2-4 Tutorials with python -> Routing

本文介绍如何使用RabbitMQ的直连交换机实现基于消息类型的日志路由,通过配置不同的绑定键来过滤消息,实现精准的日志处理。

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路由(Routing)

(使用pika 0.9.5 Python客户端)

在前面的教程中,我们实现了一个简单的日志系统。可以把日志消息广播给多个接收者。

本篇教程中我们打算新增一个功能 —— 使得它能够只订阅消息的一个字集。例如,我们只需要把严重的错误日志信息写入日志文件(存储到磁盘),但同时仍然把所有的日志信息输出到控制台中

绑定(Bindings)

前面的例子,我们已经创建过绑定(bindings),代码如下:

channel.queue_bind(exchange=exchange_name,
                   queue=queue_name)

绑定(binding)是指交换机(exchange)和队列(queue)的关系。可以简单理解为:这个队列(queue)对这个交换机(exchange)的消息感兴趣。

绑定的时候可以带上一个额外的routing_key参数。为了避免与basic_publish的参数混淆,我们把它叫做绑定键(binding key)。以下是如何创建一个带绑定键的绑定。

channel.queue_bind(exchange=exchange_name,
                   queue=queue_name,
                   routing_key='black')

绑定键的意义取决于交换机(exchange)的类型。我们之前使用过的扇型交换机(fanout exchanges)会忽略这个值。

直连交换机(Direct exchange)

我们的日志系统广播所有的消息给所有的消费者(consumers)。我们打算扩展它,使其基于日志的严重程度进行消息过滤。例如我们也许只是希望将比较严重的错误(error)日志写入磁盘,以免在警告(warning)或者信息(info)日志上浪费磁盘空间。

我们使用的扇型交换机(fanout exchange)没有足够的灵活性 —— 它能做的仅仅是广播。

我们将会使用直连交换机(direct exchange)来代替。路由的算法很简单 —— 交换机将会对绑定键(binding key)和路由键(routing key)进行精确匹配,从而确定消息该分发到哪个队列。

下图能够很好的描述这个场景:

在这个场景中,我们可以看到直连交换机 X和两个队列进行了绑定。第一个队列使用orange作为绑定键,第二个队列有两个绑定,一个使用black作为绑定键,另外一个使用green。

这样以来,当路由键为orange的消息发布到交换机,就会被路由到队列Q1。路由键为black或者green的消息就会路由到Q2。其他的所有消息都将会被丢弃。

多个绑定(Multiple bindings)

多个队列使用相同的绑定键是合法的。这个例子中,我们可以添加一个X和Q1之间的绑定,使用black绑定键。这样一来,直连交换机就和扇型交换机的行为一样,会将消息广播到所有匹配的队列。带有black路由键的消息会同时发送到Q1和Q2。

发送日志

我们将会发送消息到一个直连交换机,把日志级别作为路由键。这样接收日志的脚本就可以根据严重级别来选择它想要处理的日志。我们先看看发送日志。

我们需要创建一个交换机(exchange):

channel.exchange_declare(exchange='direct_logs',
                         type='direct')

然后我们发送一则消息:

channel.basic_publish(exchange='direct_logs',
                      routing_key=severity,
                      body=message)

我们先假设“severity”的值是info、warning、error中的一个。

订阅

处理接收消息的方式和之前差不多,只有一个例外,我们将会为我们感兴趣的每个严重级别分别创建一个新的绑定。

result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue

for severity in severities:
    channel.queue_bind(exchange='direct_logs',
                       queue=queue_name,
                       routing_key=severity)

代码整合

emit_log_direct.py的代码:

#!/usr/bin/env python
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='direct_logs',
                         type='direct')

severity = sys.argv[1] if len(sys.argv) > 1 else 'info'
message = ' '.join(sys.argv[2:]) or 'Hello World!'
channel.basic_publish(exchange='direct_logs',
                      routing_key=severity,
                      body=message)
print " [x] Sent %r:%r" % (severity, message)
connection.close()

receive_logs_direct.py的代码:

