semvec/pypi-packages huggingface dataset:
types-docker 7.1.0.20260109
Typing stubs for docker
Requires Python: >=3.9

## Typing stubs for docker

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`docker`](https://github.com/docker/docker-py) package. It can be used by type checkers
to check code that uses `docker`. This version of
`types-docker` aims to provide accurate annotations for
`docker==7.1.*`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/docker`](https://github.com/python/typeshed/tree/main/stubs/docker)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.19.1
* [pyright](https://github.com/microsoft/pyright) 1.1.408

It was generated from typeshed commit
[`9a175299c4cc4ffe0c9aafc33b2248916b23c3fa`](https://github.com/python/typeshed/commit/9a175299c4cc4ffe0c9aafc33b2248916b23c3fa).
types-peewee 3.19.0.20260109
Typing stubs for peewee
Requires Python: >=3.9

## Typing stubs for peewee

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`peewee`](https://github.com/coleifer/peewee) package. It can be used by type checkers
to check code that uses `peewee`. This version of
`types-peewee` aims to provide accurate annotations for
`peewee==3.19.0`.

This stub package is marked as [partial](https://typing.python.org/en/latest/spec/distributing.html#partial-stub-packages).
If you find that annotations are missing, feel free to contribute and help complete them.


This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/peewee`](https://github.com/python/typeshed/tree/main/stubs/peewee)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.19.1
* [pyright](https://github.com/microsoft/pyright) 1.1.408

It was generated from typeshed commit
[`9a175299c4cc4ffe0c9aafc33b2248916b23c3fa`](https://github.com/python/typeshed/commit/9a175299c4cc4ffe0c9aafc33b2248916b23c3fa).
dl-d2l 1.0.28
dl_d2l

# dl_d2l

This repository contains PyTorch implementations of the Deep Learning book "Dive into Deep Learning" (D2L).   
It provides code examples, tutorials, and exercises to help you learn deep learning concepts using PyTorch.

## Installation

```
pip install dl_d2l
```

## Code Examples

You can find the code examples in the `dl_d2l` package. Here is a simple example of how to use it:

```python
from dl_d2l import d2l_torch as d2l

x = d2l.arange(4)
print(x)
```

```python
# enable_matplotlib_chinese
from dl_d2l.util import matplotlib_util

matplotlib_util.enable_chinese()

# example plot with Chinese characters
import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(-10, 10, 100)
y = x

plt.plot(x, y)
plt.title("中文标题：y = x")
plt.xlabel("横轴（x）")
plt.ylabel("纵轴（y）")
plt.show()

```

```python
# get_available_device
from dl_d2l.util import device_util

device = device_util.get_available_device()

print(f"Available device: {device}")
```

```python
# Check if running in Google Colab
from dl_d2l.util import colab_util

if colab_util.is_colab():
    print("Running in Google Colab")
else:
    print("Not running in Google Colab")
```

```python
# Get base data directory in Google Colab or local environment
import os
from dl_d2l.util import colab_util

base_data_dir = colab_util.get_base_data_dir()
print(f'base data dir: {base_data_dir}')

datasets_dir = os.path.join(base_data_dir, 'ML', 'Datasets')
os.makedirs(datasets_dir, exist_ok=True)
print(f'datasets dir: {datasets_dir}')
```


```python
# flush_drive in Google Colab
from dl_d2l.util import colab_util

colab_util.flush_drive()
```



## Build & upload to pypi (For Developers)

prerequirement: twine is installed. If not, run the following command to install it:

```bash
pip install -U twine
```

build and upload:

```bash
## package
python setup.py sdist

## upload
twine upload dist/*
```

## D2L
For more information about the "Dive into Deep Learning" book, visit the [official website](https://d2l.ai/).