slodl

Slow deep learning. A deep-learning framework written from scratch to see how the pieces actually work — the tensor, autograd, the operations built on them — rather than to compete with the established frameworks.

It will always be slower than PyTorch or NumPy, which is where the name comes from. Nothing is hidden behind a library call: the storage, the views, the computation graph and every derivative are written out in a compiled C++17 core, with a typed Python API on top.

If you want a framework to train real models with, use PyTorch. If you want to read one end to end, this is meant to be small enough to do that.

Install

pip install slodl

NumPy is the only runtime dependency. Wheels cover CPython 3.9–3.14 on Linux, macOS and Windows; building from source needs nothing but a C++17 compiler.

What you get

A tensor that behaves like NumPy's array — Tensor([[1, 2], [3, 4]]) takes data, zeros/ones/full take a shape, indexing gives views, and repr prints the values rather than a summary of the object.
Gradients — mark a tensor with requires_grad_(), compute a loss, call backward(), and read .grad. Recording can be switched off with no_grad(), and detach() cuts a single tensor loose.
The operations to build a layer — + - * / @, unary -, sum, mean and transpose, all differentiable, with NumPy-style broadcasting so a bias row adds to a whole batch and plain numbers work as operands.
NumPy either way — build a tensor from any array, and hand one to np.asarray without copying, so the two share memory.
Type hints — ships py.typed and stubs, so editors and type checkers see the API.

Quickstart

import numpy as np
from slodl import Tensor

t = Tensor([[1, 2], [3, 4]])
t
# Tensor([[1, 2],
#         [3, 4]])

t.shape        # [2, 2]
t[1][0]        # 3.0  — a float, not a tensor
len(t)         # 2

As in NumPy and PyTorch, the argument is the data, not the dimensions — so Tensor([2, 3]) is a 1-dimensional tensor holding 2.0 and 3.0. Creating by shape has its own functions:

from slodl import zeros, ones, full

zeros([2, 3])             # 2x3, all 0.0
ones([2, 2])              # 2x2, all 1.0
full([2, 2], 7.0)         # 2x2, all 7.0
Tensor(5.0)               # 0-dimensional; read it with .item()

Indexing gives a view, so writing through it changes the original:

row = t[0]
row[1] = 50.0
t[0][1]        # 50.0

Assigning a tensor copies its values into the destination, and clone gives an independent tensor when you want one:

t[1] = t[0]        # row 1 now holds row 0's values
copy = t.clone()   # shares nothing with t

Autograd

Mark the tensors you want gradients for, compute a scalar, and call backward():

from slodl import Tensor

a = Tensor([2.0, 3.0]).requires_grad_()
b = Tensor([10.0, 20.0]).requires_grad_()

(a * b).sum().backward()
a.grad        # Tensor([10, 20])
b.grad        # Tensor([2, 3])

backward() needs a 0-dimensional tensor, which is what sum and mean are for. Gradients accumulate, so a second pass adds to the first.

Matrix multiplication is @, and a row broadcasts across a batch, so a linear layer is one line:

import slodl

inputs = Tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])   # batch of 2
weights = slodl.full([3, 2], 0.5).requires_grad_()
bias = slodl.full([2], 0.1).requires_grad_()

outputs = inputs @ weights + bias
outputs.mean().backward()

weights.grad.shape   # [3, 2]
bias.grad.shape      # [2]

Recording can be turned off, and a single tensor can be cut loose from its history:

from slodl import no_grad

with no_grad():
    prediction = inputs @ weights + bias   # computes, records nothing

weights.detach()      # same storage, no history

NumPy goes in and out. Building from an array copies; np.asarray shares memory, so writes through the array reach the tensor:

t = Tensor(np.array([[1.0, 2.0], [3.0, 4.0]]))

array = np.asarray(t)
array[0, 0] = 99.0
t[0][0]        # 99.0

Arrays that are not float64, not contiguous, or not row-major — a transpose, a stepped slice, an int32 array — are converted on the way in:

Tensor(np.arange(6).reshape(2, 3).T).shape   # [3, 2]

Status

Early and incomplete, but a linear model trains end to end: element-wise arithmetic, matmul, reductions, broadcasting and reverse-mode autograd all work.

