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@ -1,208 +0,0 @@
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[#]: collector: (lujun9972)
|
||||
[#]: translator: (bodhix)
|
||||
[#]: reviewer: ( )
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||||
[#]: publisher: ( )
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||||
[#]: url: ( )
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[#]: subject: (How to Restart a Network in Ubuntu [Beginner’s Tip])
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[#]: via: (https://itsfoss.com/restart-network-ubuntu)
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[#]: author: (Sergiu https://itsfoss.com/author/sergiu/)
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How to Restart a Network in Ubuntu [Beginner’s Tip]
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======
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You’re [using an Ubuntu-based system and you just can’t seem to connect to your network][1]? You’d be surprised how many problems can a simple restart fix.
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In this article, I’ll go over multiple ways you can restart network in Ubuntu and other Linux distributions, so you can use whatever suits your needs. The methods are basically divided into two parts:
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![Ubuntu Restart Network][2]
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### Restart network in Ubuntu using command line
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If you are using Ubuntu server edition, you are already in the terminal. If you are using the desktop edition, you can access the terminal using Ctrl+Alt+T [keyboard shortcut in Ubuntu][3].
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Now you have several commands at your disposal to restart network in Ubuntu. Some (or perhaps most) commands mentioned here should be applicable for restarting network in Debian and other Linux distributions as well.
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#### 1\. network manager service
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This is the easiest way to restart your network using the command line. It’s equivalent to the graphical way of doing it (restarts the Network-Manager service).
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```
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sudo service network-manager restart
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```
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The network icon should disappear for a moment and then reappear.
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#### 2\. systemd
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The **service** command is just a wrapper for this method (and also for init.d scripts and Upstart commands). The **systemctl** command is much more versatile than **service**. This is what I usually prefer.
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```
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sudo systemctl restart NetworkManager.service
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```
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The network icon (again) should disappear for a moment. To check out other **systemctl** options, you can refer to its man page.
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#### 3\. nmcli
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This is yet another tool for handling networks on a Linux machine. It is a pretty powerful tool that I find very practical. Many sysadmins prefer it since it is easy to use.
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There are two steps to this method: turning the network off, and then turning it back on.
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```
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sudo nmcli networking off
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```
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The network will shut down and the icon will disappear. To turn it back on:
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```
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sudo nmcli networking on
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```
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You can check out the man page of nmcli for more options.
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#### 4\. ifup & ifdown
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This commands handle a network interface directly, changing it’s state to one in which it either can or can not transmit and receive data. It’s one of the [must know networking commands in Linux][4].
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To shut down all network interfaces, use ifdown and then use ifup to turn all network interfaces back on.
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A good practice would be to combine both of these commands:
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```
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sudo ifdown -a && sudo ifup -a
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```
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**Note:** This method will not make the network icon in your systray disappear, and yet you won’t be able to have a connection of any sort.
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**Bonus tool: nmtui (click to expand)**
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This is another method often used by system administrators. It is a text menu for managing networks right in your terminal.
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```
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nmtui
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```
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This should open up the following menu:
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![nmtui Menu][5]
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**Note** that in **nmtui** , you can select another option by using the **up** and **down arrow keys**.
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Select **Activate a connection** :
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![nmtui Menu Select "Activate a connection"][6]
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Press **Enter**. This should now open the **connections** menu.
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![nmtui Connections Menu][7]
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Here, go ahead and select the network with a **star (*)** next to it. In my case, it’s MGEO72.
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![Select your connection in the nmtui connections menu.][8]
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Press **Enter**. This should **deactivate** your connection.
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![nmtui Connections Menu with no active connection][9]
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Select the connection you want to activate:
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![Select the connection you want in the nmtui connections menu.][10]
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Press **Enter**. This should reactivate the selected connection.
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![nmtui Connections Menu][11]
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Press **Tab** twice to select **Back** :
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![Select "Back" in the nmtui connections menu.][12]
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Press **Enter**. This should bring you back to the **nmtui** main menu.
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![nmtui Main Menu][13]
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Select **Quit** :
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![nmtui Quit Main Menu][14]
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This should exit the application and bring you back to your terminal.
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That’s it! You have successfully restarted your network
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### Restart network in Ubuntu graphically
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This is, of course, the easiest way of restarting the network for Ubuntu desktop users. If this one doesn’t work, you can of course check the command line options mentioned in the previous section.
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NM-applet is the system tray applet indicator for [NetworkManager][15]. That’s what we’re going to use to restart our network.
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First of all, check out your top panel. You should find a network icon in your system tray (in my case, it is a Wi-Fi icon, since that’s what I use).
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Go ahead and click on that icon (or the sound or battery icon). This will open up the menu. Select “Turn Off” here.
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![Restart network in Ubuntu][16]Turn off your network
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The network icon should now disappear from the top panel. This means the network has been successfully turned off.
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Click again on your systray to reopen the menu. Select “Turn On”.
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![Restarting network in Ubuntu][17]Turn the network back on
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Congratulations! You have now restarted your network.
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#### Bonus Tip: Refresh available network list
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Suppose you are connected to a network already but you want to connect to another network. How do you refresh the WiFi to see what other networks are available? Let me show you that.
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Ubuntu doesn’t have a ‘refresh wifi networks’ option directly. It’s sort of hidden.
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You’ll have to open the setting menu again and this time, click on “Select Network”.
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![Refresh wifi network list in Ubuntu][18]Select Network to change your WiFi connection
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Now, you won’t see the list of available wireless networks immediately. When you open the networks list, it takes around 5 seconds to refresh and show up other available wireless networks.
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![Select another wifi network in Ubuntu][19]Wait for around 5- seconds to see other available networks
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And here, you can select the network of your choice and click connect. That’s it.
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**Wrapping Up**
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Restarting your network or connection is something that every Linux user has to go through at some point in their experience.
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We hope that we helped you with plenty of methods for handling such issues!
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What do you use to restart/handle your network? Is there something we missed? Leave us a comment below.
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--------------------------------------------------------------------------------
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via: https://itsfoss.com/restart-network-ubuntu
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作者：[Sergiu][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://itsfoss.com/author/sergiu/
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[b]: https://github.com/lujun9972
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[1]: https://itsfoss.com/fix-no-wireless-network-ubuntu/
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[2]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/ubuntu-restart-network.png?resize=800%2C450&ssl=1
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[3]: https://itsfoss.com/ubuntu-shortcuts/
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[4]: https://itsfoss.com/basic-linux-networking-commands/
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[5]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmtui_menu.png?fit=800%2C602&ssl=1
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[6]: https://i2.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmtui_menu_select_option.png?fit=800%2C579&ssl=1
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[7]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_connection_menu_on.png?fit=800%2C585&ssl=1
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[8]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_select_connection_on.png?fit=800%2C576&ssl=1
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[9]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_connection_menu_off.png?fit=800%2C572&ssl=1
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[10]: https://i0.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_select_connection_off.png?fit=800%2C566&ssl=1
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[11]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_connection_menu_on-1.png?fit=800%2C585&ssl=1
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[12]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_connection_menu_back.png?fit=800%2C585&ssl=1
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[13]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmtui_menu_select_option-1.png?fit=800%2C579&ssl=1
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[14]: https://i2.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_menu_quit.png?fit=800%2C580&ssl=1
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[15]: https://wiki.gnome.org/Projects/NetworkManager
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[16]: https://i2.wp.com/itsfoss.com/wp-content/uploads/2019/03/restart-network-ubuntu-1.jpg?resize=800%2C400&ssl=1
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[17]: https://i2.wp.com/itsfoss.com/wp-content/uploads/2019/03/restart-network-ubuntu-2.jpg?resize=800%2C400&ssl=1
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[18]: https://i2.wp.com/itsfoss.com/wp-content/uploads/2019/03/select-wifi-network-ubuntu.jpg?resize=800%2C400&ssl=1
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[19]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/select-wifi-network-ubuntu-1.jpg?resize=800%2C400&ssl=1
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[20]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/ubuntu-restart-network.png?fit=800%2C450&ssl=1
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@ -1,91 +0,0 @@
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[#]: collector: (lujun9972)
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[#]: translator: (MjSeven)
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[#]: reviewer: ( )
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||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
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||||
[#]: subject: (Command line quick tips: Cutting content out of files)
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[#]: via: (https://fedoramagazine.org/command-line-quick-tips-cutting-content-out-of-files/)
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||||
[#]: author: (Stephen Snow https://fedoramagazine.org/author/jakfrost/)
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Command line quick tips: Cutting content out of files
|
||||
======
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||||
|
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![][1]
|
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|
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The Fedora distribution is a full featured operating system with an excellent graphical desktop environment. A user can point and click their way through just about any typical task easily. All of this wonderful ease of use masks the details of a powerful command line under the hood. This article is part of a series that shows you some common command line utilities. So let’s drop into the shell, and have a look at **cut**.
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Often when you work in the command line, you are working with text files. Sometimes these files may be quite long. Reading them in their entirety, while feasible, can be time consuming and prone to errors. In this installment you’ll learn how to extract content from text files, and get the information you want from them.
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|
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It’s important to recognize that there are many ways to accomplish similar command line tasks in Fedora. The Fedora repositories include entire language systems for parsing and working with text, as an example. Also, there are multiple command line utilities available for just about any purpose conceivable in the shell. This article will only focus on using a few of those utility choices, to extract some information from a file and present it in a readable format.
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### Making the cut
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To illustrate this example use a standard sizable file on the system like _/etc/passwd_. As seen in a prior article in this series, you can execute the _cat_ command to view an entire file:
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```
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$ cat /etc/passwd
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root:x:0:0:root:/root:/bin/bash
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bin:x:1:1:bin:/bin:/sbin/nologin
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daemon:x:2:2:daemon:/sbin:/sbin/nologin
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adm:x:3:4:adm:/var/adm:/sbin/nologin
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...
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```
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This file contains information on all accounts present on the system. It has a specific format:
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```
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name:password:user-id:group-id:comment:home-directory:shell
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```
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Imagine that you want to simply have a list of all the account names on the system. If you could only cut out the _name_ value from each line. This is where the _cut_ command comes in handy! This command treats any input one line at a time, and extracts a specific part of the line.
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The _cut_ command provides options for selecting parts of a line differently, and in this example two of them are needed, _-d_ which is an option to specify a delimiter type to use, and _-f_ which is an option to specify which field of the line to cut. The _-d_ option lets you declare the _delimiter_ that separates values in a line. In this case a colon (:) is used to separate values. The _-f_ option lets you choose which field value or values to extract. So for this example the command entered would be:
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```
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$ cut -d: -f1 /etc/passwd
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root
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bin
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daemon
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adm
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...
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```
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That’s great, it worked! But you get the printout to the standard output, which in a terminal session at least means the screen. What if you needed the information for another task to be done later? It would be really nice if there was a way to put the output of the _cut_ command into a text file to save it. There is an easy builtin shell function for such a task, the redirect function ( _>_ ).
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```
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$ cut -d: -f1 /etc/passwd > names.txt
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```
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This will place the output of cut into a file called _names.txt_ and you can check the contents with _cat:_
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```
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$ cat names.txt
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root
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bin
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daemon
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adm
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...
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```
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With two commands and one shell function, it was easy to identify using _cat_ , extract using _cut_ , and redirect the extracted information from one file, saving it to another file for later use.
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* * *
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_Photo by _[ _Joel Mbugua_][2]_ on _[_Unsplash_][3]_._
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--------------------------------------------------------------------------------
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|
||||
via: https://fedoramagazine.org/command-line-quick-tips-cutting-content-out-of-files/
|
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|
||||
作者：[Stephen Snow][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://fedoramagazine.org/author/jakfrost/
|
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[b]: https://github.com/lujun9972
|
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[1]: https://fedoramagazine.org/wp-content/uploads/2019/04/commandline-cutting-816x345.jpg
|
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[2]: https://unsplash.com/photos/tA5eSY_hay8?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText
|
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[3]: https://unsplash.com/search/photos/command-line?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText
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@ -1,262 +0,0 @@
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[#]: collector: (lujun9972)
|
||||
[#]: translator: (arrowfeng)
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (How To Install And Configure NTP Server And NTP Client In Linux?)
|
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[#]: via: (https://www.2daygeek.com/install-configure-ntp-server-ntp-client-in-linux/)
|
||||
[#]: author: (Magesh Maruthamuthu https://www.2daygeek.com/author/magesh/)
|
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|
||||
How To Install And Configure NTP Server And NTP Client In Linux?
|
||||
======
|
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|
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You might heard this word many times and also you might had worked on this.
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|
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However, i will tell you clearly in this article about NTP Server setup and NTP Client setup
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We will see about **[Chrony NTP Client setup][1]** later.
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|
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### What Is NTP Server?
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|
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NTP stands for Network Time Protocol.
|
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|
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It is a networking protocol that synchronize the clock between computer systems over the network.
