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1. Pull the Logstash image (keep consistent with ES version) docker pull logstash:7.5.1 2. Start the container docker run --name logstash -d -p 5044:5044 --net esnet 8b94897b4254 The network set by -- net in the command should be consistent with ES and kibana 3. Modify Logstash configuration file // 0.0.0.0: allow any IP access http.host: "0.0 ...

1. Self built yum warehouse, network source and local source respectively Steps to create yum warehouse: 1) mount Centos system CD to a directory or copy network resources to a directory 2) find the repodata directory, which is located in the upper directory of yum warehouse address (network sharing is required if creating a network warehouse ...

Text preprocessing Common four steps: Read in text participle Build a dictionary to map each word to a unique index Convert the text from the sequence of words to the sequence of indexes to facilitate the input of models import collections import re def read_time_machine(): with open('/ho ...

write explain Command name: write Command path / usr/bin/write Execute permission: all users Function Description: send information to the user, and save with Ctrl+D grammar Write < user name > Example # Send a message to tenentity write eternity [root@izm5e2q95pbpe1hh0kkwoiz ~]# write eternity write: eternity is logged in more than o ...

LeNet model 1. LeNet model 2. PyTorch implementation 2.1 model implementation 2.2 data acquisition and training 1. LeNet model LeNet is divided into convolution layer block and full connection layer block. We will introduce the two modules respectively. The basic unit in the convolution layer ...

Main points:1. Overview of kubectl2. kubectl syntax3. kubectl Management Commands4. Similarities and differences between create and apply commands5. Example demonstration1. Overview of kubectl Kubectl is a command line interface for running commands against the Kubernetes cluster.The configuration file for Kubectl is in the $HOME/.kube directo ...

Task 04: machine translation and related technologies; attention mechanism and Seq2seq model; Transformer 1. Machine translation and related technologies Machine translation and datasets Machine translation (MT): automatic translation of a text from one language to another. Solving this problem wi ...

14.1 hidden Markov model HMM and Viterbi algorithm Hidden Markov model predicts hidden sequence by given observation sequence. It is often used to mine hidden information from the surface information of sequence, such as speech recognition and handwriting recognition. In principle, HMM can also perform ...

Image augmentation As mentioned in Section 5.6 (Deep Convolution Neural Network), large-scale datasets are the prerequisite for successful application of deep neural networks.Image augmentation technology enlarges the size of training dataset by making a series of random changes to the training image ...

Glide is now the most widely used image loading framework, and has always wanted to work on it. Every time, it is deeply involved in it... This time, I will make a thorough analysis of it and try to cover the whole process and its details. This article's Glide parsing is based on the latest version 4.1 ...