semvec/pypi-packages huggingface dataset:
goodtime 0.4.1
A time library that prioritises clean and safe APIs.
Requires Python: >=3.10

Welcome to **goodtime**, a Python date/time library that prioritises safety and correctness.

```python
>>> from goodtime import *
>>> Instant.now()
Instant(unix_timestamp_ns=1770029789026987530)
>>> Instant.now() + hours(1)
Instant(unix_timestamp_ns=1770033404038049963)
>>> Instant.now().to_unix_seconds()
1770029824
>>> Instant.from_unix_seconds(1770029824)
Instant(unix_timestamp_ns=1770029824000000000)
```

Handling of timestamps, durations and human-readable dates and times is
notoriously error-prone due to issues with time zones, ambiguous arithmetics,
ambiguous units, etc. This is especially true in distributed systems, where
interacting systems may be written by multiple teams that did not have shared
assumptions about date/time semantics.

The goal of **goodtime** is to minimise the potential for errors by:

* using types that have well-defined semantics,
* preventing ambiguous conversions (e.g. timestamp to integer),
* providing explicit interfaces to interoperate with non-goodtime code.

See package documentation for more information on provided types, operations,
and best practices.

## Getting Started

Install **goodtime** from PyPI using your favourite Python package manager:

```console
$ python -m pip install goodtime
$ poetry add goodtime
$ uv add goodtime
```

## Contributing

We are open to feedback and contributions under the terms of the MIT license.
Feel free to open a Github issue or a pull request.
craft-ls 0.4.2
A Language Server Protocol implementation for *craft files.
Requires Python: >=3.12
Keywords: charmcraft, lsp, rockcraft, snapcraft

# craft-ls

![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/batalex/craft-ls/ci.yaml)

Get on\
[![PyPI - Version](https://img.shields.io/pypi/v/craft-ls)](https://pypi.org/project/craft-ls/)
[![FlakeHub](https://img.shields.io/badge/FlakeHub-5277C3)](https://flakehub.com/flake/Batalex/craft-ls)
[![Snap - Version](https://img.shields.io/snapcraft/v/craft-ls/latest/edge)](https://snapcraft.io/craft-ls)
[![VSCode Marketplace](https://vsmarketplacebadges.dev/version-short/abatisse.craft-ls.svg)](https://marketplace.visualstudio.com/items?itemName=abatisse.craft-ls)

`craft-ls` is a [Language Server Protocol](https://microsoft.github.io/language-server-protocol/) implementation for *craft[^1] tools.

`craft-ls` enables editors that support the LSP to get quality of life improvements while working on *craft configuration files.

## Features

| Feature                | Snapcraft | Rockcraft | Charmcraft[^1] |
| :--------------------- | :-------: | :-------: | :------------: |
| Diagnostics            |    ✅     |    ✅     |       ✅       |
| Documentation on hover |    ✅     |    ✅     |       ✅       |
| Symbols                |    ✅     |    ✅     |       ✅       |
| Autocompletion         |    ✅     |    ✅     |       ✅       |

https://github.com/user-attachments/assets/e4b831b5-dcac-4efd-aabb-d3040899b52b

## Usage

### Installation

Using `uv` or `pipx`

```shell
uv tool install craft-ls

pipx install craft-ls
```

### Setup

#### Helix

```toml
# languages.toml
[[language]]
name = "yaml"
language-servers = ["craft-ls"]

[language-server.craft-ls]
command = "craft-ls"
```

#### VSCode

The VSCode extension can be installed from the marketplace.
It requires a Python 3.12 interpreter.
If not automatically picked, you may configure it using the following key:

```json
"craft-ls.interpreter": [
  "/usr/bin/python3.12"
]
```

TBD: neovim

## Roadmap

Project availability:

- Python package
- Snap
- Nix flake
- VSCode extension

Features:

- Diagnostics
- Autocompletion **on typing**
- Symbol documentation

Ecosystem:

- Encourage *craft tools to refine their JSONSchemas even further

[^1]: snapcraft, rockcraft and partial support for charmcraft (all-in-one `charmcraft.yaml` only)
dask-cuda 26.2.0
Utilities for Dask and CUDA interactions
Requires Python: >=3.10

Dask CUDA
=========

Various utilities to improve deployment and management of Dask workers on
CUDA-enabled systems.

This library is experimental, and its API is subject to change at any time
without notice.

Example
-------

```python
from dask_cuda import LocalCUDACluster
from dask.distributed import Client

cluster = LocalCUDACluster()
client = Client(cluster)
```

Documentation is available [here](https://docs.rapids.ai/api/dask-cuda/nightly/).

What this is not
----------------

This library does not automatically convert your Dask code to run on GPUs.

It only helps with deployment and management of Dask workers in multi-GPU
systems.  Parallelizing GPU libraries like [RAPIDS](https://rapids.ai) and
[CuPy](https://cupy.chainer.org) with Dask is an ongoing effort.  You may wish
to read about this effort at [blog.dask.org](https://blog.dask.org) for more
information.  Additional information about Dask-CUDA can also be found in the
[docs](https://docs.rapids.ai/api/dask-cuda/nightly/).