NVIDIA DALI
1.2.0 -c4e86b5
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Getting Started

  • Installation
    • Prerequisites
    • DALI in NGC Containers
    • pip - Official Releases
      • nvidia-dali
      • nvidia-dali-tf-plugin
    • pip - Nightly and Weekly Releases
      • Nightly Builds
      • Weekly Builds
    • pip - Legacy Releases
  • Platform Support
  • Getting Started Tutorial
    • Overview
    • Pipeline
      • Defining the Pipeline
      • Building the Pipeline
      • Running the Pipeline
    • Adding Augmentations
      • Random Shuffle
      • Augmentations
      • Tensors as Arguments and Random Number Generation
    • Adding GPU Acceleration
      • Copying Tensors to GPU
        • Important Notice
      • Hybrid Decoding

Python API Documentation

  • Pipeline
    • Data Processing Graphs
    • Current Pipeline
    • Pipeline Decorator
    • DataNode
  • Types
    • TensorList
      • TensorListCPU
      • TensorListGPU
    • Tensor
      • TensorCPU
      • TensorGPU
    • Data Layouts
      • Tensor Layout String format
      • Interpreting Tensor Layout Strings
    • Constant wrapper
      • Constant
    • Enums
      • DALIDataType
      • DALIIterpType
      • DALIImageType
      • SampleInfo
      • TensorLayout
      • PipelineAPIType
  • Operations
    • Modules
      • nvidia.dali.fn
      • nvidia.dali.fn.decoders
      • nvidia.dali.fn.noise
      • nvidia.dali.fn.random
      • nvidia.dali.fn.readers
      • nvidia.dali.fn.reductions
      • nvidia.dali.fn.segmentation
      • nvidia.dali.fn.transforms
      • nvidia.dali.plugin.numba.fn.experimental
      • nvidia.dali.plugin.pytorch.fn
  • Mathematical Expressions
    • Type Promotion Rules
    • Supported Arithmetic Operations
    • Mathematical Functions
      • Exponents and logarithms
      • Trigonometric Functions
      • Hyperbolic Functions
  • DL Framework Plugins
    • MXNet
      • MXNet Plugin API reference
      • MXNet Framework
        • Gluon example with DALI
        • ExternalSource operator
        • Using MXNet DALI plugin: using various readers
    • PyTorch
      • PyTorch Plugin API reference
      • Pytorch Framework
        • Using DALI in PyTorch
        • ExternalSource operator
        • Using PyTorch DALI plugin: using various readers
        • Using DALI in PyTorch Lightning
    • TensorFlow
      • TensorFlow Plugin API reference
      • Tensorflow Framework
        • Using Tensorflow DALI plugin: DALI and tf.data
        • Using Tensorflow DALI plugin: DALI tf.data.Dataset with multiple GPUs
        • Using Tensorflow DALI plugin with sparse tensors
        • Using Tensorflow DALI plugin: simple example
        • Using Tensorflow DALI plugin: using various readers
    • PaddlePaddle
      • PaddlePaddle Plugin API reference
      • PaddlePaddle Framework
        • Using DALI in PaddlePaddle
        • ExternalSource operator
        • Using Paddle DALI plugin: using various readers
  • Operator Objects (Legacy)
    • Mapping to Functional API
    • Modules
      • nvidia.dali.ops
      • nvidia.dali.ops.decoders
      • nvidia.dali.ops.noise
      • nvidia.dali.ops.random
      • nvidia.dali.ops.readers
      • nvidia.dali.ops.reductions
      • nvidia.dali.ops.segmentation
      • nvidia.dali.ops.transforms
      • nvidia.dali.plugin.numba.experimental
      • nvidia.dali.plugin.pytorch

