semvec/pypi-packages huggingface dataset:
RunRunner 0.2.0.4
RunRunner is a wrapper library for creating and managing subprocesses and their status, mainly focussed on using Slurm but (in absence) can also work with local jobs.

# RunRunner - A Python handler for managing (Slurm)Jobs

RunRunner is a wrapper library for creating and managing subprocesses and their status', mainly focussed on using Slurm but (in absence) can also work with local jobs.

## Installation

Installation of the latest version is done using Pip:

pip install RunRunner

## Usage

RunRunner works with 'queues', either the local version or Slurm version. To use RunRunner, simply add the bash call you want to be executed to the queue with:

``add_to_queue''

By specifying Runner (an Enum from base.py), RunRunner determines whether to add to the local queue (local.py) or Slurm (Slurm.py). To check what arguments each queue accepts, see the add_to_queue implementations at the top of each respective file. The call to the queue returns a structured object (a LocalRun or SlurmRun) containing the data regarding the subprocess. A run consists of one or more jobs which will contains the actual subprocess call. To start a run after creation, one can simply .submit the run and .wait for it to be done. To verify if everything went correctly, a .Status is supplied which uses .Status enum from base.py.


## Credits

Developers:
* Thijs Snelleman
* Jeroen Rook
* Koen van der Blom (Inactive)
* Jeremy Gobeil (Original developer, inactive)
huscy.projects 1.12.0
Managing projects in a research context.

# huscy.projects

![PyPi Version](https://img.shields.io/pypi/v/huscy-projects.svg)
![PyPi Status](https://img.shields.io/pypi/status/huscy-projects)
![PyPI Downloads](https://img.shields.io/pypi/dm/huscy-projects)
![PyPI License](https://img.shields.io/pypi/l/huscy-projects?color=yellow)
![Python Versions](https://img.shields.io/pypi/pyversions/huscy-projects.svg)
![Django Versions](https://img.shields.io/pypi/djversions/huscy-projects)
[![Coverage Status](https://coveralls.io/repos/bitbucket/huscy/projects/badge.svg?branch=master)](https://coveralls.io/bitbucket/huscy/projects?branch=master)



## Requirements

- Python 3.8+
- A supported version of Django

Tox tests on Django versions 4.2, 5.0 and 5.1.



## Installation

To install `husy.projects` simply run:

	pip install huscy.projects


Add `huscy.projects` and further required apps to `INSTALLED_APPS` in your settings module:

```python
INSTALLED_APPS = (
	...
	'guardian',
	'rest_framework',

	'huscy.projects',
)
```

Add Django Guardian ObjectPermissionBackend to AUTHENTICATION_BACKENDS

```python
AUTHENTICATION_BACKENDS = (
    'django.contrib.auth.backends.ModelBackend',
    'guardian.backends.ObjectPermissionBackend',
)
```

Hook the urls from `huscy.projects` into your urls.py:

```python
urlpatterns = [
    ...
	path('/api/', include('huscy.projects.urls')),
]
```

Create `huscy.projects` database tables by running:

	python manage.py migrate



## Development

Install PostgreSQL and create a database user called `huscy` and a database called `huscy`.

	sudo -u postgres createdb huscy
	sudo -u postgres createuser -d huscy
	sudo -u postgres psql -c "GRANT ALL PRIVILEGES ON DATABASE huscy TO huscy;"
	sudo -u postgres psql -c "ALTER USER huscy WITH PASSWORD '123';"

Checking out the repository start your virtual environment (if necessary).

Install all development and test dependencies:

	make install

Create Database tables:

	make migrate

Run tests to see if everything works fine:

	make test
young-chdb 0.1.0
A Well-Encapsulated ClickHouse Database APIs Lib
Requires Python: >=3.11

