Jyväskylä University (JAMK) erbjuder sina 8 500 studenter utbildning med hög kvalitet, utvecklad för att möta arbetsmarknadens behov.
Quelle: CloudForms
JAMK eli Jyväskylän ammattikorkeakoulu tarjoaa 8500 opiskelijalle korkealaatuista opetusta, jonka halutaan vastaavan hyvin työmarkkinoiden tarpeeseen.
Quelle: CloudForms
If you want to move massive amounts of data quickly and at scale, you want Apache Kafka. But Kafka can be challenging to use. In this post, we take a look at some of the common challenges of using Apache Kafka—and how to deal with them.
Quelle: CloudForms
The Friday Five is a weekly Red Hat® blog post with 5 of the week’s top news items and ideas from or about Red Hat and the technology industry. Consider it your weekly digest of things that caught our eye.
Quelle: CloudForms
In a few short days, you’ll surely be asked: What are your New Year’s resolutions for 2022? If starting a blogging habit is one of them, we’re here to help! At WordPress.com, we’re trying something new for 2022. Instead of individual New Year’s resolutions, we’ll be focusing on a shared goal we can accomplish together.
Say Hello to Bloganuary
We’re running a month-long blogging challenge in January and we invite you to join us! Each day, you’ll receive a new writing prompt to inspire you to publish a post on your blog. There is no right or wrong way to respond to the prompts. Take this opportunity to exercise your creativity and have fun. Maybe you’ll choose to respond with a story, a drawing, a poem, a photo, a comic strip, a recipe, or even a playlist. Anything goes!
By participating in Bloganuary, you’ll join bloggers around the world in the shared goal of creating and/or solidifying a strong blogging habit. You’ll get access to the Bloganuary community site where you can meet and get to know others working toward similar goals. You can share tips, learn from others, reach a new audience for your blog, and make some new blogging friends.
Here’s a badge you can add to your blog to show others what you’re doing and encourage them to join in the fun.
Join the Bloganuary challenge, stay motivated, and start the new year off on the write track!
Learn More
If you’d prefer to journal privately instead of blog publicly, check out the Day One app:
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Quelle: RedHat Stack
In this guide, we’ll show you how to get started with a free hosted trial of Mirantis Container Cloud in just a few simple steps. By the end, you’ll have a Kubernetes cluster running a sample application, all manageable through the Lens integrated development environment. We’ll also walk you through scaling up with additional nodes … Continued
Quelle: Mirantis
On December 9, 2021, Apache disclosed a critical severity vulnerability in its Log4j 2 logging utility, which records activity within Java applications. The vulnerability impacts all Apache Log4j 2 versions prior to 2.15.0. The Mirantis team has confirmed that most of our products are unaffected by the vulnerability; the few issues we found were of … Continued
Quelle: Mirantis
Research organizations today recognize the challenge of sifting through siloed data sets, and analyzing and sharing this data with the global research community—all while staying secure and compliant within a range of national and international standards. It is precisely these constraints that led the U.S. National Cancer Institute (NCI) to create Cloud Resources, which are components of the NCI Cancer Research Data Commons that allow scientists to analyze cancer datasets in a cloud environment (vs. having to download data and use custom hardware). Included in these resources is the Institute for Systems Biology-Cancer Gateway in the Cloud (ISB-CGC). ISB-CGC relies on Google Cloud to securely host terabytes of genomic and proteomic data, and provide flexible and scalable analytics tools that can be integrated into research models. Complex computations that traditionally required days to complete are now executed in just minutes or hours. And ISB-CGC can now deliver open data, compute, and analytics resources to the global research community.Enabling faster time-to-discoverySpeed and scale can make all the difference when it comes to potentially life-saving research. Take breast cancer for example. It’s the world’s most prevalent cancer and according to the World Health Organization, more than two million women were diagnosed with it in 2020 alone. With such a large number of impacted women, each with unique biological features and personal paths through the disease, breast cancer research is particularly data intensive. And processing this on-premises is too slow, expensive, and burdensome to patients.By working with Google, NCI’s ISB has not only made data more useful to cancer researchers around the world, but also has fundamentally changed how cancer investigators conduct research. BigQuery, Google Cloud’s highly scalable mulitcloud data warehouse, underpins the cloud-based platform that connects researchers to a wide collection of cancer datasets, as well as the analytical and computational infrastructure to analyze that data quickly. “We