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PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research. PyTorch code with Lightning enables seamless training on multiple-GPUs and uses best practices such as checkpointing, logging, sharding, and mixed precision. In this post, we walk you through building speech models with PyTorch Lightning on NVIDIA GPU-powered AWS instances managed by the Grid.ai platform.

AI is driving the fourth Industrial Revolution with machines that can hear, see, understand, analyze, and then make smart decisions at superhuman levels. However, the effectiveness of AI depends on the quality of the underlying models. So, whether you’re an academic researcher or a data scientist, you want to quickly build models with a variety of parameters and identify the most effective ones for your solutions.

In this post, we walk you through building speech models with PyTorch Lightning on NVIDIA GPU-powered AWS instances.

PyTorch Lightning + Grid.ai: Build models faster, at scale

PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research. Organizing PyTorch code with Lightning enables seamless training on multiple GPUs, TPUs, CPUs, and the use of difficult to implement best practices such as checkpointing, logging, sharding, and mixed precision. A PyTorch Lightning container and developer environment is available on the NGC catalog.

Grid enables you to scale training from your laptop to the cloud without having to modify your code. Running on cloud providers such as AWS, Grid supports Lightning as well as all the classic machine learning frameworks such as Sci Kit, TensorFlow, Keras, PyTorch and more. With Grid, you can scale the training of models from the NGC catalog.

NGC: The hub for GPU-optimized AI software

The NGC catalog is the hub for GPU-optimized software including AI/ML containers, pretrained models, and SDKs that can be easily deployed across on-premises, cloud, edge, and hybrid environments. NGC offers NVIDIA TAO Toolkit that enables retraining models with custom data and NVIDIA Triton Inference Server to run predictions on CPU and GPU-powered systems.

The rest of this post walks you through how to leverage models from the NGC catalog and the NVIDIA NeMo framework to train an automatic speech recognition (ASR) model with PyTorch Lightning using the following tutorial based on the ASR with NeMo tutorial.

Training NGC models with Grid sessions, PyTorch Lightning, and NVIDIA NeMo

ASR is the task of transcribing spoken language to text and is a critical component of Speech to Text systems. When training ASR models, your goal is to generate text from a given audio input that minimizes the word error rate (WER) metric on human transcribed speech. The NGC catalog contains state-of-the-art pretrained models for ASR.

In the remainder of this post, we show you how to use Grid sessions, NVIDIA NeMo, and PyTorch Lightning to fine-tune these models on the AN4 dataset.

The AN4 dataset, also known as the Alphanumeric dataset, was collected and published by Carnegie Mellon University. It consists of recordings of people spelling out addresses, names, telephone numbers, and so on, one letter or number at a time, as well as their corresponding transcripts.

Step 1: Create a Grid session optimized for Lightning and pretrained NGC models

Grid sessions run on the same hardware that you need to scale while providing you with preconfigured environments to iterate the research phase of the machine learning process faster than before. Sessions are linked to GitHub, loaded with JupyterHub, and can be accessed through SSH and your IDE of choice without having to do any setup yourself.

With sessions, you pay only for the compute that you need to get a baseline operational, and then you can scale your work to the cloud with Grid runs. Grid sessions are optimized for PyTorch Lightning and models hosted on the NGC catalog. They even provide specialized Spot pricing.

For an in-depth walkthrough, see the Grid Session tour (requires a Grid.ai account).

Step 2: Clone the ASR demo repo and open the tutorial notebook

Now that you have a developer environment optimized for PyTorch Lightning, the next step is to clone the NGC-Lightning-Grid-Workshop repo.

You can do this directly from a terminal in your Grid Session with the following command:

git clone https://github.com/aribornstein/NGC-Lightning-Grid-Workshop.git

After you’ve cloned the repo, you can open up the notebook to use to fine-tune the NGC hosted model with NeMo and PyTorch Lightning.

