semvec/pypi-packages huggingface dataset:
dask-opentelemetry 0.0.1a0
Instrument Dask with OpenTelemetry
Requires Python: <4.0,>=3.8

# dask-opentelemetry

[![PyPI - Version](https://img.shields.io/pypi/v/dask-opentelemetry.svg)](https://pypi.org/project/dask-opentelemetry)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dask-opentelemetry.svg)](https://pypi.org/project/dask-opentelemetry)

-----

**Table of Contents**

- [dask-opentelemetry](#dask-opentelemetry)
  - [Installation](#installation)
  - [License](#license)

## Installation

```console
pip install dask-opentelemetry
```

## License

`dask-opentelemetry` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
chatllama 0.0.4
ChatLLaMA: Open and Efficient Foundation Language Models Runnable In A Single GPU
Keywords: ChatLLaMA,LLaMA

# ChatLLaMA

> 📢 Open source implementation for LLaMA-based ChatGPT runnable in a single GPU. 15x faster training process than `ChatGPT`

Meta has recently released LLaMA, a collection of foundational large language models ranging from 7 to 65 billion parameters.
LLaMA is creating a lot of excitement because it is smaller than GPT-3 but has better performance. For example, LLaMA's 13B architecture outperforms GPT-3 despite being 10 times smaller. This new collection of fundamental models opens the door to faster inference performance and chatGPT-like real-time assistants, while being cost-effective and running on a single GPU.

However, LLaMA was not fine-tuned for instruction task with a Reinforcement Learning from Human Feedback (RLHF) training process.

The good news is that we introduce `ChatLLaMA`, the first open source implementation of LLaMA based on RLHF:

- A complete open source implementation that enables you to build a ChatGPT-style service based on pre-trained LLaMA models.
- Compared to the original ChatGPT, the training process and single-GPU inference are much faster and cheaper by taking advantage of the smaller size of LLaMA architectures.
- ChatLLaMA has built-in support for DeepSpeed ZERO to speedup the fine-tuning process.
- The library also supports all LLaMA model architectures (7B, 13B, 33B, 65B), so that you can fine-tune the model according to your preferences for training time and inference performance.


<img width="1032" alt="Screen Shot 2023-02-26 at 10 56 13 PM" src="https://user-images.githubusercontent.com/83510798/221439813-5972d029-dae5-4561-ab3d-5a55fa5cde09.png">

Image from [OpenAI’s blog](https://openai.com/blog/chatgpt).


# Installation

```
pip install chatllama
```


# Get started with ChatLLaMA

> :warning: Please note this code represents the algorithmic implementation for RLHF training process of LLaMA and does not contain the model weights. To access the model weights, you need to apply to Meta's [form](https://forms.gle/jk851eBVbX1m5TAv5).

ChatLLaMA allows you to easily train LLaMA-based architectures in a similar way to ChatGPT, using RLHF.
For example, below is the code to start the training in the case of ChatLLaMA 7B.

```python
from chatllama.rlhf.trainer import RLTrainer
from chatllama.rlhf.config import Config

path = "path_to_config_file.yaml"
config = Config(path=path)
trainer = RLTrainer(config.trainer)
trainer.distillate()
trainer.train()
trainer.training_stats.plot()
```

Note that you should provide Meta's original weights and your custom dataset before starting the fine-tuning process. Alternatively, you can generate your own dataset using LangChain's agents.

```python
python generate_dataset.py
```
librarypy 0.0.1
LibraryPy is a Python library built for Python 3.11.0.
Requires Python: >=3.11

# LibraryPy
LibraryPy is a Python library built for Python 3.11.0.

## Installation
To install LibraryPy, you can run the following command in your terminal or command line:
pip install librarypy

## Usage
Here's an example of how you can use LibraryPy in your own Python project:
import librarypy
result = librarypy.do_something()
print(result)

## Dependencies
LibraryPy requires that Python 3.11.0 or later is installed on your system.

## Collaborators
LibraryPy was created by Felipe and Edwin as a collaboration to create a library for their projects.

## Contributing
If you would like to contribute to the development of LibraryPy, please follow the guidelines in our CONTRIBUTING.md file.

## Projektnavn

Dette prosjektet er tilgjengelig som en Python-pakke på Python Package Index (PyPI). Du kan installere den ved hjelp av pip:
pip install librarypi

For å publisere nye versjoner av prosjektet til PyPI, kan du bruke verktøyet twine. Du kan finne mer informasjon om hvordan du publiserer til PyPI på denne siden: https://pypi.org/

Vi håper du finner dette prosjektet nyttig i ditt eget arbeid. Hvis du har noen spørsmål eller forslag til forbedringer, vennligst åpne en issue på GitHub.
icepack 1.1.0
Encrypting File Archiver
Requires Python: >=3.8

# icepack - Encrypting File Archiver

icepack combines [age encryption][], [SSH signatures][] and ZIP files to create
encrypted and verifiable archives. Except for the number of files and their
approximate size, no metadata is visible.