Examples and Tutorials

  • Data Loading
    • ExternalSource Operator
      • Define the Data Source
      • Defining the Pipeline
      • Using the Pipeline
        • Interacting with the GPU Input
    • Data Loading: LMDB Database
      • Overview
      • Caffe LMDB Format
      • Caffe 2 LMDB Format
    • Data loading: MXNet recordIO
      • Overview
      • Creating an Index
      • Defining and Running the Pipeline
    • Data Loading: TensorFlow TFRecord
      • Overview
      • Creating index
      • Defining and Running the Pipeline
    • COCO Reader
  • Operations
    • General Purpose
      • DALI Expressions and Arithmetic Operations
        • DALI Expressions and Arithmetic Operators
        • DALI Binary Arithmetic Operators - Type Promotions
        • Custom Augmentations with Arithmetic Operations
        • Conditional-Like Execution and Masking
      • Reduction Operators
      • Tensor Joining
        • Concatenation
        • Stacking
      • Reinterpreting Tensors
        • Fixed Output Shape
        • Reshape with Wildcards
        • Removing and Adding Unit Dimensions
        • Rearranging Dimensions
        • Adding and Removing Dimensions
        • Relative Shape
        • Reinterpreting Data Type
      • Normalize Operator
        • Introduction
        • Using the Normalize Operator
        • Adjusting Output Dynamic Range
        • Externally Provided Parameters
        • Batch Normalization
      • Geometric Transforms
        • Affine Transform
        • Transform Catalogue
        • Case Study: Transforming Keypoints
        • Adding Transforms to the Pipeline
        • Combining Transforms
        • Keypoint Cropping
        • Transform Gallery
      • Erase Operator
    • Image Processing
      • Augmentation Gallery
      • BrightnessContrast Example
        • Brighness and Contrast Adjustment
        • Step-by-Step Guide
      • Color Space Conversion
        • Defining the Pipeline
        • Building and Running the Pipeline
        • Visualizing the Results
      • Image Decoder examples (CPU)
        • Common code
        • Image Decoder (CPU)
        • Image Decoder (CPU) with Random Cropping Window Size and Anchor
        • Image Decoder with Fixed Cropping Window Size and External Anchor
        • Image Decoder (CPU) with External Window Size and Anchor
      • Image Decoder (Hybrid)
        • Image Decoder (Hybrid) with Random Cropping Window Size and Anchor
        • Image Decoder (Hybrid) with Fixed Cropping Window Size and External Anchor
        • Image Decoder (Hybrid) with External Window Size and Anchor
      • HSV Example
        • Introduction
        • Step-by-Step Guide
      • Using HSV to implement RandomGrayscale operation
      • Interpolation methods
        • Downscaling
        • Upscaling
      • Resize operator
        • Output Size Parameters
        • Scaling Modes
        • Region of Interest (RoI) Processing
        • Fused Flip
        • Input and Output Types
        • Subpixel Scale
      • WarpAffine
        • Introduction
        • Usage Example
        • Example Output
      • 3D Transforms
        • Warp Operators
        • Usage Example
        • Example Output
    • Audio Processing
      • Audio Decoder in DALI
        • Step-by-Step Guide
        • Verification
      • Audio spectrogram
        • Background
        • Reference implementation
        • Calculating the Spectrogram using DALI
        • Mel Spectrogram
        • Mel-Frequency Cepstral Coefficients (MFCCs)
    • Video Processing
      • Simple Video Pipeline Reading From Multiple Files
        • Goal
        • Visualizing the Results
      • Video Pipeline Reading Labelled Videos from a Directory
        • Setting Up
        • Running the Pipeline
        • Visualizing the Results
      • Video Pipeline Demonstrating Applying Labels Based on Timestamps or Frame Numbers
        • Defining the Pipeline
        • Visualizing the Results
      • Reading Video Frames Stored as Images
        • Preparing the Data
        • Frame Sequence Reader
      • Optical Flow
        • Using Dali
  • Use Cases
    • Video Super-Resolution
      • Dataloaders
      • Data Loader Performance
      • Requirements
      • FlowNet2-SD Implementation and Pre-trained Model
      • Data
      • Training
      • Results on Myanmar Validation Set
      • Reference
    • ImageNet Training in PyTorch
      • Requirements
      • Training
      • Usage
    • Single Shot MultiBox Detector Training in PyTorch
      • Requirements
      • Usage
    • ResNet-N with TensorFlow and DALI
      • Training in Keras Fit/Compile mode
      • Predicting in Keras Fit/Compile mode
      • Training in CTL (Custom Training Loop) mode
      • Predicting in CTL (Custom Training Loop) mode
      • Other useful options
        • Requirements
    • PaddlePaddle Use-Cases
      • ResNet Training in PaddlePaddle
        • Training
        • Usage
      • Single Shot MultiBox Detector Training in PaddlePaddle
        • Requirements
        • Usage
      • Temporal Shift Module Inference in PaddlePaddle
        • Requirements
        • Usage
    • MXNet with DALI - ResNet 50 example
      • Overview
      • ResNet 50 Pipeline
        • The Training Pipeline
        • Using the MXNet Plugin
      • Training with MXNet
    • COCO Reader with Augmentations
  • Other
    • Multiple GPU Support
      • Overview
      • Run Pipeline on Selected GPU
      • Sharding
    • Custom Operations
      • Create a Custom Operator in C++
        • Prerequisites
        • Operator Definition
        • CPU Operator Implementation
        • GPU operator implementation
        • Building the Plugin
        • Importing the Plugin
      • Python Operators
        • Defining an Operation
        • Defining a Pipeline
        • Running the Pipeline and Visualizing the Results
        • Variety of Python Operators
        • Limitations of Python Operators
      • Processing GPU Data with Python Operators
        • CuPy Operations
        • Defining a Pipeline
        • Running the Pipeline and Visualizing the Results
        • Advanced: Device Synchronization in the DLTensorPythonFunction
      • Numba Function - Running a Compiled C Callback Function
        • Define the shape function swapping the width and height
        • Define the processing function that fills the output sample based on the input sample
        • Define the Pipeline
    • Serialization
      • Overview
      • Serialization
    • Operator Objects (Legacy)
      • Overview
        • Defining the Pipeline
        • Building the Pipeline
        • Running the Pipeline
      • Adding Augmentations
        • Random Shuffle
        • Augmentations
        • Tensors as Arguments and Random Number Generation
      • Adding GPU Acceleration
        • Copying Tensors to GPU
        • Hybrid Decoding