Step 3: Install NeMo ASR dependencies

First, install all the session dependencies. Run tools such as PyTorch Lightning and NeMo and process the AN4 dataset to do this. Run the first cell in the tutorial notebook, which runs the following bash commands to install the dependencies.

## Install dependencies
!pip install wget
!sudo apt-get install sox libsndfile1 ffmpeg -y
!pip install unidecode
!pip install matplotlib>=3.3.2
## Install NeMo
BRANCH = 'main'
!python -m pip install --user git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]
## Grab the config we'll use in this example
!mkdir configs
!wget -P configs/ https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/examples/asr/conf/config.yaml

Step 4: Convert and visualize the AN4 dataset

The AN4 dataset comes in raw Sof audio files, but most models process on mel spectrograms.  Convert the Sof files to the Wav format so that you can use NeMo audio processing.

import librosa
import IPython.display as ipd
import glob
import os
import subprocess
import tarfile
import wget

# Download the dataset. This will take a few moments...
print("******")
if not os.path.exists(data_dir + '/an4_sphere.tar.gz'):
    an4_url = 'http://www.speech.cs.cmu.edu/databases/an4/an4_sphere.tar.gz'
    an4_path = wget.download(an4_url, data_dir)
    print(f"Dataset downloaded at: {an4_path}")
else:
    print("Tarfile already exists.")
    an4_path = data_dir + '/an4_sphere.tar.gz'

if not os.path.exists(data_dir + '/an4/'):
    # Untar and convert .sph to .wav (using sox)
    tar = tarfile.open(an4_path)
    tar.extractall(path=data_dir)

    print("Converting .sph to .wav...")
    sph_list = glob.glob(data_dir + '/an4/**/*.sph', recursive=True)
    for sph_path in sph_list:
        wav_path = sph_path[:-4] + '.wav'
        cmd = ["sox", sph_path, wav_path]
        subprocess.run(cmd)
print("Finished conversion.n******")
# Load and listen to the audio file
example_file = data_dir + '/an4/wav/an4_clstk/mgah/cen2-mgah-b.wav'
audio, sample_rate = librosa.load(example_file)
ipd.Audio(example_file, rate=sample_rate)

You can then visualize the audio example as images of the audio waveform. Figure 3 shows the activity in the waveform that corresponds to each letter in the audio, as your speaker here enunciates quite clearly!

Each spoken letter has a different “shape.” It’s interesting to note that the last two blobs look relatively similar, which is expected because they are both the letter N.

Spectrograms

Modeling audio is easier in the context of frequencies of sound over time. You can get a better representation than this raw sequence of 57,330 values. A spectrogram is a good way of visualizing how the strengths of various frequencies in the audio vary over time. It is obtained by breaking up the signal into smaller, usually overlapping chunks, and performing a short-time Fourier transform (STFT) on each.

Figure 4 shows what the spectrogram of the sample looks like.

As in the earlier waveform, you see each letter being pronounced. How do you interpret these shapes and colors? Just as in the earlier waveform plot, you see time passing on the x-axis (all 2.6s of audio). However, now the y-axis represents different frequencies (on a log scale), and the color on the plot shows the strength of a frequency at a particular point in time.

Mel spectrograms

You’re still not done, as you can make one more potentially useful tweak by visualizing the data using the mel spectrogram. Change the frequency scale from linear (or logarithmic) to the mel scale, which better represents the pitches that are perceivable to the human ear. Mel spectrograms are intuitively useful for ASR. Because you are processing and transcribing human speech, mel spectrograms reduce background noise that can affect the model.

Step 5: Load and inference a pretrained QuartzNet model from NGC

Now that you’ve loaded and properly understood the AN4 dataset, look at how to use NGC to load an ASR model to be fine-tuned with PyTorch Lightning. NeMo’s ASR collection comes with many building blocks and even complete models that you can use for training and evaluation. Moreover, several models come with pretrained weights.