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|
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In other hand I can say. It will keep the same time (It keep an accurate time) to all the systems which are connected to NTP server through NTP or Chrony client.
|
||||
|
||||
NTP can usually maintain time to within tens of milliseconds over the public Internet, and can achieve better than one millisecond accuracy in local area networks under ideal conditions.
|
||||
|
||||
It uses User Datagram Protocol (UDP) on port number 123 for send and receive timestamps. It’s a client/server application.
|
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|
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It send and receive timestamps using the User Datagram Protocol (UDP) on port number 123.
|
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|
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### What Is NTP Client?
|
||||
|
||||
NTP client will synchronize its clock to the network time server.
|
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|
||||
### What Is Chrony Client?
|
||||
|
||||
Chrony is replacement of NTP client. It can synchronize the system clock faster with better time accuracy and it can be particularly useful for the systems which are not online all the time.
|
||||
|
||||
### Why We Need NTP Server?
|
||||
|
||||
To keep all the servers in your organization in-sync with an accurate time to perform time based jobs.
|
||||
|
||||
To clarify this, I will tell you a scenario. Say for example, we have two servers (Server1 and Server2). The server1 usually complete the batch jobs at 10:55 then the server2 needs to run another job at 11:00 based on the server1 job completion report.
|
||||
|
||||
If both the system is using in a different time (if one system is ahead of the others, the others are behind that particular one) then we can’t perform this. To achieve this, we should setup NTP. Hope it cleared your doubts about NTP.
|
||||
|
||||
In this article, we are going to use the following setup to test this.
|
||||
|
||||
* **`NTP Server:`** HostName: CentOS7.2daygeek.com, IP:192.168.1.8, OS:CentOS 7
|
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* **`NTP Client:`** HostName: Ubuntu18.2daygeek.com, IP:192.168.1.5, OS:Ubuntu 18.04
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|
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### NTP SERVER SIDE: How To Install NTP Server In Linux?
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|
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There is no different packages for NTP server and NTP client since it’s a client/server model. The NTP package is available in distribution official repository so, use the distribution package manger to install it.
|
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|
||||
For **`Fedora`** system, use **[DNF Command][2]** to install ntp.
|
||||
|
||||
```
|
||||
$ sudo dnf install ntp
|
||||
```
|
||||
|
||||
For **`Debian/Ubuntu`** systems, use **[APT-GET Command][3]** or **[APT Command][4]** to install ntp.
|
||||
|
||||
```
|
||||
$ sudo apt install ntp
|
||||
```
|
||||
|
||||
For **`Arch Linux`** based systems, use **[Pacman Command][5]** to install ntp.
|
||||
|
||||
```
|
||||
$ sudo pacman -S ntp
|
||||
```
|
||||
|
||||
For **`RHEL/CentOS`** systems, use **[YUM Command][6]** to install ntp.
|
||||
|
||||
```
|
||||
$ sudo yum install ntp
|
||||
```
|
||||
|
||||
For **`openSUSE Leap`** system, use **[Zypper Command][7]** to install ntp.
|
||||
|
||||
```
|
||||
$ sudo zypper install ntp
|
||||
```
|
||||
|
||||
### How To Configure The NTP Server In Linux?
|
||||
|
||||
Once you have installed the NTP package, make sure you have to uncomment the following configuration in the `/etc/ntp.conf` file on server side.
|
||||
|
||||
By default the NTP server configuration relies on `X.distribution_name.pool.ntp.org`. If you want you can use the default configuration or you can change it as per your location (country specific) by visiting <https://www.ntppool.org/zone/@> site.
|
||||
|
||||
Say for example. If you are in India then your NTP server will be `0.in.pool.ntp.org` and it will work for most of the countries.
|
||||
|
||||
```
|
||||
# vi /etc/ntp.conf
|
||||
|
||||
restrict default kod nomodify notrap nopeer noquery
|
||||
restrict -6 default kod nomodify notrap nopeer noquery
|
||||
restrict 127.0.0.1
|
||||
restrict -6 ::1
|
||||
server 0.asia.pool.ntp.org
|
||||
server 1.asia.pool.ntp.org
|
||||
server 2.asia.pool.ntp.org
|
||||
server 3.asia.pool.ntp.org
|
||||
restrict 192.168.1.0 mask 255.255.255.0 nomodify notrap
|
||||
driftfile /var/lib/ntp/drift
|
||||
keys /etc/ntp/keys
|
||||
```
|
||||
|
||||
We have allowed only `192.168.1.0/24` subnet clients to access the NTP server.
|
||||
|
||||
Since firewall is enabled by default on RHEL 7 based distributions so, allow the ntp server/service.
|
||||
|
||||
```
|
||||
# firewall-cmd --add-service=ntp --permanent
|
||||
# firewall-cmd --reload
|
||||
```
|
||||
|
||||
Bounce the service once you update the configuration.
|
||||
|
||||
For sysvinit systems. For Debian based system we need to run `ntp` instead of ntpd.
|
||||
|
||||
```
|
||||
# service ntpd restart
|
||||
|
||||
# chkconfig ntpd on
|
||||
```
|
||||
|
||||
For systemctl systems. For Debian based system we need to run `ntp` instead of ntpd.
|
||||
|
||||
```
|
||||
# systemctl restart ntpd
|
||||
|
||||
# systemctl enable ntpd
|
||||
```
|
||||
|
||||
### NTP CLIENT SIDE: How To Install NTP Client On Linux?
|
||||
|
||||
As I mentioned earlier in this article. There is no specific package for NTP server and client. So, install the same package on client also.
|
||||
|
||||
For **`Fedora`** system, use **[DNF Command][2]** to install ntp.
|
||||
|
||||
```
|
||||
$ sudo dnf install ntp
|
||||
```
|
||||
|
||||
For **`Debian/Ubuntu`** systems, use **[APT-GET Command][3]** or **[APT Command][4]** to install ntp.
|
||||
|
||||
```
|
||||
$ sudo apt install ntp
|
||||
```
|
||||
|
||||
For **`Arch Linux`** based systems, use **[Pacman Command][5]** to install ntp.
|
||||
|
||||
```
|
||||
$ sudo pacman -S ntp
|
||||
```
|
||||
|
||||
For **`RHEL/CentOS`** systems, use **[YUM Command][6]** to install ntp.
|
||||
|
||||
```
|
||||
$ sudo yum install ntp
|
||||
```
|
||||
|
||||
For **`openSUSE Leap`** system, use **[Zypper Command][7]** to install ntp.
|
||||
|
||||
```
|
||||
$ sudo zypper install ntp
|
||||
```
|
||||
|
||||
I have installed and configured the NTP server on `CentOS7.2daygeek.com` so, append the same into all the client machines.
|
||||
|
||||
```
|
||||
# vi /etc/ntp.conf
|
||||
|
||||
restrict default kod nomodify notrap nopeer noquery
|
||||
restrict -6 default kod nomodify notrap nopeer noquery
|
||||
restrict 127.0.0.1
|
||||
restrict -6 ::1
|
||||
server CentOS7.2daygeek.com prefer iburst
|
||||
driftfile /var/lib/ntp/drift
|
||||
keys /etc/ntp/keys
|
||||
```
|
||||
|
||||
Bounce the service once you update the configuration.
|
||||
|
||||
For sysvinit systems. For Debian based system we need to run `ntp` instead of ntpd.
|
||||
|
||||
```
|
||||
# service ntpd restart
|
||||
|
||||
# chkconfig ntpd on
|
||||
```
|
||||
|
||||
For systemctl systems. For Debian based system we need to run `ntp` instead of ntpd.
|
||||
|
||||
```
|
||||
# systemctl restart ntpd
|
||||
|
||||
# systemctl enable ntpd
|
||||
```
|
||||
|
||||
Wait for few minutes post restart of the NTP service to get synchronize time from the NTP server.
|
||||
|
||||
Run the following commands to verify the NTP server synchronization status on Linux.
|
||||
|
||||
```
|
||||
# ntpq –p
|
||||
Or
|
||||
# ntpq -pn
|
||||
|
||||
remote refid st t when poll reach delay offset jitter
|
||||
==============================================================================
|
||||
*CentOS7.2daygee 133.243.238.163 2 u 14 64 37 0.686 0.151 16.432
|
||||
```
|
||||
|
||||
Run the following command to get the current status of ntpd.
|
||||
|
||||
```
|
||||
# ntpstat
|
||||
synchronised to NTP server (192.168.1.8) at stratum 3
|
||||
time correct to within 508 ms
|
||||
polling server every 64 s
|
||||
```
|
||||
|
||||
Finally run the `date` command.
|
||||
|
||||
```
|
||||
# date
|
||||
Tue Mar 26 23:17:05 CDT 2019
|
||||
```
|
||||
|
||||
If you are observing a significant offset in the NTP output. Run the following command to sync clock manually from the NTP server. Make sure that your NTP client should be inactive state when you perform the command.
|
||||
|
||||
```
|
||||
# ntpdate –uv CentOS7.2daygeek.com
|
||||
```
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://www.2daygeek.com/install-configure-ntp-server-ntp-client-in-linux/
|
||||
|
||||
作者：[Magesh Maruthamuthu][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://www.2daygeek.com/author/magesh/
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://www.2daygeek.com/configure-ntp-client-using-chrony-in-linux/
|
||||
[2]: https://www.2daygeek.com/dnf-command-examples-manage-packages-fedora-system/
|
||||
[3]: https://www.2daygeek.com/apt-get-apt-cache-command-examples-manage-packages-debian-ubuntu-systems/
|
||||
[4]: https://www.2daygeek.com/apt-command-examples-manage-packages-debian-ubuntu-systems/
|
||||
[5]: https://www.2daygeek.com/pacman-command-examples-manage-packages-arch-linux-system/
|
||||
[6]: https://www.2daygeek.com/yum-command-examples-manage-packages-rhel-centos-systems/
|
||||
[7]: https://www.2daygeek.com/zypper-command-examples-manage-packages-opensuse-system/
|
||||
@ -1,78 +0,0 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: (warmfrog)
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (This is how System76 does open hardware)
|
||||
[#]: via: (https://opensource.com/article/19/4/system76-hardware)
|
||||
[#]: author: (Don Watkins https://opensource.com/users/don-watkins)
|
||||
|
||||
This is how System76 does open hardware
|
||||
======
|
||||
What sets the new Thelio line of desktops apart from the rest.
|
||||
![metrics and data shown on a computer screen][1]
|
||||
|
||||
Most people know very little about the hardware in their computers. As a long-time Linux user, I've had my share of frustration while getting my wireless cards, video cards, displays, and other hardware working with my chosen distribution. Proprietary hardware often makes it difficult to determine why an Ethernet controller, wireless controller, or mouse performs differently than we expect. As Linux distributions have matured, this has become less of a problem, but we still see some quirks with touchpads and other peripherals, especially when we don't know much—if anything—about our underlying hardware.
|
||||
|
||||
Companies like [System76][2] aim to take these types of problems out of the Linux user experience. System76 manufactures a line of Linux laptops, desktops, and servers, and even offers its own Linux distro, [Pop! OS][3], as an option for buyers, Recently I had the privilege of visiting System76's plant in Denver for [the unveiling][4] of [Thelio][5], its new desktop product line.
|
||||
|
||||
### About Thelio
|
||||
|
||||
System76 says Thelio's open hardware daughterboard, named Thelio Io after the fifth moon of Jupiter, is one thing that makes the computer unique in the marketplace. Thelio Io is certified [OSHWA #us000145][6] and has four SATA ports for storage and an embedded controller for fan and power button control. Thelio Io SAS is certified [OSHWA #us000146][7] and has four U.2 ports for storage and no embedded controller. During a demonstration, System76 showed how these components adjust fans throughout the chassis to optimize the unit's performance.
|
||||
|
||||
The controller also runs the power button and the LED ring around the button, which glows at 100% brightness when it is pressed. This provides both tactile and visual confirmation that the unit is being powered on. While the computer is in use, the button is set to 35% brightness, and when it's in suspend mode, it pulses between 2.35% and 25%. When the computer is off, the LED remains dimly lit so that it's easy to find the power control in a dark room.
|
||||
|
||||
Thelio's embedded controller is a low-power [ATmega32U4][8] microchip, and the controller's setup can be prototyped with an Arduino Micro. The number of Thelio Io boards changes depending on which Thelio model you purchase.
|
||||
|
||||
Thelio is also perhaps the best-designed computer case and system I have ever seen. You'll probably agree if you have ever skinned your knuckles trying to operate inside a typical PC case. I have done this a number of times, and I have the scars to prove it.
|
||||
|
||||
### Why open hardware?
|
||||
|
||||
The boards were designed in [KiCAD][9], and you can access all of Thelio's design files under GPL on [GitHub][10]. So, why would a company that competes with other PC manufacturers design a unique interface then license it openly? It's because the company recognizes the value of open design and the ability to share and adjust an I/O board to your needs, even if you're a competitor in the marketplace.
|
||||
|
||||
![Don Watkins speaks with System76 CEO Carl Richell at the Thelio launch event.][11]
|
||||
|
||||
Don Watkins speaks with System76 CEO Carl Richell at the [Thelio launch event][12].
|
||||
|
||||
I asked [Carl Richell][13], System76's founder and CEO, whether the company is concerned that openly licensing its hardware designs means someone could take its unique design and use it to drive System76 out of business. He said:
|
||||
|
||||
> Open hardware benefits all of us. It's how we further advance technology and make it more available to everyone. We welcome anyone who wishes to improve on Thelio's design to do so. Opening the hardware not only helps advance improvements of our computers more quickly, but it also empowers our customers to truly own 100% of their device. Our goal is to remove as much proprietary functioning as we can, while still producing a competitive Linux computer for customers.
|
||||
>
|
||||
> We've been working with the Linux community for over 13 years to create a flawless and digestible experience on all of our laptops, desktops, and servers. Our long tenure serving the Linux community, providing our customers with a high level of service, and our personability are what makes System76 unique.
|
||||
|
||||
I also asked Carl why open hardware makes sense for System76 and the PC business in general. He replied:
|
||||
|
||||
> System76 was founded on the idea that technology should be open and accessible to everyone. We're not yet at the point where we can create a computer that is 100% open source, but with open hardware, we're one large, essential step closer to reaching that point.
|
||||
>
|
||||
> We live in an era where technology has become a utility. Computers are tools for people at every level of education and across many industries. With everyone's needs specific, each person has their own ideas on how they might improve the computer and its software as their primary tool. Having our computers open allows these ideas to come to fruition, which in turn makes the technology a more powerful tool. In an open environment, we constantly get to iterate a better PC. And that's kind of cool.
|
||||
|
||||
We wrapped up our conversation by talking about System76's roadmap, which includes open hardware mini PCs and, eventually, laptops. Existing mini PCs and laptops sold under the System76 brand are manufactured by other vendors and are not based on open hardware (although their Linux software is, of course, open source).
|
||||
|
||||
Designing and supporting open hardware is a game-changer in the PC business, and it is what sets System76's new Thelio line of desktop computers apart.