Not there yet: activations (relu, exp, log), dimension-wise reductions such as sum(dim=...), reshape and slicing, and anything above tensors — no zero_grad, optimizers, layers or datasets. matmul is 2-dimensional only, and backward() starts from a 0-dimensional tensor.

CHANGELOG.md tracks what has landed and the known limitations.

Contributing

Building from a checkout, running the test suites and the layout the code follows are in CONTRIBUTING.md.

Metadata

Release files for slodl 0.1.0

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

Source distribution (sdist)

Source distribution for slodl 0.1.0
File	Size	Uploaded
slodl-0.1.0.tar.gz	42.0 kB	Sep 30, 2026	Details

Built distributions (wheels)

Table of built distributions (wheels) for slodl 0.1.0
File	Interpreter	ABI	Platform	Reset
slodl-0.1.0-cp314-cp314-win_amd64.whl 366.1 kB Sep 30, 2026	CPython 3.14	CPython 3.14	Windows x86-64	Details
slodl-0.1.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 221.3 kB Sep 30, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
slodl-0.1.0-cp314-cp314-macosx_11_0_arm64.whl 158.7 kB Sep 30, 2026	CPython 3.14	CPython 3.14	macOS 11.0+ ARM64	Details
slodl-0.1.0-cp314-cp314-macosx_10_15_x86_64.whl 176.1 kB Sep 30, 2026	CPython 3.14	CPython 3.14	macOS 10.15+ x86-64	Details
slodl-0.1.0-cp313-cp313-win_amd64.whl 354.8 kB Sep 30, 2026	CPython 3.13	CPython 3.13	Windows x86-64	Details
slodl-0.1.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 221.2 kB Sep 30, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
slodl-0.1.0-cp313-cp313-macosx_11_0_arm64.whl 158.3 kB Sep 30, 2026	CPython 3.13	CPython 3.13	macOS 11.0+ ARM64	Details
slodl-0.1.0-cp313-cp313-macosx_10_13_x86_64.whl 175.9 kB Sep 30, 2026	CPython 3.13	CPython 3.13	macOS 10.13+ x86-64	Details
slodl-0.1.0-cp312-cp312-win_amd64.whl 354.8 kB Sep 30, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details
slodl-0.1.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 221.1 kB Sep 30, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
slodl-0.1.0-cp312-cp312-macosx_11_0_arm64.whl 158.2 kB Sep 30, 2026	CPython 3.12	CPython 3.12	macOS 11.0+ ARM64	Details
slodl-0.1.0-cp312-cp312-macosx_10_13_x86_64.whl 175.9 kB Sep 30, 2026	CPython 3.12	CPython 3.12	macOS 10.13+ x86-64	Details
slodl-0.1.0-cp311-cp311-win_amd64.whl 351.4 kB Sep 30, 2026	CPython 3.11	CPython 3.11	Windows x86-64	Details
slodl-0.1.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 219.1 kB Sep 30, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
slodl-0.1.0-cp311-cp311-macosx_11_0_arm64.whl 156.6 kB Sep 30, 2026	CPython 3.11	CPython 3.11	macOS 11.0+ ARM64	Details
slodl-0.1.0-cp311-cp311-macosx_10_9_x86_64.whl 173.5 kB Sep 30, 2026	CPython 3.11	CPython 3.11	macOS 10.9+ x86-64	Details
slodl-0.1.0-cp310-cp310-win_amd64.whl 350.2 kB Sep 30, 2026	CPython 3.10	CPython 3.10	Windows x86-64	Details
slodl-0.1.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 218.5 kB Sep 30, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
slodl-0.1.0-cp310-cp310-macosx_11_0_arm64.whl 155.4 kB Sep 30, 2026	CPython 3.10	CPython 3.10	macOS 11.0+ ARM64	Details
slodl-0.1.0-cp310-cp310-macosx_10_9_x86_64.whl 172.0 kB Sep 30, 2026	CPython 3.10	CPython 3.10	macOS 10.9+ x86-64	Details
slodl-0.1.0-cp39-cp39-win_amd64.whl 351.0 kB Sep 30, 2026	CPython 3.9	CPython 3.9	Windows x86-64	Details
slodl-0.1.0-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 218.2 kB Sep 30, 2026	CPython 3.9	CPython 3.9	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
slodl-0.1.0-cp39-cp39-macosx_11_0_arm64.whl 155.6 kB Sep 30, 2026	CPython 3.9	CPython 3.9	macOS 11.0+ ARM64	Details
slodl-0.1.0-cp39-cp39-macosx_10_9_x86_64.whl 172.1 kB Sep 30, 2026	CPython 3.9	CPython 3.9	macOS 10.9+ x86-64	Details