Advanced

  • Performance Tuning
    • Thread Affinity
    • Memory Consumption
    • Operator Buffer Presizing
    • Prefetching Queue Depth
  • Sharding
  • Pipeline Run Methods
  • Experimental
    • C++ API
  • Compiling DALI from Source
    • Using Docker builder - recommended
      • Prerequisites
      • Building Python Wheel
    • Bare Metal build
      • Prerequisites
      • Build DALI
        • Install Python Bindings
      • Verify the Build (Optional)
        • Obtain Test Data
        • Set Test Data Path
        • Run Tests
      • Building DALI with Clang (Experimental)
      • Optional CMake Build Parameters
    • Cross-compiling for aarch64 Jetson Linux (Docker)
      • Setup
      • Build the aarch64 Jetson Linux Build Container
      • Compile

Reference

  • Release Notes
  • GitHub
NVIDIA DALI
  • »
  • Python Module Index

Python Module Index

n
 
n
- nvidia
    nvidia.dali.fn
    nvidia.dali.fn.decoders
    nvidia.dali.fn.noise
    nvidia.dali.fn.random
    nvidia.dali.fn.readers
    nvidia.dali.fn.reductions
    nvidia.dali.fn.segmentation
    nvidia.dali.fn.transforms
    nvidia.dali.ops
    nvidia.dali.ops.decoders
    nvidia.dali.ops.noise
    nvidia.dali.ops.random
    nvidia.dali.ops.readers
    nvidia.dali.ops.reductions
    nvidia.dali.ops.segmentation
    nvidia.dali.ops.transforms
    nvidia.dali.plugin.mxnet
    nvidia.dali.plugin.numba.experimental
    nvidia.dali.plugin.numba.fn.experimental
    nvidia.dali.plugin.paddle
    nvidia.dali.plugin.pytorch
    nvidia.dali.plugin.pytorch.fn
    nvidia.dali.plugin.tf

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