Archive for the ‘Cloud’ Category

Best Practices for running HPC on AWS

December 11th, 2018
Best Practices for running HPC on AWS

Use placement group A cluster placement group is a logical grouping of instances within a single Availability Zone. Cluster placement groups are recommended for applications that benefit from low network latency, high network throughput, or both, and if the majority of the network traffic is between the instances in the group. Reference: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/placement-groups.html   Disable

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Cloud weekly news digest (9.12.2018)

December 9th, 2018
Cloud weekly news digest (9.12.2018)

Machine Learning: AWS Inferentia – a machine learning inference chip designed to deliver high performance at low cost: https://aws.amazon.com/about-aws/whats-new/2018/11/announcing-amazon-inferentia-machine-learning-inference-microchip/ https://aws.amazon.com/machine-learning/inferentia/ AWS-Optimized TensorFlow Now Scales to 256 GPUs: https://aws.amazon.com/about-aws/whats-new/2018/11/tensorflow-scalability-to-256-gpus/ New features for AWS SageMaker: https://aws.amazon.com/about-aws/whats-new/2018/11/amazon-sagemaker-announces-support-for-reinforcement-learning/ https://aws.amazon.com/about-aws/whats-new/2018/11/introducing-amazon-sagemaker-groundtruth/ Azure Machine Learning service: A look under the hood https://azure.microsoft.com/en-us/blog/azure-machine-learning-service-a-look-under-the-hood/ ONNX Runtime – a high-performance inference engine for machine learning

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Cloud weekly news digest (25.11.2018)

November 25th, 2018
Cloud weekly news digest (25.11.2018)

Machine Learning: Automated Machine Learning in Azure and Google cloud: https://medium.com/@santiagof/auto-is-the-new-black-google-automl-microsoft-automated-ml-autokeras-and-auto-sklearn-80d1d3c3005c Translating Between Statistics and Machine Learning: https://insights.sei.cmu.edu/sei_blog/2018/11/translating-between-statistics-and-machine-learning.html News about Amazon SageMaker: https://aws.amazon.com/blogs/machine-learning/new-features-for-amazon-sagemaker-workflows-algorithms-and-accreditation/ https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-automatic-model-tuning-becomes-more-efficient-with-warm-start-of-hyperparameter-tuning-jobs/ https://aws.amazon.com/blogs/machine-learning/easily-monitor-and-visualize-metrics-while-training-models-on-amazon-sagemaker/ Batch processing: If you need to run tightly coupled and high compute research, Amazon batch service now supports multi-node parallel jobs: https://aws.amazon.com/about-aws/whats-new/2018/11/aws-batch-now-supports-multi-node-parallel-jobs/ https://docs.aws.amazon.com/batch/latest/userguide/multi-node-parallel-jobs.html Data Science: How Data Science Is Improving

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Cloud weekly news digest (18.11.2018)

November 18th, 2018
Cloud weekly news digest (18.11.2018)

HPC: Three Approaches to HPC and AI Convergence: https://openlab.cern/sites/openlab.web.cern.ch/files/2018-09/Three%20Approaches%20to%20HPC%20and%20AI%20Convergence%20-%20IT%20Peer%20Network.pdf Amazon released a new service called ParallelCluster in-order to simplify HPC cluster management: https://aws.amazon.com/blogs/opensource/aws-parallelcluster/ GPU: Case study from the Italian National Institute of Astrophysics who migrated to AWS for astronomy research: https://aws.amazon.com/blogs/publicsector/the-italian-national-institute-of-astrophysics-explores-the-universe-with-the-cloud/ Storage and security related: AWS now offers a policy for securing S3 buckets using

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Cloud weekly news digest (11.11.2018)

November 11th, 2018
Cloud weekly news digest (11.11.2018)