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://opensource.com/article/19/4/system76-hardware
|
||||
|
||||
作者：[Don Watkins ][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://opensource.com/users/don-watkins
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/metrics_data_dashboard_system_computer_analytics.png?itok=oxAeIEI- (metrics and data shown on a computer screen)
|
||||
[2]: https://system76.com/
|
||||
[3]: https://opensource.com/article/18/1/behind-scenes-popos-linux
|
||||
[4]: /article/18/11/system76-thelio-desktop-computer
|
||||
[5]: https://system76.com/desktops
|
||||
[6]: https://certification.oshwa.org/us000145.html
|
||||
[7]: https://certification.oshwa.org/us000146.html
|
||||
[8]: https://www.microchip.com/wwwproducts/ATmega32u4
|
||||
[9]: http://kicad-pcb.org/
|
||||
[10]: https://github.com/system76/thelio-io
|
||||
[11]: https://opensource.com/sites/default/files/uploads/don_system76_ceo.jpg (Don Watkins speaks with System76 CEO Carl Richell at the Thelio launch event.)
|
||||
[12]: https://trevgstudios.smugmug.com/System76/121418-Thelio-Press-Event/i-FKWFxFv
|
||||
[13]: https://www.linkedin.com/in/carl-richell-9435781
|
||||
@ -0,0 +1,212 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: (bodhix)
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (How to Restart a Network in Ubuntu [Beginner’s Tip])
|
||||
[#]: via: (https://itsfoss.com/restart-network-ubuntu)
|
||||
[#]: author: (Sergiu https://itsfoss.com/author/sergiu/)
|
||||
|
||||
如何在 Ubuntu 中重启网络【新手提示】
|
||||
======
|
||||
|
||||
你[是否正在使用基于 Ubuntu 的系统，然后发现无法连接网络][1]？你一定会很惊讶，很多很多的问题都可以简单地通过重启服务解决。
|
||||
|
||||
在这篇文章中，我会介绍在 Ubuntu 或者其他 Linux 发行版中重启网络的几种方法，你可以根据自身需要选择对应的方法。这些方法基本分为两类：
|
||||
|
||||
![Ubuntu Restart Network][2]
|
||||
|
||||
### 通过命令行方式重启网络
|
||||
|
||||
如果你使用的 Ubuntu 服务器版，那么你已经在使用命令行终端了。如果你使用的是桌面版，那么你可以通过快捷键 Ctrl+Alt+T [Ubuntu 键盘快捷键][3] 打开命令行终端。
|
||||
|
||||
在 Ubuntu 中，有多个命令可以重启网络。这些命令，一部分或者说大部分，也适用于在 Debian 或者其他的 Linux 发行版中重启网络。
|
||||
|
||||
#### 1\. network manager service
|
||||
|
||||
这是通过命令行方式重启网络最简单的方法。它相当于是通过图形化界面重启网络（重启 Network-Manager 服务）。
|
||||
|
||||
```
|
||||
sudo service network-manager restart
|
||||
```
|
||||
|
||||
此时，网络图标会消失一会儿然后重新显示。
|
||||
|
||||
#### 2\. systemd
|
||||
|
||||
**service** 命令仅仅是该命令的一个封装（同样的还有 init.d 系列脚本和 Upstart 相关命令）。 **systemctl** 命令的功能远多于 **service** 命令。通常我更喜欢使用这个命令。
|
||||
|
||||
```
|
||||
sudo systemctl restart NetworkManager.service
|
||||
```
|
||||
|
||||
这时，网络图标又会消失一会儿。 如果你想了解 **systemctl** 的其他选项, 可以参考 man 帮助文档。
|
||||
|
||||
#### 3\. nmcli
|
||||
|
||||
这是 Linux 上可以管理网络的另一个工具。这是一个功能强大而且实用的工具。很多系统管理员都喜欢使用该工具，因为它非常容易使用。
|
||||
|
||||
这种方法有两个操作步骤：关闭网络，再开启网络。
|
||||
|
||||
```
|
||||
sudo nmcli networking off
|
||||
```
|
||||
|
||||
这样就会关闭网络，网络图标会消失。接下来，再开启网络：
|
||||
|
||||
```
|
||||
sudo nmcli networking on
|
||||
```
|
||||
|
||||
你可以通过 man 帮助文档了解 nmcli 的更多用法。
|
||||
|
||||
#### 4\. ifup & ifdown
|
||||
|
||||
这两个命令直接操作网口，切换网口是否可以收发包的状态。这是 [Linux 中最应该了解的网络命令][4] 之一。
|
||||
|
||||
使用 ifdown 关闭所有网口，再使用 ifup 重新启用网口。
|
||||
|
||||
通常推荐的做法是将这两个命令一起使用。
|
||||
|
||||
```
|
||||
sudo ifdown -a && sudo ifup -a
|
||||
```
|
||||
|
||||
**注意：** 这种方法不会让网络图标从系统托盘中消失，另外，你也无法进行网络连接。
|
||||
|
||||
**其他工具： nmtui （点击展开）**
|
||||
|
||||
这是系统管理员们常用的另外一种方法。它是在命令行终端中管理网络的文本菜单工具。
|
||||
|
||||
```
|
||||
nmtui
|
||||
```
|
||||
|
||||
打开如下菜单：
|
||||
|
||||
![nmtui Menu][5]
|
||||
|
||||
**注意** 在 **nmtui** 中，可以通过 **up** 和 **down 方向键** 选择选项。
|
||||
|
||||
选择 **Activate a connection** ：
|
||||
|
||||
![nmtui Menu Select "Activate a connection"][6]
|
||||
|
||||
按下 **Enter** 键，打开 **connections** 菜单。
|
||||
|
||||
![nmtui Connections Menu][7]
|
||||
|
||||
接下来，选择前面带 **星号 （*）** 的网络。在这个例子中，就是 MGEO72。
|
||||
|
||||
![Select your connection in the nmtui connections menu.][8]
|
||||
|
||||
按下 **Enter** 键。 **关闭** 你的网络连接。
|
||||
|
||||
![nmtui Connections Menu with no active connection][9]
|
||||
|
||||
选择你要连接的网络：
|
||||
|
||||
![Select the connection you want in the nmtui connections menu.][10]
|
||||
|
||||
按下 **Enter** 键。这样就重启了所选择的网络连接。
|
||||
|
||||
![nmtui Connections Menu][11]
|
||||
|
||||
双击 **Tab** 键，选择 **Back** ：
|
||||
|
||||
![Select "Back" in the nmtui connections menu.][12]
|
||||
|
||||
按下 **Enter** 键，回到 **nmtui** 的主菜单。
|
||||
|
||||
![nmtui Main Menu][13]
|
||||
|
||||
选择 **Quit** ：
|
||||
|
||||
![nmtui Quit Main Menu][14]
|
||||
|
||||
退出该界面，返回到命令行终端。
|
||||
|
||||
就这样，你已经成功重启网络了。
|
||||
|
||||
### 通过图形化界面重启网络
|
||||
|
||||
显然，这是 Ubuntu 桌面版用户重启网络最简单的方法。如果这个方法不生效，你可以尝试使用前文提到的命令行方式重启网络。
|
||||
|
||||
NM 程序是 [NetworkManager][15] 的系统托盘程序标志。我们将使用它来重启网络。
|
||||
|
||||
首先，查看顶部状态栏。 你会在系统托盘找到一个网络图标 （因为我使用 Wi-Fi，所以这里是一个 Wi-Fi 图标）。
|
||||
|
||||
接下来，点击该图标（也可以点击音量图标或电池图标）。打开菜单。选择 “Turn Off” 关闭网络。
|
||||
|
||||
![Restart network in Ubuntu][16]
|
||||
|
||||
网络图标会在状态栏中消失，这表示你已经成功关闭网络了。
|
||||
|
||||
再次点击系统托盘重新打开菜单，选择 “Turn On”，重新开启网络。
|
||||
|
||||
![Restarting network in Ubuntu][17]
|
||||
|
||||
恭喜！你现在已经重启你的网络了。
|
||||
|
||||
#### 其他提示：刷新可用网络列表
|
||||
|
||||
如果你已经连接上一个网络，但是你想连接到另外一个网络，你如何刷新 WiFi 列表，查找其他可用的网络呢？我来向你展示一下。
|
||||
|
||||
Ubuntu 没有可以直接 “刷新 WiFi 网络” 的选项，它有点隐蔽。
|
||||
|
||||
你需要再次打开配置菜单，然后点击 “Select Network” 。
|
||||
|
||||
![Refresh wifi network list in Ubuntu][18]
|
||||
|
||||
选择对应的网络修改你的 WiFi 连接。
|
||||
|
||||
你无法马上看到可用的无线网络列表。打开网络列表之后，大概需要 5 秒才会显示其他可用的无线网络。
|
||||
|
||||
![Select another wifi network in Ubuntu][19]
|
||||
|
||||
等待大概 5 秒钟，看到其他可用的网络。
|
||||
|
||||
现在，你就可以选择你想要连接的网络，点击连接。这样就完成了。
|
||||
|
||||
**总结**
|
||||
|
||||
重启网络连接是每个 Linux 用户在使用过程中必须经历的事情。
|
||||
|
||||
我们希望这些方法可以帮助你处理这样的问题！
|
||||
|
||||
你是如何重启或管理你的网络的？我们是否还有遗漏的？请在下方留言。
|
||||
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://itsfoss.com/restart-network-ubuntu
|
||||
|
||||
作者：[Sergiu][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[bodhix](https://github.com/bodhix)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://itsfoss.com/author/sergiu/
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://itsfoss.com/fix-no-wireless-network-ubuntu/
|
||||
[2]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/ubuntu-restart-network.png?resize=800%2C450&ssl=1
|
||||
[3]: https://itsfoss.com/ubuntu-shortcuts/
|
||||
[4]: https://itsfoss.com/basic-linux-networking-commands/
|
||||
[5]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmtui_menu.png?fit=800%2C602&ssl=1
|
||||
[6]: https://i2.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmtui_menu_select_option.png?fit=800%2C579&ssl=1
|
||||
[7]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_connection_menu_on.png?fit=800%2C585&ssl=1
|
||||
[8]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_select_connection_on.png?fit=800%2C576&ssl=1
|
||||
[9]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_connection_menu_off.png?fit=800%2C572&ssl=1
|
||||
[10]: https://i0.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_select_connection_off.png?fit=800%2C566&ssl=1
|
||||
[11]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_connection_menu_on-1.png?fit=800%2C585&ssl=1
|
||||
[12]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_connection_menu_back.png?fit=800%2C585&ssl=1
|
||||
[13]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmtui_menu_select_option-1.png?fit=800%2C579&ssl=1
|
||||
[14]: https://i2.wp.com/itsfoss.com/wp-content/uploads/2019/03/nmui_menu_quit.png?fit=800%2C580&ssl=1
|
||||
[15]: https://wiki.gnome.org/Projects/NetworkManager
|
||||
[16]: https://i2.wp.com/itsfoss.com/wp-content/uploads/2019/03/restart-network-ubuntu-1.jpg?resize=800%2C400&ssl=1
|
||||
[17]: https://i2.wp.com/itsfoss.com/wp-content/uploads/2019/03/restart-network-ubuntu-2.jpg?resize=800%2C400&ssl=1
|
||||
[18]: https://i2.wp.com/itsfoss.com/wp-content/uploads/2019/03/select-wifi-network-ubuntu.jpg?resize=800%2C400&ssl=1
|
||||
[19]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/select-wifi-network-ubuntu-1.jpg?resize=800%2C400&ssl=1
|
||||
[20]: https://i1.wp.com/itsfoss.com/wp-content/uploads/2019/03/ubuntu-restart-network.png?fit=800%2C450&ssl=1
|
||||
@ -0,0 +1,89 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: (MjSeven)
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (Command line quick tips: Cutting content out of files)
|
||||
[#]: via: (https://fedoramagazine.org/command-line-quick-tips-cutting-content-out-of-files/)
|
||||
[#]: author: (Stephen Snow https://fedoramagazine.org/author/jakfrost/)
|
||||
|
||||
命令行技巧：分割文件内容
|
||||
======
|
||||
|
||||
![][1]
|
||||
|
||||
Fedora 发行版是一个功能齐全的操作系统，有出色的图形化桌面环境。用户可以很容易地通过单击动作来完成任何典型任务。所有这些美妙的易用性掩盖了其底层强大的命令行细节。本文是向你展示一些常见命令行实用程序的系列文章的一部分。让我们进入 shell 来看看 **cut**。
|
||||
|
||||
通常，当你在命令行中工作时，你处理的是文本文件。有时这些文件可能很长，虽然可以完整地阅读它们，但是可能会耗费大量时间，并且容易出错。在本文中，你将学习如何从文本文件中提取内容，并从中获取你所需的信息。
|
||||
|
||||
重要的是要意识到，在 Fedora 中有许多方法可以完成类似的命令行任务。例如，Fedora 仓库含有用于解析和处理文本的完整语言系统。此外，还有多个命令行实用程序可用于 shell 中任何可能的用途。本文只关注使用其中几个实用程序选项，从文件中提取一些信息并以可读的格式呈现。
|
||||
|
||||
### cut 使用
|
||||
|
||||
为了演示这个例子，在系统上使用一个标准的大文件，如 _/etc/passwd_。正如本系列的前一篇文章所示，你可以执行 _cat_ 命令来查看整个文件：
|
||||
```
|
||||
$ cat /etc/passwd
|
||||
root:x:0:0:root:/root:/bin/bash
|
||||
bin:x:1:1:bin:/bin:/sbin/nologin
|
||||
daemon:x:2:2:daemon:/sbin:/sbin/nologin
|
||||
adm:x:3:4:adm:/var/adm:/sbin/nologin
|
||||
...
