What is Runlog?
Runlog is an AI experiment tracking tool that monitors and controls machine learning training runs in real time for ML engineers and research teams. Built for unreliable connections, it streams metrics and terminal logs live, and its offline-first design buffers data locally so nothing is lost if the connection drops mid-run — runs even start fully offline and sync in order once you reconnect. You connect any PyTorch, HuggingFace, Keras, or XGBoost script in about three lines of code. Beyond passive logging, Runlog adds live control: adjust hyperparameters mid-run with "live knobs," pause or stop training from the dashboard without restarting, and rely on automatic crash detection for exceptions, OOM errors, and silent hangs. Metric charts are auto-detected, and you can log custom key-value pairs. For teams, it offers workspaces with role-based access control, cross-user run comparison, run annotations anchored to training steps, built-in group chat, and public read-only shareable links. Conditional email alerts fire when metrics cross thresholds, and a checkpoint ledger keeps metric snapshots. Runlog is currently in beta.
What is Runlog used for?
ML engineers and research teams use Runlog to track, monitor, and control PyTorch, HuggingFace, Keras, and XGBoost training runs in real time, even over unreliable connections.
How much does Runlog cost?
Pros and cons
Pros
- Offline-first design buffers metrics and terminal logs locally, so nothing is lost if the connection drops mid-run
- Live knobs let you adjust hyperparameters and pause/stop training from the dashboard without restarting
- Connects any PyTorch, HuggingFace, Keras, or XGBoost script in about three lines of code
- Automatic crash detection catches exceptions, OOM errors, and silent hangs (dead runs)
- Team features: role-based workspaces, cross-user run comparison, step-anchored annotations, and public read-only share links
Cons
- Currently in beta, so features and stability may still change
- No published pricing beyond a free tier, making long-term cost unclear
- Narrow focus on ML training monitoring may not suit broader MLOps or data-science needs
- Newer and less established than incumbent experiment trackers
Questions about Runlog
What is Runlog used for?
Runlog is used to monitor and control machine learning training runs in real time. It streams metrics and terminal logs live, detects crashes and hangs, and lets you adjust hyperparameters or pause training mid-run from a dashboard.
How does Runlog handle unreliable internet connections?
Runlog is offline-first. If the connection drops mid-run, data buffers locally and syncs in order once you reconnect, so nothing is lost. You can even start a run fully offline and it will be created on the dashboard when you come online.
Which ML frameworks does Runlog support?
Runlog connects to PyTorch, HuggingFace, Keras, and XGBoost scripts. Integration takes roughly three lines of code to start streaming metrics and logs.
Is Runlog free?
Runlog offers a free start and is currently in beta. No detailed paid pricing tiers are published on the site yet.
What are good alternatives to Runlog?
Common alternatives for ML experiment tracking include Weights & Biases, Neptune.ai, Comet ML, MLflow, and TensorBoard. Runlog differentiates itself with offline-first data buffering and live mid-run hyperparameter control.
What are the best alternatives to Runlog?
Reviews
Sign in to rate and review Runlog.
Sign in to reviewNo reviews yet. Be the first to review Runlog!