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A docker-compose manager.

Learn the basics of WebAssembly through code examples and try different ways to compile C and Rust programs for the browser with the help of Docker image from Evil Martians.

Learn from Docker experts to simplify and advance your app development and management with Docker. Stay up to date on Docker events and new version announcements!

Deep learning models are everywhere without us even realizing it. The number of AI use cases have been increasing exponentially with the rapid development of new algorithms, cheaper compute, and greater access to data. Almost every industry has deep learning applications, from healthcare to education to manufacturing, construction, and beyond. Many developers opt to use popular AI Frameworks like PyTorch, which simplifies the process of analyzing predictions, training models, leveraging data, and refining future results.PyTorch on AzureGet an enterprise-ready PyTorch experience in the cloud.Learn morePyTorch is a machine learning framework used for applications such as computer vision and natural language processing, originally developed by Meta AI and now a part of the Linux Foundation umbrella, under the name of PyTorch Foundation. PyTorch has a powerful, TorchScript-based implementation that transforms the model from eager to graph mode for deployment scenarios.One of the biggest challenges PyTorch developers face in their deep learning projects is model optimization and performance. Oftentimes, the question arises How can I improve the performance of my PyTorch models? As you might have read in our previous blog, Intel and Microsoft have joined hands to tackle this problem with OpenVINO Integration with Torch-ORT. Initially, Microsoft had released Torch-ORT, which focused on accelerating PyTorch model training using ONNX Runtime. Recently, this capability was extended to accelerate PyTorch model inferencing by using the OpenVINO toolkit on Intel central processing unit (CPU), graphical processing unit (GPU), and video processing unit (VPU) with just two lines of code.Figure 1 OpenVINO Integration with Torch-ORT Application Flow. This figure shows how OpenVINO Integration with Torch-ORT can be used in a Computer Vision Application.By adding just two lines of code, we achieved 2.15 times faster inference for PyTorch Inception V3 model on an 11th Gen Intel Core i7 processor1. In addition to Inception V3, we also see performance gains for many popular PyTorch models such as ResNet50, Roberta-Base, and more. Currently, OpenVINO Integration with Torch-ORT supports over 120 PyTorch models from popular model zoo's, like Torchvision and Hugging Face.Figure 2 FP32 Model Performance of OpenVINO Integration with Torch-ORT as compared to PyTorch. This chart shows average inference latency (in milliseconds) for 100 runs after 15 warm-up iterations on an 11th Gen Intel(R) Core (TM) i7-1185G7 @ 3.00GHz.FeaturesOpenVINO Integration with Torch-ORT introduces the following featuresInline conversion of static/dynamic input shape modelsGraph partitioningSupport for INT8 modelsDockerfiles/Docker ContainersInline conversion of static/dynamic input shape modelsOpenVINO Integration with Torch-ORT performs inferencing of PyTorch models by converting these models to ONNX inline and subsequently performing inference with OpenVINO Execution Provider. Currently, both static and dynamic input shape models are supported with OpenVINO Integration with Torch-ORT. You also have the ability to save the inline exported ONNX model using the DebugOptions API.Graph partitioningOpenVINO Integration with Torch-ORT supports many PyTorch models by leveraging the existing graph partitioning feature from ONNX Runtime. With this feature, the input model graph is divided into subgraphs depending on the operators supported by OpenVINO and the OpenVINO-compatible subgraphs run using OpenVINO Execution Provider and unsupported operators fall back to MLAS CPU Execution Provider.Support for INT8 modelsOpenVINO Integration with Torch-ORT extends the support for lower precision inference through post-training quantization (PTQ) technique. Using PTQ, developers can quantize their PyTorch models with Neural Network Compression Framework (NNCF) and then run inferencing with OpenVINO Integration with Torch-ORT. Note Currently, our INT8 model support is in the early stages, only including ResNet50 and MobileNetv2. We are continuously expanding our INT8 model coverage.Docker ContainersYou can now use OpenVINO Integration with Torch-ORT on Mac OS and Windows OS through Docker. Pre-built Docker images are readily available on Docker Hub for your convenience. With a simple docker pull, you will now be able to unleash the key to accelerating performance of PyTorch models. To build the docker image yourself, you can also find dockerfiles readily available on Github.Customer storyRoboflowRoboflow empowers ISVs to build their own computer vision applications and enables hundreds of thousands of developers with a rich catalog of services, models, and frameworks to further optimize their AI workloads on a variety of different Intel hardware. An easy-to-use developer toolkit to accelerate models, properly integrated with AI frameworks, such as OpenVINO integration with Torch-ORT provides the best of both worldsan increase in inference speed as well as the ability to reuse already created AI application code with minimal changes. The Roboflow team has showcased a case study that demonstrates performance gains with OpenVINO Integration with Torch-ORT as compared to Native PyTorch for YOLOv7 model on Intel CPU. The Roboflow team is continuing to actively test OpenVINO integration with Torch-ORT with the goal of enabling PyTorch developers in the Roboflow Community.Try it outTry out OpenVINO Integration with Torch-ORT through a collection of Jupyter Notebooks. Through these sample tutorials, you will see how to install OpenVINO Integration with Torch-ORT and accelerate performance for PyTorch models with just two additional lines of code. Stay in the PyTorch framework and leverage OpenVINO optimizationsit doesn't get much easier than this.Learn moreHere is a list of resources to help you learn moreGithub RepositorySample NotebooksSupported ModelsUsage GuidePyTorch on AzureNotes1Framework configuration ONNXRuntime 1.13.1Application configuration torch_ort_infer 1.13.1, python timeit module for timing inference of modelsInput Classification models torch.Tensor; NLP models Masked sentence; OD model .jpg imageApplication Metric Average Inference latency for 100 iterations calculated after 15 warmup iterationsPlatform Tiger LakeNumber of Nodes 1 Numa NodeNumber of Sockets 1CPU or Accelerator 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHzCores/socket, Threads/socket or EU/socket 4, 2 Threads/Coreucode 0xa4HT EnabledTurbo EnabledBIOS Version TNTGLV57.9026.2020.0916.1340System DDR Mem Config slots / cap / run-speed 2/32 GB/2667 MT/sTotal Memory/Node (DDR+DCPMM) 64GBStorage boot Sabrent Rocket 4.0 500GB – size 465.8GOS Ubuntu 20.04.4 LTSKernel 5.15.0-1010-intel-iotgNotices and disclaimersPerformance varies by use, configuration, and other factors. Learn more at www.Intel.com/PerformanceIndex.Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. See backup for configuration details. No product or component can be absolutely secure.Your costs and results may vary.Intel technologies may require enabled hardware, software, or service activation.Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from a course of performance, course of dealing, or usage in trade.Results have been estimated or simulated. Intel Corporation. Intel, the Intel logo, OpenVINO, and the OpenVINO logo are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.The post Faster inference for PyTorch models with OpenVINO Integration with Torch-ORT appeared first on Microsoft Open Source Blog.