|
||||
```
|
||||
|
||||
此文件包含系统上所有所有账户的信息。它有一个特定的格式：
|
||||
|
||||
```
|
||||
name:password:user-id:group-id:comment:home-directory:shell
|
||||
```
|
||||
假设你只想要系统上所有账户名的列表，如果你只能从每一行中删除 _name_ 值。这就是 _cut_ 命令派上用场的地方！它一次处理一行输入，并提取该行的特定部分。
|
||||
|
||||
_cut_ 命令提供了以不同方式选择一行的部分的选项，在本示例中需要两个，_d_ 是指定要使用的分隔符类型，_f_ 是指定要删除行的哪个字段。_-d_ 选项允许你声明用于分隔行中值的 _delimiter_。在本例中，冒号(:)用于分隔值。_-f_ 选项允许你选择要提取哪个字段或哪些字段值。因此，在本例中，输入的命令是：
|
||||
|
||||
```
|
||||
$ cut -d: -f1 /etc/passwd
|
||||
root
|
||||
bin
|
||||
daemon
|
||||
adm
|
||||
...
|
||||
```
|
||||
|
||||
太棒了，成功了！但是你将输出打印到标准输出，在终端会话中意味着它需要占据屏幕。如果你需要稍后完成另一项任务所需的信息，这该怎么办？如果有办法将 _cut_ 命令的输出保存到文本文件中，那就太好了。对于这样的任务，shell 有一个简单的内置功能，重定向功能(_>_)。
|
||||
|
||||
```
|
||||
$ cut -d: -f1 /etc/passwd > names.txt
|
||||
```
|
||||
|
||||
这会将 cut 的输出放到一个名为 _names.txt_ 的文件中，你可以使用 _cat_ 来查看它的内容:
|
||||
|
||||
```
|
||||
$ cat names.txt
|
||||
root
|
||||
bin
|
||||
daemon
|
||||
adm
|
||||
...
|
||||
```
|
||||
|
||||
使用两个命令和一个 shell 功能，可以很容易地使用 _cat_ 从一个文件进行识别、提取和重定向一些信息，并将其保存到另一个文件以供以后使用。
|
||||
|
||||
* * *
|
||||
|
||||
_[ _Joel Mbugua_][2]_ 在 _[_Unsplash_][3] 上的照片._
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://fedoramagazine.org/command-line-quick-tips-cutting-content-out-of-files/
|
||||
|
||||
作者：[Stephen Snow][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[MjSeven](https://github.com/MjSeven)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://fedoramagazine.org/author/jakfrost/
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://fedoramagazine.org/wp-content/uploads/2019/04/commandline-cutting-816x345.jpg
|
||||
[2]: https://unsplash.com/photos/tA5eSY_hay8?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText
|
||||
[3]: https://unsplash.com/search/photos/command-line?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText
|
||||
@ -0,0 +1,262 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: (arrowfeng)
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (How To Install And Configure NTP Server And NTP Client In Linux?)
|
||||
[#]: via: (https://www.2daygeek.com/install-configure-ntp-server-ntp-client-in-linux/)
|
||||
[#]: author: (Magesh Maruthamuthu https://www.2daygeek.com/author/magesh/)
|
||||
|
||||
如何在Linux上安装、配置NTP服务和NTP客户端？
|
||||
======
|
||||
|
||||
你也许听说过这个词很多次或者你可能已经在使用它了。
|
||||
但是，在这篇文章中我将会清晰的告诉你NTP服务和NTP客户端的安装。
|
||||
|
||||
之后我们将会了解 **[Chrony NTP 客户端的安装][1]**。
|
||||
|
||||
|
||||
### 什么是NTP服务?
|
||||
|
||||
NTP 表示为网络时间协议。
|
||||
|
||||
它是通过网络在电脑系统之间进行时钟同步的网络协议。
|
||||
|
||||
另一方面，我可以说，它可以让那些通过NTP或者Chrony客户端连接到NTP服务的系统保持时间上的一致（它能保持一个精确的时间）。
|
||||
|
||||
|
||||
NTP在公共互联网上通常能够保持时间延迟在几十毫秒以内的精度，并在理想条件下，它能在局域网下达到优于一毫秒的延迟精度。
|
||||
|
||||
它使用用户数据报协议（UDP）在端口123上发送和接受时间戳。它是C/S架构的应用程序。
|
||||
|
||||
|
||||
|
||||
### 什么是NTP客户端？
|
||||
|
||||
NTP客户端将其时钟与网络时间服务器同步。
|
||||
|
||||
### 什么是Chrony客户端？
|
||||
Chrony是NTP客户端的替代品。它能以更精确的时间更快的同步系统时钟，并且它对于那些不总是在线的系统很有用。
|
||||
|
||||
### 为什么我们需要NTP服务？
|
||||
|
||||
为了使你组织中的所有服务器与基于时间的作业保持精确的时间同步。
|
||||
|
||||
为了说明这点，我将告诉你一个场景。比如说，我们有两个服务器（服务器1和服务器2）。服务器1通常在10:55完成离线作业，然后服务器2在11:00需要基于服务器1完成的作业报告去运行其他作业。
|
||||
|
||||
如果两个服务器正在使用不同的时间（如果服务器2时间比服务器1提前，服务器1的时间就落后于服务器2），然后我们就不能去执行这个作业。为了达到时间一致，我们应该安装NTP。
|
||||
希望上述能清除你对于NTP的疑惑。
|
||||
|
||||
|
||||
在这篇文章中，我们将使用下列设置去测试。
|
||||
|
||||
* **`NTP Server:`** HostName: CentOS7.2daygeek.com, IP:192.168.1.8, OS:CentOS 7
|
||||
* **`NTP Client:`** HostName: Ubuntu18.2daygeek.com, IP:192.168.1.5, OS:Ubuntu 18.04
|
||||
|
||||
|
||||
|
||||
### NTP服务端： 如何在Linux上安装NTP？
|
||||
|
||||
因为它是c/s架构，所以NTP服务端和客户端的安装包没有什么不同。在发行版的官方仓库中都有NTP安装包，因此可以使用发行版的包管理器安装它。
|
||||
|
||||
对于 **`Fedora`** 系统, 使用 **[DNF 命令][2]** 去安装ntp.
|
||||
|
||||
```
|
||||
$ sudo dnf install ntp
|
||||
```
|
||||
|
||||
对于 **`Debian/Ubuntu`** 系统, 使用 **[APT-GET 命令][3]** 或者 **[APT 命令][4]** 去安装 ntp.
|
||||
|
||||
```
|
||||
$
|
||||
```
|
||||
|
||||
对基于 **`Arch Linux`** 的系统, 使用 **[Pacman 命令][5]** 去安装 ntp.
|
||||
|
||||
```
|
||||
$ sudo pacman -S ntp
|
||||
```
|
||||
|
||||
对 **`RHEL/CentOS`** 系统, 使用 **[YUM 命令][6]** 去安装 ntp.
|
||||
|
||||
```
|
||||
$ sudo yum install ntp
|
||||
```
|
||||
|
||||
对于 **`openSUSE Leap`** 系统, 使用 **[Zypper 命令][7]** 去安装 ntp.
|
||||
|
||||
```
|
||||
$ sudo zypper install ntp
|
||||
```
|
||||
|
||||
### 如何在Linux上配置NTP服务？
|
||||
|
||||
安装NTP软件包后，请确保在服务器端的`/etc/ntp.conf`文件中，必须取消以下配置的注释。
|
||||
|
||||
默认情况下，NTP服务器配置依赖于`X.distribution_name.pool.ntp.org`。 如果有必要，可以使用默认配置，也可以访问<https://www.ntppool.org/zone/@>站点，根据你所在的位置（特定国家/地区）进行更改。
|
||||
|
||||
比如说如果你在印度，然后你的NTP服务器将是`0.in.pool.ntp.org`，并且这个地址适用于大多数国家。
|
||||
|
||||
```
|
||||
# vi /etc/ntp.conf
|
||||
|
||||
restrict default kod nomodify notrap nopeer noquery
|
||||
restrict -6 default kod nomodify notrap nopeer noquery
|
||||
restrict 127.0.0.1
|
||||
restrict -6 ::1
|
||||
server 0.asia.pool.ntp.org
|
||||
server 1.asia.pool.ntp.org
|
||||
server 2.asia.pool.ntp.org
|
||||
server 3.asia.pool.ntp.org
|
||||
restrict 192.168.1.0 mask 255.255.255.0 nomodify notrap
|
||||
driftfile /var/lib/ntp/drift
|
||||
keys /etc/ntp/keys
|
||||
```
|
||||
|
||||
我们仅允许`192.168.1.0/24`子网的客户端访问NTP服务器。
|
||||
|
||||
由于默认情况下基于RHEL7的发行版的防火墙是打开的，因此允许ntp服务通过。
|
||||
|
||||
```
|
||||
# firewall-cmd --add-service=ntp --permanent
|
||||
# firewall-cmd --reload
|
||||
```
|
||||
|
||||
更新配置后重启服务。
|
||||
|
||||
对于基于Debian的sysvinit系统，我们需要去运行`ntp`而不是`ntpd`。
|
||||
|
||||
```
|
||||
# service ntpd restart
|
||||
|
||||
# chkconfig ntpd on
|
||||
```
|
||||
对于基于Debian的systemctl系统，我们需要去运行`ntp`和`ntpd`。
|
||||
|
||||
```
|
||||
# systemctl restart ntpd
|
||||
|
||||
# systemctl enable ntpd
|
||||
```
|
||||
|
||||
### NTP客户端：如何在Linux上安装NTP客户端？
|
||||
|
||||
正如我在这篇文章中前面所说的。NTP服务端和客户端的安装包没有什么不同。因此在客户端上也安装同样的软件包。
|
||||
|
||||
对于 **`Fedora`** 系统, 使用 **[DNF 命令][2]** 去安装ntp.
|
||||
|
||||
```
|
||||
$ sudo dnf install ntp
|
||||
```
|
||||
|
||||
对于 **`Debian/Ubuntu`** 系统, 使用 **[APT-GET 命令][3]** 或者 **[APT 命令][4]** 去安装 ntp.
|
||||
|
||||
```
|
||||
$
|
||||
```
|
||||
|
||||
对基于 **`Arch Linux`** 的系统, 使用 **[Pacman 命令][5]** 去安装 ntp.
|
||||
|
||||
```
|
||||
$ sudo pacman -S ntp
|
||||
```
|
||||
|
||||
对 **`RHEL/CentOS`** 系统, 使用 **[YUM 命令][6]** 去安装 ntp.