To model the data for this post, you use a Jasper architecture called QuartzNet from the NGC Model Hub. Jasper architecture consists of repeated block structures that uses 1D convolutions to model spectrogram data (Figure 6).

QuartzNet is a better variant of Jasper with a key difference in that it uses time-channel separable 1D convolutions. This enables it to reduce the number of weights dramatically while keeping similar accuracy.

The following command downloads the pretrained QuartzNet15x5 model from the NGC catalog and instantiates it for you.

tgmuartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")

Step 6: Fine-tune the model with Lightning

When you have a model, you can fine-tune it with PyTorch Lightning, as follows.

import pytorch_lightning as pl
from omegaconf import DictConfig
trainer = pl.Trainer(gpus=1, max_epochs=10)
params['model']['train_ds']['manifest_filepath'] = train_manifest
params['model']['validation_ds']['manifest_filepath'] = test_manifest
first_asr_model = nemo_asr.models.EncDecCTCModel(cfg=DictConfig(params['model']), trainer=trainer)

# Start training!!!
trainer.fit(first_asr_model)

Because you are using this Lightning Trainer, you get some key advantages, such as model checkpointing and logging by default. You can also use 50+ best-practice tactics without needing to modify the model code, including multi-GPU training, model sharding, deep speed, quantization-aware training, early stopping, mixed precision, gradient clipping, and profiling.

Step 7: Inference and deployment

Now that you have a baseline model, inference it.

Step 8: Pause session

Now that you have trained the model, you can pause the session and all the files that you need are persisted.

Paused sessions are free of charge and can be resumed as needed.

Conclusion

Now, you should have a better understanding of PyTorch Lightning, NGC, and Grid. You’ve fine-tuned your first NGC NeMo model and optimized it with Grid runs. We are excited to see what you do next with Grid and NGC.

Ray Tracing Games II is now available as a hardcover on Apress and Amazon.

Ray Tracing Games II is now available to download for free via Apress and Amazon and as a hardcover on Apress and Amazon as well. For those who love books as a physical medium, we recommend purchasing a copy for your home library, while also downloading the free PDF version for easy digital access on the go. 

This Open Access book is a must-have for anyone interested in real-time rendering. Ray tracing is the holy grail of gaming graphics, simulating the physical behavior of light to bring real-time, cinematic-quality rendering to even the most visually intense games. Ray tracing is also a fundamental algorithm used for architecture applications, visualization, sound simulation, deep learning, and more.

We’ve collaborated with our partners to make four limited edition versions of the book, featuring custom covers that highlight real-time ray tracing in Fortnite, Control, Watch Dogs: Legion, and Quake II RTX.

To win a limited edition print copy of Ray Tracing Gems II, enter the giveaway contest here: https://developer.nvidia.com/ray-tracing-gems-ii

Accelerated computing is humming in the background, making life better even on a quiet night at home. It prevents credit card fraud when you buy a movie to stream. It recommends a dinner you might like and arranges a fast delivery. Maybe it even helped the movie’s director win an Oscar for stunning visuals. So, Read article >

The post What Is Accelerated Computing? appeared first on The Official NVIDIA Blog.

Machine Learning Frameworks Interoperability part 3 covers on the implementation of an end-to-end pipeline demonstrating the discussed techniques for optimal data transfer across data science frameworks.

Introduction

Efficient pipeline design is crucial for data scientists. When composing complex end-to-end workflows, you may choose from a wide variety of building blocks, each of them specialized for a dedicated task. Unfortunately, repeatedly converting between data formats is an error-prone and performance-degrading endeavor. Let’s change that!

In this blog series, we discuss different aspects of efficient framework interoperability:

• In the first post, we discussed pros and cons of distinct memory layouts as well as memory pools for asynchronous memory allocation to enable zero-copy functionality.
• In the second post, we highlighted bottlenecks occurring during data loading/transfers and how to mitigate them using Remote Direct Memory Access (RDMA) technology.
• In this post, we dive into the implementation of an end-to-end pipeline demonstrating the discussed techniques for optimal data transfer across data science frameworks.

