DataBloom

container array [ matrix 1 [ vector 1 [a, b, c, d] ... vector 1061 [e, f, g, h] ] ... matrix 7 [ vector 1 [i, j, k, l] ... vector 1061 [m, n, o, p] ] ] 

input_shape = (1061, 4) network = Sequential() network.add(Input(shape=input_shape)) network.add(Dense(2**6, activation="relu")) network.add(Dense(2**3, activation="relu")) network.add(Dense(2, activation="linear")) network.compile( loss="mean_squared_error", optimizer="adam", ) 

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It’s Thursday, which means it’s GFN Thursday — when GeForce NOW members can learn what new games and updates are streaming from the cloud. This GFN Thursday, we’re checking in on one of our favorite gaming franchises, the Stronghold series from Firefly Studios. We’re also sharing some sales Firefly is running on the Stronghold franchise. Read article >

The post Siege the Day as Stronghold Series Headlines GFN Thursday appeared first on The Official NVIDIA Blog.

Shalini De Mello, a principal research scientist at NVIDIA who’s made her mark inventing computer vision technology that contributes to driver safety, finished 2020 with a bang — presenting two posters at the prestigious NeurIPS conference in December. A 10-year NVIDIA veteran, De Mello works on self-supervised and few-shot learning, 3D reconstruction, viewpoint estimation and Read article >

The post NVIDIA’s Shalini De Mello Talks Self-Supervised AI, NeurIPS Successes appeared first on The Official NVIDIA Blog.

With everyone shifting to a remote work environment, game development and professional visualization teams around the world need a solution for real-time collaboration and more efficient workflows.

With everyone shifting to a remote work environment, game development and professional visualization teams worldwide need a solution for real-time collaboration and more efficient workflows.

To boost creativity and innovation, developers need access to powerful technology accelerated by GPUs and easy access to secure datasets, no matter where they’re working from. And as many developers concurrently work on a project, they need to be able to manage version control of a dataset to ensure everyone is working on the latest assets.

NVIDIA Omniverse addresses these challenges. It is an open, multi-GPU enabled platform that makes it easy to accelerate development workflows and collaborate in real time. 

The primary goal of Omniverse is to support universal interoperability across various applications and 3D ecosystems. Using Pixar’s Universal Scene Description and NVIDIA RTX technology, Omniverse allows people to easily work with leading 3D applications and collaborate simultaneously with colleagues and customers, wherever they may be.

USD is the foundation for Omniverse — the open-source 3D scene description is easily extensible, originally developed to simplify content creation and facilitate frictionless interchange of assets between disparate software tools. 

The Omniverse platform is comprised of multiple components designed to help developers connect 3D applications and transform workflows:

• Move assets throughout your pipeline seamlessly. Omniverse Connect opens the portals that allow content creation tools to connect to the Omniverse platform. With Omniverse Connect, users can work in their favorite industry software applications. 
• Manage and store assets. Omniverse Nucleus allows users to store, share, and collaborate on project data and provides the unique ability to collaborate live across multiple applications. Nucleus works on a local machine, on-premises, or in the cloud. 
• Quickly build tools. Omniverse Kit is a powerful toolkit for developers to create new Omniverse apps and extensions.
• Access new technology, such as Omniverse Simulation including PhysX 5, Flow, and Blast – plus NVIDIA AI SDKs and apps such as Omniverse Audio2Face, Omniverse Deep Search, and many more. 

Learn more about NVIDIA Omniverse, which is currently in open beta. 

In addition to Omniverse, there are several other SDKs that enable developers to create more rich and lifelike content.

NVIDIA OptiX Ray Tracing Engine

OptiX provides a programmable GPU-accelerated Ray-Tracing Pipeline that is scalable across multiple NVIDIA GPU architectures. Developers can easily use this framework with other existing NVIDIA tools and OptiX has already been successfully deployed in a broad range of commercial applications.

NanoVDB

Accelerates OpenVDB applications which is the industry standard for motion picture visual effects. NanoVDB is fully optimized for high performance and quality in real time on NVIDIA GPUs and is completely compatible with OpenVDB structures, which allows for efficient creation and visualization.