|
||||
|
||||
```
|
||||
$ sudo yum install ntp
|
||||
```
|
||||
|
||||
对于 **`openSUSE Leap`** 系统, 使用 **[Zypper 命令][7]** 去安装 ntp.
|
||||
|
||||
```
|
||||
$ sudo zypper install ntp
|
||||
```
|
||||
|
||||
我已经在`CentOS7.2daygeek.com`这台主机上安装和配置了NTP服务器，因此将其附加到所有的客户端机器上。
|
||||
|
||||
```
|
||||
# vi /etc/ntp.conf
|
||||
|
||||
restrict default kod nomodify notrap nopeer noquery
|
||||
restrict -6 default kod nomodify notrap nopeer noquery
|
||||
restrict 127.0.0.1
|
||||
restrict -6 ::1
|
||||
server CentOS7.2daygeek.com prefer iburst
|
||||
driftfile /var/lib/ntp/drift
|
||||
keys /etc/ntp/keys
|
||||
```
|
||||
|
||||
更新配置后重启服务。
|
||||
|
||||
对于基于Debian的sysvinit系统，我们需要去运行`ntp`而不是`ntpd`。
|
||||
|
||||
```
|
||||
# service ntpd restart
|
||||
|
||||
# chkconfig ntpd on
|
||||
```
|
||||
对于基于Debian的systemctl系统，我们需要去运行`ntp`和`ntpd`。
|
||||
|
||||
```
|
||||
# systemctl restart ntpd
|
||||
|
||||
# systemctl enable ntpd
|
||||
```
|
||||
|
||||
重新启动NTP服务后等待几分钟以便从NTP服务器获取同步的时间。
|
||||
|
||||
在Linux上运行下列命令去验证NTP服务的同步状态。
|
||||
|
||||
```
|
||||
# ntpq –p
|
||||
或
|
||||
# ntpq -pn
|
||||
|
||||
remote refid st t when poll reach delay offset jitter
|
||||
==============================================================================
|
||||
*CentOS7.2daygee 133.243.238.163 2 u 14 64 37 0.686 0.151 16.432
|
||||
```
|
||||
|
||||
运行下列命令去得到ntpd的当前状态。
|
||||
|
||||
```
|
||||
# ntpstat
|
||||
synchronised to NTP server (192.168.1.8) at stratum 3
|
||||
time correct to within 508 ms
|
||||
polling server every 64 s
|
||||
```
|
||||
|
||||
最后运行`date`命令。
|
||||
|
||||
```
|
||||
# date
|
||||
Tue Mar 26 23:17:05 CDT 2019
|
||||
```
|
||||
|
||||
如果你观察到NTP中输出的偏移很大。运行下列命令从NTP服务器手动同步时钟。当你执行下列命令的时候，确保你的NTP客户端应该为未激活状态。
|
||||
|
||||
```
|
||||
# ntpdate –uv CentOS7.2daygeek.com
|
||||
```
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://www.2daygeek.com/install-configure-ntp-server-ntp-client-in-linux/
|
||||
|
||||
作者：[Magesh Maruthamuthu][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[arrowfeng](https://github.com/arrowfeng)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://www.2daygeek.com/author/magesh/
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://www.2daygeek.com/configure-ntp-client-using-chrony-in-linux/
|
||||
[2]: https://www.2daygeek.com/dnf-command-examples-manage-packages-fedora-system/
|
||||
[3]: https://www.2daygeek.com/apt-get-apt-cache-command-examples-manage-packages-debian-ubuntu-systems/
|
||||
[4]: https://www.2daygeek.com/apt-command-examples-manage-packages-debian-ubuntu-systems/
|
||||
[5]: https://www.2daygeek.com/pacman-command-examples-manage-packages-arch-linux-system/
|
||||
[6]: https://www.2daygeek.com/yum-command-examples-manage-packages-rhel-centos-systems/
|
||||
[7]: https://www.2daygeek.com/zypper-command-examples-manage-packages-opensuse-system/
|
||||
@ -1,5 +1,5 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: ( )
|
||||
[#]: translator: (warmfrog)
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
@ -7,79 +7,78 @@
|
||||
[#]: via: (https://opensource.com/article/19/4/detecting-malaria-deep-learning)
|
||||
[#]: author: (Dipanjan Sarkar https://opensource.com/users/djsarkar)
|
||||
|
||||
Detecting malaria with deep learning
|
||||
======
|
||||
Artificial intelligence combined with open source tools can improve
|
||||
diagnosis of the fatal disease malaria.
|
||||
使用深度学习检测疟疾
|
||||
==================
|
||||
人工智能结合开源硬件工具能够提升严重传染病疟疾的诊断。
|
||||
![][1]
|
||||
|
||||
Artificial intelligence (AI) and open source tools, technologies, and frameworks are a powerful combination for improving society. _"Health is wealth"_ is perhaps a cliche, yet it's very accurate! In this article, we will examine how AI can be leveraged for detecting the deadly disease malaria with a low-cost, effective, and accurate open source deep learning solution.
|
||||
人工智能（AI）和开源工具，技术，和框架是促进社会进步的强有力的结合。_“健康就是财富”_可能有点陈词滥调，但它却是非常准确的！在本篇文章，我们将测试 AI 是如何与低花费，有效，精确的开源深度学习方法一起被利用来检测致死的传染病疟疾。
|
||||
|
||||
While I am neither a doctor nor a healthcare researcher and I'm nowhere near as qualified as they are, I am interested in applying AI to healthcare research. My intent in this article is to showcase how AI and open source solutions can help malaria detection and reduce manual labor.
|
||||
我既不是一个医生，也不是一个医疗保健研究者，我也绝不像他们那样合格，我只是对将 AI 应用到医疗保健研究感兴趣。在这片文章中我的想法是展示 AI 和开源解决方案如何帮助疟疾检测和减少人工劳动的方法。
|
||||
|
||||
![Python and TensorFlow][2]
|
||||
|
||||
Python and TensorFlow: A great combo to build open source deep learning solutions
|
||||
Python and TensorFlow: 一个构建开源深度学习方法的很棒的结合
|
||||
|
||||
Thanks to the power of Python and deep learning frameworks like TensorFlow, we can build robust, scalable, and effective deep learning solutions. Because these tools are free and open source, we can build solutions that are very cost-effective and easily adopted and used by anyone. Let's get started!
|
||||
感谢 Python 的强大 和像 TensorFlow 这样的深度学习框架，我们能够构建鲁棒的，大规模的，有效的深度学习方法。因为这些工具是自由和开源的，我们能够构建低成本的能够轻易被任何人采纳和使用的解决方案。让我们开始吧！
|
||||
|
||||
### Motivation for the project
|
||||
### 项目动机
|
||||
|
||||
Malaria is a deadly, infectious, mosquito-borne disease caused by _Plasmodium_ parasites that are transmitted by the bites of infected female _Anopheles_ mosquitoes. There are five parasites that cause malaria, but two types— _P. falciparum_ and _P. vivax_ —cause the majority of the cases.
|
||||
疟疾是由_疟原虫_造成的致死的，有传染性的，蚊子传播的疾病，主要通过受感染的雌性按蚊叮咬传播。共有五种寄生虫能够造成疟疾，但是样例中的大多数是这两种类型- _恶性疟原虫_ 和 _间日疟原虫_ 造成的。
|
||||
|
||||
![Malaria heat map][3]
|
||||
![疟疾热图][3]
|
||||
|
||||
This map shows that malaria is prevalent around the globe, especially in tropical regions, but the nature and fatality of the disease is the primary motivation for this project.
|
||||
这个地图显示了疟疾在全球传播分布形势，尤其在热带地区，但疾病的性质和致命性是该项目的主要动机。
|
||||
|
||||
If an infected mosquito bites you, parasites carried by the mosquito enter your blood and start destroying oxygen-carrying red blood cells (RBC). Typically, the first symptoms of malaria are similar to a virus like the flu and they usually begin within a few days or weeks after the mosquito bite. However, these deadly parasites can live in your body for over a year without causing symptoms, and a delay in treatment can lead to complications and even death. Therefore, early detection can save lives.
|
||||
如果一个雌性蚊子咬了你，蚊子携带的寄生虫进入你的血液并且开始破坏携带氧气的红细胞（RBC）。通常，疟疾的最初症状类似于流感病毒，在蚊子叮咬后，他们通常在几天或几周内发作。然而，这些致死的寄生虫可以在你的身体里生存长达一年并且不会造成任何症状，延迟治疗可能造成并发症甚至死亡。因此，早期的检查能够挽救生命。
|
||||
|
||||
The World Health Organization's (WHO) [malaria facts][4] indicate that nearly half the world's population is at risk from malaria, and there are over 200 million malaria cases and approximately 400,000 deaths due to malaria every year. This is a motivatation to make malaria detection and diagnosis fast, easy, and effective.
|
||||
世界健康组织（WHO）的[疟疾事件][4]暗示世界近乎一半的人口面临疟疾的风险，有超过 2 亿 的疟疾病例，每年由于疟疾造成的死亡近乎 40 万。这是使疟疾检测和诊断快速，简单和有效的一个动机。
|
||||
|
||||
### Methods of malaria detection
|
||||
### 检测疟疾的方法
|
||||
|
||||
There are several methods that can be used for malaria detection and diagnosis. The paper on which our project is based, "[Pre-trained convolutional neural networks as feature extractors toward improved Malaria parasite detection in thin blood smear images][5]," by Rajaraman, et al., introduces some of the methods, including polymerase chain reaction (PCR) and rapid diagnostic tests (RDT). These two tests are typically used where high-quality microscopy services are not readily available.
|
||||
有几种方法能够用来检测和诊断疟疾。该文中的项目就是基于 Rajaraman，et al. 的论文：“[预先训练的卷积神经网络作为特征提取器，用于改善薄血涂片图像中的疟疾寄生虫检测][5]”，介绍了一些方法，包含聚合酶链反应（PCR）和快速诊断测试（RDT）。这两种测试通常在高质量的显微镜下使用，但这样的设备不是轻易能够获得的。
|
||||
|
||||
The standard malaria diagnosis is typically based on a blood-smear workflow, according to Carlos Ariza's article "[Malaria Hero: A web app for faster malaria diagnosis][6]," which I learned about in Adrian Rosebrock's "[Deep learning and medical image analysis with Keras][7]." I appreciate the authors of these excellent resources for giving me more perspective on malaria prevalence, diagnosis, and treatment.
|
||||
标准的疟疾诊断通常使基于血液涂片工作流的，根据 Carlos Ariza 的文章“[Malaria Hero: 一个更快诊断疟原虫的网络应用][6]”，我从中了解到 Adrian Rosebrock 的“[使用 Keras 的深度学习和医学图像分析][7]”。我感激这些优秀的资源的作者，让我在疟原虫预防，诊断和治疗方面有了更多的想法。
|
||||
|
||||
![Blood smear workflow for Malaria detection][8]
|
||||
![疟原虫检测的血涂片工作流程][8]
|
||||
|
||||
A blood smear workflow for Malaria detection
|
||||
一个疟原虫检测的血涂片工作流程
|
||||
|
||||
According to WHO protocol, diagnosis typically involves intensive examination of the blood smear at 100X magnification. Trained people manually count how many red blood cells contain parasites out of 5,000 cells. As the Rajaraman, et al., paper cited above explains:
|
||||
根据 WHO 草案，诊断通常包括对放大 100 倍的血涂片的集中检测。训练人们人工计数在 5000 个细胞中有多少红细胞中包含疟原虫。正如上述解释中引用的 Rajaraman， et al. 的论文：
|
||||
|
||||
> Thick blood smears assist in detecting the presence of parasites while thin blood smears assist in identifying the species of the parasite causing the infection (Centers for Disease Control and Prevention, 2012). The diagnostic accuracy heavily depends on human expertise and can be adversely impacted by the inter-observer variability and the liability imposed by large-scale diagnoses in disease-endemic/resource-constrained regions (Mitiku, Mengistu, and Gelaw, 2003). Alternative techniques such as polymerase chain reaction (PCR) and rapid diagnostic tests (RDT) are used; however, PCR analysis is limited in its performance (Hommelsheim, et al., 2014) and RDTs are less cost-effective in disease-endemic regions (Hawkes, Katsuva, and Masumbuko, 2009).
|
||||
> 薄血涂片帮助检测疟原虫的存在性并且帮助识别造成传染（疾病控制和抑制中心，2012）的物种。诊断准确性在很大程度上取决于人类的专业知识，并且可能受到观察者间差异和疾病流行/资源受限区域大规模诊断所造成的不利影响（Mitiku， Mengistu， and Gelaw， 2003）。可替代的技术是使用聚合酶链反应（PCR）和快速诊断测试（RDT）；然而，PCR 分析受限于它的性能（Hommelsheim， et al.， 2014），RDT 在疾病流行的地区成本效益低(Hawkes，Katsuva， and Masumbuko， 2009)。
|
||||
|
||||
Thus, malaria detection could benefit from automation using deep learning.