To learn more on framework interoperability, check out our presentation at NVIDIA’s GTC 2021 Conference.

Let’s dive into the implementation details of a fully functional pipeline for:

• Parsing of 20 hours of continuously measured electrocardiograms (ECGs) from plain CSV files.
• Unsupervised segmentation of bespoke ECG stream into individual heart beats using traditional signal processing techniques.
• Subsequent training of a variational autoencoder (VAE) for outlier detection.
• Final visualization of the results.

For each of the previous steps, a different data science library is used, so making efficient data conversion a crucial task. Most importantly, you should avoid costly CPU roundtrips when copying data from one GPU-based framework to another.

Zero-Copy in Action: End-to-End pipeline

Enough talk! Let’s see framework interoperability in action. In the following, we will discuss the end-to-end pipeline step by step. If you are an impatient person, you can directly download the full Jupyter notebook here. The source code can be executed from within a recent RAPIDS docker container. 

View this code snippet on GitHub.

Step 1: Data loading

In the first step, we download 20 hours of electrocardiograms as a CSV file and write it to disk (see Cell 1). After, we parse the 500 MB of scalar values from the CSV file and transfer them directly to the GPU using RAPIDS’ blazing fast CSV reader (see Cell 2). Now, the data resides on the GPU and will stay there until the very end. Next, we plot the whole time series consisting of 20 million scalar data points using the cuxfilter (ku-cross-filter) framework (see Cell 3).

View this code snippet on GitHub.

Step 2: Data segmentation

In the next step, we segment the 20 hours ECG into individual heartbeats using traditional signal processing techniques. We achieve that by convolving the ECG stream with the second derivative of a Gaussian distribution, also known as Ricker wavelet, in order to isolate the corresponding frequency band of the initial peak in a prototypical heartbeat. Both the sampling of the wavelet as well as the FFT-based convolution are facilitated using CuPy, a CUDA-accelerated library for dense linear algebra and array operations. As a direct result, the RAPIDS cuDF dataframe storing the ECG data has to be converted to a CuPy array using DLPack as a zero-copy mechanism. 

The feature response (result) of the convolution measures the presence of a fixed frequency content for each position in the stream. Note that we have chosen the wavelet in a way so that local maxima correspond to the initial peak of a heartbeat. 

View this code snippet on GitHub.

Step 3: Local maxima detection

In the next step, we map these extremal points to a binary gate using a 1D variant of non-maximum suppression (NMS). NMS determines for every position in the stream if the corresponding value is the maximum in a predefined window (neighborhood). A CUDA implementation of this embarrassingly parallel problem is straightforward. In our example, we use the just-in-time compiler Numba to allow for seamless Python integration. Both Numba and Cupy implement the CUDA array interface as a zero-copy mechanism and thus the explicit cast from CuPy arrays to Numba device arrays can be fully avoided. 

View this code snippet on GitHub.

The length of each heartbeat is determined by computing the adjacent difference (finite step derivative) of the gate positions. We facilitate this by filtering the index domain with the predicate gate==1 followed by a call to cupy.diff(). The resulting histogram depicts the length distribution.

View this code snippet on GitHub.

Step 4: Candidate pruning and embedding

We intend to train a (convolutional) Variational Autoencoder (VAE) on the set of heartbeats using a fixed-length input matrix. The embedding of heartbeats in a vector of zeros can be realized with a CUDA kernel. Here, we again use Numba for both candidate pruning and embedding. 

View this code snippet on GitHub.

Step 5: Outliers detection

In this step, we train the VAE model on 75% of the data. DLPack is used again as the zero-copy mechanism to map the CuPy data matrix to a PyTorch tensor.

View this code snippet on GitHub.

Step 6: Results visualization

In a final step, we visualize the latent space of the remaining 25% of the data.

View this code snippet on GitHub.