#!/usr/bin/env python
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='direct_logs',
                         type='direct')

result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue

severities = sys.argv[1:]
if not severities:
    print >> sys.stderr, "Usage: %s [info] [warning] [error]" % \
                         (sys.argv[0],)
    sys.exit(1)

for severity in severities:
    channel.queue_bind(exchange='direct_logs',
                       queue=queue_name,
                       routing_key=severity)

print ' [*] Waiting for logs. To exit press CTRL+C'

def callback(ch, method, properties, body):
    print " [x] %r:%r" % (method.routing_key, body,)

channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=True)

channel.start_consuming()

如果你希望只是保存warning和error级别的日志到磁盘,只需要打开控制台并输入:

$ python receive_logs_direct.py warning error > logs_from_rabbit.log

如果你希望所有的日志信息都输出到屏幕中,打开一个新的终端,然后输入:

$ python receive_logs_direct.py info warning error
 [*] Waiting for logs. To exit press CTRL+C

如果要触发一个error级别的日志,只需要输入:

$ python emit_log_direct.py error "Run. Run. Or it will explode."
 [x] Sent 'error':'Run. Run. Or it will explode.'

这里是完整的代码:(emit_log_direct.pyreceive_logs_direct.py)


The Network Simulator, Version 3 -------------------------------- Table of Contents: ------------------ 1) An overview 2) Building ns-3 3) Running ns-3 4) Getting access to the ns-3 documentation 5) Working with the development version of ns-3 Note: Much more substantial information about ns-3 can be found at http://www.nsnam.org 1) An Open Source project ------------------------- ns-3 is a free open source project aiming to build a discrete-event network simulator targeted for simulation research and education. This is a collaborative project; we hope that the missing pieces of the models we have not yet implemented will be contributed by the community in an open collaboration process. The process of contributing to the ns-3 project varies with the people involved, the amount of time they can invest and the type of model they want to work on, but the current process that the project tries to follow is described here: http://www.nsnam.org/developers/contributing-code/ This README excerpts some details from a more extensive tutorial that is maintained at: http://www.nsnam.org/documentation/latest/ 2) Building ns-3 ---------------- The code for the framework and the default models provided by ns-3 is built as a set of libraries. User simulations are expected to be written as simple programs that make use of these ns-3 libraries. To build the set of default libraries and the example programs included in this package, you need to use the tool 'waf'. Detailed information on how use waf is included in the file doc/build.txt However, the real quick and dirty way to get started is to type the command ./waf configure --enable-examples followed by ./waf in the the directory which contains this README file. The files built will be copied in the build/ directory. The current codebase is expected to build and run on the set of platforms listed in the RELEASE_NOTES file. Other platforms may or may not work: we welcome patches to improve the portability of the code to these other platforms. 3) Running ns-3 --------------- On recent Linux systems, once you have built ns-3 (with examples enabled), it should be easy to run the sample programs with the following command, such as: ./waf --run simple-global-routing That program should generate a simple-global-routing.tr text trace file and a set of simple-global-routing-xx-xx.pcap binary pcap trace files, which can be read by tcpdump -tt -r filename.pcap The program source can be found in the examples/routing directory. 