Self-Hosted Experiment Tracking and MLOps
Track training runs, metrics and model versions on a server you run, with MLflow, ClearML, DVC or Aim.
Open-source data version control for ML projects on macOS, Windows and Linux, with local experiment tracking and a VS Code extension.
15.9KUpdated 2 months agoApache-2.0
macOS · Windows · Linux · VS Code#Git integration
DVC connects data and model versions to the code in your Git repository, so you can reproduce a machine learning experiment with the inputs it used. It's a free, open-source tool under Apache 2.0 for individual data scientists and small projects. It runs on macOS, Windows and Linux.
Experiment Tracking and MLOps+0
Self-hosted ML experiment tracker for comparing training runs and querying metadata, with a Python SDK and an Apache 2.0 license.
6.3KUpdated 9 months agoApache-2.0
Docker · Web
Aim is a free, open source ML experiment tracker for researchers and teams who want to keep training records on their own infrastructure. It runs in your training environment or on a self-hosted server, with Docker and Kubernetes deployment support. Its Apache 2.0 license permits use and modification.
Experiment Tracking and MLOps+0
An open-source LLM fine-tuning tool with a browser interface. Runs on Ubuntu with NVIDIA GPUs or in Docker, under the Apache 2.0 license.
5.2KUpdated 4 days agoApache-2.0
Linux · Docker · Web#Hugging Face integration#LoRA#Quantization
H2O LLM Studio is a self-hosted tool for teams that want to adapt language models to their own datasets without writing training code. Its browser interface brings training experiments, evaluation, and model testing into one place. The project is open source under Apache 2.0.
Experiment Tracking and MLOpsLLM Evaluation and TestingLLM Fine-Tuning+0
Self-hostable MLOps software for experiment tracking, pipelines, dataset versioning and model serving, with an Apache 2.0 Python SDK.
6.9KUpdated 2 days agoApache-2.0
Docker · Web#Code execution#Git integration#Multi-user access
ClearML is an MLOps suite for recording experiments, managing datasets and running ML workloads. Its Apache 2.0 Python SDK connects to a ClearML Server, available as a hosted service or open-source software you deploy yourself. ClearML Agent handles job orchestration and reproducibility.
Experiment Tracking and MLOpsGPU Management and MonitoringInference Servers and ClustersKubernetes and Cloud-Native AILLM Fine-Tuning+0
An open-source ML visualization tool that runs locally or on your server, works offline, and compares TensorFlow training metrics and model graphs.
7.2KUpdated 1 month agoApache-2.0
macOS · Web#Works offline
TensorBoard is a browser-based toolkit for inspecting TensorFlow experiments on your own machine or server. It's for researchers and ML developers who need to understand training behavior, compare runs, and investigate model performance. It works entirely offline, including behind a corporate firewall or in a datacenter, so experiment data can stay within your own environment.
Experiment Tracking and MLOpsModel Inspection and Visualization+0
An open source AI platform for Kubernetes that supports self-hosted ML workflows, distributed training and model management under Apache 2.0.
15.9KUpdated 1 month agoApache-2.0
Web#MLX#Multi-user access
Kubeflow is a self-hosted AI platform for teams that run machine learning workloads on Kubernetes. It brings model development, training and production workflows into a modular stack that can run on a local laptop, on-premises infrastructure or a cloud Kubernetes cluster. It's open source under Apache 2.0.
Experiment Tracking and MLOpsKubernetes and Cloud-Native AILLM Fine-TuningTraining Frameworks+0
Self-hosted AI observability platform for tracing and evaluating LLM apps. Run it locally or in Docker, with Ollama integration and OpenTelemetry support.
11.7KUpdated 23 hours ago
Docker · Web#LLM tracing#MCP#Ollama integration
Arize Phoenix is a self-hosted platform for developers who need to understand why an AI agent failed and test changes before shipping them. It runs on a laptop, in Docker, or on Kubernetes. Self-hosting keeps traces on your infrastructure; Phoenix Cloud provides a hosted alternative. Phoenix uses the Elastic License 2.0 (ELv2), a source-available license.
Experiment Tracking and MLOpsLLM Evaluation and TestingLLM Observability and TracingMCP Servers and Agent SkillsPrompt Management and Playgrounds+0
Self-hosted AI engineering platform for tracing agents, evaluating LLMs, and tracking models. Apache 2.0 licensed, with Ollama support.
28.2KUpdated 1 day agoApache-2.0
Docker · Web#Batch processing#LLM tracing#MCP
MLflow brings agent tracing, LLM evaluation, and model experiment tracking into a platform you can run locally or on your own servers. It's for developers and teams who need to understand failures, compare changes, and monitor AI applications in production. It's open source under Apache 2.0.
Experiment Tracking and MLOpsLLM Evaluation and TestingLLM Gateways and RoutersLLM Observability and TracingPrompt Management and Playgrounds+0
