DataBloom

2021-08-27 10:20:22.799484: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll 2021-08-27 10:20:26.443509: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library nvcuda.dll 2021-08-27 10:20:26.488972: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 2060 computeCapability: 7.5 coreClock: 1.2GHz coreCount: 30 deviceMemorySize: 6.00GiB deviceMemoryBandwidth: 245.91GiB/s 2021-08-27 10:20:26.489580: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll 2021-08-27 10:20:26.532650: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublas64_11.dll 2021-08-27 10:20:26.533109: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublasLt64_11.dll 2021-08-27 10:20:26.559639: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cufft64_10.dll 2021-08-27 10:20:26.565224: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library curand64_10.dll 2021-08-27 10:20:26.637251: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusolver64_11.dll 2021-08-27 10:20:26.660612: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusparse64_11.dll 2021-08-27 10:20:26.661815: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudnn64_8.dll 2021-08-27 10:20:26.662201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 2021-08-27 10:20:26.662770: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2021-08-27 10:20:26.664656: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 2060 computeCapability: 7.5 coreClock: 1.2GHz coreCount: 30 deviceMemorySize: 6.00GiB deviceMemoryBandwidth: 245.91GiB/s 2021-08-27 10:20:26.665660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 2021-08-27 10:20:27.284225: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix: 2021-08-27 10:20:27.284551: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 2021-08-27 10:20:27.284738: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N 2021-08-27 10:20:27.285152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3961 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5) 2021-08-27 10:20:28.198186: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2) Epoch 1/50 2021-08-27 10:20:29.513381: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudnn64_8.dll Process finished with exit code -1073740791 (0xC0000409) 

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Healthcare giant Johnson & Johnson is injecting data science across its business to improve its manufacturing, clinical trial enrollment, forecasting and more. “I actually like to call it decision science,” said Jim Swanson, the company’s executive vice president and enterprise chief information officer, in a panel discussion at the most recent NVIDIA GPU Technology Conference. Read article >

The post Down to a Science: How Johnson & Johnson Boosts Its Business With MLOps appeared first on The Official NVIDIA Blog.

NVIDIA Decision Support (NDS) is our adaptation of an industry-standard data science benchmark often used in the Apache Spark community. NDS consists of the same 105 SQL queries as the industry standard benchmark TPC-DS, but has modified parts for dataset generation and execution scripts.

Introduction

The August release (21.08) of RAPIDS Accelerator for Apache Spark is now available. It has been a little over a year since the first release at NVIDIA GTC 2020. We have improved in so many ways, particularly in terms of ease-of-use with minimal to no-code change for Apache Spark applications. This last year, the team has been focused on adding both functionality and continuously improving performance. As a testament to that, we periodically measure performance and functionality over time with the NVIDIA Data Science (NDS) benchmark at a scale factor of 3,000 (3 TB uncompressed). In this release, apart from adding new features, we are extremely proud to make progress on improving end-to-end speed for all passing queries and lowering the total cost of ownership for NVIDIA EGX servers.

Benchmark updates

NVIDIA Decision Support (NDS) is our adaptation of an industry-standard data science benchmark often used in the Apache Spark community. NDS consists of the same 105 SQL queries as the industry standard benchmark TPC-DS, but has modified parts for dataset generation and execution scripts. In our GTC 2021 update, we had 95 queries passing. With the 21.08 release, with new features such as out-of-core group by, window rank, and dense_rank, we have enabled all of the 105 queries to run on the GPU.

Benchmark setup

• Scale Factor — 3K (3TB Dataset with floats)
• Systems: 4x NVIDIA Certified EGX Server
• EGX Server Hardware Spec: 4-node Dell R740xd, each with (2) 24-core CPUs, 512GB RAM, HDFS on NVMe, (1) CX-6 Dx 25/100Gb NIC, 2x NVIDIA A30 GPU
• CPU Hardware Spec: 4-node Dell R740xd, each with (2) 24-core CPUs, 512GB RAM, HDFS on NVMe, (1) CX-6 Dx 25/100Gb NIC
• Software: RAPIDS Accelerator v21.08.0, cuDF 21.08.0, Apache Spark 3.1.1, UCX 1.10.1

Results summary

Based on this release, we are excited to show that all the 105 queries can now run without any code change on the GPU.

