CPUTorch

CPUTorch is a lightweight tensor and neural-network framework built from scratch, with a C++ numerical core, Python API, CPU execution, and Vulkan compute support.

The project is designed as a general-purpose computational foundation for machine learning and artificial intelligence systems.

Author

Frandika Imam Arifin

Repository

https://github.com/Mangono1/minitorch

Architecture

  • C++ numerical core
  • Python API
  • CPU backend
  • Vulkan compute backend
  • Automatic differentiation
  • Tensor operations
  • Neural-network modules
  • Optimizers
  • Hardware profiling
  • Hardware-aware execution
  • Adaptive execution architecture

Current Version

CPUTorch V0.5.4

V0.5.4

Current development includes:

  • Tensor engine
  • requires_grad
  • Computational graph infrastructure
  • Automatic differentiation infrastructure
  • Neural-network modules
  • Linear layers
  • ReLU
  • Sequential models
  • MSE loss
  • SGD optimizer
  • Hardware profiler
  • CPU backend
  • Vulkan compute infrastructure
  • Vulkan buffer management
  • Vulkan compute pipeline
  • Vector arithmetic backend operations
  • Scalar multiplication operation
  • Python package
  • PyPI distribution
  • Multi-platform wheel build automation

Installation

Install the released package directly from PyPI:

python -m pip install cputorch

Or install a specific version:

python -m pip install cputorch==0.5.4

Quick Example

import cputorch

x = cputorch.Tensor(
    [1.0, 2.0, 3.0, 4.0],
    [2, 2],
    requires_grad=True
)

y = x.multiply_scalar(2.0)

print(y.data)

Design Philosophy

CPUTorch is intended to remain a general computational framework.

It is not tied to a specific domain or dataset.

It can serve as a foundation for:

  • Machine learning
  • Deep learning
  • Computer vision
  • Scientific computing
  • Robotics
  • Simulation
  • Reinforcement learning
  • Custom AI systems
  • Domain-specific AI frameworks

Higher-level frameworks and models can use CPUTorch as their computational backend.

Roadmap

  • V0.1 Tensor core
  • V0.2 Automatic differentiation
  • V0.3 Neural-network modules
  • V0.4 Optimizers
  • V0.5 Hardware profiler
  • V0.5.x Vulkan compute development
  • V0.6 Expanded Vulkan backend
  • V0.7 GPU watchdog and adaptive scheduler
  • V0.8 Transformer architecture
  • V0.9 Tokenizer and language-model infrastructure
  • V1.0 General training engine

License

MIT License.

Project links

Data verified by PyPI on Aug 26, 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 (0.6.0)

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 26, 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

MIT License (MIT)

Requires

Python >=3.9

Tags

machine-learning deep-learning tensor neural-network cpu vulkan ai

Classifiers

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0.6.0

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0.5.9

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0.5.8

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0.5.7

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0.5.5

55 files

This release

0.5.4 This release

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0.5.3

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