semvec/pypi-packages huggingface dataset:
nmsi 0.1.7
No More Search for Installation - Easy installation tool for open source software
Requires Python: >=3.9
Keywords: install, tool, snippet

# nmsi

No More Search for Installation. I am tired to search the same opens source 
installation method again, again...
This tool intends to be a quick and handy tool to install open source software.

## Installation

```bash
pip install nmsi
```

or run directly via uvx.

```bash
uvx nmsi update && uvx nmsi install uv
```

## Usage

Update installation scripts

```bash
nmsi update
```

List available tools

```bash
nmsi list
```

Install a tool

```bash
nmsi install <tool_name>
```

Add a custom script

```bash
nmsi add --name <tool_name> <script.sh>
```

## Add your own installation scripts as a repository

NMSI supports to update installation scripts from a your own repository.
The repository name is generated from basename of the repository URL.
Supported schemes are http, https, file, git.

```bash
nmsi update --from git@github.com:myusername/myapp.git
```

For example, you can prepare a source repository as follows:

```
myapp
├── linux
│   ├── general
│   │   └── install.sh
│   └── x86_64
│       └── install.sh
└── macos
    ├── general
    │   └── install.sh
    └── arm64
        └── install.sh
```

## List your own repositories

```bash
nmsi list --all
```

## Configuration

Set `NMSI_PATH` environment variable to customize the installation directory (default: `~/.local/share/nmsi`).

```bash
export NMSI_PATH="/custom/path"
```
argoflow 0.2.1
A Python library for building Argo Workflows programmatically
Requires Python: >=3.7
Keywords: argo workflows kubernetes devops ci/cd

# ArgoFlow

A Python library for building Argo Workflows in pure object oriented programming style.

## Description

ArgoFlow provides a simple and intuitive Python API for creating Argo Workflows without writing YAML (which I dislike to my core). It allows you to define workflows, tasks, dependencies and artifacts using Python code, making it easier to build dynamic and complex workflows without ever touching YAML.

But if you do still want more granular control, you can use it to generate intermediate Workflow objects as well, for validation or sanity checks, instead of directly submitting the them from the Python script.

## Installation

```bash
pip install argoflow
```

## Quick Start

```python
from argoflow import Workflow, WorkflowTask

# Create a workflow
workflow = Workflow(
    name="my-workflow",
    workflow_server_url="argo-server.example.com",
    namespace="default"
)

# Define tasks
task1 = WorkflowTask(
    name="task-1",
    template="task-1",
    image="python:3.9",
    script="print('Hello from task 1')"
)

task2 = WorkflowTask(
    name="task-2",
    template="task-2",
    image="python:3.9",
    script="print('Hello from task 2')",
    depends_on=[task1]
)

# Add tasks to workflow
workflow.add_tasks(task1, task2)

# Build and save workflow
workflow.save_to_file("workflow.json")

# Submit workflow to Argo
workflow.run_workflow(
    serverUrl="argo-server.example.com",
    executorToken="your-token-here"
)
```

## Features

- Object oriented workflow creation
- Task dependencies and DAG construction
- Input/output artifact management
- Resource allocation (CPU, memory)
- Environment variables and configuration
- Script execution from files or inline code
- Workflow submission to Argo server

## Documentation

For submitting feature requests and contributing to the project, please visit the [GitHub repository](https://github.com/D-Ajay-Kumar/argowf-python).

## License

This project is licensed under the MIT License.