[age encryption]: https://age-encryption.org/
[ssh signatures]: https://www.agwa.name/blog/post/ssh_signatures

## Installation

Requirements:

- Python 3.8
- age 1.0
- OpenSSH 8.0

Install with `pip` or [pipx][]:

```
$ pip install icepack
```

If `pigz` or `pbzip2` are installed, they will be used for faster compression.

[pipx]: https://pypa.github.io/pipx/

## Basic Usage

### Initialize the keys

```
$ icepack init
Enter passphrase (empty for no passphrase): *****
Enter same passphrase again: *****
The keys have been initialized in /home/username/.config/icepack
Make sure to protect and backup this directory!
```

### Create an archive

```
$ icepack create $HOME/Documents/ $HOME/my-documents.zip
Documents/Cat Pictures
Documents/Cat Pictures/awww.jpg
Documents/Cat Pictures/grumpy.jpg
Documents/Cat Pictures/socute.jpg
Documents/world-domination.txt
Enter passphrase: *****
```

| Option | Description |
| --- | --- |
| `--comment` | Archive comment. |
| `--compression`, `-c` | Compression type: `bz2`, `gz` or `none` (Default: `gz`) |
| `--mode` | Store file/directory modes. |
| `--mtime` | Store file/directory modification times. |
| `--recipient`, `-r` | Allow another public key/alias to extract. |

### Extract an archive

```
$ icepack extract $HOME/my-documents.zip $HOME/
Enter passphrase for "/home/username/.config/icepack/identity": *****
Documents/Cat Pictures
Documents/Cat Pictures/awww.jpg
Documents/Cat Pictures/grumpy.jpg
Documents/Cat Pictures/socute.jpg
Documents/world-domination.txt
```

| Option | Description |
| --- | --- |
| `--mode` | Restore file/directory modes. |
| `--mtime` | Restore file/directory modification times. |

### Check the version and dependencies

```
$ icepack version --dependencies
icepack 1.0.0
✅ age found. (Version: v1.0.0)
✅ age-keygen found.
✅ ssh found. (Version: OpenSSH_8.2p1)
✅ ssh-keygen found.
```

| Option | Description |
| --- | --- |
| `--dependencies`, `-d` | Check the dependencies. |

## Signer Management

To extract archives created by other parties, their public keys need to be
added to the list of allowed signers. The `signer` command supports this.

### List allowed signers

```
$ icepack signer list
ssh-ed25519 AAAAC3NzaC... (Your Key)
ssh-ed25519 AAAAC3NzaC... (Bob)
```

### Add an allowed signer

```
$ icepack signer add "ssh-ed25519 AAAAC3NzaC..." --alias Alice
$ icepack signer list
ssh-ed25519 AAAAC3NzaC... (Your Key)
ssh-ed25519 AAAAC3NzaC... (Bob)
ssh-ed25519 AAAAC3NzaC... (Alice)
```

| Option | Description |
| --- | --- |
| `--alias`, `-a` | Key alias. |

### Remove an allowed signer

```
$ icepack signer remove Bob
$ icepack signer list
ssh-ed25519 AAAAC3NzaC... (Your Key)
ssh-ed25519 AAAAC3NzaC... (Alice)
```

When removing a key, you can specify the key or its alias.
rhclient 0.4.2
Python REST Service Management Client
Requires Python: >=3.6

#### <strong>This software is the sole property of Prime Solutions Group, Inc.</strong>

# <strong>REST Harness - Python Client</strong>

REST Harness is a robust tool that acts to stand in place of another RESTful service. This can be used to mock out a service that doesn't exist yet or where there is a need to reproduce a particular set of inputs from an opaque service. It can also serve as a simple shared cache. This software runs as a daemon service by default. This library contains a client developed in Java that can be used to interact with the REST Harness web server.

### Requirements
Python3, boto3 and requests.

### Building the client
To build:
1. pull the source into any folder.
2. Open a terminal and CD to the source folder.
3. Use any of the python lifecycle phase flags with the prefix -m (so it is ran as a script), such as `build`.

### Importing the client and using it in an application
Importing the client is like installing and importing any other python dependency. 
To import:
1. In your project environment, type `pip install rhclient`
2. To use it in your scripts, type `from rhclient import client` where you'd normally import dependencies