Conclusion

A key takeaway from this and the preceding blog posts is that interoperability is crucial for the design of efficient data pipelines. Copy-and-converting data between different frameworks is an expensive and incredibly time-consuming task that adds zero value to data science pipelines. Data science workloads are becoming increasingly complex, and the interaction between multiple software libraries is common practice. DLPack and the CUDA Array Interface are the de facto data format standards that guarantee zero-copy data exchange among GPU-based frameworks.

Support for external memory managers is a nice-to-have feature to consider when appraising which software libraries your pipeline will use. For instance, if your task requires both DataFrame and array data manipulation, a great choice of libraries is RAPIDS cuDF + CuPy. They both benefit from GPU acceleration, support DLPack to exchange data with zero-copy, and share the same memory manager, RMM. Alternatively, RAPIDS cuDF + JAX would also be an excellent option. Nevertheless, the latter combination might require extra development efforts to leverage memory usage because of JAX’s lack of support for external memory allocators.

Data loading and data transfer bottlenecks are frequent when dealing with large datasets. NVIDIA GPUDirect technology comes to the rescue, supporting moving data into or out of the GPU memory without burdening the CPU and reducing to one the number of data copies needed when transferring data between GPUs on different nodes.

This post discusses tensor methods, how they are used in NVIDIA, and how they are central to the next generation of AI algorithms. Tensors in modern machine learning Tensors, which generalize matrices to more than two dimensions, are everywhere in modern machine learning. From deep neural networks features to videos or fMRI data, the structure … Continued

This post discusses tensor methods, how they are used in NVIDIA, and how they are central to the next generation of AI algorithms.

Tensors in modern machine learning

Tensors, which generalize matrices to more than two dimensions, are everywhere in modern machine learning. From deep neural networks features to videos or fMRI data, the structure in these higher-order tensors is often crucial.

Deep neural networks typically map between higher-order tensors. In fact, it is the ability of deep convolutional neural networks to preserve and leverage local structure that made the current levels of performance possible, along with large datasets and efficient hardware. Tensor methods enable you to preserve and leverage that structure further, for individual layers or whole networks.

Combining tensor methods and deep learning can lead to better models, including:

• Better performance and generalization, through better inductive biases
• Improved robustness, from implicit (low-rank structure) or explicit (tensor dropout) regularization
• Parsimonious models, with a large reduction in the number of parameters
• Computational speed-ups by operating directly and efficiently on factorized tensors

One example is factorized convolution. With a CP structure, it is possible to decompose the kernel of a convolution and express it efficiently as a separable one. This decouples the dimensions and enables you to transduct, such as training on 2D and generalizing to 3D while leveraging the information learned in 2D.

The proper implementation of tensor-based deep neural networks can be tricky. Major neural networks libraries such as PyTorch or TensorFlow do not provide layers based on tensor algebraic methods and have limited support for sparse tensors. In NVIDIA, we lead the development of a series of tools to make the use of tensor methods in deep learning seamless, through the TensorLy project and Minkowski Engine.

TensorLy ecosystem

TensorLy offers a high-level API for tensor methods, including decomposition and algebra.

It enables you to use tensor methods easily without requiring a lot of background knowledge. You can choose and seamlessly integrate with your computational backend of choice (NumPy, PyTorch, MXNet, TensorFlow, CuPy, or JAX), without having to change the code.

TensorLy-Torch is a new library that builds on top of TensorLy and provides PyTorch layers implementing these tensor operations. They can be used out-of-the-box and readily integrated in any deep neural network. At the core of it is the concept of factorized tensors: tensors are represented, stored, and manipulated directly in decomposed form. Whenever possible, operations then directly operate on these decomposed tensors.

These factorized tensors can then be used to parametrize deep neural network layers efficiently, such as factorized convolutions and linear layers. Finally, tensor hooks enable you to apply techniques such as generalized lasso and tensor dropout seamlessly for improved generalization and robustness.

