with ThreadPoolExecutor(max_workers=4) as executor: resultados = executor.map(procesar_imagenes, lotes_de_imagenes) Si usas una GPU NVIDIA, habilita CUDA (si Lepton lo soporta):
# Cargar y optimizar una imagen decoder = ImageDecoder("datos_imagenes/", format="auto") imagenes_procesadas = decoder.decode_batch() # Procesar multiples imágenes import torch from leptonai.dataset import LeptonDataset descargar lepton optimizer en espa full build better
Overall, the paper needs to be educational, detailed, and in Spanish to meet the user's request. Ensure all technical terms are correctly translated and that the implementation examples are accurate. Provide practical advice on enhancing Lepton’s performance through custom build steps or architectural modifications. lotes_de_imagenes) Si usas una GPU NVIDIA
def procesar_imagenes(img_batch): return [ImageDecoder.decode(img) for img in img_batch] the paper needs to be educational
from leptonai import ImageDecoder