Parse
Readable indentation-aware parser emits typed dataclass AST nodes.
Edition 2026.3
Python + PyTorch backend
CSV tabular MVP
Hyperbridge Digital · Kynetra language systems
KynML
A Python-like ML training language that turns one readable `.kyn` file into a complete PyTorch training script.
kynml train examples/house_price.kyn
parse -> validate -> generate -> train
5
language blocks
4
CLI commands
3
working examples
1
runnable PyTorch script
Language surface
Write the training intent. Let the compiler carry the boilerplate.
KynML v1 is intentionally narrow: CSV datasets, dense networks, core losses, metrics, optimizers, and Torch export. That restraint is the feature.
Compiler pipeline
Small language, real compiler shape.
The MVP stays approachable while leaving clean boundaries for ONNX, JAX, MLIR, distributed training, and cloud jobs.
Validate
Semantic checks catch missing blocks, unknown references, unsupported layers, and bad ranges.
Generate
PyTorch backend writes imports, loaders, model class, loop, metrics, export, and main.
Train
The CLI compiles and executes the generated script with the project Python runtime.
Proof, not theater
The first milestone is executable.
`kynml train examples/house_price.kyn` parses the source, validates the program, generates Python, trains a model on sample CSV data, prints metrics, and exports a Torch `state_dict`.
$ kynml validate examples/house_price.kyn
Valid KynML program: examples/house_price.kyn
$ kynml compile examples/house_price.kyn --out generated/house_price.py
Generated generated/house_price.py
$ kynml train examples/house_price.kyn
Epoch 20/20 - loss: ...
mae: ...
rmse: ...
Saved model to models/house_price_model.ptSupported in v1
A compact surface that can grow without collapsing.
Data
CSV source, target selection, split, shuffle, StandardScaler normalization.
Models
Input layer plus dense layers with relu, sigmoid, tanh, and linear activations.
Training
MSE, BCE, cross entropy, Adam, SGD, epochs, batch size, auto/cpu/cuda device.
Evaluation
MAE, MSE, RMSE, and accuracy metrics on the generated test dataloader.
Export
Torch `state_dict` export with automatic directory creation.
CLI
`validate`, `ast`, `compile`, and `train` commands for local model development.
Leadership
Built by operators who have shipped the stack before.
KynML is led from the HyperBridge Digital and Kynetra ecosystem: product narrative, AI architecture, platform engineering, and production delivery moving as one system.
KR
KR
Founder & CEO
Supercharged entrepreneur and content producer. BE (CS), Anna University · 20 yrs. Architect of Kynetra AI — 423 regents across 25 domains. Fractional CMO to 200+ brands including Sony, Samsung, Tata Motors, HDFC, and GRT. Founded The Reel Factory.
CS
CS
Co-founder & CTO
BE (CS), Anna University · 22 yrs. Engineering force behind every HyperBridge platform. Owns the architecture across the Kynetra Cognitive Grid: telemetry, router, evaluator, memory, audit, prompts, and 30 production platforms.
Roadmap
From tabular PyTorch MVP to sovereign training substrate.
Phase 2 Image datasets, CNN layers, text datasets, transformer blocks.
Phase 3 ONNX export, TorchScript export, registry, experiment tracking.
Phase 4 JAX backend, MLIR backend, distributed training, cloud jobs.
Phase 5 KynML Studio, AutoML layer, SaaS deployment, API generation.