Comet
Experiment tracking, model registry & production monitoring for ML teams
Aim
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.
Overview
Overview
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.
Capabilities & Deployment
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.
Who Benefits
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.
Highlights
Beautiful UI for visualizing and comparing runs
Python SDK for flexible metadata queries
Built‑in converters for TensorBoard, MLflow, and Weights & Biases
Integrations with major ML frameworks and remote server support
Pros
- Scales to tens of thousands of runs
- Real‑time alerting and resource tracking
- Easy migration from other trackers
- Self‑hosted, no vendor lock‑in
Considerations
- Requires self‑hosting and maintenance
- Limited out‑of‑the‑box cloud SaaS offering
- UI may need tuning for very large datasets
- Advanced features may need enterprise support
Managed products teams compare with
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.
Fit guide
Great for
- ML teams needing on‑prem experiment tracking
- Researchers who want programmatic access to metadata
- Projects migrating from TensorBoard or MLflow
- Organizations preferring self‑hosted solutions
Not ideal when
- Teams looking for a fully managed cloud service
- Users requiring extensive built‑in model serving
- Small scripts with minimal tracking needs
- Environments without Python support
How teams use it
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.
Tech snapshot
Python92%
Cython5%
JavaScript2%
HTML1%
CSS1%
Shell1%
Frequently asked questions
How do I install Aim?
Run `pip install aim` in your training environment.
Can I migrate logs from other trackers?
Yes, Aim includes converters for TensorBoard, MLflow, and Weights & Biases.
Is there a hosted version of Aim?
Aim is self‑hosted; enterprise support and hosted options are offered by AimStack.
Which machine‑learning frameworks are supported?
Integrations cover PyTorch Ignite, PyTorch Lightning, Hugging Face, Keras, XGBoost, LightGBM, fastai, MXNet, Optuna, and many others.
How do I launch the Aim UI?
After logging runs, execute `aim up` to start the web interface.
Project at a glance
Active- Stars
- 5,960
- Watchers
- 5,960
- Forks
- 368
LicenseApache-2.0
Repo age6 years old
Last commit2 days ago
Primary languagePython
Last synced yesterday