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
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:
Call purem.configure(license_key='<your-license-key>')
The system will download the native Purem engine
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…
Release files for purem 3.0.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| purem-3.0.6.tar.gz | 14.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| purem-3.0.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 28.5 kB
Release files / purem-3.0.6.tar.gz
| Download URL | purem-3.0.6.tar.gz |
|---|---|
| Size | 14.3 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.0
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Release files / purem-3.0.6-py3-none-any.whl
| Download URL | purem-3.0.6-py3-none-any.whl |
|---|---|
| Size | 14.2 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.0
|