semvec/pypi-packages huggingface dataset:
vws-python-mock 2026.2.21
A mock for the Vuforia Web Services (VWS) API.
Requires Python: >=3.13
Keywords: client, fake, mock, vuforia, vws

|Build Status| |PyPI|

VWS Mock
========

.. contents::
   :local:

Mock for the Vuforia Web Services (VWS) API and the Vuforia Web Query API.

Mocking calls made to Vuforia with Python ``requests``
------------------------------------------------------

Using the mock redirects requests to Vuforia made with `requests`_ to an in-memory implementation.

.. code-block:: shell

    pip install vws-python-mock

This requires Python |minimum-python-version|\+.

.. code-block:: python

    """Make a request to the Vuforia Web Services API mock."""

    import requests

    from mock_vws import MockVWS
    from mock_vws.database import CloudDatabase

    with MockVWS() as mock:
        database = CloudDatabase()
        mock.add_cloud_database(cloud_database=database)
        # This will use the Vuforia mock.
        requests.get(url="https://vws.vuforia.com/summary", timeout=30)

By default, an exception will be raised if any requests to unmocked addresses are made.

.. _requests: https://pypi.org/project/requests/

Using Docker to mock calls to Vuforia from any language
-------------------------------------------------------

It is possible run a Mock VWS instance using Docker containers.

This allows you to run tests against a mock VWS instance regardless of the language or tooling you are using.

See the `the instructions <https://vws-python.github.io/vws-python-mock/docker.html>`__ for how to do this.

Full documentation
------------------

See the `full documentation <https://vws-python.github.io/vws-python-mock/>`__.
This includes details on how to use the mock, options, and details of the differences between the mock and the real Vuforia Web Services.


.. |Build Status| image:: https://github.com/VWS-Python/vws-python-mock/actions/workflows/test.yml/badge.svg?branch=main
   :target: https://github.com/VWS-Python/vws-python-mock/actions
.. |PyPI| image:: https://badge.fury.io/py/VWS-Python-Mock.svg
    :target: https://badge.fury.io/py/VWS-Python-Mock
.. |minimum-python-version| replace:: 3.13
phlo 0.5.1
Lakehouse platform
Requires Python: >=3.11

<p align="center">
  <img src="docs/assets/phlo.png" alt="Phlo" width="400">
</p>

<p align="center">
  <strong>Modern data lakehouse platform built on Dagster, DLT, Iceberg, Nessie, and dbt.</strong>
</p>

<p align="center">
  <a href="https://github.com/iamgp/phlo/actions/workflows/ci.yml"><img src="https://github.com/iamgp/phlo/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
  <a href="https://pypi.org/project/phlo/"><img src="https://img.shields.io/pypi/v/phlo" alt="PyPI"></a>
  <img src="https://img.shields.io/badge/python-3.11+-blue.svg" alt="Python 3.11+">
</p>

## Features

- **Decorator-driven development** - Reduce boilerplate by 74% with `@phlo.operations.ingestion` and `@phlo.quality`
- **Write-Audit-Publish pattern** - Git-like branching with automatic quality gates and promotion
- **Type-safe data quality** - Pandera schemas enforce validation and generate Iceberg tables
- **Plugin architecture** - Extensible via service, source, quality, and transformation plugins
- **Observatory UI** - Web-based interface for data exploration, lineage, and monitoring
- **Production-ready patterns** - Auto-publishing to Postgres, configurable merge strategies, freshness policies
- **Modern tooling** - Built on Dagster, DLT, Iceberg, Nessie, dbt, and Trino

## Quick Start

```bash
# Install with default services
uv pip install phlo[defaults]

# Initialize a new project
phlo init my-project
cd my-project

# Start services and run
phlo services start
phlo materialize --select "dlt_glucose_entries+"
```

## Documentation

Full documentation at [docs/index.md](docs/index.md):

- [Installation Guide](docs/getting-started/installation.md)
- [Quickstart Guide](docs/getting-started/quickstart.md)
- [Core Concepts](docs/getting-started/core-concepts.md)
- [Developer Guide](docs/guides/developer-guide.md)
- [Plugin Development](docs/guides/plugin-development.md)
- [CLI Reference](docs/reference/cli-reference.md)
- [Configuration Reference](docs/reference/configuration-reference.md)
- [Operations Guide](docs/operations/operations-guide.md)
- [Blog Series](docs/blog/README.md) - 13-part deep dive

## Development

```bash
# Services
phlo services start    # Start all services
phlo services stop     # Stop services
phlo services logs -f  # View logs

# Development
uv pip install -e .    # Install Phlo
ruff check src/        # Lint
ruff format src/       # Format
basedpyright src/      # Type check
phlo test              # Run tests
```
xlm-core 0.1.4a0
XLM Framework
Requires Python: >=3.11
Keywords: AI, ML, Machine Learning, Deep Learning, Non-Autoregressive Language Models


    XLM is a unified framework for developing and comparing small non-autoregressive language models. It uses PyTorch as the deep learning framework, PyTorch Lightning for training utilities, and Hydra for configuration management. XLM provides core components for flexible data handling and training, useful architectural implementations for non-autoregressive workflows, and support for arbitrary runtime code injection. Custom model implementations that leverage the core components of xlm can be found in the xlm-models package. The package also includes a few preconfigured synthetic planning and language-modeling datasets.

    Usage:
        pip install xlm-core

    Command usage:
        xlm job_type=[JOB_TYPE] job_name=[JOB_NAME] experiment=[CONFIG_PATH]
       
        The job_type argument can be one of train ,eval and generate. The experiment argument should point to the root hydra config file.
tiledb-client 3.0b11
TileDB Python client
Requires Python: >=3.9

# tiledb-client

The next generation Python client for TileDB.

This project provides a `tiledb.client` module and a `tiledb.cloud` module. The
latter provides some backwards compatibility by re-exporting `tiledb.client`
names from the `tiledb.cloud` namespaces. Installing the tiledb-client package
installs both those modules.

tiledb-client is incompatible with tiledb-cloud versions < 1 (all versions on
PyPI). Avoid installing tiledb-cloud in Python environments where tiledb-client
wil be installed.

## Installation

`pip install tiledb-client`

## Quickstart

```python
import tiledb.client

# First, configure your credentials (this saves them to a profile)
tiledb.client.configure(
    username="USERNAME",
    password="PASSWORD",
    workspace="WORKSPACE"
)

# Then login using the stored credentials
tiledb.client.login()

# Now you can use TileDB Client
tiledb.client.teamspaces.list_teamspaces()
```

## Documentation

API documentation is hosted on GitHub: https://tiledb-inc.github.io/TileDB-Client-Py/.