linux 下发布项目

昨天都今天忙着发布一个项目,不怕笑话,还是我第一次在linux下发布,以前都是在windows下本地整,什么都不管,想怎么弄就怎么弄,大不了重装tomcat,毕竟是服务器,不可乱来,所以都有点畏首畏尾的。
一:拷贝整个webroot下面的内容到机器指定位置。
二:打包代码成jar文件,切忌不能把有些配置文件打包进去。放到webroot-》lib里面。
三:配置tomcat,部署项目有三种方式,意识配置一个xml文件到conf-》Catalina-》localhost下面文件内容:
<?xml version="1.0" encoding="UTF-8" ?>
  <Context docBase="项目路径" privileged="true" antiResourceLocking="false" antiJARLocking="false" />
四:重启tomcat,查看日志文件,很重要,有什么错误就一个一个出来,处理方法很多,一步步来。
  1,ÑÏÖØ: Error configuring application listener of class org.springframework.web.util.Log4jConfigListener
java.lang.ClassNotFoundException: org.springframework.web.util.Log4jConfigListener
        at org.apache.catalina.loader.WebappClassLoader.loadClass(WebappClassLoader.java:1355)
        at org.apache.catalina.loader.WebappClassLoader.loadClass(WebappClassLoader.java:1201)
        at org.apache.catalina.core.StandardContext.listenerStart(StandardContext.java:3711)
        at org.apache.catalina.core.StandardContext.start(StandardContext.java:4211)
        at org.apache.catalina.core.ContainerBase.addChildInternal(ContainerBase.java:759)
        at org.apache.catalina.core.ContainerBase.addChild(ContainerBase.java:739)
        at org.apache.catalina.core.StandardHost.addChild(StandardHost.java:524)
        at org.apache.catalina.startup.HostConfig.deployDirectory(HostConfig.java:904)
        at org.apache.catalina.startup.HostConfig.deployDirectories(HostConfig.java:867)
        at org.apache.catalina.startup.HostConfig.deployApps(HostConfig.java:474)
        at org.apache.catalina.startup.HostConfig.start(HostConfig.java:1122)
        at org.apache.catalina.startup.HostConfig.lifecycleEvent(HostConfig.java:310)
        at org.apache.catalina.util.LifecycleSupport.fireLifecycleEvent(LifecycleSupport.java:119)
        at org.apache.catalina.core.ContainerBase.start(ContainerBase.java:1021)
        at org.apache.catalina.core.StandardHost.start(StandardHost.java:718)
        at org.apache.catalina.core.ContainerBase.start(ContainerBase.java:1013)
        at org.apache.catalina.core.StandardEngine.start(StandardEngine.java:442)
        at org.apache.catalina.core.StandardService.start(StandardService.java:450)
        at org.apache.catalina.core.StandardServer.start(StandardServer.java:709)
        at org.apache.catalina.startup.Catalina.start(Catalina.java:551)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
        at java.lang.reflect.Method.invoke(Method.java:597)
        at org.apache.catalina.startup.Bootstrap.start(Bootstrap.java:294)
        at org.apache.catalina.startup.Bootstrap.main(Bootstrap.java:432)
2010-2-5 9:08:05 org.apache.catalina.core.StandardContext listenerStart
ÑÏÖØ: Error configuring application listener of class org.springframework.web.context.ContextLoaderListener
java.lang.ClassNotFoundException: org.springframework.web.context.ContextLoaderListener
        at org.apache.catalina.loader.WebappClassLoader.loadClass(WebappClassLoader.java:1355)
        at org.apache.catalina.loader.WebappClassLoader.loadClass(WebappClassLoader.java:1201)
        at org.apache.catalina.core.StandardContext.listenerStart(StandardContext.java:3711)
        at org.apache.catalina.core.StandardContext.start(StandardContext.java:4211)



java.lang.ClassNotFoundException: org.springframework.web.context.ContextLoaderListener  缺少spring.jar
java.lang.ClassNotFoundException: org.springframework.web.util.Log4jConfigListener      缺少 logg。jar


at org.apache.catalina.startup.Bootstrap.main(Bootstrap.java:432)
ERROR 2010-02-05 09:35:59,446 StandardContext:listenerStart - Exception sending context initialized event to listener instance of class org.springframework.web.context.ContextLoaderListener
org.springframework.beans.factory.BeanDefinitionStoreException: Line 4 in XML document from URL [file:/home/cqxs3/www/thirdpartyweb/WEB-INF/classes/applicationContext.xml] is invalid; nested exception is org.xml.sax.SAXParseException: Document root element "beans", must match DOCTYPE root "null".
org.xml.sax.SAXParseException: Document root element "beans", must match DOCTYPE root "null".


