Metadata-Version: 2.3
Name: ultima
Version: 1.2.0
Summary: Intuitive parallel map calls for Python
License: MIT
Author: Yonatan Perry
Requires-Python: >=3.10,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Dist: dill
Project-URL: Repository, https://github.com/epic-framework/ultima
Description-Content-Type: text/markdown

# Ultima - intuitive parallel `map()` for Python
[![Ultima CI](https://github.com/epic-framework/ultima/actions/workflows/ci.yml/badge.svg)](https://github.com/epic-framework/ultima/actions/workflows/ci.yml)

## What is it?

**ultima** is a Python package that provides a simple yet powerful interface to do `map()` in parallel.
It uses `concurrent.futures` as its execution backend and can run tasks in either threads or sub-processes.
It is designed to squeeze maximum performance (let those CPUs burn 🔥) with the same simple interface.

## Usage examples:

Run a heavy function in sub-processes:

```python
from ultima import Workforce

inputs = open("input_data.txt")

with Workforce() as wf:
    for result in wf.map(cpu_intensive_function, inputs):
        ...
```

An equivalent one-liner:

```python
from ultima import ultimap

for result in ultimap(cpu_intensive_function, inputs):
    ...
```

The default backend is multiprocessing, but you can easily use threads instead:

```python
from ultima import ultimap

for result in ultimap(io_bound_function, inputs, backend='threading', n_workers=64):
    ...
```

To chain an IO-intensive task with a CPU-intensive task:

```python
from ultima import ultimap

def io_intensive(url):
    import requests
    return requests.get(url).text

def cpu_intensive(page):
    import hashlib
    return hashlib.sha1(page.encode()).hexdigest()

urls = open("urls.txt")
webpages = ultimap(io_intensive, urls, backend='threading', n_workers=64)
hashes = ultimap(cpu_intensive, webpages, backend='multiprocessing')
print(len(set(hashes)))
```

You can also map a function recursively:

```python
from ultima import ultimap

def visit(graph, node, add_input):
    for child_node in graph.get_children(node):
        add_input(graph, child_node)
    return f"visited {node}"

print(list(ultimap(visit, [(graph, graph.root)], recursive=True)))
```

