NVIDIA數(shù)據(jù)加載庫介紹

The NVIDIA Data Loading Library (DALI) is a portable, open source library for decoding and augmenting images and videos to accelerate deep learning applications. DALI reduces latency and training time, mitigating bottlenecks, by overlapping training and pre-processing. It provides a drop-in replacement for built in data loaders and data iterators in popular deep learning frameworks for easy integration or retargeting to different frameworks.

Training neural networks with images requires developers to first normalize those images. Moreover images are often compressed to save on storage. Developers have therefore built multi-stage data processing pipelines that include loading, decoding, cropping, resizing, and many other augmentation operators. These data processing pipelines, which are currently executed on the CPU, have become a bottleneck, limiting overall throughput.

DALI is a high performance alternative to built-in data loaders and data iterators. Developers can now run their data processing pipelines on the GPU, reducing the total time it takes to train a neural network. Data processing pipelines implemented using DALI are portable because they can easily be retargeted to TensorFlow, PyTorch and MXNet.。

DALI is a high performance alternative to built-in data loaders and data iterators. Developers can now run their data processing pipelines on the GPU, reducing the total time it takes to train a neural network. Data processing pipelines implemented using DALI are portable because they can easily be retargeted to TensorFlow, PyTorch and MXNet.

DALI的主要特點

Easy-to-use Python API
Transparently scales across multiple GPUs
Accelerates image classification (ResNet-50) and object detection (SSD) workloads
Flexible graphs lets developers create custom pipelines
Supports multiple data formats - LMDB, RecordIO, TFRecord, COCO, JPEG, H.264 and HEVC
Developers can add custom image and video processing operators

DALI的目的——解決CPU瓶頸

Training deep learning models with vast amounts of data is necessary to achieve accurate results. Data in the wild, or even prepared data sets, is usually not in the form that can be directly fed into neural network. This is where NVIDIA DALI data preprocessing comes into play.

There are various reasons for that:

（1）Different storage formats
（2）Compression
（3）Data format and size may be incompatible
（4）Limited amount of high quality data

Addressing the above issues requires your training pipeline provide extensive data preprocessing capabilities, such as loading, decoding, decompression, data augmentation, format conversion, and resizing. You may have used the native implementation in existing machine learning frameworks, such as Tensorflow, Pytorch, MXnet, and others, for these pre-processing steps. However, this creates portability issues due to use of framework-specific data format, set of available transformations, and their implementations. Training in a truly portable fashion needs augmentations and portability in the data pipeline.
Data preprocessing for deep learning workloads has garnered little attention until recently, eclipsed by the tremendous computational resources required for training complex models. As such, preprocessing tasks typically ran on the CPU due to simplicity, flexibility, and availability of libraries such as OpenCV or Pillow.

Recent advances in GPU architectures introduced in the NVIDIA Volta and Turing architectures, have significantly increased GPU throughput in deep learning tasks. In particular, half-precision arithmetic and Tensor Cores accelerate certain types of FP16 matrix calculations useful for training DNNs. Dense multi-GPU systems like NVIDIA’s DGX-1 and DGX-2 train a model much faster than data can be provided by the processing framework, leaving the GPUs starved for data.

Today’s DL applications include complex, multi-stage data processing pipelines consisting of many serial operations. To rely on the CPU to handle these pipelines limits your performance and scalability.

DALI Key features

DALI offers a simple Python interface where you can implement a data processing pipeline in a few steps:

1. Select Operators from this extensive list of supported operators
2. Define the operation flow as a symbolic graph in an imperative way (as in most of the current deep learning frameworks)
3. Build an operation pipeline
4. Run graph on demand
5. Integrate with your target deep learning framework by dedicated plugin

Let us now deep dive into the inner working of DALI, followed by how to use it.

DALI 內(nèi)部原理

DALI defines data pre-processing pipeline as a dataflow graph, with each node representing a data processing Operator. DALI has 3 types of Operators as follows:

1.CPU: accepts and produces data on CPU
2.Mixed: accepts data from CPU and produces the output at the GPU side
3.GPU: accepts and produces data on the GPU

Although DALI is developed mostly with GPUs in mind, it also provides a variety of CPU-operator variants. This enables utilizing available CPU cycles for use cases where the CPU/GPU ratio is high or network traffic completely consumes available GPU cycles. You should experiment with CPU/GPU operator placement to find the sweet spot.

For the performance reasons, DALI only transfers data from CPU->Mixed->GPU as shown in figure 3.

Dali example pipeline

Existing frameworks offer prefetching, which calculates necessary data fetches before they’re needed. DALI prefetches transparently, providing the ability to define prefetch queue length flexibly during pipeline construction, as shown in figure 4. This makes it straightforward to hide high variation in the batch-to-batch processing time.

How data processing overlaps with training

DALI 表現(xiàn)

NVIDIA showcases DALI in our implementations of SSD and ResNet-50 since it was one of the contributing factors in MLPerf benchmark success.

Figure 6 compares DALI with the RN50 network running with the different GPU configurations:

Note how the core/GPU ratio becomes smaller (DGX1V has 5 CPU cores per GPU, while DGX2 only 3) the performance improvement gets better.