DataBloomFrom factories and farms to refineries and construction sites, the world is full of places that are hot, dirty, noisy, potentially dangerous — and critical to keeping industry humming. These places all need inspection and maintenance alongside their everyday operations, but, given safety concerns and working conditions, it’s not always best to send in humans. Read article >
The post Tough Customer: NVIDIA Unveils Jetson AGX Xavier Industrial Module appeared first on The Official NVIDIA Blog.
Hi all,
I’m a node developer and extremly new to tensorflow.js and really the entire ml space.
I’m looking for any recommendations to courses or learning material that covers Tensorflow.js with multi labeling/classify images and computer vision?
I’ve been on Udemy but the reviews does not seem too good on those courses that I had a look at.
Thanks in advance,
submitted by /u/wbuc1
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The docs state that it’s possible to compile with compute 3.0 support and I’ve tried to compile it but it fails stating it requires min 3.5 and building always fails when compiling the GPU section. I’ve even tried using anaconda’s tensorflow-gpu package.
I have CUDA toolkit 10.1 and cudnn 7.6 which I think are right. When running a `f.config.list_physical_devices(‘GPU’)`, I see the error output “Ignoring visible gpu device (device: 0, name: NVIDIA Quadro K2100M, pci bus id: 0000:01:00.0, compute capability: 3.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.”
Am I SOL?
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NVIDIA NetQ is a highly-scalable modern network operations tool leveraging fabric-wide telemetry data for visibility and troubleshooting of the overlay and underlay network in real-time.
NVIDIA NetQ 4.0.0 was recently released with many new capabilities. NVIDIA NetQ is a highly-scalable modern network operations tool leveraging fabric-wide telemetry data for visibility and troubleshooting of the overlay and underlay network in real-time. NetQ can be deployed on customer premises, or can be consumed as cloud-based service (SaaS). For more details, refer to the NetQ datasheet.
NetQ 4.0.0 includes the following notable new features:
Refer to the NetQ 4.0.0 User Guide for details and all the other capabilities introduced.
Validation enhancements
In the physical production network, NetQ validations provide insight into the live state of the network and helps with troubleshooting. NetQ 4.0.0 provides the ability to:
gNMI streaming of WJH events
NVIDIA What Just Happened (WJH) is a hardware-accelerated telemetry feature available on NVIDIA Spectrum switches, which streams detailed and contextual telemetry data for analysis. WJH provides real-time visibility into problems in the network, such as hardware packet drops due to misconfigurations, buffer congestion, ACL, or layer 1 problems.
NetQ 4.0.0 supports gNMI ( gRPC network management interface) to collect What Just Happened data from the NetQ Agent. YANG Model details are available in the User Guide.
SONiC support
NetQ now monitors the switches with SONiC (Software for Open Networking in the Cloud) operating system as well as Cumulus Linux. SONiC support includes traces, validations, snapshots, events, service visibility and What Just Happened. This is an early access feature.
RoCE Monitoring
RDMA over Converged Ethernet (RoCE) provides the ability to write to compute or storage elements using remote direct memory access (RDMA) over an Ethernet network instead of using host CPUs. RoCE relies on congestion control and lossless Ethernet to operate. Cumulus Linux supports features that can enable lossless Ethernet for RoCE environments. NetQ allows users to view RoCE configuration and monitor RoCE counters with threshold crossing alerts.
User interface enhancements
The NetQ GUI is enhanced to show switch details in the topology view. Using the GUI, premises can be renamed and deleted.
NVIDIA AIR is updated with NetQ 4.0.0, check out and upgrade your environment to take advantage of all the new capabilities. To learn more, visit the NVIDIA ethernet switching solutions webpage.
Vyasa, a leading provider of tools in the field of biomedical analytics, developed a suite of products that efficiently integrate with existing compute infrastructure via an extensive RESTful API architecture.
Data is the back-bone to building state-of-the-art accurate AI models. And easy access to high-quality data sets can reduce the overall development time significantly. However, the required data may be siloed, can come from different sources (for example, sensors, images, documents) and can be in structured as well as unstructured formats. Manually moving and transforming data from different sources and formats to derive meaningful insights can be tedious and time consuming.
Vyasa, a leading provider of tools in the field of biomedical analytics, developed a suite of products that efficiently integrate with existing compute infrastructure via an extensive RESTful API architecture. Allowing users to derive insights from analytical modules including question answering, named entity recognition, PDF table extraction and image classification, irrespective of where that data resides. Vyasa technologies can integrate external data sources (for example, Pubmed, patents, and clinical trials) with a client’s internal data sources including documents, images and database content.
Vyasa’s solutions are being used by data scientists, researchers, and IT managers in the field of life sciences and healthcare, ranging from pharmaceutical and biotechnology companies, to consulting firms and healthcare organizations.
Available through the NGC catalog, NVIDIA’s GPU-optimized hub of HPC and AI software, Vyasa’s product suite includes the following features:
Layar – A secure, highly scalable, data fabric solution that can be added to existing enterprise data architectures to augment analytics capabilities or can operate as a standalone data fabric for text, image, and data stream integration and analytics.
Axon – A knowledge graph application that enables derivation of dynamically generated knowledge graphs directly from integrated data and documents sources integrated in a Layar data fabric.
Retina – An image analytics application that offers a wide range of deep learning image-related tasks, including management, annotation, and deep learning analytics on images.
Synapse – Provides “Smart Table Technology” that directly connects a user’s spreadsheet content to the analytical capabilities of Layar Data Fabrics.
