Register for CNC Process Optimizer

Optimizes cutting parameters to reduce wear and defects

Industrial
Benchmark Requirements
Proposal ID
18
Model Name
CNC Process Optimizer
Token Ticker
HKCNC
Benchmark Metric
Mse
Target / Baseline Value
4.0%
Dataset / Test Data Reference
kaggle
Token Reward
1,000 HKCNC per verified DeltaOne
Performance target

Reach 4.0% on Mse where lower is better.

Mean squared error

How to Submit Your Model
1

Register Your Model

Use the proposal-owned values below to attach your model registration to the existing proposal through the SDK.

Install SDK

pip install "hokusai-ml-platform[ml]"

Configure API Key

export HOKUSAI_API_KEY="your-hokusai-api-key-here"

Register your model

hokusai model register \
  --token-id HKCNC \
  --model-path ./models/final_model.pkl \
  --metric Mse \
  --baseline 4.0%
Python SDK option also available for MLflow users. See complete guide for details.
2

Trigger Evaluation

Once your model is registered, trigger an evaluation run against the benchmark dataset. The system will automatically measure your model's performance against the target criteria.

Evaluation process:

  • Your model runs on the specified evaluation dataset
  • Performance is measured using Mse
  • Results are verified and recorded on-chain
  • Token rewards are calculated based on performance improvement
3

Claim Your Rewards

If your model meets or exceeds the benchmark target, you'll earn token rewards. Tokens are automatically minted and can be claimed from your dashboard.

What to Expect

Evaluation Timeline

Evaluation runs typically complete within 5-30 minutes, depending on model complexity and dataset size. You'll receive notifications when evaluation completes.

Performance Verification

All evaluation results are verified and recorded on-chain to ensure transparency and prevent manipulation. Your model's performance will be publicly visible.

Token Distribution

Tokens are minted automatically when your model achieves performance improvements. The amount is calculated based on the delta between your model's performance and the baseline, multiplied by the tokens-per-delta-one rate.

Support & Documentation

Need help? Visit our model submission guide for detailed instructions, code examples, and troubleshooting tips.