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The Banzai Cloud Productinfo service retrieves product and pricing information from cloud providers and exposes it through a RESTful API, and UI. Our Kubernetes based Pipeline platform and Telescopes recommendation engine make use of this information when they advise users on cluster layout and resourcing. Here’s a quick primer of how and why we utilize the Productinfo service: Pipeline platform users have the option of launching clusters or deploying applications based only on resource- and SLA-requirements (price, IO, memory, CPU, GPU, etc.

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A few weeks back we released Telescopes, our Kubernetes cluster layout recommender application. That application has evolved quite a bit, and in this post we’ll provide insight into some its new features and recent changes. Cloud cost management series: Overspending in the cloud Managing spot instance clusters on Kubernetes with Hollowtrees Monitor AWS spot instance terminations Diversifying AWS auto-scaling groups Draining Kubernetes nodes tl;dr: We added new features to Telescopes to provide support for blacklisting or whitelisting instance types Recommendation accuracies can now be checked There is now support that allows asking cloud instance types for CPU, memory and network performance.

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When we started to work on our cluster infrastructure recommender, Telescopes, we soon realized how difficult it was to get instance type attributes and pricing information from cloud providers programatically. While EC2, Google Cloud, and Azure all provide some kind of API from which to query this information, in some cases these APIs respond with partially inconsistent data, or their responses are large chunks of JSON files that are very cumbersome to parse.

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Creating Kubernetes clusters in the cloud and deploying (or CI/CDing) applications to those clusters is not always simple. There are a few conventional options, but they are either cloud or distribution specific. While we were working on our open source Pipeline Platform, we needed a solution which covered (here follows an inclusive but not exhaustive list of requirements): provisioning of Kubernetes clusters on all major cloud providers (via REST, UI and CLI) using a unified interface application lifecycle management (on-demand deploy, CI/CD, dependency management, etc) preferably over a REST interface support for multi tenancy, and advanced security scenarios (app to app security with dynamic secrets, standards, multi-auth backends, and more) ability to build cross-cloud or hybrid Kubernetes environments This posts highlights the ease of creating Kubernetes clusters using the Pipeline API on the following providers:

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Cloud cost management series: Overspending in the cloud Managing spot instance clusters on Kubernetes with Hollowtrees Monitor AWS spot instance terminations Diversifying AWS auto-scaling groups Draining Kubernetes nodes A few months ago we posted on this blog about overspending in the cloud. We discussed how difficult it is to keep track of the vast array of instance types and pricing options offered by cloud providers, especially on AWS with spot pricing.

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While we build our open source, cloud agnostic Heroku/Cloud Foundry-like Paas - Pipeline - on top of Kubernetes, we continue to launch lots of clusters on different cloud providers. Most of these clusters are launched on spot or preemptible instances, and managed by Hollowtrees. However, there are many smaller development clusters, control planes, instances and PoCs we launch that are marginally related to, or launched with, Pipeline. Naturally, these have an associated cost that we want to keep tight control over.

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Apache Spark on Kubernetes series: Introduction to Spark on Kubernetes Scaling Spark made simple on Kubernetes The anatomy of Spark applications on Kubernetes Monitoring Apache Spark with Prometheus Apache Spark CI/CD workflow howto Spark History Server on Kubernetes Spark scheduling on Kubernetes demystified Spark Streaming Checkpointing on Kubernetes Deep dive into monitoring Spark and Zeppelin with Prometheus Spark Streaming Checkpointing on Kubernetes Deep dive into monitoring Spark and Zeppelin with Prometheus Apache Spark application resilience on Kubernetes

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Cloud cost management series: Overspending in the cloud Managing spot instance clusters on Kubernetes with Hollowtrees Monitor AWS spot instance terminations Diversifying AWS auto-scaling groups Draining Kubernetes nodes Cluster recommender Cloud instance type and price information as a service Kubernetes was designed in such a way as to be fault tolerant of worker node failures. If a node goes missing because of a hardware problem, a cloud infrastructure problem, or if Kubernetes simply ceases to receive heartbeat messages from a node for any reason, the Kubernetes control plane is clever enough to handle it.

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Cloud cost management series: Overspending in the cloud Managing spot instance clusters on Kubernetes with Hollowtrees Monitor AWS spot instance terminations Diversifying AWS auto-scaling groups Draining Kubernetes nodes Cluster recommender Cloud instance type and price information as a service You may remember the Hollowtrees project we open sourced a few weeks ago: a framework for the management of AWS spot instance clusters, batteries included: Hollowtrees, an alert-react based framework that’s part of the Pipeline PaaS, which coordinates monitoring, applies rules and dispatches action chains to plugins using standard CNCF interfaces AWS spot instance termination Prometheus exporter AWS autoscaling group Prometheus exporter AWS Spot Instance recommender Kubernetes action plugin to execute k8s operations (e.

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Cloud cost management series: Overspending in the cloud Managing spot instance clusters on Kubernetes with Hollowtrees Monitor AWS spot instance terminations Diversifying AWS auto-scaling groups Draining Kubernetes nodes Cluster recommender Cloud instance type and price information as a service Last week we open sourced the Hollowtrees project, a framework that manages AWS spot instance clusters - batteries included: Hollowtrees - an alert-react based framework that’s part of the Pipeline PaaS, which coordinates monitoring, applies rules and dispatches action chains to plugins using standard CNCF interfaces AWS spot instance termination Prometheus exporter AWS autoscaling group Prometheus exporter AWS Spot Instance recommender Kubernetes action plugin to execute k8s operations (e.

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Last time we discussed how our Pipeline PaaS deploys and provisions an AWS EFS filesystem on Kubernetes and what the performance benefits are for Spark or TensorFlow. This post is gives: An introduction to TensorFlow on Kubernetes The benefits of EFS for TensorFlow (image data storage for TensorFlow jobs) Pipeline uses the kubeflow framework to deploy: A JupyterHub to create & manage interactive Jupyter notebooks A TensorFlow Training Controller that can be configured to use CPUs or GPUs A TensorFlow Serving container Note that Pipeline also has default Spotguides for Spark and Zeppelin to help support your datascience experience

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At Banzai Cloud we provision different frameworks and tools like Spark, Zeppelin and, most recently, Tensorflow, all of which run on our Pipeline PaaS (built on Kubernetes). One of Pipeline’s early adopters runs a Tensorflow Training Controller using GPUs on AWS EC2, wired into our CI/CD pipeline, which needs significant parallelization for reading training data. We’ve introduced support for Amazon Elastic File System and made it publicly available in the forthcoming release of Pipeline.

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