purem 2.1.5

The official high-performance mapping function for mixed-type arrays powered by Work TIF Ltd.

Overview

Purem is an advanced high-performance computational library optimized for vectorized mathematical operations. This project focuses on efficient execution of element-wise transformations, including softmax, exp, and other mathematical functions, leveraging highly optimized assembly code for modern architectures.

Key Features

Purem is a blazing-fast AI math engine that turns your Python formulas into native-speed vectorized execution. Achieve up to 429× faster performance on CPU/GPU/TPU – no rewrites, no dependencies, just speed. Designed for ML researchers, scientific computing, and production-grade workloads that can’t afford to wait.

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🔧 Installation

Install the Python wrapper via pip:

pip install purem

📦 Note: Installation is quick, but purem must be initialized with a license before use. Setup takes less than a minute – we’re ready when you are: https://worktif.com/#start

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🚀 Quickstart

1. Import and Initialize:

   from purem import purem
   
   purem.configure(license_key='your-license-key')  # Auto-downloads and configures backend

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🔐 License-Based Activation

The system will download the licensed native Purem engine:

1. Call purem.configure(license_key='<your-license-key>')

2. The system will download the native Purem engine

3. All functions will become available instantly after initialization

Without a valid license key:

• No native Purem engine will be downloaded

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🧠 Available Functions

After initialization, you can call:

from purem import purem

# Transforms a list of numbers into a probability distribution.
# Each output value is between 0 and 1, and all outputs sum to 1.
# Commonly used in classification tasks to interpret scores as probabilities.
purem.softmax([...])
...

Full function list: https://worktif.com/docs/basic-usage

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📦 Packaging Notes

This package does not bundle the native Purem engine. You are required to:

• Use a license key to download it dynamically

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🧪 Benchmark Tutorial

Visit the Benchmark Tutorial: https://worktif.com/#benchmarks

• How Purem compares to NumPy, PyTorch and Numba

• How it reaches low-level performance via native execution

• Why it’s faster than traditional Python-based computation

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📧 Distribution and Licensing

The native Purem engine is distributed exclusively through license-based activation. All users must:

• Use their license key to install

For access, contact us or visit: https://worktif.com/documents/terms-of-service

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📚 Full Example

# Import required modules
import numpy as np
from purem import purem

# Automatic setup using license key
try:
    purem.configure(license_key='<your-license-key>')
except Exception as e:
    print(f"Setup failed: {e}")

data = np.array([1.0, 2.0, 3.0], dtype=float)
output = purem.softmax(data)

print(output)

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🧠 Why Purem?

• 🔥 High level performance with zero Python overhead

• 🧪 Built-in benchmarking and scientific accuracy

• 🧩 Easy plug-and-play design

• 🔐 Secure and license-aware system

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🛠 Advanced Usage & API Docs

Coming soon…