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Experiment tracking, model registry & production monitoring for ML teams
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Track, visualize, and compare AI experiments effortlessly
Aim logs training runs and any AI metadata, offering a fast UI for comparison, real‑time alerts, and a Python SDK for programmatic queries and automation.
Aim is a self‑hosted experiment tracking tool built for machine‑learning teams that need to manage tens of thousands of training runs. It captures system information, resource usage, and arbitrary metadata, then presents it in a responsive web UI where runs can be grouped, compared, and visualized.
The Python SDK lets you log parameters, metrics, images, and custom objects directly from your code and query them later with expressive Python expressions. Built‑in converters simplify migration from TensorBoard, MLflow, and Weights & Biases. Aim integrates with popular frameworks such as PyTorch Lightning, Hugging Face, Keras, XGBoost, and many more. For distributed training, a remote tracking server aggregates logs from multiple hosts, and the official Docker image enables quick deployment on Kubernetes or any container platform.
Researchers and engineers who prefer on‑prem control, need real‑time alerts, and want programmatic access to experiment data will find Aim a flexible foundation for building reproducible AI pipelines.
When teams consider Aim, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Training a deep translation model
Track loss, accuracy, and resource usage, visualize progress, and compare hyperparameter variations across runs.
GAN research experiments
Log image samples and metrics, enabling visual comparison of generator quality over epochs.
Hyperparameter sweep with Optuna
Store trial parameters and scores, then query the best configurations via the SDK.
Multi‑node training with remote server
Collect logs from distributed workers into a central dashboard for unified monitoring.
Run `pip install aim` in your training environment.
Yes, Aim includes converters for TensorBoard, MLflow, and Weights & Biases.
Aim is self‑hosted; enterprise support and hosted options are offered by AimStack.
Integrations cover PyTorch Ignite, PyTorch Lightning, Hugging Face, Keras, XGBoost, LightGBM, fastai, MXNet, Optuna, and many others.
After logging runs, execute `aim up` to start the web interface.
Project at a glance
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