|
||||
因此，疟疾检测可能受益于使用机器学习的自动化。
|
||||
|
||||
### Deep learning for malaria detection
|
||||
### 疟原虫检测的深度学习
|
||||
|
||||
Manual diagnosis of blood smears is an intensive manual process that requires expertise in classifying and counting parasitized and uninfected cells. This process may not scale well, especially in regions where the right expertise is hard to find. Some advancements have been made in leveraging state-of-the-art image processing and analysis techniques to extract hand-engineered features and build machine learning-based classification models. However, these models are not scalable with more data being available for training and given the fact that hand-engineered features take a lot of time.
|
||||
人工诊断血涂片是一个加强的人工过程，需要专业知识来分类和计数被寄生虫感染的和未感染的细胞。这个过程可能不能很好的规模化，尤其在那些专业人士不足的地区。在利用最先进的图像处理和分析技术提取人工选取特征和构建基于机器学习的分类模型方面取得了一些进展。然而，这些模型不能大规模推广，因为没有更多的数据用来训练，并且人工选取特征需要花费很长时间。
|
||||
|
||||
Deep learning models, or more specifically convolutional neural networks (CNNs), have proven very effective in a wide variety of computer vision tasks. (If you would like additional background knowledge on CNNs, I recommend reading [CS231n Convolutional Neural Networks for Visual Recognition][9].) Briefly, the key layers in a CNN model include convolution and pooling layers, as shown in the following figure.
|
||||
深度学习模型，或者更具体地讲，卷积神经网络（CNNs），已经被证明在各种计算机视觉任务中非常有效。（如果你想有额外的关于 CNNs 的背景知识，我推荐你阅读[视觉识别的 CS2331n 卷积神经网络][9]。）简单地讲，CNN 模型的关键层包含卷积和池化层，正如下面图像显示。
|
||||
|
||||
![A typical CNN architecture][10]
|
||||
|
||||
A typical CNN architecture
|
||||
一个典型的 CNN 架构
|
||||
|
||||
Convolution layers learn spatial hierarchical patterns from data, which are also translation-invariant, so they are able to learn different aspects of images. For example, the first convolution layer will learn small and local patterns, such as edges and corners, a second convolution layer will learn larger patterns based on the features from the first layers, and so on. This allows CNNs to automate feature engineering and learn effective features that generalize well on new data points. Pooling layers helps with downsampling and dimension reduction.
|
||||
卷积层从数据中学习空间层级模式，它是平移不变的，因此它们能够学习不同方面的图像。例如，第一个卷积层将学习小的和本地图案，例如边缘和角落，第二个卷积层学习基于第一层的特征的更大的图案，等等。这允许 CNNs 自动化提取特征并且学习对于新数据点通用的有效的特征。池化层帮助下采样和降维。
|
||||
|
||||
Thus, CNNs help with automated and scalable feature engineering. Also, plugging in dense layers at the end of the model enables us to perform tasks like image classification. Automated malaria detection using deep learning models like CNNs could be very effective, cheap, and scalable, especially with the advent of transfer learning and pre-trained models that work quite well, even with constraints like less data.
|
||||
因此，CNNs 帮助自动化和规模化的特征工程。同样，在模型末尾加上密集层允许我们执行像图像分类这样的任务。使用像 CNNs 者的深度学习模型自动的疟疾检测可能非常有效，便宜和具有规模性，尤其是迁移学习和预训练模型效果非常好，甚至在少量数据的约束下。
|
||||
|
||||
The Rajaraman, et al., paper leverages six pre-trained models on a dataset to obtain an impressive accuracy of 95.9% in detecting malaria vs. non-infected samples. Our focus is to try some simple CNN models from scratch and a couple of pre-trained models using transfer learning to see the results we can get on the same dataset. We will use open source tools and frameworks, including Python and TensorFlow, to build our models.
|
||||
Rajaraman， et al. 的论文在一个数据集上利用六个预训练模型在检测疟疾 vs 无感染样本获取到令人吃惊的 95.9% 的准确率。我们的关注点是从头开始尝试一些简单的 CNN 模型和用一个预训练的训练模型使用迁移学习来查看我们能够从相同的数据集中得到什么。我们将使用开源工具和框架，包括 Python 和 TensorFlow，来构建我们的模型。
|
||||
|
||||
### The dataset
|
||||
### 数据集
|
||||
|
||||
The data for our analysis comes from researchers at the Lister Hill National Center for Biomedical Communications (LHNCBC), part of the National Library of Medicine (NLM), who have carefully collected and annotated the [publicly available dataset][11] of healthy and infected blood smear images. These researchers have developed a mobile [application for malaria detection][12] that runs on a standard Android smartphone attached to a conventional light microscope. They used Giemsa-stained thin blood smear slides from 150 _P. falciparum_ -infected and 50 healthy patients, collected and photographed at Chittagong Medical College Hospital, Bangladesh. The smartphone's built-in camera acquired images of slides for each microscopic field of view. The images were manually annotated by an expert slide reader at the Mahidol-Oxford Tropical Medicine Research Unit in Bangkok, Thailand.
|
||||
我们分析的数据来自 Lister Hill 国家生物医学交流中心（LHNCBC），国家医学图书馆（NLM）的一部分，他们细心收集和标记了健康和受感染的血涂片图像的[公众可获得的数据集][11]。这些研究者已经开发了一个运行在 Android 智能手机的移动[疟疾检测应用][12]，连接到一个传统的光学显微镜。它们使用 吉姆萨染液 将 150 个受恶性疟原虫感染的和 50 个健康病人的薄血涂片染色，这些薄血涂片是在孟加拉的吉大港医学院附属医院收集和照相的。使用智能手机的内置相机获取每个显微镜视窗内的图像。这些图片由在泰国曼谷的马希多-牛津热带医学研究所的一个专家使用幻灯片阅读器标记的。
|
||||
|
||||
Let's briefly check out the dataset's structure. First, I will install some basic dependencies (based on the operating system being used).
|
||||
让我们简洁的查看数据集的结构。首先，我将安装一些基础的依赖（基于使用的操作系统）。
|
||||
|
||||
![Installing dependencies][13]
|
||||
|
||||
I am using a Debian-based system on the cloud with a GPU so I can run my models faster. To view the directory structure, we must install the tree dependency (if we don't have it) using **sudo apt install tree**.
|
||||
我使用的是云上的带有一个 GPU 的基于 Debian 的操作系统，这样我能更快的运行我的模型。为了查看目录结构，我们必须安装 tree 依赖（如果我们没有安装的话）使用 **sudo apt install tree**。
|
||||
|
||||
![Installing the tree dependency][14]
|
||||
|
||||
We have two folders that contain images of cells, infected and healthy. We can get further details about the total number of images by entering:
|
||||
我们有两个文件夹包含血细胞的图像，包括受感染的和健康的。我们可以获取关于图像总数更多的细节通过输入：
|
||||
|
||||
|
||||
```
|
||||
@ -98,7 +97,7 @@ len(infected_files), len(healthy_files)
|
||||
(13779, 13779)
|
||||
```
|
||||
|
||||
It looks like we have a balanced dataset with 13,779 malaria and 13,779 non-malaria (uninfected) cell images. Let's build a data frame from this, which we will use when we start building our datasets.
|
||||
看起来我们有一个平衡的 13,779 张疟疾的 和 13,779 张非疟疾的（健康的）血细胞图像。让我们根据这些构建数据帧，我们将用这些数据帧来构建我们的数据集。
|
||||
|
||||
|
||||
```
|
||||
@ -117,9 +116,9 @@ files_df.head()
|
||||
|
||||
![Datasets][15]
|
||||
|
||||
### Build and explore image datasets
|
||||
### 构建和参所图像数据集
|
||||
|
||||
To build deep learning models, we need training data, but we also need to test the model's performance on unseen data. We will use a 60:10:30 split for train, validation, and test datasets, respectively. We will leverage the train and validation datasets during training and check the performance of the model on the test dataset.
|
||||
为了构建深度学习模型，我们需要训练数据，但是我们还需要使用不可见的数据测试模型的性能。相应的，我们将使用 60：10：30 的划分用于训练，验证和测试数据集。我们将在训练期间应用训练和验证数据集并用测试数据集来检查模型的性能。
|
||||
|
||||
|
||||
```
|
||||
@ -143,7 +142,7 @@ Val: Counter({'healthy': 970, 'malaria': 959})
|
||||
Test: Counter({'malaria': 4193, 'healthy': 4075})
|
||||
```
|
||||
|
||||
The images will not be of equal dimensions because blood smears and cell images vary based on the human, the test method, and the orientation of the photo. Let's get some summary statistics of our training dataset to determine the optimal image dimensions (remember, we don't touch the test dataset at all!).
|
||||
这些图片维度并不相同，因此血涂片和细胞图像是基于人类，测试方法，图片的朝向。让我们总结我们的训练数据集的统计信息来决定最佳的图像维度（牢记，我们根本不会碰测试数据集）。
|
||||
|
||||
|
||||
```
|
||||
@ -183,7 +182,7 @@ Median Dimensions: [130. 130. 3.]
|
||||
Max Dimensions: [385 394 3]
|
||||
```
|
||||
|
||||
We apply parallel processing to speed up the image-read operations and, based on the summary statistics, we will resize each image to 125x125 pixels. Let's load up all of our images and resize them to these fixed dimensions.
|
||||
我们应用并行处理来加速图像读取，并且在总结统计时，我们将重新调整每幅图片到 125x125 像素。让我们载入我们所有的图像并重新调整它们为这些固定的大小。
|
||||
|
||||
|
||||
```
|
||||
@ -246,7 +245,7 @@ ThreadPoolExecutor-1_8: working on img num: 8267
|
||||
((17361, 125, 125, 3), (1929, 125, 125, 3), (8268, 125, 125, 3))
|
||||
```
|
||||
|
||||
We leverage parallel processing again to speed up computations pertaining to image load and resizing. Finally, we get our image tensors of the desired dimensions, as depicted in the preceding output. We can now view some sample cell images to get an idea of how our data looks.
|
||||
我们再次应用并行处理来加速有关图像载入和重新调整大小。最终，我们获得了想要的维度的图片张量，正如之前描述的。我们现在查看一些血细胞图像样本来对我们的数据什么样有个印象。
|
||||
|
||||
|
||||
```
|
||||
@ -267,9 +266,9 @@ plt.xticks([]) , plt.yticks([])
|
||||
|
||||
![Malaria cell samples][16]
|
||||
|
||||
Based on these sample images, we can see some subtle differences between malaria and healthy cell images. We will make our deep learning models try to learn these patterns during model training.
|
||||
基于这些样本图像，我们看到一些疟疾和健康细胞图像的细微不同。我们将使我们的深度学习模型试图在模型训练中学习这些模式。
|
||||
|
||||
Before can we start training our models, we must set up some basic configuration settings.
|
||||
开始我们的模型训练前，我们必须建立一些基础的配置设置。
|
||||
|
||||
|
||||
```
|
||||
@ -295,7 +294,7 @@ print(train_labels[:6], train_labels_enc[:6])
|
||||
['malaria' 'malaria' 'malaria' 'healthy' 'healthy' 'malaria'] [1 1 1 0 0 1]
|
||||
```
|
||||
|
||||
We fix our image dimensions, batch size, and epochs and encode our categorical class labels. The alpha version of TensorFlow 2.0 was released in March 2019, and this exercise is the perfect excuse to try it out.
|
||||
我们修复我们的图像维度，批大小，和历元并编码我们的分类类标签。TensorFlow 2.0 于 2019 年三月发布，这个练习是非常好的借口来试用它。
|
||||
|
||||
|
||||
```
|
||||
@ -311,13 +310,13 @@ tf.__version__
|
||||
'2.0.0-alpha0'
|
||||
```
|
||||
|
||||
### Deep learning model training
|
||||
### 深度学习训练
|
||||
|
||||
In the model training phase, we will build three deep learning models, train them with our training data, and compare their performance using the validation data. We will then save these models and use them later in the model evaluation phase.
|
||||
在模型训练阶段，我们将构建三个深度训练模型，使用我们的训练集训练，使用验证数据比较它们的性能。我们然后保存这些模型并在之后的模型评估阶段使用它们。
|
||||
|
||||
#### Model 1: CNN from scratch
|
||||
#### 模型 1：从头开始的 CNN
|
||||
|
||||
Our first malaria detection model will build and train a basic CNN from scratch. First, let's define our model architecture.
|
||||
我们的第一个疟疾检测模型将从头开始构建和训练一个基础的 CNN。首先，让我们定义我们的模型架构，
|
||||
|
||||
|
||||
```
|
||||
@ -376,7 +375,7 @@ Non-trainable params: 0
|
||||
_________________________________________________________________
|
||||
```
|
||||
|
||||
Based on the architecture in this code, our CNN model has three convolution and pooling layers, followed by two dense layers, and dropouts for regularization. Let's train our model.
