The document discusses various aspects of Docker and Kubernetes, including container runtimes, deployment strategies, and container networking. It references resources from the Cloud Native Computing Foundation (CNCF) and outlines the roles of different components such as Kubelet, CRI-O, and containerd. Additionally, it covers technical details about container orchestration and management within Kubernetes.
The document discusses containerd, a core container runtime used with various orchestration frameworks, and highlights its CNFC graduation status and integration with platforms like Docker and Kubernetes. It details features of containerd, including lazy pulling, runtime options, and plugins such as the stargz snapshotter, enhancing image management and performance. Furthermore, it references multiple resources and ongoing developments related to containerd's architecture and functionalities.
Comparing Next-Generation Container Image Building ToolsAkihiro Suda
?
The document presents a comparison of next-generation container image building tools, highlighting issues with the traditional Docker build process, such as inefficient caching and access to private assets. It focuses on various tools like BuildKit, img, Buildah, Kaniko, and others, detailing their features, advantages, and limitations. The insights include the evolution of Docker towards Moby, the introduction of multi-stage builds, and the need for improved security and efficiency in building container images.
The document is comprised of technical documentation copyrighted by Recruit Marketing Partners Co., Ltd. It includes code snippets and configuration examples for tools like AWS Lambda, Kinesis, Presto, Hive, Embulk and Treasure Data. The documentation provides guidance on building data pipelines, ETL processes, and reporting solutions using these technologies.
The document discusses various aspects of Docker and Kubernetes, including container runtimes, deployment strategies, and container networking. It references resources from the Cloud Native Computing Foundation (CNCF) and outlines the roles of different components such as Kubelet, CRI-O, and containerd. Additionally, it covers technical details about container orchestration and management within Kubernetes.
The document discusses containerd, a core container runtime used with various orchestration frameworks, and highlights its CNFC graduation status and integration with platforms like Docker and Kubernetes. It details features of containerd, including lazy pulling, runtime options, and plugins such as the stargz snapshotter, enhancing image management and performance. Furthermore, it references multiple resources and ongoing developments related to containerd's architecture and functionalities.
Comparing Next-Generation Container Image Building ToolsAkihiro Suda
?
The document presents a comparison of next-generation container image building tools, highlighting issues with the traditional Docker build process, such as inefficient caching and access to private assets. It focuses on various tools like BuildKit, img, Buildah, Kaniko, and others, detailing their features, advantages, and limitations. The insights include the evolution of Docker towards Moby, the introduction of multi-stage builds, and the need for improved security and efficiency in building container images.
The document is comprised of technical documentation copyrighted by Recruit Marketing Partners Co., Ltd. It includes code snippets and configuration examples for tools like AWS Lambda, Kinesis, Presto, Hive, Embulk and Treasure Data. The documentation provides guidance on building data pipelines, ETL processes, and reporting solutions using these technologies.
Semi-Supervised Classification with Graph Convolutional Networks @ICLR2017iみ氏Eiji Sekiya
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This document describes research on semi-supervised learning on graph-structured data using graph convolutional networks. It proposes a layer-wise propagation model for graph convolutions that is more efficient than previous methods. The model is tested on several datasets, achieving state-of-the-art results for semi-supervised node classification while training faster than alternative methods. Future work to address limitations regarding memory requirements, directed graphs, and locality assumptions is also discussed.
The document describes reinforcement learning algorithms. It defines equations for the policy, reward, and value functions in a reinforcement learning problem. It then derives the policy gradient theorem, which gives an expression for the gradient of expected returns with respect to the policy parameters that can be used to optimize the policy via gradient ascent. Subsequent equations adjust the policy gradient derivation for use in actor-critic methods.
Introducton to Convolutional Nerural Network with TensorFlowEtsuji Nakai
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The document serves as an introduction to convolutional neural networks (CNN) using TensorFlow, detailing the structure and training process for effective image classification. It explains concepts such as logistic regression, loss functions, and gradient descent, along with practical coding examples for implementing these techniques in TensorFlow. Additionally, it covers the multi-layer approach of CNNs and their advantages in extracting features from images for enhanced accuracy in categorization.
Machine Learning Basics for Web Application DevelopersEtsuji Nakai
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This document provides an overview of machine learning basics for web application developers. It discusses linear binary classifiers and logistic regression, how to measure model fitness with loss functions, and graphical understandings of linear classifiers. It then covers linear multiclass classifiers using softmax functions, image classification with neural networks, and ways to improve accuracy using convolutional neural networks. Finally, it discusses client applications that use pre-trained machine learning models through API services and examples of smile detection and cucumber classification.
Your first TensorFlow programming with JupyterEtsuji Nakai
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This document provides an introduction and overview of TensorFlow and how to use it with Jupyter notebooks on Google Cloud Platform (GCP). It explains that TensorFlow is Google's open source library for machine learning and was launched in 2015. It is used for many production machine learning projects. Jupyter is introduced as an interactive web-based platform for data analysis that can also be used as a TensorFlow runtime environment. The document then provides details on the programming paradigm and model of TensorFlow, giving an example of using it for a least squares method problem to predict temperatures. It explains the key components of defining a model, loss function, and training algorithm to optimize variables in a session.
This document provides an introduction to deep Q-networks (DQN) for beginners. It explains that DQNs can be used to learn optimal actions in video games by collecting data on screen states, player actions, rewards, and next states without knowing the game's rules. The key idea is to approximate a "Q function" that represents the total expected rewards if optimal actions are taken from each state onward. A deep neural network is used as the candidate function, and its parameters are adjusted using an error function to satisfy the Q-learning equation. To collect the necessary state-action data, the game is played with a mix of random exploration and exploiting the current best actions from the Q-network.
25. Google のインフラを匯違_慧した Google Cloud Platform
VIRTUAL NETWORK
LOAD BALANCING
CDN
DNS
INTERCONNECT
Management Compute Storage Networking Data
Machine
Learning
STACKDRIVER
IDENTITY AND
ACCESS
MANAGEMENT
CLOUD ML
SPEECH API
VISION API
TRANSLATE API
NATURAL
LANGUAGE API