Spatially sparse tensors and Minkowski Engine

In many high-dimensional problems, data become sparse as the volume of the space increases faster. The sparsity is mostly embedded in the spatial dimension where you can compute distance. The most well-known example of such sparsity is 3D data, such as meshes and scans.

Here’s an example 3D reconstruction of a room with two beds. The 3D bounding volume that it occupies can be quite large, but the data, or the 3D surface reconstruction, occupies only a fraction of the space. In this example, 95.5% of the space is empty and less than 5% contains a valid surface. Using a dense tensor to represent such data results in wasting not just large amounts of memory but also computation if you were to process such data.

For such cases, you could use a sparse representation that does not waste memory and computation on the empty space for building a neural network or a learning algorithm. Specifically, you use sparse tensors to represent such data, which is one of the most widely used representations for sparse data. Sparse tensors represent data using a pair of positions and values of nonzero values.

Minkowski Engine is a PyTorch extension that provides an extensive set of neural network layers for sparse tensors. All functions in Minkowski Engine support CPU and CUDA operations where CUDA operations accelerate over 100x over the top-of-the-line CPUs.

For other interesting projects, see all NVIDIA Research posts.

○ Quality assurance and audits are necessary for deep learning models. Current AI models require large data sets for training or a designed reward function that must be optimized. Algorithmically, AI is prone to optimizing behaviors that were not intended by the human designer. To help combat this, the AuditAI framework was developed to help audit these problems, which increases safety and ethical use of deep learning models during deployment.

When you purchased your last car, did you check for safety ratings or quality assurances from the manufacturer. Perhaps, like most consumers, you simply went for a test drive to see if the car offered all the features and functionality you were looking for, from comfortable seating to electronic controls.

Audits and quality assurance are the norm across many industries. Consider car manufacturing, where the production of the car is followed by rigorous tests of safety, comfort, networking, and so on, before deployment to end users. Based on this, we ask the question, “How can we design a similarly motivated auditing scheme for deep learning models?”

AI has enjoyed widespread success in real-world applications. Current AI models—deep neural networks in particular—do not require exact specifications of the type of desired behavior. Instead, they require large datasets for training, or a designed reward function that must be optimized over time.

While this form of implicit supervision provides flexibility, it often leads to the algorithm optimizing for behavior that was not intended by the human designer. In many cases, it also leads to catastrophic consequences and failures in safety-critical applications, such as autonomous driving and healthcare.

As these models are prone to failure, especially under domain shifts, it is important to know before their deployment when they might fail. As deep learning research becomes increasingly integrated with real world applications, we must come up with schemes of formally auditing deep learning models.

Semantically aligned unit tests

One of the biggest challenges in auditing is in understanding how we can obtain human-interpretable specifications that are directly useful to end users. We addressed this challenge through a sequence of semantically aligned unit tests. Each unit test verifies whether a predefined specification (for example, accuracy over 95%) is satisfied with respect to controlled and semantically aligned variations in the input space (for example, in face recognition, the angle relative to the camera).

We perform these unit tests by directly verifying the semantically aligned variations in an interpretable latent space of a generative model. Our framework, AuditAI, bridges the gap between interpretable formal verification of software systems and scalability of deep neural networks.     

Consider a typical machine learning production pipeline with three parties: the end user of the deployed model, verifier, and model designer. The verifier plays the critical role of verifying whether the model from the designer satisfies the need of the end user. For example, unit test 1 could be verifying whether a given face classification model maintains over 95% accuracy when the face angle is within d degrees. Unit test 2 could be checking under what lighting condition the model has over 86% accuracy. After verification, the end user can then use the verified specification to determine whether to use the trained DL model during deployment.

Verified deployment

To verify the deep network for semantically aligned properties, we bridge it with a generative model, such that they share the same latent space and the same encoder that projects inputs to latent codes. In addition to verifying whether a unit test is satisfied, we can also perform certified training to ensure that the unit test is likely to be satisfied in the first place. The framework has appealing theoretical properties, and we show in our paper how the verifier is guaranteed to be able to generate a proof of whether the verification is true or false. For more information, see Auditing AI models for Verified Deployment under Semantic Specifications [LINK].

