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codeAugust 7, 2026•8 min read•RTSPLink Dev Team

Streaming PyTorch Lightning DataLoader for Live RTSP Video Feeds

Train and evaluate neural networks on continuous live security camera streams without storing video files on disk.

#PyTorch#PyTorch Lightning#Deep Learning#Python#DataLoader
Live RTSP Test Feed

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#1PyTorch IterableDataset RTSP Implementation

Implement a non-blocking stream generator for PyTorch:

PyTorch RTSP Dataset
import torch
from torch.utils.data import IterableDataset, DataLoader
import cv2

class RTSPIterableDataset(IterableDataset):
    def __init__(self, rtsp_url):
        self.rtsp_url = rtsp_url

    def __iter__(self):
        cap = cv2.VideoCapture(self.rtsp_url)
        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            tensor = torch.from_numpy(frame).permute(2, 0, 1).float() / 255.0
            yield tensor

dataset = RTSPIterableDataset("rtsp://rtsplink.com/live/test?token=demo")
loader = DataLoader(dataset, batch_size=4)

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