VENLACPU

CPU-first deep learning framework modern C++17.

VENLACPU is an actively developed deep learning framework focused on a clean, portable CPU implementation. The native engine is written in C++17 and is designed to work across desktop and mobile environments without requiring CUDA.

Status: Alpha / active development. APIs may change before the first stable release.

Author

VENLACPU is created and developed by Frandika Imam Arifin.

Current capabilities

The current C++ core includes:

  • Tensor storage, shape, stride, dtype, and device abstractions
  • CPU tensor operations and manipulation
  • Autograd and gradient propagation
  • Elementwise mathematics and reductions
  • Linear layers and sequential models
  • Activation functions
  • MSE and cross-entropy loss
  • Embedding
  • Positional encoding
  • Layer normalization
  • Multi-head attention
  • Feed-forward networks
  • Transformer encoder
  • Transformer decoder
  • KV cache and cached attention
  • Causal language model
  • Causal language-model dataset batching
  • Training loop with gradient accumulation
  • Adam optimizer
  • Evaluation and autoregressive generation
  • Tokenizer and vocabulary components

The project currently contains a comprehensive C++ test suite covering the implemented components.

Architecture

Physical CPU engine
           |
          T
ensor / Storage / Shape / Stride / Dtype / Device
           |
          Autograd
          |
          Math Ops
           |
          Neural network
          |
      Transformer / Language Model
          |
          Training System

The native implementation is the primary implementation at this stage.

Build from source

Requirements:

  • CMake 3.16 or newer
  • C++17 compiler
  • Git
cmake -S . -B build
cmake --build build -j2

Run the complete test suite:

ctest --test-dir build --output-on-failure

Python / PyPI

VENLACPU has a modular Python package prepared for PyPI. The current Python package exposes the package version, while the native C++ engine remains the primary implementation.

Platform-specific native wheels will be added when the Python binding layer is ready. GitHub Actions is used as the build and packaging engine.

Causal language-model training

The current training API supports:

  • Causal token shifting
  • Dataset batching
  • Padding with ignore index
  • Cross-entropy loss
  • Autograd and gradient propagation
  • Gradient accumulation
  • Adam optimization
  • Evaluation
  • Autoregressive generation

CPU-first design

VENLACPU is intentionally CPU-first. The current implementation does not require CUDA or a GPU runtime.

This makes the project suitable for desktop CPUs and ARM/mobile development environments.

Repository layout

venlacpu/
└ ── include/venla/
    └ ── autograd/
    │      core/
    └        math/
    └        nn/
    └ ── optim/
    │        tensor/
    └ ── tokenizer/
    └        training/
  │   src/
    │      core/
    └        math/
    └        nn/
    └ ── optim/
    │      tensor/
    │      tokenizer/
    │      training/
  │   tests/
  │  examples/
   │  benchmarks/
   │   docs/
   └ ── python/
   │  CMakeLists.tx
  │  pyproject.toml
   └ ── README.md

Development philosophy

VENLACPU begins with the low-level foundations and builds upward. The long-term goal is a portable CPU-first framework with a native C++ core and convenient Python access.

License

The project is currently in alpha and the final public-release license is being finalized.

Links

Project links

Data verified by PyPI on Aug 31, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (2.4.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for dika03 from gravatar.com dika03

Credits

Author: Frandika Imam Arifin

GitHub Statistics

Data verified by PyPI on Aug 31, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

License to be finalized before the first public release

Requires

Python >=3.9

Tags

deep-learning machine-learning neural-network tensor autograd transformer language-model cpu cpp

Classifiers

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2.4.0

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2.3.1

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