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
8
CLI commands
5
working examples
4
adjacent tools mapped
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.ptCapability matrix
A compact surface that can grow without collapsing.
Layers
Input + dense layers. Activations: relu, sigmoid, tanh, linear. Softmax output inferred from cross_entropy loss.
Losses
mse · bce · cross_entropy — covers regression, binary and multiclass classification.
Optimizers
adam(lr) · sgd(lr, momentum) with per-param learning rate. Scheduler: cosine_annealing.
Metrics
mae · mse · rmse · accuracy — computed on the held-out test split after training.
Export formats
torch state_dict .pt and onnx .onnx — both with automatic directory creation.
CLI commands
validate · ast · compile · train · sweep · compare · fmt · lsp — eight commands for local compiler workflows.
Performance
One line to unlock AMP, compile, and workers.
The compiler emits opt-in flags for PyTorch 2 torch.compile, mixed-precision AMP, and multi-worker data loading. No boilerplate edits — just set the flag in the train block.
AMP
device = cuda
Emits autocast + GradScaler when CUDA is available. Half-precision training at full fidelity.
compile
compile = true
Wraps the model in torch.compile() for PyTorch 2 graph capture and kernel fusion.
workers
workers = 4
Passes num_workers to the DataLoader for parallel data prefetch, keeping the GPU fed.
Connectivity
Serve, orchestrate, integrate — without touching the compiler.
The optional kynml.serving, kynml.mcp, and kynml.integrations packages extend the base compiler into production workflows. Heavy deps are lazy-imported so the core package stays minimal.
01
REST serving
kynml serve
Compiles a .kyn spec and generates a FastAPI service with /predict and /health endpoints. Run kynml serve model.kyn — the server boots in seconds.
from kynml.serving import generate_service
02
MCP server
kynml mcp
Exposes KynML tools over the Model Context Protocol so any MCP-compatible agent can validate, compile, and trigger training runs programmatically.
python -m kynml.mcp.server
03
HuggingFace
HF datasets
Pull any HuggingFace dataset directly into a .kyn pipeline with source = hf("dataset/name"). The integration handles splits, column mapping, and tensor conversion.
from kynml.integrations.huggingface import load_hf_dataset
Competitive map
Not another wrapper. A training language that can sit above the stack.
Lightning strengthens PyTorch projects. Keras gives a refined Python API. Ludwig pushes low-code breadth. ZenML orchestrates MLOps pipelines. KynML takes a sharper lane: compiler-checked training intent that emits readable Python and PyTorch.
kynml compare --format markdown
Tool
Best at
KynML edge
PyTorch Lightning
Trainer, callbacks, accelerators, distributed runs
Source-first training specs before boilerplate exists
Keras
Human-friendly Python API and backend portability
Reviewable DSL, deterministic codegen, explicit compiler boundary
Ludwig
Declarative low-code breadth and production integrations
Smaller language surface with syntax, IR, linting, and diffs
ZenML
Pipeline orchestration, stacks, artifacts, and MLOps governance
Compiler layer ZenML-like systems can orchestrate
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 1 — shipped PyTorch transpiler, CSV datasets, dense models, regression + binary + multiclass, ONNX export, REST serving, MCP server, HuggingFace integration, 6-command CLI.
Phase 2 Image datasets, CNN layers, text datasets, transformer blocks, richer parser diagnostics, dataset schema validation.
Phase 3 TorchScript export, model registry, experiment tracking, generated script extension hooks.
Phase 4 JAX backend, MLIR backend, distributed training, cloud training jobs.
Phase 5 KynML Studio, AutoML layer, SaaS deployment, interactive compiler playground.