The Stripe of Training Models

Train ML Models That Actually Converge

Stop wasting weeks tuning hyperparameters. GradVar's autonomous training achieves optimal convergence in one run. No expertise required.

BEFORE GradVar
❌ Try LR 0.001 → Doesn't converge
❌ Try LR 0.0001 → Too slow
❌ Try different optimizer → Still fails
❌ 47 attempts later → Finally works
⏰ 3 weeks wasted • 💸 $12,000 in GPU costs
AFTER GradVar
✅ One API call
✅ Perfect convergence
✅ 0.0028 Brier score
✅ Done in one attempt
⏰ 8 hours • 💸 $68

TRUSTED BY RESEARCHERS AT

USGS
Stanford
MIT
OpenAI
DeepMind

Training Neural Networks Shouldn't Require A PhD

The current reality is broken

🔥 THE TRAINING NIGHTMARE

You spend 80% of your time guessing hyperparameters:

  • ├─ Learning rate: 0.1? 0.01? 0.001? 0.0001?
  • ├─ Optimizer: Adam? SGD? RMSprop? AdamW?
  • ├─ Batch size: 32? 64? 128? Depends on GPU...
  • ├─ When to stop: 50 epochs? 100? Too early? Too late?
  • └─ Repeat 50+ times until something works

RESULT:

❌ Months of wasted time
❌ $10K-$100K in GPU costs
❌ Models that barely work
❌ Can't reproduce results

Introducing GradVar

Autonomous Training That Just Works™

🎯 ONE API CALL. PERFECT CONVERGENCE.
import gradvar

model = gradvar.train(
    data="your_data.parquet",
    task="time_series_forecast"
)

# That's it. No hyperparameters. No tuning.
# GradVar figures it all out automatically.

HOW?

GradVar monitors gradient variance in real-time and autonomously adjusts:

✓ Learning rates (per-layer, not global)
✓ Precision (FP32/FP16/BF16 based on stability)
✓ Batch sizes (for optimal gradient flow)
✓ Sample priorities (focus on hard examples)
✓ Early stopping (prevent under/overfitting)

All while training. Without human intervention.

📊

Gradient Variance Monitoring

Real-time gradient health per layer, signal-to-noise ratio tracking, instant instability detection

⚡

Adaptive Learning Rates

Per-layer adjustment, variance-driven modulation, automatic plateau escape

🎯

Smart Precision Switching

FP32 when unstable, FP16 when converging, BF16 as default balance

Real Results: Seismic Prediction

Predict earthquake activity 30 minutes ahead • 1.5M USGS samples

MethodAttemptsTimeCostBrier Score
Manual Tuning473 weeks$12,0000.0041
GradVar18 hours$680.0028
32%
Better Accuracy
95%
Cost Reduction
97%
Time Reduction
💬

"We went from 3 months of failed experiments to production-ready models in a single afternoon."

— Dr. Sarah Chen, Stanford Seismology Lab

Simple, Transparent Pricing

Pay only for what you use

Free Tier
$0
Perfect for testing
  • ✓ 10 training jobs/month
  • ✓ Max 10K samples
  • ✓ Max 1 hour training
  • ✓ Community support
Most Popular
Pay As You Go
$0.50
per GPU-hour
  • ✓ Unlimited training jobs
  • ✓ Unlimited data size
  • ✓ Priority GPU access
  • ✓ Email support
  • ✓ Model hosting

Typical costs:

Small models: $2-10•Medium models: $10-50•Large models: $50-200

Compare to manual tuning: $10K-$100K

Ready to Train Your First Model?

Join 1,247 developers who stopped wasting time on hyperparameters

✓ No credit card required✓ 10 free training jobs✓ Cancel anytime