d2l

library1.0.3pypypiunverified

The d2l package (v1.0.3) provides utility functions and dataset loaders for the textbook 'Dive into Deep Learning' (d2l.ai). It includes common deep learning building blocks, data iterators, and plotting helpers to accompany the book. Release cadence is irregular, tied to book updates.

ai-ml

pip install d2l

package ↗homepage ↗

INSTALL

IMPORT

SIG · D2L

d2l

ai-mlpythonv1.0.3

Install

29.5s avg

Import

—

Disk

591MB

Pass rate

3/ 10

Env Coverage3 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.0.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 28.2s

py 3.11

✕ build_error

✓ 27.8s

py 3.12

✕ build_error

py 3.13

✕ build_error

py 3.9

✕ build_error

✓ 32.5s

591MB installed

● package 591MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

d2l

✓ import d2l

Commonly imported as a module; subpackages like d2l.torch, d2l.mxnet, d2l.tensorflow exist but torch is default

torch

✓ from d2l import torch as d2l_torch

✗ from d2l.torch import *

d2l.torch is deprecated; use from d2l import torch and then call d2l.torch.xxx or simply import d2l and use d2l.xxx

data

✓ from d2l import data

✗ from d2l.utils import data

d2l.utils was restructured; direct submodules are preferred

Animator

✓ from d2l import Animator

✗ from d2l.plot import Animator

Animator is now top-level in d2l

Accumulator

✓ from d2l import Accumulator

Top-level utility; no common mistake

Quickstart: Load Fashion-MNIST using d2l's data loader. The d2l package simplifies dataset access and common training loops.

import torch from d2l import torch as d2l # Load a small dataset (Fashion-MNIST) train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=256) # Check batch for X, y in train_iter: print(X.shape, y.shape) break

Debug

Known issues

deprecatedd2l.torch (submodule) is deprecated; use `from d2l import torch` or `import d2l` directly. The old import path `d2l.torch.nn` etc. may break in future versions.

fix

Replace `from d2l.torch import ...` with `from d2l import torch as d2l` or simply `import d2l` and use `d2l.xxx`.

affects: >=1.0.0

breakingIn d2l 1.0.0, many utility functions were moved to top-level (e.g., Animator, Accumulator, Timer) and removed from submodules like d2l.utils. Scripts using old paths will raise ImportError.

fix

Update imports: use `from d2l import Animator, Accumulator` instead of `from d2l.utils import Animator`.

affects: >=1.0.0

gotchaThe d2l package downloads datasets on first use from a server that may be slow or unreachable in certain regions. If dataset download hangs or fails, check network connectivity or manually download from the book's website.

fix

Set environment variable `D2L_DATA_DIR` to a local directory with pre-downloaded datasets. For offline use, manually copy datasets to ~/.d2l/data/.

affects: all

deprecatedThe `d2l.mxnet` and `d2l.tensorflow` backends are deprecated and no longer maintained. Only PyTorch (torch) backend is actively developed.

fix

Use the torch backend: `from d2l import torch` or `import d2l` (defaults to torch). Avoid importing d2l.mxnet or d2l.tensorflow.

affects: >=1.0.0

Upgrade

Version history

1.0.3latest on PyPI · released Aug 18, 2023

Audit

Dependencies

torchrequiredd2l is built on PyTorch; core dependency for all tensor operations and models

matplotlibrequiredUsed for plotting utilities (e.g., show_images)

requestsrequiredUsed for downloading datasets from the d2l data server

Agent activity

18 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/d2l/ ↗

homepagehttps://d2l.ai ↗