Metadata-Version: 2.4
Name: wirehead
Version: 0.9.3
Summary: Caching system for scaling of synthetic data generators using MongoDB
Home-page: https://github.com/neuroneural/wirehead
Author: Neuroneural Lab
Author-email: spikedoanz@gmail.com
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pymongo
Requires-Dist: torch
Requires-Dist: numpy
Requires-Dist: PyYaml
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Dynamic: author-email
Dynamic: classifier
Dynamic: description
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# Wirehead

Caching system for scaling of synthetic data generators using MongoDB.

## Features
- Cache and efficiently serve synthetic data from generators
- Scalable architecture using MongoDB for storage
- Support for numpy and torch tensors
- Configurable caching behavior

## Quick Start
```python
# Generator example
import numpy as np
from wirehead import WireheadGenerator 

def create_generator():
    while True: 
        img = np.random.rand(256,256,256)
        lab = np.random.rand(256,256,256)
        yield (img, lab)

brain_generator = create_generator()
wirehead_runtime = WireheadGenerator(
    generator = brain_generator,
    config_path = "config.yaml" 
)
wirehead_runtime.run_generator()

# Dataset example
from wirehead import MongoheadDataset
dataset = MongoheadDataset(config_path = "config.yaml")
data = dataset[[0]]
```

## MongoDB Setup Required
Requires a running MongoDB instance.

## Documentation
For full documentation and examples, visit: https://github.com/neuroneural/wirehead