Verification and certified training of neural networks for pixel-based perturbations covers a much narrower range of semantic variations in the latent space, compared to AuditAI. To perform a quantitative comparison, for the same verified error, we project the pixel-bound to the latent space and compare it with the latent-space bound for AuditAI. We show that AuditAI can tolerate around 20% larger latent variations compared to pixel-based counterparts as measured by L2 norm, for the same verified error. For the implementation and experiments, we used NVIDIA V100 GPUs and Python with the PyTorch library. 

In Figure 3, we show qualitative results for generated outputs corresponding to controlled variations in the latent space. The top row shows visualizations for AuditAI, and the bottom row shows visualizations for pixel-perturbations for images of class hen on ImageNet, chest X-ray images with the condition pneumonia, and human faces with different degrees of smile respectively. From the visualizations, it is evident that wider latent variations correspond to a wider set of semantic variations in the generated outputs.

Figure 3. Top row: AuditAI visualizations, Bottom row: Visualizations for pixel-perturbations

Future work

In this paper, we developed a framework for auditing of deep learning (DL) models. There are increasingly growing concerns about innate biases in the DL models that are deployed in a wide range of settings and there have been multiple news articles about the necessity for auditing DL models before deployment. Our framework formalizes this audit problem, which we believe is a step towards increasing safety and the ethical use of DL models during deployment.

One of the limitations of AuditAI is that its interpretability is limited by that of the built-in generative model. While exciting progress has been made for generative models, we believe it is important to incorporate domain expertise to mitigate potential dataset biases and human error in both training and deployment.

Currently, AuditAI doesn’t directly integrate human domain experts in the auditing pipeline. It indirectly uses domain expertise in the curation of the dataset used for creating the generative model. Incorporating the former would be an important direction for future work.

Although we have demonstrated AuditAI primarily for auditing computer vision classification models, we hope that this would pave the way for more sophisticated domain-dependent AI-auditing tools and frameworks in language modeling and decision-making applications.

Acknowledgments

This work was conducted wholly at NVIDIA. We thank our co-authors De-An Huang, Chaowei Xiao, and Anima Anandkumar for helpful feedback, and everyone in the AI Algorithms team at NVIDIA for insightful discussions during the project.

Register now for the Sept. 21 instructor-led training from DLI covering training, accelerating, and optimizing a defect detection classifier.

NVIDIA GPUs are used to develop the most accurate automated inspection solutions for manufacturing semiconductors, electronics, automotive components, and assemblies. Along with accompanying software tools, GPUs enable efficient training of models for greater accuracy and optimized inference deployment at the edge. These models dramatically improve the accuracy of industrial inspection, resulting in reduced test escapes and increased yield at greater throughput.

The NVIDIA Deep Learning Institute (DLI) is offering an instructor-led class about training, accelerating, and optimizing a defect detection classifier. 

You will start by exploring key challenges around industrial inspection, and problem formulation along with data curation, exploration, and formatting. 

Then you will learn about the fundamentals of transfer learning, online augmentation, modeling, and fine-tuning. 

By the end of the workshop, you’ll be familiar with the key concepts of optimized inference, performance assessment, and interpretation of deep learning models.

By participating in this workshop, you will learn how to: 

• Formulate an industrial inspection case study and curate datasets generated by automated optical inspection (AOI) machines.
• Deal with the logistics and challenges of data handling in an industrial inspection workflow.
• Extract meaningful insights from our dataset using pandas DataFrame and NumPy library.
• Apply transfer learning to a deep learning classification model (Inception v3). 
• Fine-tune the deep learning model and set up evaluation metrics.
• Optimize the trained Inception v3 model on an NVIDIA V100 Tensor Core GPU using NVIDIA TensorRT 5.
• Experiment with FP16 half-precision fast inferencing with the V100’s Tensor Cores.

