{"library":"nvidia-dali-cuda120","title":"NVIDIA DALI for CUDA 12.0","description":"NVIDIA DALI (Data Loading Library) is a GPU-accelerated data loading and augmentation library for deep learning. This package (nvidia-dali-cuda120) is built specifically for CUDA 12.0. Current version is 2.1.0, with a rapid release cadence (about monthly). Supports Python 3.10–3.14. Requires NVIDIA GPU with CUDA 12.0 driver (R525+) and nvJPEG2000 support. For CUDA 12.0 users, install this package instead of the generic nvidia-dali.","language":"python","status":"active","last_verified":"Sat May 09","install":{"commands":["pip install nvidia-dali-cuda120"],"cli":null},"imports":["from nvidia.dali import pipeline_def","from nvidia.dali import fn","from nvidia.dali import types","from nvidia.dali.pipeline import Pipeline"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from nvidia.dali import pipeline_def, fn, types\nfrom nvidia.dali.plugin.pytorch import DALIGenericIterator\n\n@pipeline_def(batch_size=4, num_threads=2, device_id=0)\ndef simple_pipeline():\n    jpegs, labels = fn.readers.file(file_root='/data/images', random_shuffle=True)\n    images = fn.decoders.image(jpegs, device='mixed')\n    images = fn.resize(images, resize_x=224, resize_y=224)\n    images = fn.crop_mirror_normalize(\n        images,\n        dtype=types.FLOAT,\n        output_layout='CHW',\n        mean=[0.485*255,0.456*255,0.406*255],\n        std=[0.229*255,0.224*255,0.225*255])\n    return images, labels\n\npipe = simple_pipeline()\npipe.build()\ntrain_loader = DALIGenericIterator(pipe, ['images', 'labels'])\nfor data in train_loader:\n    print(data[0]['images'].shape)\n    break","lang":"python","description":"Basic image classification pipeline using DALI with PyTorch integration. Ensure /data/images contains subdirectories per class with JPEG images.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}