4) Getting access to the ns-3 documentation ------------------------------------------- Once you have verified that your build of ns-3 works by running the simple-point-to-point example as outlined in 4) above, it is quite likely that you will want to get started on reading some ns-3 documentation. All of that documentation should always be available from the ns-3 website: http:://www.nsnam.org/documentation/. This documentation includes: - a tutorial - a reference manual - models in the ns-3 model library - a wiki for user-contributed tips: http://www.nsnam.org/wiki/ - API documentation generated using doxygen: this is a reference manual, most likely not very well suited as introductory text: http://www.nsnam.org/doxygen/index.html 5) Working with the development version of ns-3 ----------------------------------------------- If you want to download and use the development version of ns-3, you need to use the tool 'mercurial'. A quick and dirty cheat sheet is included in doc/mercurial.txt but reading through the mercurial tutorials included on the mercurial website is usually a good idea if you are not familiar with it. If you have successfully installed mercurial, you can get a copy of the development version with the following command: "hg clone http://code.nsnam.org/ns-3-dev"
03-08
<think>我们被要求生成相关问题,并且根据用户的问题,用户想要获取有关使用PyTorch的深度学习教程资源,特别是提到了一个名为"Deep Learning with PyTorch Tutorials"的GitHub仓库。 由于用户的问题非常具体,我们可以先尝试回答这个仓库的相关信息,然后根据我们的知识生成一些相关问题。 但是,请注意,我们并没有直接关于这个特定仓库的引用。不过,我们可以根据一般知识来回答,并利用引用中提到的资源来提供一些相关的PyTorch学习资源。 根据引用[1],有一个GitHub资源列表,其中包含450个NLP/CV/SP、论文实现、教程、示例。我们可以从中挑选一些教程资源。 然而,用户明确提到了一个名为"Deep Learning with PyTorch Tutorials"的GitHub仓库。实际上,PyTorch官方有一个教程仓库,名为pytorch/tutorials,这可能是用户所指的。 因此,我们可以这样回答: 官方PyTorch教程仓库:https://github.com/pytorch/tutorials 这个仓库包含了一系列从基础到高级的教程,涵盖了深度学习的不同方面,包括图像分类、序列模型、生成对抗网络、强化学习等。 此外,引用[1]中还列出了一些其他资源,例如: - deep-auto-punctuation: 一个基于字符的自动标点符号学习的PyTorch实现。 - Realtime_Multi-Person_Pose_Estimation: 实时多人姿态估计的PyTorch版本。 - 等等。 我们也可以推荐一些其他优秀的教程资源。 然后,我们生成相关问题。 注意:由于引用[2]和[3]主要涉及模型部署和训练(如Triton服务器和DeepSpeed),如果用户的问题扩展到这些领域,我们也可以提及。 因此,回答结构如下: 1. 首先介绍用户明确询问的官方教程仓库。 2. 然后介绍引用[1]中其他相关的教程和示例项目。 3. 简要提及模型部署(引用[2])和训练优化(引用[3])的资源,作为扩展。 最后,生成相关问题。 注意:我们被要求使用中文回答。</think>以下是关于PyTorch深度学习教程资源的推荐,特别关注官方及高质量GitHub资源: ### 1. **PyTorch官方教程仓库** GitHub地址:[pytorch/tutorials](https://github.com/pytorch/tutorials) - **核心内容**: - 基础操作(张量运算、自动微分) - 图像分类(ResNet、CNN实战) - NLP应用(RNN、Transformer) - 生成模型(GAN、VAE) - 部署指南(TorchScript、ONNX) - **特点**: 所有教程附带完整代码和数据集,支持Jupyter Notebook交互式学习[^1]。 ### 2. **精选第三方资源(引用[1])** - **论文复现项目**: - [deep-auto-punctuation](https://github.com/geyang/deep-auto-punctuation):基于字符的标点生成模型 - [Realtime_Multi-Person_Pose_Estimation](https://github.com/tensorboy/pytorch_Realtime_Multi-Person_Pose_Estimation):实时人体姿态估计 - **进阶实现**: - [pytorch_value-iteration-networks](https://github.com/kentsommer/pytorch-value-iteration-networks):强化学习算法实现 - [pytorch_Highway](https://github.com/kefth/pytorch-highway):高速神经网络实现 ### 3. **部署与优化(引用[2])** - **Triton推理服务器**: 支持Torch-TensorRT模型部署,优化并发执行和gRPC通信,详见[Triton部署文档](https://github.com/triton-inference-server/server)[^2]。 - **训练加速(引用[3])**: 使用`DeepSpeed ZeRO 3`和混合精度(`--bf16`/`--fp16`)提升训练效率[^3]。 ### 4. **学习路径建议** ```mermaid graph LR A[基础] --> B(张量操作/自动微分) B --> C[图像分类-CNN] B --> D[NLP-RNN/Transformer] C --> E[生成模型-GAN/VAE] D --> E E --> F[部署优化-TorchScript/Triton] ```
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