|
||||
基于这些代码的架构，我们的 CNN 模型有三个卷积和一个池化层，跟随两个致密层，以及用于正则化的丢失。让我们训练我们的模型。
|
||||
|
||||
|
||||
```
|
||||
@ -411,7 +410,7 @@ Epoch 25/25
|
||||
17361/17361 [====] - 30s 2ms/sample - loss: 0.0034 - accuracy: 0.9994 - val_loss: 0.3699 - val_accuracy: 0.9559
|
||||
```
|
||||
|
||||
We get a validation accuracy of 95.6%, which is pretty good, although our model looks to be overfitting slightly (based on looking at our training accuracy, which is 99.9%). We can get a clear perspective on this by plotting the training and validation accuracy and loss curves.
|
||||
我们获得了 95.6% 的验证精确率，这很好，尽管我们的模型看起来有些过拟合（通过查看我们的训练精确度，是 99.9%）。通过绘制训练和验证的精度和损失曲线，我们可以清楚地看到这一点。
|
||||
|
||||
|
||||
```
|
||||
@ -440,47 +439,47 @@ l2 = ax2.legend(loc="best")
|
||||
|
||||
![Learning curves for basic CNN][17]
|
||||
|
||||
Learning curves for basic CNN
|
||||
基础 CNN 学习曲线
|
||||
|
||||
We can see after the fifth epoch that things don't seem to improve a whole lot overall. Let's save this model for future evaluation.
|
||||
我们可以看在在第五个历元，情况并没有改善很多。让我们保存这个模型用于将来的评估。
|
||||
|
||||
|
||||
```
|
||||
`model.save('basic_cnn.h5')`
|
||||
```
|
||||
|
||||
#### Deep transfer learning
|
||||
#### 深度迁移学习
|
||||
|
||||
Just like humans have an inherent capability to transfer knowledge across tasks, transfer learning enables us to utilize knowledge from previously learned tasks and apply it to newer, related ones, even in the context of machine learning or deep learning. If you are interested in doing a deep-dive on transfer learning, you can read my article "[A comprehensive hands-on guide to transfer learning with real-world applications in deep learning][18]" and my book [_Hands-On Transfer Learning with Python_][19].
|
||||
就像人类有与生俱来的能力在不同任务间传输知识，迁移学习允许我们利用从以前任务学到的知识用到新的任务，相关的任务，甚至在机器学习或深度学习的上下文中。如果想深入探究迁移学习，你应该看我的文章“[一个易于理解与现实应用一起学习深度学习中的迁移学习的指导实践][18]”和我的书[ Python 迁移学习实践][19]。
|
||||
|
||||
![Ideas for deep transfer learning][20]
|
||||
![深度迁移学习的想法][20]
|
||||
|
||||
The idea we want to explore in this exercise is:
|
||||
在这篇实践中我们想要探索的想法是：
|
||||
|
||||
> Can we leverage a pre-trained deep learning model (which was trained on a large dataset, like ImageNet) to solve the problem of malaria detection by applying and transferring its knowledge in the context of our problem?
|
||||
> 在我们的问题上下文中，我们能够利用一个预训练深度学习模型（在大数据集上训练的，像 ImageNet）通过应用和迁移知识来解决疟疾检测的问题吗？
|
||||
|
||||
We will apply the two most popular strategies for deep transfer learning.
|
||||
我们将应用两个深度迁移学习的最流行的策略。
|
||||
|
||||
* Pre-trained model as a feature extractor
|
||||
* Pre-trained model with fine-tuning
|
||||
* 预训练模型作为特征提取器
|
||||
* 微调的预训练模型
|
||||
|
||||
|
||||
|
||||
We will be using the pre-trained VGG-19 deep learning model, developed by the Visual Geometry Group (VGG) at the University of Oxford, for our experiments. A pre-trained model like VGG-19 is trained on a huge dataset ([ImageNet][21]) with a lot of diverse image categories. Therefore, the model should have learned a robust hierarchy of features, which are spatial-, rotational-, and translation-invariant with regard to features learned by CNN models. Hence, the model, having learned a good representation of features for over a million images, can act as a good feature extractor for new images suitable for computer vision problems like malaria detection. Let's discuss the VGG-19 model architecture before unleashing the power of transfer learning on our problem.
|
||||
我们将使用预训练的 VGG-19 深度训练模型，由剑桥大学的视觉几何组（VGG）开发，作为我们的实验。一个像 VGG-19 的预训练模型在一个大的数据集上使用了很多不同的图像分类训练（[Imagenet][21]）。因此，这个模型应该已经学习到了鲁棒的特征层级结构，相对于你的 CNN 模型学到的特征，是空间不变的，转动不变的，平移不变的。因此，这个模型，已经从百万幅图片中学习到了一个好的特征显示，对于像疟疾检测这样的计算机视觉问题，可以作为一个好的合适新图像的特征提取器。在我们的问题中释放迁移学习的能力之前，让我们先讨论 VGG-19 模型。
|
||||
|
||||
##### Understanding the VGG-19 model
|
||||
##### 理解 VGG-19 模型
|
||||
|
||||
The VGG-19 model is a 19-layer (convolution and fully connected) deep learning network built on the ImageNet database, which was developed for the purpose of image recognition and classification. This model was built by Karen Simonyan and Andrew Zisserman and is described in their paper "[Very deep convolutional networks for large-scale image recognition][22]." The architecture of the VGG-19 model is:
|
||||
VGG-19 模型是一个构建在 ImageNet 数据库之上的 19 层（卷积和全连接的）的深度学习网络，该数据库为了图像识别和分类的目的而开发。该模型由 Karen Simonyan 和 Andrew Zisserman 构建，在它们的论文”[大规模图像识别的非常深的卷积网络][22]“中描述。VGG-19 的架构模型是：
|
||||
|
||||
![VGG-19 Model Architecture][23]
|
||||
![VGG-19 模型架构][23]
|
||||
|
||||
You can see that we have a total of 16 convolution layers using 3x3 convolution filters along with max pooling layers for downsampling and two fully connected hidden layers of 4,096 units in each layer followed by a dense layer of 1,000 units, where each unit represents one of the image categories in the ImageNet database. We do not need the last three layers since we will be using our own fully connected dense layers to predict malaria. We are more concerned with the first five blocks so we can leverage the VGG model as an effective feature extractor.
|
||||
你可以看到我们总共有 16 个使用 3x3 卷积过滤器的卷积层，与最大的池化层来下采样，和由 4096 个单元组成的两个全连接的隐藏层，每个隐藏层之后跟随一个由 1000 个单元组成的致密层，每个单元代表 ImageNet 数据库中的一个分类。我们不需要最后三层，因为我们将使用我们自己的全连接致密层来预测疟疾。我们更关心前五块，因此我们可以利用 VGG 模型作为一个有效的特征提取器。
|
||||
|
||||
We will use one of the models as a simple feature extractor by freezing the five convolution blocks to make sure their weights aren't updated after each epoch. For the last model, we will apply fine-tuning to the VGG model, where we will unfreeze the last two blocks (Block 4 and Block 5) so that their weights will be updated in each epoch (per batch of data) as we train our own model.
|
||||
我们将使用模型之一作为一个简单的特征提取器通过冻结五个卷积块的方式来确保它们的位权在每个时期后不会更新。对于最后一个模型，我们会应用微调到 VGG 模型，我们会解冻最后两个块（第 4 和第 5）因此当我们训练我们的模型时，它们的位权在每个时期（每批数据）被更新。
|
||||
|
||||
#### Model 2: Pre-trained model as a feature extractor
|
||||
#### 模型 2：预训练的模型作为一个特征提取器
|
||||
|
||||
For building this model, we will leverage TensorFlow to load up the VGG-19 model and freeze the convolution blocks so we can use them as an image feature extractor. We will plug in our own dense layers at the end to perform the classification task.
|
||||
为了构建这个模型，我们将利用 TensorFlow 载入 VGG-19 模型并且冻结卷积块因此我们用够将他们用作特征提取器。我们插入我们自己的致密层在末尾来执行分类任务。
|
||||
|
||||
|
||||
```
|
||||
@ -541,7 +540,7 @@ Non-trainable params: 20,024,384
|
||||
_________________________________________________________________
|
||||
```
|
||||
|
||||
It is evident from this output that we have a lot of layers in our model and we will be using the frozen layers of the VGG-19 model as feature extractors only. You can use the following code to verify how many layers in our model are indeed trainable and how many total layers are present in our network.
|
||||
输出是很明白的，在我们的模型中我们有了很多层，我们将只利用 VGG-19 模型的冻结层作为特征提取器。你可以使用下列代码来验证我们的模型有多少层是实际训练的，我们的网络中总共存在多少层。
|
||||
|
||||
|
||||
```
|
||||
@ -554,22 +553,22 @@ Total Layers: 28
|
||||
Total trainable layers: 6
|
||||
```
|
||||
|
||||
We will now train our model using similar configurations and callbacks to the ones we used in our previous model. Refer to [my GitHub repository][24] for the complete code to train the model. We observe the following plots showing the model's accuracy and loss.
|
||||
我们将使用和我们之前的模型相似的配置和回调来训练我们的模型。参考 [我的 GitHub 仓库][24] 获取训练模型的完整代码。我们观察下列显示模型精确度和损失曲线。
|
||||
|
||||
![Learning curves for frozen pre-trained CNN][25]
|
||||
|
||||
Learning curves for frozen pre-trained CNN
|
||||
冻结的预训练的 CNN 的学习曲线
|
||||
|
||||
This shows that our model is not overfitting as much as our basic CNN model, but the performance is slightly less than our basic CNN model. Let's save this model for future evaluation.
|
||||
这显示了我们的模型没有像我们的基础 CNN 模型那样过拟合，但是性能有点不如我们的基础的 CNN 模型。让我们保存这个模型用户将来的评估。
|
||||
|
||||
|
||||
```
|
||||
`model.save('vgg_frozen.h5')`
|
||||
```
|
||||
|
||||
#### Model 3: Fine-tuned pre-trained model with image augmentation
|
||||
#### 模型 3：使用图像增强来微调预训练的模型
|
||||
|
||||
In our final model, we will fine-tune the weights of the layers in the last two blocks of our pre-trained VGG-19 model. We will also introduce the concept of image augmentation. The idea behind image augmentation is exactly as the name sounds. We load in existing images from our training dataset and apply some image transformation operations to them, such as rotation, shearing, translation, zooming, and so on, to produce new, altered versions of existing images. Due to these random transformations, we don't get the same images each time. We will leverage an excellent utility called **ImageDataGenerator** in **tf.keras** that can help build image augmentors.
|
||||
在我们的最后一个模型中，我们微调预定义好的 VGG-19 模型的最后两个块中层的位权。我们同样引入图像增强的概念。图像增强背后的想法和名字一样。我们从训练数据集中载入已存在的图像，并且应用转换操作，例如旋转，裁剪，转换，放大缩小，等等，来产生新的，改变的版本。由于这些随机的转换，我们每次获取到的图像不一样。我们将应用一个在 **tf.keras** 的优秀的工具叫做 **ImageDataGenerator** 来帮助构建图像增强器。
|
||||
|
||||
|
||||
```
|
||||
@ -588,7 +587,7 @@ train_generator = train_datagen.flow(train_data, train_labels_enc, batch_size=BA
|
||||
val_generator = val_datagen.flow(val_data, val_labels_enc, batch_size=BATCH_SIZE, shuffle=False)
|
||||
```
|
||||
|
||||
We will not apply any transformations on our validation dataset (except for scaling the images, which is mandatory) since we will be using it to evaluate our model performance per epoch. For a detailed explanation of image augmentation in the context of transfer learning, feel free to check out my [article][18] cited above. Let's look at some sample results from a batch of image augmentation transforms.
|
||||
我们不会应用任何转换在我们的验证数据集上（除非是调整大小，它是强制性适应的）因为我们将在每个时期来评估我们的模型性能。对于在传输学习上下文中的图像增强的详细解释，请自由查看我们上述引用的[文章][18]。让我们从一批图像增强转换中查看一些样本结果。
|
||||
|
||||
|
||||
```
|
||||
@ -603,7 +602,7 @@ l = [ax[i].imshow(sample[i][0][0]) for i in range(0,5)]
|
||||
|
||||
![Sample augmented images][26]
|
||||
|
||||
You can clearly see the slight variations of our images in the preceding output. We will now build our deep learning model, making sure the last two blocks of the VGG-19 model are trainable.
|
||||
你可以清晰的看到与之前的输出中我们图像的轻微变化。我们现在构建我们的学习模型，确保 VGG-19 模型的最后两块是可以训练的。
|
||||
|
||||
|
||||
```
|
||||
@ -644,7 +643,7 @@ Total Layers: 28
|
||||
Total trainable layers: 16
|
||||
```
|
||||
|
||||
We reduce the learning rate in our model since we don't want to make to large weight updates to the pre-trained layers when fine-tuning. The model's training process will be slightly different since we are using data generators, so we will be leveraging the **fit_generator(…)** function.
