Open-source alternatives to Tensorflow Tfx

BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package, deploy, and scale AI models as production-ready REST APIs. It functions as an AI model lifecycle manager and an inference graph orchestrator, enabling the chaining of multiple models and custom logic into complex pipelines for advanced task sequences. The framework distinguishes itself through a dynamic batching engine that optimizes GPU throughput and an artifact-based packaging system that bundles model weights and dependencies into immutable archives for consistent deployment. It

BytePS is a distributed deep neural network training framework and communication library designed to scale model training across multiple GPUs and compute nodes. It functions as a GPU cluster orchestrator and RDMA network optimizer, providing the necessary primitives to synchronize gradients and data across a server cluster. The project distinguishes itself through high-performance network optimizations, utilizing remote direct memory access and page-aligned memory to reduce latency. It employs topology-aware communication tuning and CPU core affinity management to maximize hardware throughpu

High Accuracy and efficiency multi-task fine-tuning framework for Code LLMs. This work has been accepted by KDD 2024.

MLeap: Deploy ML Pipelines to Production

Avalanche: an End-to-End Library for Continual Learning based on PyTorch.

3FS is a distributed file system and RDMA storage cluster designed for high-performance AI training and inference workloads. It functions as a strongly consistent storage layer that utilizes a disaggregated architecture to pool SSDs and memory resources across multiple nodes. The system provides specialized storage implementations including an AI training checkpoint store for parallel state preservation and a distributed key-value cache store for decoder layer vectors to optimize inference processing. It ensures data integrity through chain replication and apportioned query distribution. The

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.

Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ

Skaffold is a command-line tool that automates the build, push, and deployment lifecycle for containerized applications on Kubernetes. It functions as a continuous development engine, monitoring source code for changes to trigger incremental updates, manifest hydration, and automated deployments to a cluster. By abstracting the underlying build and deployment tools, it provides a unified interface for managing the inner development loop. The platform distinguishes itself through its environment-aware configuration and flexible build orchestration. It supports diverse build strategies, includi

h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and deploying predictive models using distributed in-memory computing. It functions as a deep learning framework and a distributed model scoring engine, capable of operating as a Kubernetes ML cluster to process large datasets in parallel. The platform distinguishes itself through automated machine learning capabilities that automatically select the best algorithms and hyperparameters to optimize model performance. It provides specialized deep learning toolkits for tasks including i

Horovod is a distributed deep learning framework and gradient synchronizer designed to scale model training across multiple GPUs and compute nodes. It functions as a distributed training orchestrator and an elastic training engine, utilizing an MPI collective communication library to synchronize weights and gradients across TensorFlow, PyTorch, Keras, and MXNet models. The system distinguishes itself through dynamic elastic scaling, which allows it to adjust the number of active workers at runtime and recover from node failures. It optimizes communication efficiency using tensor fusion batchi

This project is a multimodal model trainer and machine learning fine-tuning tool that provides a containerized workflow for adapting pre-trained models to specific tasks. It features a no-code web interface and a dashboard for training large language models and other machine learning datasets without writing code. The system distinguishes itself by integrating a no-code interface with remote GPU orchestration, allowing users to deploy containerized training environments on cloud infrastructure or local hardware. It includes a dedicated integrator for uploading trained model weights and config

🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code

Minimalistic large language model 3D-parallelism training

Sacred is an experiment management tool and reproducibility framework designed to organize multiple runs of a process with different configurations. It functions as a machine learning experiment tracker and hyperparameter configuration manager, logging hyperparameters, metrics, and metadata to a database to ensure that experimental executions remain trackable. The project focuses on scientific result reproducibility by automatically managing random seeds and tracking system dependencies. It allows for the execution of experiment variants through command-line parameter overrides and dynamic pa

CML is a pipeline automation tool for training and evaluating machine learning models, functioning as a CI/CD system for machine learning. It serves as a cloud compute orchestrator and Git-based workflow manager that automates model training cycles through branch management, automated commits, and integrated reporting. The project distinguishes itself by provisioning ephemeral cloud instances or Kubernetes nodes to provide specialized hardware for compute-heavy tasks. It also manages remote compute runners, allowing the connection of self-hosted GPU clusters or on-premise machines to execute

DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models. It functions as a system for managing large data artifacts by storing lightweight metadata in version control while keeping the actual binaries in a separate cache. The project serves as an experiment tracker and remote storage synchronizer, enabling the execution and comparison of machine learning iterations based on hyperparameters and performance metrics. It provides a bridge for pushing and pulling these large data artifacts between local environments and cloud or on-premi

Hopsworks - Data-Intensive AI platform with a Feature Store

Ludwig is a multimodal machine learning platform and low-code framework designed for building, training, and deploying neural networks. It enables the construction of models that process text, images, audio, and tabular data through a unified interface using declarative configuration files rather than custom code. The system features a specialized low-code framework for large language models, supporting supervised fine-tuning, preference alignment, and a constrained decoding tool to force structured data output via logit extraction. It also includes an automated model architecture search to i

NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

Resource scheduling and cluster management for AI

Debugging, monitoring and visualization for Python Machine Learning and Data Science

Metaflow is a Python machine learning framework and MLOps workflow orchestrator designed to manage the lifecycle of data pipelines from local prototyping to production. It serves as a distributed compute manager and an experiment tracking system, enabling the creation of reproducible pipelines that transition between development and high-availability production environments. The framework distinguishes itself through an integrated checkpointing system that automatically persists intermediate data artifacts to remote storage, allowing failed runs to be resumed from the last successful step. It