# chdb: A Well-Encapsulated ClickHouse Database APIs Lib

## Quick Start

An example of how to use chdb:

```python

from chdb import ClickHouseDatabase

# To connect your clickhouse database, you need to setup your config, in which the '.env' method is recommmended for security
config = {
            "host": os.getenv("DB_HOST", "localhost"),
            "port": int(os.getenv("DB_PORT", 9000)),
            "user": os.getenv("DB_USER", "default"),
            "password": os.getenv("DB_PASSWORD", ""),
            "database": os.getenv("DB_DATABASE", "default")
        }

# 'terminal' and 'file_log' control the log records. 'True' denotes the corresponding log method will be executed. You can control the logs' file path by  the 'log_file' param. 
db = ClickHouseDatabase(config=config, terminal=True, file_log=False)

# Fetch data from clickhouse database
sql = "SELECT * FROM stocks.snap ORDER BY date DESC LIMIT 5"
df = db.fetch(sql)

# Execute sql commands
sql = f"""
CREATE TABLE IF NOT EXISTS etf.kline_1m(
	`exg` UInt8 NOT NULL COMMENT '交易所标识，沪市为1，深市为0， 北交所为2',
    `code` String NOT NULL COMMENT '股票代码',
    `date` Date NOT NULL COMMENT '日期',
    `date_time` DateTime('Asia/Shanghai') NOT NULL COMMENT '日期时间，最高精度为秒',
    `time_int` UInt32 NOT NULL COMMENT '从当日开始至当前时刻的毫秒数',
    `open` Float32 NULL COMMENT 'K线开始价格',
    `high` Float32 NULL COMMENT 'K线内最高价',
    `low` Float32 NULL COMMENT 'K线内最低价',
    `close` Float32 NULL COMMENT 'K线结束价格',
    `volume` Float32 NULL COMMENT 'K线内成交量',
    `amount` Float32 NULL COMMENT 'K线内成交额'
)Engine = ReplacingMergeTree()
ORDER BY (code, date_time);
"""
db.execute(sql)

# Insert dataframe into clickhouse database. Before you insert your dataframe, you need to make sure the corresponding database and table are existed.

db.insert_dataframe(
            df=df,
            table_name="etf.kline_1m",
            columns=["exg", "code", "date_time", "date", "time_int", "open", "high", "low", "close", "volume", "amount"]
        )
```
slurm-mcp 0.0.1
Add your description here
Requires Python: >=3.13

# slurm_mcp

[![CI](https://github.com/lebrice/slurm_mcp/actions/workflows/ci.yml/badge.svg)](https://github.com/lebrice/slurm_mcp/actions/workflows/ci.yml)

Simple MCP server to interact with SLURM clusters in natural language.

## Features

- Allows LLM agents to retrieve information on your running jobs from `squeue`, prior jobs from `sacct`, and to get compute usage information based on Prometheus (once you configure it, see instructions below).


## Demo

<img width="974" height="903" alt="Screenshot from 2025-10-03 15-04-03" src="https://github.com/user-attachments/assets/4f277e02-ab8e-468a-9b4e-a9c18f3256a8" />


## Limitations

- This can only fetch your own SLURM job information, not of other users.
- This can't be used to launch jobs. It is not a good idea to let an LLM submit compute jobs for you.
- The GPU utilization metrics are only available for the Mila cluster. For other clusters, you will need to provide the prometheus URL to use in order to fetch job compute stats.
   - Some jobs are missing GPU compute stats because of a bug in the DCGMI / slurm job exporter / nvidia driver / something, that causes the gpu util to be a very very very large number. This tool filters those and displays them as having no usable compute metrics.



## Setup

You need to have SSH access to a SLURM compute cluster.

1. Install [UV](https://docs.astral.sh/uv) following the instructions [here](https://docs.astral.sh/uv/getting-started/installation).

2. Create a `.vscode/mcp.json` file with the following content:

```json
{
    "servers": {
        "slurm_mcp": {
            "type": "stdio",
            "command": "uvx",
            "args": [
                "--from",
                "git+https://www.github.com/lebrice/slurm_mcp",
                "slurm_mcp"
            ]
        }
    }
}
```

3. To get GPU metrics, you need to set the `PROMETHEUS_URL_<CLUSTER>` environment variable to the Prometheus URL of your cluster. If the SLURM cluster requires authentication to connect to prometheus, you also need to set the `PROMETHEUS_HEADERS_FILE_<CLUSTER>` environment variable to point to a JSON file containing the headers to use for authentication (e.g., `{"Authorization": "Bearer <TOKEN>"}`).

   - you can set these environment variables in your shell configuration file (e.g., `.bashrc`, `.bash_aliases`, `.zshrc`, etc.):

   ```bash
   export PROMETHEUS_URL_MILA="THE_MILA_PROMETHEUS_URL"
   export PROMETHEUS_HEADERS_FILE_MILA="secrets/prometheus_headers_mila.json"
    ```