这是applicationContext.xml的配置问题,spring1.x 和spring2.x 的冲突,解析applicationContext.xml失败,替换成<!DOCTYPE beans PUBLIC "-//SPRING//DTD BEAN 2.0//EN" "http://www.springframework.org/dtd/spring-beans-2.0.dtd" >

ERROR 2010-02-05 09:58:17,939 StandardContext:listenerStart - Exception sending context initialized event to listener instance of class org.springframework.web.context.ContextLoaderListener
org.springframework.beans.factory.BeanDefinitionStoreException: IOException parsing XML document from URL [file:/home/cqxs3/www/thirdpartyweb/WEB-INF/classes/applicationContext.xml]; nested exception is java.net.UnknownHostException: www.springframework.orgj

因为那台服务器不能连接外网,而jar里面有没有spring—bean。jar,默认情况,显示寻找本地的http://www.springframework.org/dtd/spring-beans-2.0.dtd如果失败再从网上找,解决办法可以吧http://指引到本地  改成file:// 或者添加spring—bean。jar。

实在无奈,我把所有的spring包到传上去了,
ERROR 2010-02-05 10:30:46,535 StandardContext:filterStart - Exception starting filter struts2
The package name 'struts' at location package - jar:file:/home/cqxs3/www/thirdpartyweb/WEB-INFb/thirdpartyweb.jar!/struts.xml:10:50 is already been used by another package at location package - file:/home/cqxs3/www/thirdpartyweb/WEB-INF/classes/struts.xml:10:50 - package - jar:file:/home/cqxs3/www/thirdpartyweb/WEB-INFb/thirdpartyweb.jar!/struts.xml:10:50
  这是之前提到的,我们java代码打包成jar时不能把配置文件也打进去。


经过这么一次,学习不少东西,遇到问题,先要努力找到问题的原因,找到原因了就成功一半了。比如发现部署失败了,先看tomcat日志,查看日志提示的是什么错误,才好对症下药。努力ing!!!!在此也感谢我们老刘同志,谢谢!。

DQN(Deep Q-Network)是一种使用深度神经网络实现的强化学习算法,用于解决离散动作空间的问题。在PyTorch中实现DQN可以分为以下几个步骤: 1. 定义神经网络:使用PyTorch定义一个包含多个全连接层的神经网络,输入为状态空间的维度,输出为动作空间的维度。 ```python import torch.nn as nn import torch.nn.functional as F class QNet(nn.Module): def __init__(self, state_dim, action_dim): super(QNet, self).__init__() self.fc1 = nn.Linear(state_dim, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, action_dim) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x ``` 2. 定义经验回放缓存:包含多条经验,每条经验包含一个状态、一个动作、一个奖励和下一个状态。 ```python import random class ReplayBuffer(object): def __init__(self, max_size): self.buffer = [] self.max_size = max_size def push(self, state, action, reward, next_state): if len(self.buffer) < self.max_size: self.buffer.append((state, action, reward, next_state)) else: self.buffer.pop(0) self.buffer.append((state, action, reward, next_state)) def sample(self, batch_size): state, action, reward, next_state = zip(*random.sample(self.buffer, batch_size)) return torch.stack(state), torch.tensor(action), torch.tensor(reward), torch.stack(next_state) ``` 3. 定义DQN算法:使用PyTorch定义DQN算法,包含训练和预测两个方法。 ```python class DQN(object): def __init__(self, state_dim, action_dim, gamma, epsilon, lr): self.qnet = QNet(state_dim, action_dim) self.target_qnet = QNet(state_dim, action_dim) self.gamma = gamma self.epsilon = epsilon self.lr = lr self.optimizer = torch.optim.Adam(self.qnet.parameters(), lr=self.lr) self.buffer = ReplayBuffer(100000) self.loss_fn = nn.MSELoss() def act(self, state): if random.random() < self.epsilon: return random.randint(0, action_dim - 1) else: with torch.no_grad(): q_values = self.qnet(state) return q_values.argmax().item() def train(self, batch_size): state, action, reward, next_state = self.buffer.sample(batch_size) q_values = self.qnet(state).gather(1, action.unsqueeze(1)).squeeze(1) target_q_values = self.target_qnet(next_state).max(1)[0].detach() expected_q_values = reward + self.gamma * target_q_values loss = self.loss_fn(q_values, expected_q_values) self.optimizer.zero_grad() loss.backward() self.optimizer.step() def update_target_qnet(self): self.target_qnet.load_state_dict(self.qnet.state_dict()) ``` 4. 训练模型:使用DQN算法进行训练,并更新目标Q网络。 ```python dqn = DQN(state_dim, action_dim, gamma=0.99, epsilon=1.0, lr=0.001) for episode in range(num_episodes): state = env.reset() total_reward = 0 for step in range(max_steps): action = dqn.act(torch.tensor(state, dtype=torch.float32)) next_state, reward, done, _ = env.step(action) dqn.buffer.push(torch.tensor(state, dtype=torch.float32), action, reward, torch.tensor(next_state, dtype=torch.float32)) state = next_state total_reward += reward if len(dqn.buffer.buffer) > batch_size: dqn.train(batch_size) if step % target_update == 0: dqn.update_target_qnet() if done: break dqn.epsilon = max(0.01, dqn.epsilon * 0.995) ``` 5. 测试模型:使用训练好的模型进行测试。 ```python total_reward = 0 state = env.reset() while True: action = dqn.act(torch.tensor(state, dtype=torch.float32)) next_state, reward, done, _ = env.step(action) state = next_state total_reward += reward if done: break print("Total reward: {}".format(total_reward)) ``` 以上就是在PyTorch中实现DQN强化学习的基本步骤。需要注意的是,DQN算法中还有很多细节和超参数需要调整,具体实现过程需要根据具体问题进行调整。
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