semvec/pypi-packages huggingface dataset:
bio-SMART 0.1.2
SMART: Spatial multi-omic aggregation using graph neural networks and metric learning
License: MIT
Requires Python: >=3.9

Spatial multi-omics enables the exploration of tissue microenvironments and heterogeneity from the perspective of different omics modalities across distinct spatial domains within tissues. To jointly analyze the spatial multi-omics data, computational methods are desired to integrate multiple omics with spatial information into a unified space. 

Here, we present **SMART** (Spatial Multi-omic Aggregation using gRaph neural networks and meTric learning), a computational framework for spatial multi-omic integration. SMART leverages a modality-independent modular and stacking framework with spatial coordinates and adjusts the aggregation using triplet relationships. SMART excels at accurately identifying spatial regions of anatomical structures, compatible with spatial datasets of any type and number of omics layers, while demonstrating exceptional computational efficiency and scalability on large datasets. 

Moreover, a variant of SMART, **SMART-MS**, expands its capabilities to integrate spatial multi-omics data across multiple tissue sections. 

In summary, SMART provides a versatile, efficient, and scalable solution for integrating spatial multi-omics data.
pytornis 0.0.2b0
Industrial Deep Learning framework with CPU/GPU support, tensors, autograd, and CUDA
License: MIT
Requires Python: >=3.10
Keywords: deep-learning, tensor, cuda, cpu, autograd, AI, industrial

# Pytornis

Pytornis es una biblioteca de **cómputo industrial para IA y Deep Learning** completamente autocontenida, inspirada en PyTorch y NumPy. Su diseño permite entrenar modelos de manera **real, industrial y distribuida**, sin dependencias externas (solo C++ embebido con compilación automática opcional con clang/nvcc).

## Características Principales

- Tensores multidimensionales en CPU y GPU (FP16, FP32, INT8).
- Kernels optimizados propios para cálculos lineales y convoluciones.
- AutoGrad: diferenciación automática completa.
- Capas y módulos: Linear, Conv2D, Pooling, BatchNorm, Dropout.
- Optimizadores nativos: SGD, Adam, RMSProp, Lion, con búsqueda automática de hiperparámetros.
- Entrenamiento distribuido y pipelines asincrónicos.
- Vectorización avanzada y tokenización nativa (soporte GPT/CPT).
- Tensores de audio/video para entrenamiento de modelos multimodales.
- Precisión mixta y autotuning dinámico de kernels.
- DataLoader con prefetching y mini-batches.
- Propio sistema ONNX-like para importar/exportar modelos.
- Autocontenido: no requiere PyTorch, NumPy, TensorFlow ni CUDNN.
- nota puede contener errores primera version estable hasta ahora.

## Instalación

```bash
pip install pytornis