DeepTensor 🔥

mexican cat dance

  • DeepTensor: A minimal PyTorch-like deep learning library focused on custom autograd and efficient tensor operations.

Features at a Glance 🚀

  • Automatic gradient computation with a custom autograd engine.
  • Weight initialization schemes:
    • Xavier/Glorot and He initialization in both uniform and normal variants.
  • Activation functions:
    • ReLU, GeLU, Sigmoid, Tanh, SoftMax, LeakyReLU, and more.
  • Built-in loss functions:
    • Mean Squared Error (MSE), Cross Entropy, and Binary Cross Entropy.
  • Optimizers:
    • SGD, Momentum, AdaGrad, RMSprop, and Adam.

Why DeepTensor?

DeepTensor offers a hands-on implementation of deep learning fundamentals with a focus on customizability and learning the internals of deep learning frameworks like PyTorch.

Installation

pip install deeptensor

Setup the project for development

git clone --recurse-submodules -j8 git@github.com:deependujha/DeepTensor.git
cd DeepTensor

# run ctests
make ctest

# install python package in editable mode
pip install -e .

# run pytest
make test

Checkout Demo

demo

Check Docs

loss curve

Basic Usage

from deeptensor import (
    # model
    Model,

    # Layers
    Conv2D,
    MaxPooling2D,
    Flatten,
    LinearLayer,

    # activation layers
    GeLu,
    LeakyReLu,
    ReLu,
    Sigmoid,
    SoftMax,
    Tanh,

    # core objects
    Tensor,
    Value,

    # optimizers
    SGD,
    Momentum,
    AdaGrad,
    RMSprop,
    Adam,

    # losses
    mean_squared_error,
    cross_entropy,
    binary_cross_entropy,
)

model = Model(
    [
        LinearLayer(2, 16),
        ReLu(),
        LinearLayer(16, 16),
        LeakyReLu(0.1),
        LinearLayer(16, 1),
        Sigmoid(),
    ],
    False,  # using_cuda
)

opt = Adam(model, 0.01) # learning rate

print(model)

tensor_input = Tensor([2])
tensor_input.set(0, Value(2.4))
tensor_input.set(1, Value(5.2))

out = model(tensor_input)

loss = mean_squared_error(out, YOUR_EXPECTED_OUTPUT)

# backprop
loss.backward()
opt.step()
opt.zero_grad()

Features expected to be added

  • Save & Load model
  • Train a character-level transformer model
  • Add support for DDP
  • Add support for CUDA execution ⭐️

Open to Opportunities 🎅🏻🎁

I am actively seeking new opportunities to contribute to impactful projects in the deep learning and AI space.

If you are interested in collaborating or have a position that aligns with my expertise, feel free to reach out!

You can connect with me on GitHub, X (formerly twitter), or email me: deependujha21@gmail.com.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

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

Credits

Data verified by PyPI on Jan 26, 2025
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI. Confirms that the provided email matches a verified, public email address on a PyPI user account associated with the project.

Author: Deependu Jha

License

MIT License

Requires

Python >=3.9

Provides Extra

test

Classifiers

Report project as malware

Metadata

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

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

Credits

Data verified by PyPI on Jan 26, 2025
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI. Confirms that the provided email matches a verified, public email address on a PyPI user account associated with the project.

Author: Deependu Jha

License

MIT License

Requires

Python >=3.9

Provides Extra

test

Classifiers

Report project as malware

Release files for deeptensor 0.5.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for deeptensor 0.5.0
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deeptensor-0.5.0.tar.gz 1.5 MB Details

Built distributions (wheels)

Table of built distributions (wheels) for deeptensor 0.5.0
File
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deeptensor-0.5.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl 781.0 kB PyPy 3.9 PyPy 3.9 7.3 Linux glibc 2.17+ x86-32 Details
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deeptensor-0.5.0-cp313-cp313-win32.whl 1.6 MB CPython 3.13 CPython 3.13 Windows x86-32 Details
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deeptensor-0.5.0-cp312-cp312-musllinux_1_2_i686.whl 1.9 MB CPython 3.12 CPython 3.12 Linux musl 1.2+ x86-32 Details
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deeptensor-0.5.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl 783.9 kB CPython 3.12 CPython 3.12 Linux glibc 2.17+ x86-32 Details
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deeptensor-0.5.0-cp311-cp311-musllinux_1_2_x86_64.whl 1.8 MB CPython 3.11 CPython 3.11 Linux musl 1.2+ x86-64 Details
deeptensor-0.5.0-cp311-cp311-musllinux_1_2_i686.whl 1.9 MB CPython 3.11 CPython 3.11 Linux musl 1.2+ x86-32 Details
deeptensor-0.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 775.6 kB CPython 3.11 CPython 3.11 Linux glibc 2.17+ x86-64 Details
deeptensor-0.5.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl 783.1 kB CPython 3.11 CPython 3.11 Linux glibc 2.17+ x86-32 Details
deeptensor-0.5.0-cp311-cp311-macosx_11_0_arm64.whl 692.7 kB CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
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deeptensor-0.5.0-cp310-cp310-win_amd64.whl 2.1 MB CPython 3.10 CPython 3.10 Windows x86-64 Details
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deeptensor-0.5.0-cp310-cp310-musllinux_1_2_i686.whl 1.9 MB CPython 3.10 CPython 3.10 Linux musl 1.2+ x86-32 Details
deeptensor-0.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 774.0 kB CPython 3.10 CPython 3.10 Linux glibc 2.17+ x86-64 Details
deeptensor-0.5.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl 780.9 kB CPython 3.10 CPython 3.10 Linux glibc 2.17+ x86-32 Details
deeptensor-0.5.0-cp310-cp310-macosx_11_0_arm64.whl 691.4 kB CPython 3.10 CPython 3.10 macOS 11.0+ ARM64 Details
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deeptensor-0.5.0-cp39-cp39-musllinux_1_2_x86_64.whl 1.8 MB CPython 3.9 CPython 3.9 Linux musl 1.2+ x86-64 Details
deeptensor-0.5.0-cp39-cp39-musllinux_1_2_i686.whl 1.9 MB CPython 3.9 CPython 3.9 Linux musl 1.2+ x86-32 Details
deeptensor-0.5.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 774.3 kB CPython 3.9 CPython 3.9 Linux glibc 2.17+ x86-64 Details
deeptensor-0.5.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl 782.0 kB CPython 3.9 CPython 3.9 Linux glibc 2.17+ x86-32 Details
deeptensor-0.5.0-cp39-cp39-macosx_11_0_arm64.whl 691.5 kB CPython 3.9 CPython 3.9 macOS 11.0+ ARM64 Details
deeptensor-0.5.0-cp39-cp39-macosx_10_9_x86_64.whl 699.4 kB CPython 3.9 CPython 3.9 macOS 10.9+ x86-64 Details

Total release size: 63.0 MB

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0.5.0 This release

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0.2.0

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0.1.0

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0.0.1

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