You will have access to a GPU-accelerated server in the cloud and earn an NVIDIA DLI certificate to demonstrate subject-matter competency and accelerate your career growth.

This workshop will be offered twice to accommodate both CEST and PDT timezones on:
Tue, Sept. 21, 2021, 9 am–5 pm, CEST/EMEA, UTC+2
Tue, Sept. 21, 2021, 9 am–5 pm, PDT, UTC-7

Register now, space is limited!

• Learn more about NVIDIA DLI and request this workshop for your organization >>
• Watch this short video of the NVIDIA DLI remote Instructor-led training experience >>
• Visit the NVIDIA DLI homepage for a full list of online and instructor-led training>>

Unreal Engine developers receive access to several NVIDIA updates. Our custom branch of Unreal Engine 4.27 (NvRTX) improves Deep Learning Super Sampling (DLSS), RTX Global Illumination (RTXGI), RTX Direct Illumination (RTXDI), and NVIDIA Real-Time Denoisers (NRD).

Custom RTX Branch of UE4.27 Drops, Global Illumination and Low Latency Solutions Level Up 

Today, Unreal Engine developers receive access to several NVIDIA updates. Our custom branch of Unreal Engine 4.27 (NvRTX) improves Deep Learning Super Sampling (DLSS), RTX Global Illumination (RTXGI), RTX Direct Illumination (RTXDI), and NVIDIA Real-Time Denoisers (NRD). The Unreal Engine RTXGI plugin has been updated, making it easy to add the latest version of this global illumination SDK (1.1.40) to your game. And NVIDIA Reflex is now a standard feature in Unreal Engine 4.27. Details for each product are below.

NvRTX 4.27 (Download here)

NVIDIA has made it easy for game developers to add leading-edge technologies to their Unreal Engine (UE4) games by providing custom UE4 branches for NVIDIA technologies on GitHub. NvRTX 4.27 will shorten your development cycles and help make your games look even more stunning.

In NvRTX 4.27, RTX Direct Illumination (RTXDI) and NVIDIA Real-Time Denoisers (NRD) improved support for Metahuman hair. Deep Learning Super Sampling (DLSS) added softening capability to the sharpness slider, and provided a workaround for packaged builds not initializing DLSS on d3d11 devices. RTXGI increased the number of volumes supported in the indirect lighting pass from 6 to 12, made screen-space indirect lighting resolution adjustable through cvars, and supported blending of more than 2 volumes.

RTXGI 1.1.40 UE4 plugin (Download here) 

Leveraging the power of ray tracing, RTXGI provides scalable solutions to compute multi-bounce indirect lighting without bake times, light leaks, or expensive per-frame costs. RTXGI is supported on any DXR-enabled GPU, and is an ideal starting point to bring the benefits of ray tracing to your existing tools, knowledge, and capabilities. 

As in the NvRTX update, this plugin update doubles the number of volumes supported in the indirect lighting pass, enables developers to adjust screen-space indirect lighting resolution, and supports the blending of more than 2 volumes.

Reflex (now available in Unreal Engine 4.27)

The NVIDIA Reflex SDK allows game developers to implement a low latency mode that aligns game engine work to complete just-in-time for rendering, eliminating the GPU render queue and reducing CPU back pressure in GPU-bound scenarios. As a developer, System Latency (click-to-display) can be one of the hardest metrics to optimize for. In addition to latency reduction functions, the SDK also features measurement markers to calculate both Game and Render Latency – great for debugging and in-game performance counters.

NVIDIA Reflex is now mainlined within UE4.27 and natively supported. To get started adding NVIDIA Reflex to your game, check out our easy step by step integration guide. 

Additional Resources:

• Instructions to get started with Unreal Engine on GitHub
• Access our latest NvRTX documentation
• Contact our support alias