are spreading the message of the cost-effectiveness of the cloud,” said Dr. Kawther Abdilleh, lead bioinformatics scientist at General Dynamics Information Technology, a partner of ISB. “With Google Cloud’s BigQuery, we’ve successfully demonstrated that researchers can inexpensively analyze large amounts of data, and do so faster than ever before.”Integrating diverse tools and datasetsTraditionally, researchers have downloaded source data and performed analysis locally on their personal machines using programming languages like R and Python. As the volume and complexity of cancer data has grown, this approach has become unsustainable. Through the use of Google Cloud services, like Notebooks and BigQuery application programming interfaces (APIs), researchers can now use their desired methods to analyze data on the ISB-CGC platform, directly in the cloud—without the need to download data. For example, in their September 2020 paper on data integration and analysis in the cloud, Dr. Abdilleh and Dr. Boris Aguilar, senior research scientists at ISB, demonstrated how cloud-based data analysis can be used to identify novel biological associations between clinical and molecular features of breast cancer. “Google’s AI platform, for example, allows us to easily create notebooks to use R or Python in combination with BigQuery or machine learning to perform large-scale statistical analysis of genomic data, all in the cloud,” Aguilar wrote. “This type of analysis is particularly effective when the data is large and heterogenous, which is the case for cancer-related data.” Drs. Abdilleh and Aguilar developed a set of BigQuery user-defined functions (UDFs) to perform statistical tests and gain a more holistic picture of breast cancer. Performing these statistical functions directly on the massive data stored in BigQuery vs. in an on-premises computer program later in the analysis workflow saved a significant amount of time. In fact, by using UDFs with BigQuery, analysis that typically required supercomputers and days of computation was complete in minutes. Drs. Abdilleh and Aguilar have now made their UDFs available for use by the broader research community via BigQuery, opening doors for fellow breast cancer researchers to build on this progress and make strides in their life-saving work. Global access to critical cancer dataWith so many lives and families impacted by cancer—and researchers worldwide diligently seeking answers—the need to accelerate and improve the means by which cancer research is conducted is critical. ISB-CGC’s success using Google Cloud as the foundation of its infrastructure and data cloud strategy has opened the door for the cancer research community to gain real-time, secure access to data that plays a significant role in the early detection of cancer. Read the case study for more detail on how Google Cloud is supporting breast cancer research.
Quelle: Google Cloud Platform
We are thrilled that Gartner has positioned Google as a Leader for the second year in a row in the 2021 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMS).We believe the report evaluated Google’s unified capabilities across both transactional and analytical use cases, and showcases innovation progress in areas like data management consistency, high speed processing and ingestion, security, elasticity, advanced analytics, and more. With the recent announcement of Dataplex, organizations can centrally manage, monitor, and govern their data across data lakes, data warehouses, and data marts with consistent controls. Solutions like BigQuery ML provide a “built-in” approach for advanced analytics capabilities and Analytics Hub offer the infrastructure customers need to share data analytics solutions securely and at scale in ways never before achieved. For example, over a seven-day period in April, more than 3,000 different organizations shared over 200 petabytes of data using BigQuery. Research shows 90% of organizations have a multicloud strategy, which is why we have invested in a cross-cloud data analytics solution for Google Cloud, AWS, and Azure with BigQuery Omni. Additionally, our progress with Anthos and our Distributed Cloud this past year further advance our ability to support multi and hybrid cloud scenarios. To gain a competitive advantage using data, organizations need a data platform that transcends transactional and analytical workloads and can be run with the highest level of reliability, availability and security. Cloud Spanner, our globally distributed relational database has redefined the scale, global consistency, and availability of Online Transaction Processing (OLTP) systems. Spanner processes over 1 billion requests per second at peak, and has been battle-tested with some of the most-demanding applications, including Google services such as Search, YouTube, Gmail, Maps, and Payments. And what’s unique about our core services Spanner and BigQuery, is that they leverage common infrastructure such as our highly durable distributed file system (Colossus), our large-scale cluster management system (Borg), and Jupiter, our high-performance networking infrastructure, enabling