Texture Tools Exporter

Enables both the creation of highly compressed texture files, saving memory in their applications and the processing of complex high quality images. Texture Tools Exporter supports all modern compression algorithms, making it a very seamless and versatile tool for developers. 

At GTC, starting on Monday, April 12, there will be over 70 technical sessions that dive into NVIDIA Omniverse. Register for free and experience its impact on the future of game development.

And don’t forget to check out all the game development sessions at GTC. 

Model developers no longer face a steep learning curve to accelerate model training. By utilizing two open-source software projects, Determined AI’s Deep Learning Training Platform and the RAPIDS accelerated data science toolkit, they can easily achieve up to 10x speedups in data preprocessing and train models at scale.  Making GPUs accessible As the field of … Continued

Model developers no longer face a steep learning curve to accelerate model training. By utilizing two open-source software projects, Determined AI’s Deep Learning Training Platform and the RAPIDS accelerated data science toolkit, they can easily achieve up to 10x speedups in data preprocessing and train models at scale. 

Making GPUs accessible

As the field of deep learning advances, practitioners are increasingly expected to make a significant investment in GPUs, either on-prem or from the cloud. Hardware is only half the story behind the proliferation of AI, though. NVIDIA’s success in powering data science has as much to do with software as hardware: widespread GPU adoption would be very difficult without convenient software abstractions that make GPUs easy for model developers to use. RAPIDS is a software suite that bridges the gap from CUDA primitives to data-hungry analytics and machine learning use cases.

Similarly, Determined AI’s deep learning training platform frees the model developer from hassles: operational hassles they are guaranteed to hit in a cluster setting, and model development hassles as they move from toy prototype to scale.  On the operational side, the platform handles distributed systems concerns like training job orchestration, storage layer integration, centralized logging, and automatic fault tolerance for long-running jobs.  On the model development side, machine learning engineers only need to maintain one version of code from the model prototype phase to more advanced tasks like multi-GPU (and multi-node) distributed training and hyperparameter tuning.  Further, the platform handles the boilerplate engineering required to track workload dependencies, metrics, and checkpoints.

At their core, both Determined AI and RAPIDS make the GPU accessible to machine learning engineers via intuitive APIs: Determined as the platform for accelerating and tracking deep learning training workflows, and RAPIDS as the suite of libraries speeding up parts of those training workflows.

For the remainder of this post, we’ll examine a model development process in which RAPIDS accelerates training data set construction within a Determined cluster, at which point Determined handles scaled out, fault-tolerant model training and hyperparameter tuning.

The RAPIDS is not alone in offering familiar interfaces atop GPU acceleration.  E.g., CuPy is NumPy-compatible, and OpenCV’s GPU module API interface is “kept similar with the CPU interface where possible.” experience will look familiar to ML engineers who are accustomed to tackling data manipulation with pandas or NumPy, and model training with PyTorch or TensorFlow.

Getting started

To use Determined and RAPIDS to accelerate model training, there are a few requirements to meet upfront. On the RAPIDS side, OS and CUDA version requirements are listed here.  One is worth calling out explicitly: RAPIDS requires NVIDIA P100 or later generation GPUs, ruling out the NVIDIA K80 in AWS P2 instances.

After satisfying these prerequisites, making RAPIDS available to tasks running on Determined is simple.  Because Determined supports custom Docker images for running training workloads, we can create an image that contains the appropriate version of RAPIDS¹ installed via conda.  This is as simple as specifying the RAPIDS dependency in a Conda environment file:

name: Rapids
channels:
 - rapidsai
 - nvidia
 - conda-forge
dependencies:
 - rapids=0.14

And updating the base Conda environment in your custom image Dockerfile:

FROM determinedai/environments:cuda-10.0-pytorch-1.4-tf-1.15-gpu-0.7.0 as base
COPY environment.yml /tmp/
RUN conda --version && 
   conda env update --name base --file /tmp/environment.yml && 
   conda clean --all --force-pkgs-dirs --yes
RUN eval "$(conda shell.bash hook)" && conda activate base

After building and pushing this image to a Docker repository, you can run experiments, notebooks, or shell sessions by configuring the environment image that these tasks should use.