• The benchmark servers used for these benchmarks cost little under $170,000 for four servers without GPUs and $220,000 to include one NVIDIA A100 GPU in each server.
• In simple terms, benchmark GPU servers would cost 1.29 times CPU servers.
• As shown by the chart above (figure 1), more than 95 queries are now 1.29x faster and thereby cheaper to run on GPU.
• Some of the queries that are slower on GPU are currently being addressed and we are relentlessly working to improve those queries as well as improve the overall speed-ups.
• Users can easily deduce that GPU speed-up varies from 1x to 18x and therefore it’s suggested that users qualify the right use cases for GPUs.
• The Qualification Tool would be a handy asset if users are unsure about the right use case for GPU. For more information about the Qualification Tool, refer to the section below.

Profiling & qualification tool

The Profiling & Qualification tool, released in 21.06, saw positive feedback from the user community as well as requests for new features. In 21.08 the qualification tool now has the ability to handle event logs generated by Apache Spark 2.x versions. The tool will also support event logs generated by AWS EMR 6.3.0, Google Dataproc 2.0, Microsoft Azure Synapse, and the Databricks 7.3 and 8.2 runtimes. The qualification tool will no longer require a Spark runtime. Users can now use the qualification tool with just Apache Spark 3.x jars on their machine. The latest version also has new filtering capabilities to choose event logs. The tool also looks for read data formats and types that the plugin doesn’t support and removes these from the score (based on the total task time in SQL Dataframe operations). The output will be reported in a concise format on the terminal and a detailed analysis of each of the processed event logs will be stored as a csv output.

New functionality

This release adds more functionality for arrays and structs. We can now do a union on multi-level struct data types and can also write array data types in Parquet format. We have added rank and dense_rank window functions to the existing lead, lag and row_number functionality. With this added functionality, the RAPIDS Accelerator can now support the most commonly used window operators in SQL. For the timestamp operators, we have added support for LEGACY timestamps. With this functionality, users can read legacy timestamp formats supported in Spark 2.0. For Databricks users, we have added the ability to cache data in GPU (this was already supported for all other platforms).

We continue to make the user experience better with the ability to handle datasets that spill out of GPU memory for group by and windowing operations. This improvement will save users time creating partitions to avoid out-of-memory errors on the GPU. Similarly, the adoption of UCX 1.11 has improved error handling for RAPIDS Spark Accelerated Shuffle Manager.

Growing community

Join us for “Accelerate Data Pipelines with NVIDIA RAPIDS Accelerator for Spark” to learn how Informatica is removing the barrier to using GPUs, unlocking dramatic performance improvements in operationalizing machine learning projects at scale. You can read more about this online seminar and register here.

Coming Soon

As we noted in the last release, we moved to CalVer and a bi-monthly release cadence since the last release (21.06). The upcoming versions will add expanded support for additional decimal types and continue to add more nested data type support for multi-level struct and maps. In addition, lookout for micro-benchmarks with code-samples and notebooks that will highlight operations best suited for GPUs. We want to hear from you, the users. Reach out to us on GitHub and let us know how we can continue to improve your experience using RAPIDS Spark.

GFN Thursday is here to wake you up when September begins because there are a bunch of awesome day-and-date launch games coming to GeForce NOW this month. September brings 16 new day-and-date games to the cloud — including the anticipated Life is Strange: True Colors. They’re part of the 34 games being added throughout the Read article >

The post Streaming in September: GFN Thursday Welcomes 16 Day-and-Date Game Launches, Including ‘Life Is Strange: True Colors’ appeared first on The Official NVIDIA Blog.

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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