Neuralcast records every training run — hyperparameters, metrics, code, data, and the exact environment — as one searchable lineage. Compare a thousand experiments at a glance, promote the winner into a stage-gated registry, and ship it knowing you can rebuild it down to the random seed.
Tracking the training that ships — from research labs to teams in production
No more metrics pasted into spreadsheets, no more checkpoints named model_final_v3_REAL.pt. Neuralcast is the record of everything your team has ever trained.
Stream loss, accuracy, learning rate, and any custom metric as it happens. Watch curves move mid-epoch, kill a divergent run from the dashboard, and stop losing results to a crashed notebook.
Turn a run into a versioned, stage-gated model. Staging, production, archived — each transition signed off, each change on the record.
Overlay hundreds of runs, sort by any metric, and diff the exact configs that moved the number you care about.
Version datasets, checkpoints, and configs as content-addressed artifacts. Trace any model back to the precise data that trained it.
Kick off Bayesian, grid, or random sweeps from one config. Neuralcast schedules the runs and surfaces the winner.
What reproducible ML looks like at scale
Neuralcast slots into the stack you already have. No rewrite, no proprietary trainer, no hold on your weights — it logs right beside the code you wrote.
Drop nc.init() and nc.log() into any PyTorch, TensorFlow, JAX, or scikit-learn loop. Auto-loggers handle the rest.
Git SHA, diff, pip freeze, GPU type, and CUDA version are snapshotted on every run — so 'works on my machine' is finally something you can prove.
Checkpoints and datasets stay in your own S3, GCS, or Azure storage. Neuralcast indexes them and never holds your weights hostage.
A typed Python SDK and REST API expose every run, metric, and artifact — pull results into a report or wire a threshold into CI in seconds.
One workspace carries a model from a noisy experiment to a governed, deployable asset — no tab-hopping, no second tool.
Real-time metric panels, system charts, and console logs for every run — grouped by experiment, filtered by any tag.
Parallel-coordinates and scatter plots that show which hyperparameters actually drive your target metric across hundreds of runs.
Versioned models with staging-to-production promotion, required reviewers, and a tamper-evident history of every change.
Live documents that embed real charts and run tables — how your team actually shows results to the people who ask for them.
“We were buried in checkpoints with no idea which dataset trained which model. With lineage, an audit question that used to eat a week now takes me about ten minutes.”
“A sweep used to be a spreadsheet and a prayer. Now it's one config — and the first afternoon we ran it, we found settings two points over our production model.”
“The registry is what won over our platform team. Stage-gated promotions with required reviewers gave us real model governance without bolting on a second system.”
Tracking is free for individuals and academics, for good. The bill starts when your team needs governance, collaboration, and scale.
For solo researchers and students.
For ML teams shipping to production.
For regulated and large-scale orgs.
Anything Python. First-class integrations ship for PyTorch, PyTorch Lightning, TensorFlow, Keras, JAX, Hugging Face, and scikit-learn, plus a framework-agnostic logging API for whatever isn't on that list. Two lines of code is the whole setup.
In your own cloud storage. Neuralcast indexes artifacts in your S3, GCS, or Azure bucket and tracks their versions and lineage — the bytes never leave your account. Revoke our access whenever you want and your run history stays intact.
On every run we capture the Git commit and diff, the full dependency list, the hardware and CUDA version, the random seeds, and the dataset version. From any run, you can regenerate the exact environment and config that produced it — no detective work.
Yes. Enterprise plans run entirely inside your own VPC or on-prem, so tracking data and metadata stay within your network perimeter while your team uses the same UI and SDK as the cloud product.
A checkpoint is a file; a registered model is a governed, versioned asset. The registry adds stages — staging, production, archived — plus required reviewers on promotions, an immutable audit trail, and a direct link back to the run, data, and code behind it.
No. Logging runs asynchronously and batched off the training thread, so streaming metrics adds negligible overhead even at over a thousand points a second. Lose the network and runs buffer locally, then sync the moment it's back.
pip install neuralcast, add two lines, watch the metrics stream in live. No credit card, no sales call to start.