Many container customers building applications use common software packages (e.g. operating systems, databases, and application components) that are publicly distributed as container images on Docker Hub. Docker, Inc. has announced that the Hub service will begin limiting the rate at which images are pulled under their anonymous and free plans. These limits will progressively take […]

This blog is co-authored by Edwin Cheung, Principal Software Engineering Manager and Xiaoyong Zhu, Principal Data Scientist.Feathr is an enterprise scale feature store, which facilitates the creation, engineering, and usage of machine learning features in production. It has been used by many organizations as an online/offline store, as well as for real-time streaming.Today, we are excited to announce the much-anticipated availability of the OSS Feathr 1.0. It contains many new features and enhancements since Feathr became open-source one year ago. Similar to the online transformation, rapid sandbox environment, MLOPs V2 accelerator integration really accelerates the development and deployment of machine learning projects at enterprise scale.Online transformation via domain specific language (DSL)In various machine learning scenarios, features generation is required for both training and inferences. There is a limitation where data source cannot come from online service, as currently transformation only happens before feature data is published to the online store and the transformation is required close to real-time. In such cases, there is a need for a mechanism where the user has the ability to run transformation on the inference data dynamically before inferencing via the model. The new online transformation via DSL feature addresses these challenges by using a custom transformation engine that can process transformation requests and responses close to real-time on demand. It allows definition of transformation logic declaratively using DSL syntax which is based on EBNF. It also provides extensibility, where there is a need to define custom complex transformation, by supporting user defined function (UDF) written in Python or Java.nyc_taxi_demo(pu_loc_id as int, do_loc_id as int, pu_time as string, do_time as string, trip_distance as double, fare_amount as double) project duration_second = (to_unix_timestamp(do_time, "%Y/%-m/%-d %-H%-M") - to_unix_timestamp(pu_time, "%Y/%-m/%-d %-H%-M"))| project speed_mph = trip_distance * 3600 / duration_second;This declarative logic runs in a new high-performance DSL engine. We provide HELM Chart to deploy this service in a container-based technology such as the Azure Kubernetes Service (AKS). The transformation engine can also run as a standalone executable, which is a HTTP server that can be used to transform data for testing purposes. feathrfeaturestore/feathrpiperlatest.curl -s -H"content-typeapplication/json" http//localhost8000/process -d'{"requests" [{"pipeline" "nyc_taxi_demo_3_local_compute","data" {"pu_loc_id" 41,"do_loc_id" 57,"pu_time" "2020/4/1 041","do_time" "2020/4/1 056","trip_distance" 6.79,"fare_amount" 21.0}}]}' It also provides the ability to auto-generate the DSL file if there are already predefined feature transformations, which have been created for the offline-transformation. Online transformation performance benchmarkIt is imperative that online transformation performs close to real-time and meets low latency demand with high queries per second (QPS) transformation for many of the enterprise customers’ needs. To determine the performance, we have conducted a benchmark on three tests. First, deployment on AKS with traffic going through ingress controller. Second, traffic going through AKS internal load balance, and finally, via the localhost.  