semvec/pypi-packages huggingface dataset:
llmatoolkit 2.0.0
Framework to build and manage IA based applications. Based on Langchain and LlamaIndex under the hood.
Requires Python: >=3.11.3

# LlmToolkit

![Python : 11](https://img.shields.io/badge/Python-=3.11-green)
[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
![Code style : black](https://img.shields.io/badge/Code_style-black-black)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)

[![Open Issues](https://img.shields.io/github/issues-raw/Eurelis/Eurelis-KB-Framework)](https://github.com/Eurelis/Eurelis-KB-Framework/issues)
[![GitHub star chart](https://img.shields.io/github/stars/langchain-ai/langchain?style=social)](https://star-history.com/#Eurelis/Eurelis-KB-Framework)
[![X (formerly Twitter) URL](https://img.shields.io/twitter/url?url=https%3A%2F%2Fx.com%2FAgence_Eurelis&label=Follow%20%40Eurelis)](https://x.com/Agence_Eurelis)

Framework to build and manage IA based applications.
Based on LlamaIndex under the hood.
Reboot de la librairie [Eurelis-KB-Framework](https://github.com/Eurelis/Eurelis-KB-Framework).

## General information

Eurelis-LlmaToolkit is released under the **[MIT license](/LICENSE)**.

### Changes

See [CHANGELOG.md](CHANGELOG.md)

### Security policy

See [SECURITY.md](SECURITY.md)

### Citing LlmToolkit in publications

See [CITATION.cff](CITATION.cff)
RiskQuantLib 0.4.3
RiskQuantLib is a QuantLib derivative to evaluate risk.
Requires Python: >=3.8

# RiskQuantLib
RiskQuantLib is a derivative of Quantlib, a famous quantitative library of financial engineering. Unlike QuantLib, however, RiskQuantLib is a scaffolding of financial analysis. RiskQuantLib provides default class of financial instruments and allows you to create new classes you want automatically, given the inheritance rules and other information. It also provides automation building tools to add attributes to classes automatically.

You can read the document on https://riskquantlib-doc.readthedocs.io/en/latest/index.html
hipulse 0.1.0
HIPulse — Heterogeneous Compute Telemetry. Lightweight system health monitoring for workloads with CPU, GPU, memory, and kernel telemetry.
Requires Python: >=3.9
Keywords: telemetry, monitoring

# HIPulse

**Heterogeneous Compute Telemetry**


---

<p align="center">
  <strong>HIPulse</strong> — Pulse of your heterogeneous workloads
</p>
gwatch 0.0.2
G-Watch is a toolbox for GPU profiling and program analysis.
Requires Python: >=3.7

# G-Watch

<div align="center" style="display: flex; margin:10px;">
    <img src="https://github.com/G-Watch/G-Watch/raw/main/docs/logo.jpg" style="width: 350px; margin: 0px; padding: 0px;" />
</div>

***G-Watch*** is a toolbox for GPU profiling and program analysis.
It features rich **Profiling** capabilities on both NVIDIA and AMD GPUs.
Additionally,
G-Watch offers **Program Analysis** tools for inspecting compiler-generated GPU binaries,
facilitating secondary development tasks such as register analysis and binary instrumentation.


## Features

We would release guidance for using G-Watch soon,
currently please take refer to [examples](https://github.com/G-Watch/G-Watch/tree/main/examples)


## Installation

### Install from package

You can install G-Watch directly from PyPI:

```bash
# prerequisites for gwatch
apt-get update
apt-get install -y  python3 python3-pip libelf-dev protobuf-compiler libwebsockets-dev libnuma-dev
```

``` bash
# install from PyPI source
pip install gwatch
```

### Install from source

1. Clone this repository
    ```bash
    git clone --recursive https://github.com/G-Watch/G-Watch.git
    ```

2. Start official nvidia container for building and testing
    ```bash
    cd scripts/docker
    bash run_cuda_12_8.sh -s 1  # start and enter a container with id 1

    # don't need to run, just noted here
    bash run_cuda_12_8.sh -e 1  # enter a container with id 1
    bash run_cuda_12_8.sh -c 1  # close and remove a container with id 1
    ```

3. Install prerequisites
    ```bash
    # inside the container
    apt-get update
    apt-get install -y  git pkg-config python3 python3-pip cmake meson build-essential libeigen3-dev \
                        python3-dev wget libelf-dev libwebsockets-dev libnuma-dev protobuf-compiler \
                        gdb curl libcurl4-openssl-dev xxd libdwarf-dev python-is-python3
    pip3 install PyYAML tqdm pyyaml packaging loguru ninja==1.11.1.3 --break-system-packages

    # for most of our test workloads, we use PyTorch, so it's recommended to install torch as well
    # https://pytorch.org/get-started/locally/
    pip3 install torch torchvision torchaudio --break-system-packages
    ```

4. Build and install python package from source
    ```bash
    # inside the container
    cd /root

    python3 setup.py clean

    # build and install gwatch (with dev mode)
    python3 setup.py install --dev

    # build and install gwatch (with product mode)
    python3 setup.py install
    ```