The model

To showcase the potency of integrating RAPIDS and Determined, we picked a tabular learning task that would typically benefit from nontrivial data preprocessing, based on the TabNet architecture and the pytorch-tabnet library implementing it. TabNet brings the power of deep learning to tabular data-driven use cases and offers some nice interpretability properties to boot.  One benchmark explored in the TabNet paper is the Rossman store sales prediction task of building a model to predict revenue across thousands of stores based on tabular data describing the stores, promotions, and nearby competitors.  Since Rossman dataset access requires signing off on an agreement, we train our model on generated data of a similar schema and scale so that users can more easily run this example.  All assets for this experiment are available on GitHub.

Data prep with RAPIDS, training with Determined

With multiple CSVs to ingest and denormalize, the Rossman revenue prediction task is ripe for RAPIDS.  The high level flow to develop a revenue prediction model looks like this:

• Read location and historical sales CSVs into cuDF DataFrames residing in GPU memory.
• Join these data sets into a denormalized DataFrame. This GPU-accelerated join is handled by cuDF.
• Construct a PyTorch Dataset from the denormalized DataFrame.
• Train with Determined!

RAPIDS cuDF’s familiar pandas-esque interface makes data ingest and manipulation a breeze:

df_store = cudf.read_csv(STORE_CSV)
df_train = cudf.read_csv(TRAIN_CSV).join(df_store,
                                        how='left',
                                        on='store_id',
                                        rsuffix='store')
df_valid = cudf.read_csv(VAL_CSV).join(df_store,
                                      how='left',
                                      on='store_id',
                                      rsuffix='store')

We then use CuPy to get from a cuDF DataFrame to a PyTorch Dataset and DataLoader to expose via Determined’s Trial interface.

Given that RAPIDS cuDF is a drop-in replacement for pandas, it’s trivial to toggle between the two libraries and compare performance of their analogous APIs. In this simplified case, cuDF showed a 10x speedup over pandas, requiring only 6 seconds to complete on a single NVIDIA V100 GPU that took a minute on the vCPU.

  Another option is to use DLPack as the intermediate format that both cuDF and PyTorch support, either directly, or using NVTabular’s Torch Dataloader which does the same under the covers.

On an absolute scale, this might not seem like a big deal: whether the overall training job takes 20 or 21 minutes doesn’t seem to matter much. However, given the iterative nature of deep learning model tuning, the time and cost savings quickly add up. For a hyperparameter tuning experiment training hundreds or thousands of models, on data larger than the couple of GB, and perhaps with more complex data transformations, savings on the order of GPU-minutes per trained model can translate to savings on the order of GPU-days or weeks at scale, netting your organization hundreds or thousands of dollars in infrastructure cost.

Determined and the broader RAPIDS toolkit

The RAPIDS library suite goes far beyond manipulation of data frames that we leveraged in this example.  To name a couple:

• RAPIDS cuML offers GPU-accelerated ML algorithms mirroring sklearn.
• NVTabular, which sits atop RAPIDS, offers high-level abstractions for feature engineering and building recommenders.

If you’re using these libraries, you’ll soon be able to train on a Determined cluster and get the platform’s resource management, experiment tracking, and hyperparameter tuning capabilities. We’ve heard from our users that the need for these tools isn’t limited to deep learning, so we are pushing into the broader ML space and making Determined not only the platform for PyTorch and TensorFlow model development, but for any Python-based model development. Stay tuned, and in the meantime you can learn more about this development from our 2021 roadmap discussion during our most recent community meetup.

Get started

If you’d like to learn more about (and test drive!) RAPIDS and Determined, check out the RAPIDS quick start and Determined’s quick start documentation. We’d love to hear your feedback on the RAPIDS and Determined community Slack channels. Happy training!

Here a few key sessions from industry leaders in media, delivery-on-demand, and retail at GTC Spring 2021.

Ensuring recommenders are meaningful, personalized, and relevant to a single customer is not easy. Scaling a personalized recommender experience to hundreds of thousands, or millions of customers, comes with unique challenges that data scientists and machine learning engineers tackle every day. Scaling challenges often provide obstacles to effective ETL, training, retraining, or deploying models into production.