Benchmark ATraffic going through ingress controller (AKS)Infrastructure setupTest agent runs on 1 pod on node with size Standard_D8ds_v5Transform function deployed as docker image running on 1 pod on a different node with size Standard_D8ds_v5 in same AKS.Agent sends request thru service hostname which means traffic will go thru ingress controller.Test command ab -k -c {concurrency_count} -n 1000000 (http//feathr-online.trafficmanager.net/healthz)Benchmark A resultTotal RequestsConcurrencyp90p95p99request/sec1000000100349437101000000200681543685100000030010111843378100000040013152143220100000050016192442406Benchmark BTraffic goes thru AKS internal load balancer (AKS)Benchmark BInfrastructure setupTest agent runs on 1 pod on node with size Standard_D8ds_v5Transform function deployed as docker image running on 1 pod on a different node with size Standard_D8ds_v5 in same AKS.Agent sends request thru service pip which means traffic will go thru internal load balancer.Test command ab -k -c {concurrency_count} -n 1000000 ab -k -c 100 -n 1000000 http//10.0.187.2/healthzBenchmark B resultTotal RequestsConcurrencyp90p95p99request/sec10000001003444767310000002005784703510000003009101246613100000040011121545362100000050014151944941 Benchmark CTraffic going through local host (AKS)Infrastructure setupTest agent runs on 1 pod on node with size Standard_D8ds_v5.Transform function deployed as docker image running on the same pod.Agent sends request thru localhost which means there are not network traffic at all.Test command ab -k -c {concurrency_count} -n 1000000 (http//localhost/healthz)Benchmark C resultTotal RequestsConcurrencyp90p95p99Request/sec1000000100223594661000000200445594331000000300668601841000000400891059622100000050010111459031Benchmark summaryIf transform service and up-streaming are in same host/pod, the p95 latency result is very good, stay within 10ms if concurrency < 500.If transform service and up-streaming are in different host/pod, the p95 latency result might get reduced with 2-4ms, if traffic goes thru internal load balance.If transform service and up-streaming are in different host/pod, the p95 latency result might get reduced with 2-8ms, if traffic goes thru ingress controller.Benchmark thanks to Blair Chan and Chen Xu.For more details, check out the online transformation guide.Getting started with sandbox environmentThis is an exciting feature, especially for data scientists, who may not have the necessary infrastructure background or know how to deploy the infrastructure in the cloud. The sandbox is a fully-featured, quick-start Feathr environment that enables organizations to rapidly prototype various capabilities of Feathr without the burden of full-scale infrastructure deployment. It is designed to make it easier for users to get started quickly, validate feature definitions and new ideas, and interactive experience.By default, it comes with a Jupyter notebook environment to interact with the Feathr platform. Users can also use the user experience (UX) to visualize the features, lineage, and other capabilities.To get started, check out the quick start guide to local sandbox.Feathr with MlOps V2 acceleratorMLOps V2 solution accelerator provides a modular end-to-end approach to MLOps in Azure based on pattern architecture. We are pleased to announce an initial integration of Feathr to the classical pattern that enables Terraform-based infrastructure deployment as part of the infrastructure provisioning with Azure machine learning (AML) workspace. With this integration, enterprise customers can use the templates to customize their continuous integration and continuous delivery (CI/CD) workflows to run end-to-end MlOps in their organization. Check out the Feathr integration with MLOps V2 deployment guide.Feathr GUI enhancementWe have added a number of enhancements to the graphical user interface (GUI) to improve the usability. These include support for registering features, support for deleting features, support for displaying version, and quick access to lineage via the top menu. Try out our demo UX on our live demo site.What's nextThe Feathr journey has just begun, this is the first stop to many great things to come. So, stay tuned for many enterprise enhancements, security, monitoring, and compliance features with a more enriched MLOps experience. Check out how you can also contribute to this great project, and if you have not already, you can join our slack channel here.The post Announcing the availability of Feathr 1.0 appeared first on Microsoft Open Source Blog.