Machine Learning: Latest news about Amazon SageMaker service for researchers: https://aws.amazon.com/blogs/machine-learning/lifecycle-configuration-update-for-amazon-sagemaker-notebook-instances/ https://aws.amazon.com/blogs/machine-learning/customize-your-notebook-volume-size-up-to-16-tb-with-amazon-sagemaker/ https://aws.amazon.com/blogs/machine-learning/now-easily-perform-incremental-learning-on-amazon-sagemaker/ https://aws.amazon.com/blogs/machine-learning/introduction-to-amazon-sagemaker-object2vec/ https://aws.amazon.com/about-aws/whats-new/2018/11/amazon-sagemaker-now-supports-apache-mxnet-1-3-and-tensorflow-1-/ Big Data: The pros and cons of Google Cloud Storage vs. HDFS https://cloud.google.com/blog/products/storage-data-transfer/hdfs-vs-cloud-storage-pros-cons-and-migration-tips Tips and tricks for migrating on-premises Hadoop infrastructure to Azure HDInsight https://azure.microsoft.com/en-us/blog/migrating-on-premises-hadoop-infrastructure-to-azure-hdinsight/ HPC: What is HPC in the Cloud? Exploring the Need for Speed https://blogs.oracle.com/cloud-infrastructure/what-is-hpc-in-the-cloud-exploring-the-need-for-speed Cost optimization:

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Cloud weekly news digest (4.11.2018)

November 4th, 2018
Cloud weekly news digest (4.11.2018)

Machine Learning: A case study from UCLA School of Medicine, who used AWS services for conducting a research on asthma: https://aws.amazon.com/blogs/publicsector/ucla-helps-caregivers-predict-and-prevent-asthma-attacks-in-children/ Google cloud just published AdaNet, a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention: https://ai.googleblog.com/2018/10/introducing-adanet-fast-and-flexible.html Azure ML Studio now supports R 3.4 https://blog.revolutionanalytics.com/2018/11/azure-ml-studio-r-34.html Deep Learning: Using AWS machine learning services

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Cloud weekly news digest (28.10.2018)

October 29th, 2018
Cloud weekly news digest (28.10.2018)

Machine Learning: A case study of how the Israeli startup Fiverr is using AWS SageMaker for machine learning models: https://aws.amazon.com/blogs/startups/fiverr-relies-on-sagemaker-to-streamline-and-simplify-machine-learning-models/ Step by step guidelines for securing the usage of AWS SageMaker for sensitive environments: https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-batch-transform-now-supports-amazon-vpc-and-aws-kms-based-encryption/ AI How to use GCP machine learning services for fluid annotation: https://ai.googleblog.com/2018/10/fluid-annotation-exploratory-machine.html Using Azure AI to notify seismologists on earthquake:

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The Spot / Low-Priority VM / Preemptible VM Instance Pricing Model

October 22nd, 2018
The Spot / Low-Priority VM / Preemptible VM Instance Pricing Model

Spot Instances or Low-Priority VM or Pre-Emptible VM Instances allow customers to purchase VM instances at a low prices from a pool of spare compute capacity at cloud vendors’ data centers

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Cloud weekly news digest (21.10.2018)

October 21st, 2018
Cloud weekly news digest (21.10.2018)

Research Related: News regarding the latest AWS public datasets for researchers in life science, machine learning, environmental, etc: https://aws.amazon.com/about-aws/whats-new/2018/10/public-datasets/ Full list of AWS datasets for researchers: https://registry.opendata.aws/ Full list of Google Cloud Platform datasets for researchers: https://cloud.google.com/public-datasets/ Full list of Azure datasets for researchers: https://msropendata.com/ Using Google Cloud Platform and NASA for astronomy research: https://cloud.google.com/blog/products/ai-machine-learning/is-there-life-on-other-planets-google-cloud-is-working-with-nasas-frontier-development-lab-to-find-out

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Cloud weekly news digest (14.10.2018)

October 14th, 2018
Cloud weekly news digest (14.10.2018)

Machine Learning: Microsoft has released a new open-source cross-platform framework for model-based machine learning called Infer.Net: https://www.infoworld.com/article/3311994/machine-learning/microsoft-open-sources-infernet-machine-learning.amp.html 25 Open-Source Machine Learning Repos: https://heartbeat.fritz.ai/25-open-source-machine-learning-repos-to-inspire-your-next-project-3b027a90155 AI: Case study from New York University about usage of Google open-source AI for lung cancer research: https://itsfoss.com/open-source-ai-diagnoses-lung-cancer/ NVIDIA GPU related: Article about RAPIDS – open source libraries for NVIDIA’s GPUs for

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