- 从压缩包恢复镜像 执行下面的命令将镜像读入本地:
$ docker load < chatbot_image.tar.gz
- 从镜像运行容器 执行下面的命令,从镜像运行一个容器示例:
$ docker run -it --rm --name chatbot -p 8080:8080 -p 5000:5000 chatbot:v0.3 /run.sh
- 打开本机浏览器开始使用 打开浏览器,输入地址
127.0.0.1:8080
开始使用聊天界面。
from collections import namedtuple | |
from operator import itemgetter | |
from pprint import pformat | |
import numpy as np | |
class Node(namedtuple('Node', 'location left_child right_child')): | |
def __repr__(self): | |
return pformat(tuple(self)) |
<pseudocode> | |
<pre style="display:none;"> | |
\begin{algorithm} | |
\caption{Quicksort} | |
\begin{algorithmic} | |
\PROCEDURE{Quicksort}{$A, p, r$} | |
\end{algorithmic} | |
\end{algorithm} | |
</pre> | |
</pseudocode> |
x = tf.constant([[1], [2], [3], [4]], dtype=tf.float32) | |
y_true = tf.constant([[0], [-1], [-2], [-3]], dtype=tf.float32) | |
linear_model = tf.layers.Dense(units=1) | |
y_pred = linear_model(x) | |
loss = tf.losses.mean_squared_error(labels=y_true, predictions=y_pred) | |
optimizer = tf.train.GradientDescentOptimizer(0.01) | |
train = optimizer.minimize(loss) |
#!/bin/bash | |
LENGTH=1 | |
DELAY=5 | |
while true | |
do | |
for ANGLE in 0 90 180 270 | |
do | |
xdotool mousemove_relative --polar $ANGLE $LENGTH | |
sleep $DELAY | |
done |
import itchat | |
import os | |
import math | |
from PIL import Image | |
itchat.auto_login(hotReload=True) # 扫码登录微信 | |
if not os.path.exists('img'): # 如果同目录没有img目录 | |
os.mkdir('img') # 创建img目录 |
$ docker load < chatbot_image.tar.gz
$ docker run -it --rm --name chatbot -p 8080:8080 -p 5000:5000 chatbot:v0.3 /run.sh
127.0.0.1:8080
开始使用聊天界面。""" | |
获取最新 IP 地址 | |
运行需要 requests 库: | |
$ pip install requests | |
""" | |
import requests |
\documentclass[8pt, t, aspectratio=169, compress]{beamer} | |
\usetheme{Berlin} | |
\usepackage{xeCJK} | |
\setCJKmainfont{Noto Sans CJK SC} | |
\usefonttheme[onlymath]{serif} | |
\usepackage{txfonts} | |
\usepackage[T1]{fontenc} |
import torch | |
import torch.nn as nn | |
from torch.distributions.categorical import Categorical | |
from torch.optim import Adam | |
import numpy as np | |
import gym | |
from gym.spaces import Discrete, Box | |
def mlp(sizes, activation=nn.Tanh, output_activation=nn.Identity): | |
# 构建一个前向神经网络 |