|
||||
在我们的模型中我们降低了学习率，因为我们微调的时候不想在预训练的数据集上做大的位权更新。模型的训练过程可能有轻微的不同，因为我们使用了数据生成器，因此我们应用了 **fit_generator(...)** 函数。
|
||||
|
||||
|
||||
```
|
||||
@ -672,24 +671,24 @@ Epoch 25/25
|
||||
271/271 [====] - 128s 473ms/step - loss: 0.0792 - accuracy: 0.9729 - val_loss: 0.1127 - val_accuracy: 0.9641
|
||||
```
|
||||
|
||||
This looks to be our best model yet. It gives us a validation accuracy of almost 96.5% and, based on the training accuracy, it doesn't look like our model is overfitting as much as our first model. This can be verified with the following learning curves.
|
||||
这看起来是我们的最好的模型。它给了我们近乎 96.5% 的验证精确率，基于训练精度，它看起来不像我们的第一个模型那样过拟合。这可以通过下列的学习曲线验证。
|
||||
|
||||
![Learning curves for fine-tuned pre-trained CNN][27]
|
||||
|
||||
Learning curves for fine-tuned pre-trained CNN
|
||||
微调预训练的 CNN 的学习曲线
|
||||
|
||||
Let's save this model so we can use it for model evaluation on our test dataset.
|
||||
让我们保存这个模型，因此我们能够在测试集上使用。
|
||||
|
||||
|
||||
```
|
||||
`model.save('vgg_finetuned.h5')`
|
||||
```
|
||||
|
||||
This completes our model training phase. We are now ready to test the performance of our models on the actual test dataset!
|
||||
这完成了我们的模型训练阶段。我们准备好在测试集上测试我们模型的性能。
|
||||
|
||||
### Deep learning model performance evaluation
|
||||
### 深度学习模型性能评估
|
||||
|
||||
We will evaluate the three models we built in the training phase by making predictions with them on the data from our test dataset—because just validation is not enough! We have also built a nifty utility module called **model_evaluation_utils** , which we can use to evaluate the performance of our deep learning models with relevant classification metrics. The first step is to scale our test data.
|
||||
我们将评估我们在训练阶段构建的三个模型，通过在我们的测试集上做预测，因为仅仅验证是不够的！我们同样构建了一个检测工具模块叫做 **model_evaluation_utils**，我们可以使用相关分类指标用来评估使用我们深度学习模型的性能。第一步是测量我们的数据集。
|
||||
|
||||
|
||||
```
|
||||
@ -700,7 +699,7 @@ test_imgs_scaled.shape, test_labels.shape
|
||||
((8268, 125, 125, 3), (8268,))
|
||||
```
|
||||
|
||||
The next step involves loading our saved deep learning models and making predictions on the test data.
|
||||
下一步包括载入我们保存的深度学习模型，在测试集上预测。
|
||||
|
||||
|
||||
```
|
||||
@ -722,7 +721,7 @@ vgg_ft_pred_labels = le.inverse_transform([1 if pred > 0.5 else 0
|
||||
for pred in vgg_ft_preds.ravel()])
|
||||
```
|
||||
|
||||
The final step is to leverage our **model_evaluation_utils** module and check the performance of each model with relevant classification metrics.
|
||||
下一步是应用我们的 **model_evaluation_utils** 模块根据相应分类指标来检查每个模块的性能。
|
||||
|
||||
|
||||
```
|
||||
@ -739,15 +738,15 @@ index=['Basic CNN', 'VGG-19 Frozen', 'VGG-19 Fine-tuned'])
|
||||
|
||||
![Model accuracy][28]
|
||||
|
||||
It looks like our third model performs best on the test dataset, giving a model accuracy and an F1-score of 96%, which is pretty good and quite comparable to the more complex models mentioned in the research paper and articles we mentioned earlier.
|
||||
看起来我们的第三个模型在我们的测试集上执行的最好，给出了一个模型精确性为 96% 的 F1得分，比起上述我们早期引用的研究论文和文章中提及的复杂的模型是相当好的。
|
||||
|
||||
### Conclusion
|
||||
### 总结
|
||||
|
||||
Malaria detection is not an easy procedure, and the availability of qualified personnel around the globe is a serious concern in the diagnosis and treatment of cases. We looked at an interesting real-world medical imaging case study of malaria detection. Easy-to-build, open source techniques leveraging AI can give us state-of-the-art accuracy in detecting malaria, thus enabling AI for social good.
|
||||
疟疾检测不是一个简单的程序，全球的合格的人员的可获得性在样例诊断和治疗当中是一个严重的问题。我们看到一个关于疟疾的有趣的真实世界的医学影像案例。易于构建的，开源的技术利用 AI 在检测疟疾方面可以给我们最先进的精确性，因此允许 AI 对社会是有益的。
|
||||
|
||||
I encourage you to check out the articles and research papers mentioned in this article, without which it would have been impossible for me to conceptualize and write it. If you are interested in running or adopting these techniques, all the code used in this article is available on [my GitHub repository][24]. Remember to download the data from the [official website][11].
|
||||
我鼓励你检查这片文章中提到的文章和研究论文，没有它们，我就不能形成概念并写出来。如果你对运行和采纳这些技术感兴趣，本篇文章所有的代码都可以在[我的 GitHub 仓库][24]获得。记得从[官方网站][11]下载数据。
|
||||
|
||||
Let's hope for more adoption of open source AI capabilities in healthcare to make it less expensive and more accessible for everyone around the world!
|
||||
让我们希望在健康医疗方面更多的采纳开源的 AI 能力，使它在世界范围内变得便宜些，易用些。
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
@ -755,7 +754,7 @@ via: https://opensource.com/article/19/4/detecting-malaria-deep-learning
|
||||
|
||||
作者：[Dipanjan (DJ) Sarkar (Red Hat)][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
译者：[warmfrog](https://github.com/warmfrog)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
@ -0,0 +1,80 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: (warmfrog)
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (This is how System76 does open hardware)
|
||||
[#]: via: (https://opensource.com/article/19/4/system76-hardware)
|
||||
[#]: author: (Don Watkins https://opensource.com/users/don-watkins)
|
||||
|
||||
这就是 System76 如何打造开源硬件的
|
||||
================================
|
||||
是什么让新的 Thelio 台式机系列与众不同。
|
||||
![在计算机上显示度量和数据][1]
|
||||
|
||||
大多数人对他们电脑的硬件一无所知。作为一个长期的 Linux 用户，我分享了对此的失望，就是当我想让我的无线网卡，视频卡，显示器，和其他硬件与我选择的发行版共同运行时。自主品牌的硬件通常使判断这些问题变得很困难，就是：为什么以太网驱动，无线驱动，或者鼠标驱动和我们预期的不太一样？。当 Linux 分发版已经成熟，这可能不再是问题，但是我们仍能发现触控板和其他外部设备的怪异行为，尤其是当我们对下面的硬件知道的不多时。
|
||||
|
||||
像 [System76][2] 的公司目标是解决这些问题，来提升 Linux 用户体验。System76 生产了一些列的 Linux 笔记本，台式机，和服务器，甚至提供了它自己的 Linux 发行版 [Pop! OS][3] 作为客户的一个选择。最近我有幸参观了 System76 在 Devnver 的工厂并揭开 [Thelio][5] [神秘的面纱][5]，它的新的台式机产品线。
|
||||
|
||||
### 关于 Thelio
|
||||
|
||||
System76 宣称 Thelio 的开源硬件子板，被命名为木星之后的第 5 个卫星的名字 Thelio Io，这是它在市场上独特的特点之一。Thelio Io 被证实为 [OSHWA #us000145][6]，并且有 4 个用于存储的 SATA 端口和一个控制风扇和用于电源按钮控制的嵌入式控制器。Thelio IO SAS 被证实 是 [OSHWA #us000146][7]，并且有 4 个用于存储的 U.2 端口，没有嵌入式控制器。在展示时，System76 显示了这些组件如何调整风扇通过底盘来优化部件的性能。
|
||||
|
||||
该控制器还管理电源键，和围绕该电源键的 LED 光环，当被按下时它以 100% 的亮度发光。这提供了触觉和视觉上的确认：该主机已经启动电源了。当电脑在使用中，该按钮被设置为 35% 的亮度，当在睡眠模式，它的亮度在 2.35% 和 25% 之间跳动。当计算机关闭后，LED 保持朦胧的亮度，因此能够在黑暗的房间里找到电源控制。
|
||||
|
||||
Thelio 的嵌入式控制器是一个低功耗的 [ATmega32U4][8] 微控制器，并且控制器的设置可以使用 Arduino Micro 进行原型设计。Thelio Io 主板变化的多少取决于你购买哪种 Thelio 型号。
|
||||
|
||||
Thelio 可能是我见过的设计的最好的电脑样例和系统。如果你曾经亲身体验过在一个常规的 PC 的内部进行操作的话，你可能会同意我的观点。我已经做了很多次了，因此我能以自己过往的糟糕经历来证明这点。
|
||||
|
||||
### 为什么做开源硬件？
|
||||
|
||||
该主板是在 [KiCAD][9] 设计的，你可以在 [GitHub][10] 上的 GPL 证书下访问 Thelio 所有的设计文件。因此，为什么一个与其他 PC 制造商竞争的公司会设计一个独特的接口并公开授权呢？可能是该公司认识到开源设计的价值和分享的能力，并且根据你的需要调整一个 I/O 主板，即便你是市场上的竞争者。
|
||||
|
||||
![在 Thelio 启动时 Don Watkins 与 System76 的 CEO Carl Richell 谈话][11]
|
||||
|
||||
在 [Thelio 启动时][12] Don Watkins 与 System76 的 CEO Carl Richell 谈话。
|
||||
|
||||
我问 [Carl Richell][13]，System76 的设计者和 CEO，该公司是否考虑到开放它的硬件设计意味着有人采取它的独特设计并用它来将 System76 驱逐出市场。他说：
|
||||
|
||||
> 开源硬件对我们所有人都有益。这是我们未来提升技术的方式并且使得每个人获取技术更容易。我们欢迎任何想要提高 Thelio 设计的人来这么做。开源硬件不仅更快的帮助我们的电脑提升技术，并且能够使我们的消费者 100% 信任他们的设备。我们的目标是尽可能地移除专利授权，并且仍然能够为消费者提供一个有竞争力的 Linux 主机。
|
||||
>
|
||||
> 我们已经与 Linux 社区一起工作超过 13 年来为我们的笔记本，台式机，服务器创造一个完美的顺滑的体验。我们长期专注于为 Linux 社区提供服务，提供给我们的客户高水准的服务，我们的个性使 System76 变得独特。
|
||||
|
||||
我问 Carl 为什么通常来说开源硬件对 System76 和 PC 市场是有意义的。他回复道：
|
||||
|
||||
> System76 创立之初的想法是技术应该对每个人是开放和可获取的。我们还没有到达 100% 开源创造一个电脑的程度，但是有了开源硬件，我们是接近目标的必不可少的一大步。
|
||||
>
|
||||
> 我们生活在技术变成工具的时代。计算机在每一层教育和在很多工业当中是人们的工具。由于每个人特定的需要，每个人对于如何提升电脑和软件作为他们的主要工具有他们自己的想法。开源我们的计算机允许这些想法完成，反过来促进技术成为一个更强大的工具。在一个开源环境中，我们持续迭代来获取一个更好的 PC。这有点酷。
|
||||
|
||||
我们总结了我们讨论的关于 System76 技术路线的对话，包含了开源硬件 mini PC，最终还包含了笔记本。在 System76 品牌下的已售出的 mini PC 和笔记本是由其他供应商制造的，并不是基于开源硬件的（尽管他们的 Linux 软件，当然是开源的）。
|
||||
|
||||
设计和支持开放式硬件是PC业务中改变游戏规则的因素，也正是它造就了 System76 的新 Thelio 台式机电脑产品线的不同。
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://opensource.com/article/19/4/system76-hardware
|
||||
|
||||
作者：[Don Watkins ][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[warmfrog](https://github.com/warmfrog)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://opensource.com/users/don-watkins
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/metrics_data_dashboard_system_computer_analytics.png?itok=oxAeIEI- (metrics and data shown on a computer screen)
|
||||
[2]: https://system76.com/
|
||||
[3]: https://opensource.com/article/18/1/behind-scenes-popos-linux
|
||||
[4]: /article/18/11/system76-thelio-desktop-computer
|
||||
[5]: https://system76.com/desktops
|
||||
[6]: https://certification.oshwa.org/us000145.html
|
||||
[7]: https://certification.oshwa.org/us000146.html
|
||||
[8]: https://www.microchip.com/wwwproducts/ATmega32u4
|
||||
[9]: http://kicad-pcb.org/
|
||||
[10]: https://github.com/system76/thelio-io
|
||||
[11]: https://opensource.com/sites/default/files/uploads/don_system76_ceo.jpg (Don Watkins speaks with System76 CEO Carl Richell at the Thelio launch event.)
|
||||
[12]: https://trevgstudios.smugmug.com/System76/121418-Thelio-Press-Event/i-FKWFxFv
|
||||
[13]: https://www.linkedin.com/in/carl-richell-9435781
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user