a proof-of-concept OpenAI Gym environment for Neural Architecture Search (NAS).
a framework that implements AutoML algorithms for model architecture search at scale.
a global, black box optimization engine for real world metric optimization by Yelp.
a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code.
PyTorch Meta-learning Framework for Researchers.
Katib is a Kubernetes-native project for automated machine learning (AutoML).
A framework to connect a flow of ML models by applying graph theory.
A toolset for black-box hyperparameter optimisation.
Distributed Asynchronous Hyperparameter Optimization in Python.
A General Automated Machine Learning Framework.
open source code for tuning hyperparams with Hyperband.
a library for hyperparameter optimization and black box optimization benchmarks.
a framework for distributed hyperparameter optimization.
A hyperparameter optimization framework, inspired by Optuna.
Fast and lightweight AutoML (paper).
An open source python library for AutoML.
a basic proof of concept for genetic architecture search in Keras.
An open source python library for scalable Bayesian optimisation.
an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
Automatic architecture search and hyperparameter optimization for PyTorch.
AutoML library for deep learning.
Provide an input CSV and a target field to predict, generate a model + code to run it.
An autoML framework & toolkit for machine learning on graphs
A framework to find the best performing AI/ML model for any AI problem.
a platform for Neural Network Search (NAS) that allows you to generate efficient deep networks for your applications.
a high-performance, high-precision CPU, GPU, and memory profiler for Python
octoml-profile is a python library and cloud service designed to provide the simplest experience for assessing and optimizing the performance of PyTorch models on cloud hardware with state-of-the-art ML acceleration technology.
Open deep learning compiler stack for cpu, gpu and specialized accelerators
Accessible large language models via k-bit quantization for PyTorch.
Compiler technology to transform a valid Open Neural Network Exchange (ONNX) graph into code that implements the graph with minimum runtime support.
Docker for Your ML/DL Models Based on OCI Artifacts
Open Source ML Model Versioning, Metadata, and Experiment Management
Light-weight, universal resource scheduler for container orchestrator systems.
A Cloud Native Batch System (Project under CNCF).
A Highly Scalable Workload Manager.
Kubernetes-native Job Queueing.
Enterprise-grade Voice AI simulation SDK for scenario-driven stress testing of multimodal and agentic systems.
An open-source unstructured data ETL tool to streamline the end-to-end unstructured data processing pipeline.
The easiest way to automate your data.
The fastest way to build data pipelines. Develop iteratively, deploy anywhere.
Build and manage real-life data science projects with ease!
Machine Learning Pipelines for Kubeflow.
Durable execution layer for AI agents. Checkpoints, replay, resume, and observability primitives that make agent workflows persistent and replayable — no graph DSL required.
Kubernetes-native workflow automation platform for complex, mission-critical data and ML processes at scale.
Workflow engine for Kubernetes.
An MLOps/LLMOps platform for model building, evaluation, and fine-tuning.
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
One-click machine learning deployment (LLM, text-to-image and so on) at scale on any cluster (GCP, AWS, Lambda labs, your home lab, or even a single machine).