github/stars/iterative/cml.svg?cacheSeconds=86400) - Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects.
* [CoreNet](https://github.com/apple/corenet) ![](https://img.shields.io/github/stars/apple/corenet.svg?cacheSeconds=86400) - CoreNet is a deep neural network toolkit that allows researchers and engineers to train standard and novel small and large-scale models for variety of tasks, including foundation models (e.g., CLIP and LLM), object classification, object detection, and semantic segmentation.
* [Determined](https://github.com/determined-ai/determined) ![](https://img.shields.io/github/stars/determined-ai/determined.svg?cacheSeconds=86400) - Deep learning training platform with integrated support for distributed training, hyperparameter tuning, and model management (supports Tensorflow and Pytorch).
* [dstack](https://github.com/dstackai/dstack) ![](https://img.shields.io/github/stars/dstackai/dstack.svg?cacheSeconds=86400) - dstack is an open-source container orchestrator that simplifies workload orchestration and drives GPU utilization for ML teams.
* [envd](https://github.com/tensorchord/envd) ![](https://img.shields.io/github/stars/tensorchord/envd.svg?cacheSeconds=86400) - Machine learning development environment for data science and AI/ML engineering teams.
* [Fairseq](https://github.com/facebookresearch/fairseq) ![](https://img.shields.io/github/stars/facebookresearch/fairseq.svg?cacheSeconds=86400) - Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.
* [Fire-Flyer File System](https://github.com/deepseek-ai/3FS) ![](https://img.shields.io/github/stars/deepseek-ai/3FS.svg?cacheSeconds=86400) - The Fire-Flyer File System (3FS) is a high-performance distributed file system designed to address the challenges of AI training and inference workloads. It leverages modern SSDs and RDMA networks to provide a shared storage layer that simplifies development of distributed applications.
* [H2O-3](https://github.com/h2oai/h2o-3) ![](https://img.shields.io/github/stars/h2oai/h2o-3.svg?cacheSeconds=86400) - Fast scalable Machine Learning platform for smarter applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc..
* [Hopsworks](https://github.com/logicalclocks/hopsworks) ![](https://img.shields.io/github/stars/logicalclocks/hopsworks.svg?cacheSeconds=86400) - Hopsworks is a data-intensive platform for the design and operation of machine learning pipelines.
* [Ignite](https://github.com/pytorch/ignite) ![](https://img.shields.io/github/stars/pytorch/ignite.svg?cacheSeconds=86400) - Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
* [Kubeflow](https://github.com/kubeflow/kubeflow) ![](https://img.shields.io/github/stars/kubeflow/kubeflow.svg?cacheSeconds=86400) - A cloud-native platform for machine learning based on Google’s internal machine learning pipelines.
* [Ludwig](https://github.com/ludwig-ai/ludwig) ![](https://img.shields.io/github/stars/ludwig-ai/ludwig.svg?cacheSeconds=86400) - Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks.
* [MFTCoder](https://github.com/codefuse-ai/MFTCoder) ![](https://img.shields.io/github/stars/codefuse-ai/MFTCoder.svg?cacheSeconds=86400) - MFTCoder is an open-source project of CodeFuse for accurate a