NEWARK, N.J. — As ugly as the losing slide is, it’s fair to say the Ottawa Senators dressing room is a place of mutual respect. The day after goaltender Cam Talbot put the blame for Tuesday’s 6-4 loss to the Vancouver Canucks squarely on his own shoulders, his teammates had his back. “We said it […]

tl;dr A vandal deleted NewsBlur’s MongoDB database during a migration. No data was stolen or lost.

Earlier today at TechCrunch Disrupt, venture capitalist Peter Fenton joined us to talk about a variety of issues. Among them, we discussed how he’s putting his stamp on Benchmark now that, 15 years after joining the storied firm, he’s its most senior member. Fenton said that he’s …

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How I used Docker Compose, Ansible, and Caddy to re-over-engineer my UniFi Dream Machine, PiHole-, and Cloudflare-based home network for ease of setup, maintenance, and management.

About Pachyderm   At Pachyderm, we're building an open-source enterprise-grade data science platform that lets you deploy and manage multi-stage, language-agnostic data pipelines while maintaining complete reproducibility and provenance. If you want to learn more about our grand vision, read what has become our "manifesto." Our system, developed with open source roots, shifts the paradigm of data science workflows by providing reproducibility, data provenance, and opportunity for true collaboration. Pachyderm utilizes modern technologies like Docker and Kubernetes to build an entirely new method of analyzing data.  Offered both as an in-house solution as well as hosted-service, Pachyderm brings together version-control for data with the tools to build scalable end-to-end ML/AI pipelines while empowering users to use any language, framework, or tool they want.

Wildland is a docker for your data. You can store, organize and access your data however and wherever you want.

NEWARK, N.J. — As ugly as the losing slide is, it’s fair to say the Ottawa Senators dressing room is a place of mutual respect. The day after goaltender Cam Talbot put the blame for Tuesday’s 6-4 loss to the Vancouver Canucks squarely on his own shoulders, his teammates had his back. “We said it […]

Docker has announced the public beta of their enhanced integration of Docker with AWS Elastic Container Service and AWS Fargate. The release of the ECS plugin allows for developers to work within the Docker experience to create the necessary infrastructure to operate within AWS.

Learn how to install Podman, practice its basic commands, transition away from Docker, and set up Apache HTTP Server 2.4 in a CentOS container with Podman.

When Docker announced it was selling Docker Enterprise in 2019, it was a big surprise to industry watchers. Perhaps an even bigger surprise was that the buyer was Mirantis, a company best known for commercializing the OpenStack project. After the sale, Docker pivoted to a developer-focussed company…

We provide a technical analysis of a container abuse attack that features a payload that’s specifically crafted to be able to escape privileged Docker containers.

## Intro Do you want to be apart of a startup building large scale software for the renewable energy transition? Do you have a passion for managing multiple software teams to build high quality products and code that pushes the bar? Would you enjoy being the first engineering manager with the opportunity to shape our team, engineering practices, and software stack? As we continue to grow we're looking to hire our first engineering manager that can learn our system quickly, grow with us, enable our engineers to do their best work, help us build the team further, and set the bar for EM at Raptor Maps. ## Qualities * You consistently guide several small engineering teams to maintain a healthy & productive maker environment * You love enabling the engineering team to decide where to be along the slider of “hacky enough to test an idea quickly” versus “extremely careful that we enable systems to scale”… remember, we are a startup! * When Product provides the “what”, you're quick and excited to enable the engineers to provide the “how” and follow through to make sure the engineering team delivers * You are excited to discuss architecture and technical direction and can roll up your sleeves to build, but you are also comfortable deferring to the engineers on the implementation * You like having a tight feedback loop with your direct reports, which includes check-ins with technical leads, developers, quality engineers, and interns * You enjoy determining engineering/product/design team structure and resource allocation to best achieve company goals * You love analyzing data and empower engineers to do so when making strategic implementation tradeoffs, whether for a small feature or massive system-wide change * You can recognize and encourage engineers into using good patterns and good code hygiene techniques that are easily understood and lead to a solid code base for a growing engineering team ## Qualifications * You have 2+ years of experience as a software engineering manager. * You have 4+ years of experience as a software engineer * You have experience leading a software development team that balances multiple competing priorities * You have experience collaborating with product managers and product designers to shape requirements and designs into technical specifications ready for iterative implementation by engineers * You have experience recruiting exceptional engineers * Must be primarily located in the contiguous US * Must be authorized to work in the US ## Tech Stack * Python * Javacript with React and React Native * PostgreSQL * Amazon Web Services with a general theme or structuring system into many services to enable more technologies to be used * Docker, CircleCI, Clubhouse * ML workflow orchestrations tools * MacOS fleet ## Benefits * Healthcare with dental and vision options * Unlimited vacation policy * Full remote work with paid travel for in-person meetups * Monthly remote social events and plenty of DoorDash credit * Amazing team members that tend to love memes, pets, solar, and just generally getting out and being active