features such as federation between Spanner and BigQuery.We remain focused on the integration of Google Trends, Maps, Search, Ads, and have increased industry domain expertise in areas such as retail, financial services, healthcare, and gaming. We’re continuing to develop industry white papers such as this one – How to develop Global Multiplayer Games using Cloud Spanner – and we’re proud of the work the team has done to create and share industry and horizontal architecture patterns built from industry leaders to serve as solution accelerators for customer use cases. Innovation momentum continues with a unified and open data cloudWe continue to innovate across our data cloud portfolio, especially with the innovations we announced at Google Cloud NEXT’21. BigQuery Omni is now available for AWS and Azure, supporting cross-cloud analytics for customers. We’ve added additional capabilities for enterprise data management and governance with Dataplex, which recently went GA. We’ve made migrations to Cloud SQL easier and faster with the Database Migration Service. More than 85% of all migrations are underway in under an hour, with the majority of customers migrating their databases from other clouds. We are embracing openness with Spanner by adding a PostgreSQL interface, allowing enterprises to take advantage of Spanner’s unmatched global scale, 99.999% availability, and strong consistency using skills and tools from the popular PostgreSQL ecosystem. We are also automating data processing with Spark on Google Cloud, which enables developers to spend less time on infrastructure management and more time on data science, modeling, and delivering business value. Finally, we announced Google Earth Engine on Google Cloud, allowing customers to integrate Earth Engine with BigQuery, Google Cloud’s ML technologies, and the Google Maps Platform. With these innovations, enterprises like PayPal, Deutsche Bank, and Equifax use Google Cloud to solve for their end-to-end data lifecycle use cases. Organizations like Telefonicause Google Cloud to deliver new customer experiences. Telefónica has transformed every aspect of how they store, share, and analyze data while doubling processing power and lowering costs.We continue to support an open ecosystem of data partners including Informatica, Tableau, MongoDB, Neo4j, C3.ai, and Databricks, giving customers the flexibility of choice to build their data clouds without being locked into a single approach. We are honored to be a Leader in the 2021 Gartner Magic Quadrant for Cloud Database Management Systems (DBMS), and look forward to continuing to innovate and partner with you on your digital transformation journey. Download the complimentary 2021 Gartner Magic Quadrant for Cloud Database Management Systems report. Learn more about how organizations are building their data clouds with Google Cloud solutions. Gartner, Magic Quadrant for Cloud Database Management Systems, Henry Cook, Merv Adrian, Rick Greenwald, Adam Ronthal, Philip Russom, 14 December 2021.Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Google Cloud.Related ArticleIntroducing Google Distributed Cloud—in your data center, at the edge, and in the cloudGoogle Distributed Cloud runs Anthos on dedicated hardware at the edge or hosted in your data center, enabling a new class of low-latency…Read Article
Quelle: Google Cloud Platform
It’s been a year since Ben wrote about Nvidia support on Docker Desktop. At that time, it was necessary to take part in the Windows Insider program, use Beta CUDA drivers, and use a Docker Desktop tech preview build. Today, everything has changed:
On the OS side, Windows 11 users can now enable their GPU without participating in the Windows Insider program. Windows 10 users still need to register.Nvidia CUDA drivers have been released.Last, the GPU support has been merged in Docker Desktop (in fact since version 3.1).
Nvidia used the term near-native to describe the performance to be expected.
Where to find the Docker images
Base Docker images are hosted at https://hub.docker.com/r/nvidia/cuda. The original project is located at https://gitlab.com/nvidia/container-images/cuda.
What they contain
The nvidia-smi utility allows users to query information on the accessible devices.
$ docker run -it –gpus=all –rm nvidia/cuda:11.4.2-base-ubuntu20.04 nvidia-smi
Tue Dec 7 13:25:19 2021
+—————————————————————————–+
| NVIDIA-SMI 510.00 Driver Version: 510.06 CUDA Version: 11.6 |
|——————————-+———————-+———————-+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce … On | 00000000:01:00.0 Off | N/A |
| N/A 0C P0 13W / N/A | 132MiB / 4096MiB | N/A Default |
| | | N/A |
+——————————-+———————-+———————-+
+—————————————————————————–+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+—————————————————————————–
The dmon function of nvidia-smi allows monitoring the GPU parameters :
$ docker exec -ti $(docker ps -ql) bash
root@7d3f4cbdeabb:/src# nvidia-smi dmon
# gpu pwr gtemp mtemp sm mem enc dec mclk pclk
# Idx W C C % % % % MHz MHz
0 29 69 – – – 0 0 4996 1845
0 30 69 – – – 0 0 4995 1844
The nbody utility is a CUDA sample that provides a benchmarking mode.