Total release size: 5.5 MB

Release files / slodl-0.1.0.tar.gz

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

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Release files / slodl-0.1.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

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Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

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Release files / slodl-0.1.0-cp312-cp312-macosx_11_0_arm64.whl

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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

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Release files / slodl-0.1.0-cp312-cp312-macosx_10_13_x86_64.whl

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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

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Artifact

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Release files / slodl-0.1.0-cp311-cp311-win_amd64.whl

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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

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Release files / slodl-0.1.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

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Release files / slodl-0.1.0-cp311-cp311-macosx_11_0_arm64.whl

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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

Workflow

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Artifact

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Release files / slodl-0.1.0-cp311-cp311-macosx_10_9_x86_64.whl

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Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

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Artifact

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Release files / slodl-0.1.0-cp310-cp310-win_amd64.whl

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Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

Workflow

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Artifact

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Release files / slodl-0.1.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

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Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

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Artifact

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Release files / slodl-0.1.0-cp310-cp310-macosx_11_0_arm64.whl

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Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

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Artifact

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Release files / slodl-0.1.0-cp310-cp310-macosx_10_9_x86_64.whl

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Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

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Artifact

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Release files / slodl-0.1.0-cp39-cp39-win_amd64.whl

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Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

Workflow

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Artifact

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Release files / slodl-0.1.0-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

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Upload date	Sep 30, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

Workflow

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Artifact

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SHA-256 checksum: 928ab7cc11dfd4d4389d29dee6537cbb795be6693db3fbc45017aeff3f98e25c Verifying attestations

Release files / slodl-0.1.0-cp39-cp39-macosx_11_0_arm64.whl

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Upload date	Sep 30, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

Workflow

Publishing configuration: github.com/fe-neu/slodl/blob/bf3bf7444ca0b952f578f923f993df03e55bbecd/.github/workflows/release.yml
Publishing logs: github.com/fe-neu/slodl/actions/runs/36717307000/attempts/1

Artifact

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Release files / slodl-0.1.0-cp39-cp39-macosx_10_9_x86_64.whl

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Upload date	Sep 30, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/fe-neu/slodl
Publishing commit: github.com/fe-neu/slodl/tree/bf3bf7444ca0b952f578f923f993df03e55bbecd

Workflow

Publishing configuration: github.com/fe-neu/slodl/blob/bf3bf7444ca0b952f578f923f993df03e55bbecd/.github/workflows/release.yml
Publishing logs: github.com/fe-neu/slodl/actions/runs/36717307000/attempts/1

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Subject: slodl-0.1.0-cp39-cp39-macosx_10_9_x86_64.whl
SHA-256 checksum: a9db9d4f48ffed31321f94facee2d81d8659f1fe97074e2db2522c5ef1962a2e Verifying attestations

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This release

0.1.0 This release

Sep 30, 2026 25 release files

slodl 0.1.0

slodl

Install

What you get

Quickstart

Autograd

Status

Contributing

Project links

Key dates

1 maintainer

Credits

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Provides Extra

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Metadata

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

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Requires

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Release files for slodl 0.1.0

Source distribution (sdist)

Built distributions (wheels)

Release files / slodl-0.1.0.tar.gz

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PyPI Publish Attestation

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Release files / slodl-0.1.0-cp314-cp314-win_amd64.whl

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PyPI Publish Attestation

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Release files / slodl-0.1.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

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PyPI Publish Attestation

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Release files / slodl-0.1.0-cp314-cp314-macosx_11_0_arm64.whl

Provenance

PyPI Publish Attestation

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Release files / slodl-0.1.0-cp314-cp314-macosx_10_15_x86_64.whl

Provenance

PyPI Publish Attestation

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Artifact

Release files / slodl-0.1.0-cp313-cp313-win_amd64.whl

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PyPI Publish Attestation

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Release files / slodl-0.1.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

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PyPI Publish Attestation

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Release files / slodl-0.1.0-cp313-cp313-macosx_11_0_arm64.whl

Provenance

PyPI Publish Attestation

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Workflow

Artifact