neptune.ai
AIExperiment tracker purpose-built for foundation models to monitor per-layer metrics, visualize training, and speed up debugging.
-(0 Reviews)
Paid
See pricing details
neptune.ai
AI
Experiment tracker purpose-built for foundation models to monitor per-layer metrics, visualize training, and speed up debugging.
-(0 Reviews)
Paid
See pricing details
About neptune.ai
Neptune is an experiment tracking and observability platform designed to monitor large-scale model training, including foundation models. It captures thousands of per-layer metrics (losses, gradients, activations) at scale, provides low-latency visualizations to avoid missed spikes, and enables deep drill-down into logs to speed debugging. Neptune helps ML teams reproduce experiments, share results, and simplify model handover, and has been used for GPT-scale training monitoring.
Key Features
Per-Layer Metric Monitoring: Captures and stores thousands of per-layer metrics such as losses, gradients, and activations to give fine-grained visibility into model training behavior.
Real-Time Visualization: Low-latency plotting and dashboards that let teams visualize metrics at scale with no missed spikes, enabling timely detection of anomalies during training.
Deep Drill-Down Debugging: Integrated access to logs and granular metrics allows engineers to trace and debug training issues quickly, reducing time-to-fix for complex failure modes.
Scalable Foundation Model Support: Built to handle GPT-scale and other foundation model training workloads, enabling monitoring across very large models and distributed training runs.
Experiment Reproducibility & Sharing: Stores experiment metadata, results, and artifacts to support reproducibility, result comparison, and smooth handover between team members.
Data Fetching Component: Provides a separated "Neptune Fetcher" capability (per GitHub) to optimize and streamline data retrieval for experiments and analyses.
Capture and monitor thousands of per-layer metrics (losses, gradients, activations)
Low-latency visualization for high-frequency metrics and spike detection
Log exploration and drill-down to debug training issues
Experiment metadata and result sharing to improve reproducibility and team handover
Neptune Fetcher component to separate and optimize data retrieval from the primary client package
Designed to scale to foundation-model and GPT-scale training workloads
Use Cases
Debugging Large-Scale Model Training: Monitor per-layer gradients and activations during GPT-scale training to detect vanishing/exploding gradients and locate faulty training steps.
Investigating Training Anomalies: Use low-latency visualizations and log drill-down to find and diagnose spikes or sudden metric regressions in long-running experiments.
Reproducing and Sharing Results: Archive experiment configurations, metrics, and artifacts so teammates can reproduce results and continue model development without context loss.
Model Handover Between Teams: Provide a centralized, detailed experiment record for handing over models from research to engineering or production teams with full traceability.
Comparing Experiment Runs: Compare multiple runs side-by-side at metric and per-layer granularity to evaluate hyperparameter changes or architecture modifications.
Integrating into ML Workflows: Embed experiment tracking into training pipelines to continuously monitor performance and alert on critical metric deviations.
Monitoring and debugging large-scale foundation model training runs (per-layer diagnostics)
Detecting and investigating transient metric spikes during training
Centralizing experiment metadata and logs for reproducibility and model handover
Sharing experiment results and visualizations across ML teams
Integrating with CI/ML pipelines to track and compare experiments at scale
Screenshots
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