To tackle these challenges, machine learning engineers and data scientists within the industry utilize a combination, or hybrid of tools, techniques, and algorithms. NVIDIA is committed to help streamline recommender workflows with Merlin, an open-source framework that is interoperable and designed to support machine learning engineers and data scientists with preprocessing, feature engineering, training, and inference. Merlin supports industry leaders who are tackling common recommender challenges as they provide relevant, impactful, and fresh recommenders at scale.

Here a few key sessions from industry leaders in media, delivery-on-demand, and retail at GTC Spring 2021.

• Learn How Tencent Deployed An Advertising System On The Merlin GPU Recommender Framework
  Learn how Tencent deployed their real advertising recommendation training with Merlin and achieved more than 7x speedup over the original TensorFlow solution on the same GPU platform.

• AI-First Social Media Feeds: A View From the Trenches
  Discusses efficient training of extremely large recommender models with billions of parameters distributed across multiple GPUs and workers as well as the importance of continual model updates in near-real time to deal with the key challenge of concept drift. The session includes how solutions in NVIDIA’s Merlin stack resolve key bottlenecks faced in general purpose deep learning frameworks.

• Personalization and Discovery at Postmates
  Covers the machine learning building blocks, relevant models developed in production, model optimizations to align with Postmates’ business objectives, and more.

• GPU-Accelerated Processing of Large Tabular Datasets and Distributed Neural Network Training with NVTabular+Dask+TensorFlow for Dynamic Pricing at Walmart
  Demonstrates how to use open-source tools like Dask, NVTabular, and TensorFlow to horizontally scale up GPU-accelerated training pipelines for large neural networks trained using tabular data.

• End-to-End Deployment of GPU Accelerated Recommender Systems: From ETL to Training to Inference
  Upon completion of this 4-hour in-depth workshop, attendees will be able to apply NVIDIA Merlin (NVTabular, HugeCTR and Triton Inference Server) to accelerate and scale their ETL, training and inference systems with GPUs.

Registration is free, visit the GTC website for more information.

Don’t blink. Accelerated computing is moving innovation forward faster than ever. And there’s no way to get smarter, quicker, about how it’s changing your world than to tune in to NVIDIA CEO Jensen Huang’s GTC keynote Monday, April 12, starting at 8:30 a.m. PT. The keynote, delivered again from the kitchen in Huang’s home, will Read article >

The post What Will NVIDIA CEO Jensen Huang Cook Up This Time at NVIDIA GTC? appeared first on The Official NVIDIA Blog.

We at NVIDIA are on a mission to bring the next generation data center vision to reality. Join us at NVIDIA GTC’21 (Apr 12-16, 2021) to witness the data center innovation we are pioneering.

As NVIDIA CEO Jensen Huang stated in last year’s GTC, “the data center is the new unit of computing.” Traditional way of using the server as the unit of computing is fading away quickly. More and more applications are moving to data centers that are located at the edge, in different availability zones or in private enterprise clouds. Modern workloads such as AI/ML, edge computing, cloud-native microservices and 5G services are becoming increasingly disaggregated, distributed and data hungry. These applications demand efficient, secure and accelerated computing and data processing across all the layers of the application stack. 

Computing accelerated by NVIDIA GPUs and data processing units (DPUs) is at the heart of modern data centers. DPUs are a game-changing new technology that accelerates GPU and CPU access to data while enabling software-defined, hardware-accelerated infrastructure. With DPUs, organizations can efficiently and effectively deploy networking, cyber security, and storage in virtualized as well as containerized environments.

We at NVIDIA are on a mission to bring the next generation data center vision to reality. Join us at NVIDIA GTC’21 (Apr 12-16, 2021) to witness the data center innovation we are pioneering.  Register for the top DPU sessions at GTC to learn how NVIDIA Networking solutions are powering the next generation data centers.

Palo Alto Networks and NVIDIA present  Accelerated 5G Security: DPU-Based Acceleration of Next-Generation Firewalls [S31671]

5G offers many new capabilities such as lower latency, higher reliability, throughput, agile service deployment through cloud-native architectures, greater device density, etc. A new approach is needed to achieve L7 security at these rates with software-based firewalls. Integrating Palo Alto Network next generation firewall with the NVIDIA DPU enables industry-leading high-performance security. NVIDIA’s Bluefield-2 DPU provides a rich set of network offload engines designed to address the acceleration needs of security-focused network functions in today’s most demanding markets such as 5G and the cloud.