$ docker run -it –gpus=all –rm nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -benchmark
…
> 1 Devices used for simulation
GPU Device 0: “Turing” with compute capability 7.5
> Compute 7.5 CUDA device: [NVIDIA GeForce GTX 1650 Ti]
16384 bodies, total time for 10 iterations: 25.958 ms
= 103.410 billion interactions per second
= 2068.205 single-precision GFLOP/s at 20 flops per interaction
Quick comparison to a CPU suggest a different order of magnitude of performance. GPU is 2000 times faster:
> Simulation with CPU
4096 bodies, total time for 10 iterations: 3221.642 ms
= 0.052 billion interactions per second
= 1.042 single-precision GFLOP/s at 20 flops per interaction
What can you do with a paravirtualized GPU?
Run cryptographic tools
Using a GPU is of course useful when operations can be heavily parallelized. That’s the case for hash analysis. dizcza hosted its nvidia-docker based images of hashcat on Docker hub. This image magically works on Docker Desktop!
$ docker run -it –gpus=all –rm dizcza/docker-hashcat //bin/bash
root@a6752716788d:~# hashcat -I
hashcat (v6.2.3) starting in backend information mode
clGetPlatformIDs(): CL_PLATFORM_NOT_FOUND_KHR
CUDA Info:
==========
CUDA.Version.: 11.6
Backend Device ID #1
Name………..: NVIDIA GeForce GTX 1650 Ti
Processor(s)…: 16
Clock……….: 1485
Memory.Total…: 4095 MB
Memory.Free….: 3325 MB
PCI.Addr.BDFe..: 0000:01:00.0
From there it is possible to run hashcat benchmark
hashcat -b
…
Hashmode: 0 – MD5
Speed.#1………: 11800.8 MH/s (90.34ms) @ Accel:64 Loops:1024 Thr:1024 Vec:1
Hashmode: 100 – SHA1
Speed.#1………: 4021.7 MH/s (66.13ms) @ Accel:32 Loops:512 Thr:1024 Vec:1
Hashmode: 1400 – SHA2-256
Speed.#1………: 1710.1 MH/s (77.89ms) @ Accel:8 Loops:1024 Thr:1024 Vec:1
…
Draw fractals
The project at https://github.com/jameswmccarty/CUDA-Fractal-Flames uses CUDA for generating fractals. There are two steps to build and run on Linux. Let’s see if we can have it running on Docker Desktop. A simple Dockerfile with nothing fancy helps for that.
# syntax = docker/dockerfile:1.3-labs
FROM nvidia/cuda:11.4.2-base-ubuntu20.04
RUN apt -y update
RUN DEBIAN_FRONTEND=noninteractive apt -yq install git nano libtiff-dev cuda-toolkit-11-4
RUN git clone –depth 1 https://github.com/jameswmccarty/CUDA-Fractal-Flames /src
WORKDIR /src
RUN sed ‘s/4736/1024/’ -i fractal_cuda.cu # Make the generated image smaller
RUN make
And then we can build and run:
$ docker build . -t cudafractal
$ docker run –gpus=all -ti –rm -v ${PWD}:/tmp/ cudafractal ./fractal -n 15 -c test.coeff -m -15 -M 15 -l -15 -L 15
Note that the –gpus=all is only available to the run command. It’s not possible to add GPU intensive steps during the build.
Here’s an example image:
Machine learning
Well really, looking at GPU usage without looking at machine learning would be a miss. The tensorflow:latest-gpu image can take advantage of the GPU in Docker Desktop. I will simply point you to Anca’s blog earlier this year. She described a tensorflow example and deployed it in the cloud: https://www.docker.com/blog/deploy-gpu-accelerated-applications-on-amazon-ecs-with-docker-compose/
Conclusion: What are the benefits for developers?
At Docker, we want to provide a turn key solution for developers to execute their workflows seamlessly:
With Docker Desktop, developers can run their code locally and deploy to the infrastructure of their choice.We provide support in the issue tracker https://github.com/docker/for-winDownload the latest version of Docker Desktop now.
DockerCon2022
Join us for DockerCon2022 on Tuesday, May 10. DockerCon is a free, one day virtual event that is a unique experience for developers and development teams who are building the next generation of modern applications. If you want to learn about how to go from code to cloud fast and how to solve your development challenges, DockerCon 2022 offers engaging live content to help you build, share and run your applications. Register today at https://www.docker.com/dockercon/
The post WSL 2 GPU Support for Docker Desktop on NVIDIA GPUs appeared first on Docker Blog.
Quelle: https://blog.docker.com/feed/
