semvec/pypi-packages huggingface dataset:
vulkan-object 1.4.343
An easy-to-use Python wrapper for the Vulkan API registry.
Requires Python: >=3.10

# Vulkan Object Python Package

Parsing the [`vk.xml`](https://github.com/KhronosGroup/Vulkan-Headers/blob/main/registry/vk.xml) in Vulkan is easy, processing it is hard!

It is very easy for people to mess up trying to process the `vk.xml` file, so we created `VulkanObject`

`VulkanObject` is just a python dataclass that is defined in [Vulkan-Headers/registry/vulkan_object.py](https://github.com/KhronosGroup/Vulkan-Headers/blob/main/registry/vulkan_object.py). It uses the [reg.py](https://github.com/KhronosGroup/Vulkan-Headers/blob/main/registry/reg.py) framework that the Vulkan Spec is generated with in order to populate the `VulkanObject` data structure.

This python package makes it **super easy** to get going.

```bash
pip install vulkan-object
```

and then it is as simple as

```python
#!/usr/bin/env python3
from vulkan_object import get_vulkan_object

# This builds the VulkanObject that is populated and ready to be used
vk = get_vulkan_object()

print(f'There are now {len([x for x in vk.extensions.values()])} extensions in Vulkan')

print(f'Built with the {vk.headerVersion} headers')

longest_count = 0
for struct in vk.structs.values():
    if len(struct.name) > longest_count:
        longest_count = len(struct.name)
        longest_struct = struct
print(f'Longest Struct name is {longest_struct.name} at {longest_count} characters')
```

## Quick query from the terminal

Just run the following and you can quickly use `vk` in your terminal for some quick query

`python -i -c "from vulkan_object import get_vulkan_object;vk = get_vulkan_object()"`

## Vulkan Video

> Added in 1.4.342 release

By default, the `video.xml` file is not parsed, but can be turned on. (example: [video_example.py](https://github.com/KhronosGroup/vulkan-object/blob/main/video_example.py))

```python
vk = get_vulkan_object(video=True)
```

## More example

[example.py](https://github.com/KhronosGroup/vulkan-object/blob/main/example.py) has more in depth ways to use this

## What is this package/repo actually?

The Vulkan-Headers contain a bunch of scripts in the `Vulkan-Headers/registry/` directory that repos can use to help generate code.

The main issue is the delivery mechanism for python projects. This package/repo just grabs the Vulkan-Headers scripts (**not** the C header files themselves!) and make it easier to integrate with projects.

### Updating this repo

run `python update.py /path/to/Vulkan-Headers` and it will do everything to sync this package up with the new headers
audited-runbook 0.1.0
Run multi-step shell procedures with atomic audit trail. Every step persisted before execution. Every failure recorded. No silent drops.
Requires Python: >=3.12
Keywords: runbook, audit, execution, shell, cli, devops

# Audited Runbook Executor

Multi-step shell procedures with atomic audit trail. Every step is persisted before execution. Every failure is recorded. No silent drops.

## Install

```
pip install audited-runbook
```

From source:

```
pip install -e .
```

## Verify

```
runbook --help
```

## Run a runbook

```
runbook run runbooks/test-success.yaml
```

With overrides:

```
runbook run --cwd /tmp --env FOO=bar --env BAZ=qux runbooks/test-success.yaml
runbook run --db /tmp/my.db runbooks/test-success.yaml
```

## Query history

```
runbook history
runbook history -n 5
```

## Inspect an execution

```
runbook inspect <execution-id>
```

## View audit trail

```
runbook audit <execution-id> --pretty
runbook audit <execution-id>            # raw JSON
```

## Validate a runbook (no execution)

```
runbook validate runbooks/test-success.yaml
```

## Runbook format

```yaml
name: my-deploy
description: Optional description.
steps:
  - name: run-tests
    command: pytest tests/ -x
  - name: build
    command: docker build -t myapp .
    timeout_seconds: 300
```

## Default database

`~/.runbook/runbook.db` — override with `--db PATH`.
ai-edge-model-explorer 0.1.32
A modern model graph visualizer and debugger
Requires Python: >=3.9

# Model Explorer

Model Explorer offers an intuitive and hierarchical visualization of model
graphs. It organizes model operations into nested layers, enabling users to
dynamically expand or collapse these layers. It also provides a range of
features to facilitate model exploration and debugging, including the ability to
highlight input and output operations, overlay metadata on nodes, display layers
in interactive pop-ups, perform searches, show identical layers, GPU-accelerated
graph rendering, among others. It currently supports TFLite, TF, TFJS, MLIR, and
PyTorch (Exported Program) model format, and provides an extension framework for
developers to easily add support for additional formats.

![](https://raw.githubusercontent.com/google-ai-edge/model-explorer/main/screenshots/main_ui.png)

<br>
<br>

Please check out our [Wiki](https://github.com/google-ai-edge/model-explorer/wiki) for
more details:

- [Installation](https://github.com/google-ai-edge/model-explorer/wiki/1.-Installation)
- [User Guide](https://github.com/google-ai-edge/model-explorer/wiki/2.-User-Guide)
- [Command Line Guide](https://github.com/google-ai-edge/model-explorer/wiki/3.-Command-Line-Guide)
- [API Guide](https://github.com/google-ai-edge/model-explorer/wiki/4.-API-Guide)
- [Run in Colab Notebook](https://github.com/google-ai-edge/model-explorer/wiki/5.-Run-in-Colab-Notebook)
- [Develop Adapter Extension](https://github.com/google-ai-edge/model-explorer/wiki/6.-Develop-Adapter-Extension)
personalities 0.1.0
Add your description here
Requires Python: >=3.11

# personalities

Initial PyPI package scaffold for `personalities`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```