Trace – Trace, a geospatial application that leverages structured data to plot businesses, assets, and intellectual property in relation to trend and document content derived from Layar Data Fabrics.[3]
Get started with Vyasa by pulling the Helm chart from the NGC catalog.
University of Aizu, a premier computer science and engineering institution in Japan, conducted a two-week intensive learning program based on the NVIDIA Jetson Nano edge AI platform and Jetson AI Certification.
University of Aizu, a premier computer science and engineering institution in Japan, conducted a two-week intensive learning program based on the NVIDIA Jetson Nano edge AI platform and Jetson AI Certification.
During the university’s annual Silicon Valley Learning Program, teams of six students worked on projects in robotics and intelligent IoT. Students were awarded Jetson AI Specialist certificates for their work during the program, which included several unique projects listed below.
The NVIDIA Jetson AI Certification program is designed to facilitate robotics and AI learning. Two certification tracks are offered: Jetson AI Specialist for anyone, and Jetson AI Ambassador for educators and instructors.
University of Aizu is one of the first schools in Japan to focus on Jetson AI Certification. The university received free developer kits through the NVIDIA Jetson Nano 2GB Developer Kit Grant Program. With the performance and capability to run a diverse set of AI models and frameworks, the Jetson Nano 2GB is designed for hands-on teaching and learning while providing a scalable platform for creating AI applications as they evolve in the future.
Yuji Mitsunaga, Senior Associate Professor, Promotion Office, Super Global University, University of Aizu, who is in charge of this training, said:
“We believe Jetson AI Certification is the best way for students to experience edge IoT and understand the importance of AI in the future. Since 2019, we’ve been using Jetson platform to teach AI as part of our pre-training program for undergraduate students. Using the new NVIDIA program, not only did the students efficiently learn the fundamentals of AI, but just a few days later they used their hands-on knowledge to develop a variety of systems that utilized Jetson’s capabilities.”
“For us, the aim of this training is to build a high level of confidence for students learning AI and IoT manufacturing, and NVIDIA’s Jetson AI Certification has become an important milestone that lays the foundation for our students,” Mitsunaga added. “I am confident that this experience will have a positive impact on the active development and entrepreneurship activities of the students in the future.”
NVIDIA’s Japan and US teams shared insights with the students and provided additional guidance on how to get started with Jetson. The Jetson Nano 2GB , proved invaluable as the students collaborated on ideas for their AI projects over three days.
The students created the following projects:
Portable Coronavirus Diagnostic Device by Heihao Weng
Simple coronavirus diagnostic device for healthcare professionals that analyzes chest x-ray images
Application to Prevent from Pet’s Mischief by Hiroshi Tasaki
Prevents pet mischief in life with a fulfilling pet
Automatic Car Windshield Wiper by Keigo Fukasa
Learned once water lands on glass and automatically wipes off the water droplets
Reaction When Learning Plant Images in Perspective by Banri Yasui
Identifies plant type from the pattern of a leaf
AFK Minecraft by Tarun Sreepada
Converts body posture into keyboard strokes and plays games in VR-like situations
Hermit Purple by Eri Miyaoka
Displays character effects to match jojo’s bizarre adventures when shooting style poses
The students who published their projects on GitHub and successfully completed their applications for certification were certified as Jetson AI Specialists.
Chitoku Yato, Jetson Product Marketing Manager at NVIDIA said:
“Each student’s published project uses AI at an advanced level, reaffirming that NVIDIA’s training and sample projects are being fully utilized in education. Following the example of University of Aizu, I hope more students build projects on the Jetson platform and get certified as Jetson AI Specialists. And I’m confident these initiatives will inspire youth to see themselves as builders of our AI and robotics future.”
Learn more about curriculum, grants and other offerings on the Jetson for AI Education page.
Matanya Horowitz smelled a problem in 2014. Fresh out of CalTech with a Ph.D., he saw that recycling centers lacked robotics and computer vision to pick through heaps of garbage-contaminated recyclables. Horowitz founded AMP Robotics that year to harness AI run on NVIDIA GPUs to turn sorting out the trash into cash. It’s a ripe Read article >
The post Trash to Cash: Recyclers Tap Startup with World’s Largest Recycling Network to Freshen Up Business Prospects appeared first on The Official NVIDIA Blog.
I downloaded a software that takes images of people, and creates 3d models. I’m having an issue where the encodings fail, and I’m left with the message ‘tf.ConfigProto() AttributeError: module ‘tensorflow’ has no attribute ‘ConfigProto’
I have ZERO experience working with code/python so I’m utterly confused. I can post the full text if necessary. Been trying to fix this for hours
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I’m working in a memory constrained environment and I’m trying to optimize memory usage as much as I can. Can I load train data alone or test data alone in a jupyter notebook?
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I need to implement CosineSimilarity myself because i need to work on the individual losses before calculating the batch-wide mean.
I do it like this:
a_n = tf.math.l2_normalize(a, axis=-1)
b_n = tf.math.l2_normalize(b, axis=-1)
d = -tf.math.reduce_sum(a_n * b_n, axis=-1)
# Above is _identical_ to Keras' implementation.
return d, tf.math.reduce_mean(d)
I already compared the output to Keras’ implementation by repeatedly printing
print(tf.math.reduce_sum(tf.math.abs(my_loss - keras_loss)))
However, even though this outputs straight zeros (and never any NaNs), i still encounter NaNs, while with Keras’ implementation i do not. I already tried a higher epsilon in the l2_normalize, or using multiply_no_nan, to no avail.
Update: This comment.
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