Speakers:

• Sree Koratala, VP Product Management Mobility Security, Palo Alto Networks
• John McDowall, Senior Distinguished Engineer, Palo Alto Networks
• Ash Bhalgat, Senior Director, Cloud, Telco & Security Market Development, NVIDIA

China Mobile, Nokia and NVIDIA present Turbocharge Cloud-Native Applications with Virtual Data Plane Accelerated Networking [S31563]

Great progress has been made leveraging hardware to accelerate cloud networking for a wide range of cloud-based applications. This talk will examine how cloud networking for public cloud infrastructure as a service can be accelerated using the NVIDIA’s new open standards technology called virtual data plane acceleration (vDPA). In addition, this presentation will examine the early validation results and acceleration benefits of deploying NVIDIA ASAP² vDPA technology in China Mobile’s BigCloud cloud service.

Speakers:

• Sharko Cheng, Senior Network Architect, Cloud Networking Products Department, CMCC
• Mark Iskra, Director, Nokia/Nuage Networks
• Ash Bhalgat, Senior Director, Cloud, Telco & Security Market Development, NVIDIA

Red Hat and NVIDIA present  Implementing Virtual Network Offloading Using Open Source tools on BlueField-2 [S31380]

NVIDIA and Red Hat have been working together to provide an elegant and 100% open-source solution using the BlueField DPU for hardware offloading of the software-defined networking tasks in cloud-native environments. With BlueField DPUs, we can encrypt, encapsulate, switch, and route packets right on the DPU, effectively dedicating all the server’s processing capacity to running business applications. This talk will discuss typical use cases and demonstrate the performance advantages of using BlueField’s hardware offload capabilities with Red Hat Enterprise Linux and the Red Hat OpenShift container platform.

Speakers:

• Rashid Khan, Director of Networking, Red Hat
• Rony Efraim, Networking Software and Systems Architect, NVIDIA

NVIDIA DPU team presents  Program Data Center Infrastructure Acceleration with the Release of DOCA and the Latest DPU Software [S32205]

NVIDIA is releasing the first version of DOCA, a set of libraries, SDKs, and tools for programming the NVIDIA BlueField DPU, as well as the new version 3.6 of the DPU software. Together these enable new infrastructure acceleration and management features in BlueField, simplify programming and application integration. DPU developers can offload and accelerate networking, virtualization, security, and storage features including VirtIO for NFV/VNFs, BlueField SNAP for elastic storage virtualization, regular expression matching for malware detection, and deep packet inspection to enable sophisticated routing, firewall, and load-balancing applications.

Speakers:

• Ami Badani, VP Marketing, NVIDIA
• Ariel Kit, Director of Product Marketing for Networking, NVIDIA

F5/NGINX and NVIDIA presents  kTLS Hardware Offload Performance Benchmarking for NGINX Web Server [S31551]

Encrypted communication usage is steadily growing across most internet services. TLS is the leading security protocol implemented on top of TCP. kTLS (kernel TLS) serves as a unit that offers TLS operations support in the Linux kernel layer. It was introduced in kernel v4.13 as a software offload for user-space TLS libraries and was extended in kernel v4.18 with an infrastructure for performing hardware-accelerated encryption/decryption in the SmartNIC and DPUs. This session will review the life cycle of a hardware-offloaded kTLS connection and the driver-hardware interaction to support it while demonstrating and analyzing the significant performance gain by offloading kTLS operations to the hardware, using NGINX as the target workload and NVIDIA’s mlx5e driver on top of a ConnectX-6 Dx SmartNIC.

Speakers:

• Damian Curry, Business Development Technical Director, F5
• Bar Tuaf, Software engineer, NVIDIA

Register today for free and start building your schedule. 

Once you are signed in, you can explore all GTC conference topics here. Topics include areas of interest such as data center networking and virtualization, HPC, deep learning, data science, and autonomous machines, or industries including healthcare, public sector, retail, and telecommunications.

See you at the GTC’21!