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AI Trainer
Stop training when generalization begins.
$2,500/mo
Most teams watch loss curves and guess. That guess burns GPU time. AI Trainer analyzes training dynamics — loss, train/test gap, gradient behavior, weight evolution — to detect when a run is still memorizing, when it begins to generalize, and when further training is unlikely to pay off. With a dollar figure attached.
If the transition never happens, it tells you that too — and suggests what to change: more weight decay, different optimizer, more data, smaller model. A negative result is still a result. It saves you from burning GPU hours on a dead end.
WHAT YOU GET
Grokking detection: the epoch range where generalization emerges
Multi-signal analysis (loss, weights, gradients)
Landauer energy accounting per training run
Plateau detection with escape recommendations
Memorization vs generalization classifier
Cost-per-insight tracking in real dollars
Strategy recommendations when grokking fails
Integration with PyTorch and TensorFlow callbacks
Neural Engine grokking detection (19,716 inferences/sec)
Hardware-signed training reports via Secure Enclave
WHAT THIS IS / WHAT THIS ISN'T
WHAT THIS IS
A training monitor that detects grokking (the phase transition from memorization to generalization) using 4 signals: accuracy jump, energy-per-correct drop, weight norm stabilization, and train-test gap closing. It tells you when to stop and estimates compute savings.
WHAT THIS ISN'T
W&B. No experiment tracking, no model registry, no dataset versioning, no team collaboration. This does ONE thing — tells you when to stop — and does it better than anything else. Pair it with W&B if you need the full stack.
YEAH BUT
"W&B already tracks training."
W&B shows curves. It never says STOP. You still have to decide when to stop training. We decide for you and attach a dollar figure. The difference between a dashboard and a decision.
"What's the Landauer energy accounting?"
A theoretical lower-bound lens on information processing. Memorizing every sample independently costs orders of magnitude more energy than compressing them into a pattern. We track this ratio per run — it's a signal, not a marketing number.
"What if grokking doesn't happen?"
We detect that too. If your model memorizes and never generalizes, we say CHANGE STRATEGY and suggest: more weight decay, different optimizer, more data, smaller model. A negative result is still a result — and it saves you from burning GPU hours on a dead end.
VS THE COMPETITION
Weights & Biases
$60/user/mo — records training runs. Doesn't make stop recommendations.
AI Trainer: tells you when to stop, what it costs, and what to change. Decisions, not dashboards.
Neptune.ai
$49+/mo — cloud-only. No grokking detection. Metadata tracker, not training advisor.
AI Trainer: runs locally. Detects phase transitions. Recommends strategy changes.
MLflow
Free but no access controls, basic visualization, significant DevOps overhead.
AI Trainer: zero DevOps. Grokking detection out of the box. Landauer energy accounting included.
TRY IT
3 free analyses. Fixed sample data. Your own data requires a license.
TESTING
Unit tests, adversarial input testing (None, wrong types, NaN, empty data, unicode), real user workflow testing, and cross-product integration testing. Every public function handles every input permutation without crashing. 673 quality tests across all products, zero failures. Self-verifying: the product can audit its own output.
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Get AI Trainer — $2,500/mo
After purchase: setup guide
INSTALL
pip install begump
from gump.aitrainer import *