semvec/pypi-packages huggingface dataset:
wwt-kernel-data-relay 0.3.0
Jupyter server extension to allow kernels to make data web-accessible
License: MIT

[wwt_kernel_data_relay] is a [Jupyter server extension][ext] that enables
[Jupyter kernels][kernels] to publish some of their data files to the Web; that
is, to request that the Jupyter HTTP server make them accessible at a
predictable URL. This functionality is used by [pywwt], the [WorldWide
Telescope] Python library, to expose kernel-side data assets for visualization
inside the WWT [research app][rapp].

[wwt_kernel_data_relay]: https://github.com/WorldWideTelescope/wwt_kernel_data_relay/
[ext]: https://jupyter-server.readthedocs.io/en/stable/developers/extensions.html
[kernels]: https://jupyter.readthedocs.io/en/latest/projects/kernels.html
[pywwt]: https://pywwt.readthedocs.io/
[AAS]: https://aas.org/
[WorldWide Telescope]: http://www.worldwidetelescope.org/
[rapp]: https://docs.worldwidetelescope.org/research-app/latest/


For more information, including installation instructions, please visit [the
project homepage].

[the project homepage]: https://github.com/WorldWideTelescope/wwt_kernel_data_relay/
drlx 0.0.2
DRLX is a library for distributed training of diffusion models via RL
License: MIT
Requires Python: >=3.9

# Diffuser Reinforcement Learning X

DRLX is a library for distributed training of diffusion models via RL. It is meant to wrap around 🤗 Hugging Face's [Diffusers](https://huggingface.co/docs/diffusers/) library and uses [Accelerate](https://huggingface.co/docs/accelerate/) for Multi-GPU and Multi-Node (as of yet untested)

📖 **[Documentation](https://DRLX.readthedocs.io)**

# Setup

You can install the library from pypi:
```
pip install drlx
```

or from source:

```sh
pip install git+https://github.com/CarperAI/DRLX.git
```

# How to use

Currently we have only tested the library with StableDiffusion 1.4, but the plug and play nature of it means that realistically any denoiser from any pipeline should be usable. Models saved with DRLX are compatible with the pipeline they originated from and can be loaded like any other pretrained model. Currently the only algorithm supported for training is [DDPO](https://arxiv.org/abs/2305.13301).

```python
from drlx.reward_modelling.aesthetics import Aesthetics
from drlx.pipeline.pickapic_prompts import PickAPicPrompts
from drlx.trainer.ddpo_trainer import DDPOTrainer
from drlx.configs import DRLXConfig

# We import a reward model, a prompt pipeline, the trainer and config

pipe = PickAPicPrompts()
config = DRLXConfig.load_yaml("configs/my_cfg.yml")
trainer = DDPOTrainer(config)

trainer.train(pipe, Aesthetics())
```

And then to use a trained model for inference:

```python
pipe = StableDiffusionPipeline.from_pretrained("out/ddpo_exp")
prompt = "A mad panda scientist"
image = pipe(prompt).images[0]
image.save("test.jpeg")
```

# Accelerated Training

```bash
accelerate config
accelerate launch -m [your module]
```
focalize 0.1.0
Provide a formatter for loguru loggers that indents and contextualizes log blocks
License: MIT
Requires Python: >=3.8

# Focalize

**You can't depend on your eyes when your imagination is out of focus _--Mark Twain_**

![asciicast](https://github.com/dusktreader/focalize/blob/main/docs/source/images/focalize.gif)

Focalize provides context within logs for blocks of code that are encircled by a
context manager. This helps contextualize log lines within deeply nested call chains or
within complex operations where many logical steps are taken.

## Super-quick Start

* Only requires Python 3.8 or later
* Installed with pip (`$ pip install focalize`)
* Demo available in [examples](https://github.com/dusktreader/focalize/tree/main/examples/demo.py)

## Documentation

The complete documentation can be found at the [focalize documentation page](https://dusktreader.github.io/focalize/)
pyhdfe 0.2.0
High dimensional fixed effect absorption with Python 3
License: MIT
Requires Python: >=3.6

An overview of the package, examples, and other documentation can be found on `Read the Docs <https://pyhdfe.readthedocs.io/en/stable/>`_.

.. docs-start

PyHDFE is a Python 3 implementation of algorithms for absorbing high dimensional fixed effects. This package was created by `Jeff Gortmaker <https://jeffgortmaker.com>`_ in collaboration with `Anya Tarascina <https://anyatarascina.com>`_.

What PyHDFE won't do is provide a convenient interface for running regressions. Instead, the package is meant to be incorporated into statistical projects that would benefit from performant fixed effect absorption. Another goal is facilitating fair comparison of algorithms that have been previously implemented in various languages with different convergence criteria.

Development of the package has been guided by code made publicly available by many researchers and practitioners. For a full list of papers and software cited in this documentation, refer to the `references <https://pyhdfe.readthedocs.io/en/stable/references.html>`_ section of the documentation.


Installation
------------

The PyHDFE package has been tested on `Python <https://www.python.org/downloads/>`_ versions 3.6 through 3.9. The `SciPy instructions <https://scipy.org/install/>`_ for installing related packages is a good guide for how to install a scientific Python environment. A good choice is the `Anaconda Distribution <https://www.anaconda.com/download>`_, since, along with many other packages that are useful for scientific computing, it comes packaged with PyHDFE's only required dependencies: `NumPy <https://numpy.org/>`_ and `SciPy <https://scipy.org/>`_.

You can install the current release of PyHDFE with `pip <https://pip.pypa.io/en/latest/>`_::

    pip install pyhdfe

You can upgrade to a newer release with the ``--upgrade`` flag::

    pip install --upgrade pyhdfe

If you lack permissions, you can install PyHDFE in your user directory with the ``--user`` flag::

    pip install --user pyhdfe

Alternatively, you can download a wheel or source archive from `PyPI <https://pypi.org/project/pyhdfe/>`_. You can find the latest development code on `GitHub <https://github.com/jeffgortmaker/pyhdfe/>`_ and the latest development documentation `here <https://pyhdfe.readthedocs.io/en/latest/>`_.


Bugs and Requests
-----------------

Please use the `GitHub issue tracker <https://github.com/jeffgortmaker/pyhdfe/issues>`_ to submit bugs or to request features.