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Setting Column Rules in CSS3

To set column rules, use the shorthand column-rule property, which allows you to set the following properties −

column-rule-width: set the width of the rule between columns
column-rule-style: set the style of the rule between columns
column-rule-color: set the rule of the rule between columns

The value for column rules can be set as −

column-rule: column-rule-width column-rule-style column-rule-color|initial|inherit;

Example

Let us now see another example −

 Live Demo

<!DOCTYPE html>
<html>
<head>
<style>
.demo {
   column-count: 5;
   -webkit-column-count: 5; /* Chrome, Safari, Opera */
   -moz-column-count: 5; /* Firefox */
   -webkit-column-gap: normal; /* Chrome, Safari, Opera */
   -moz-column-gap: normal; /* Firefox */
   column-gap: normal;
   -webkit-column-rule: 5px dotted orange; /* Chrome, Safari, Opera */
   -moz-column-rule: 5px dotted orange; /* Firefox */
   column-rule: 5px dotted orange;
}
</style>
</head>
<body>
<h1>PyTorch</h1>
<div class="demo">
PyTorch is defined as an open source machine learning library for Python. It is used for applications such as natural language processing. It is initially developed by Facebook artificial-intelligence research group, and Uber’s Pyro software for probabilistic programming which is built on it.
Originally, PyTorch was developed by Hugh Perkins as a Python wrapper for the LusJIT based on Torch framework. There are two PyTorch variants.
PyTorch redesigns and implements Torch in Python while sharing the same core C libraries for the backend code. PyTorch developers tuned this back-end code to run Python efficiently. They also kept the GPU based hardware acceleration as well as the extensibility features that made Lua-based Torch.
</div>
</body>
</html>

Output

Example

Let us see an example wherein we are using all the properties used above as shorthand property column-rule −

 Live Demo

<!DOCTYPE html>
<html>
<head>
<style>
.demo {
   column-count: 4;
   -webkit-column-count: 4; /* Chrome, Safari, Opera */
   -moz-column-count: 4; /* Firefox */
   -webkit-column-gap: normal; /* Chrome, Safari, Opera */
   -moz-column-gap: normal; /* Firefox */
   column-gap: normal;
   -webkit-column-rule-width: 5px; /* Chrome, Safari, Opera */
   -moz-column-rule-width: 5px; /* Firefox */
   column-rule-width: 5px;
   -webkit-column-rule-color: blue; /* Chrome, Safari, Opera */
   -moz-column-rule-color: blue; /* Firefox */
   column-rule-color: blue;
   -webkit-column-rule-style: double; /* Chrome, Safari, Opera */
   -moz-column-rule-style: double; /* Firefox */
   column-rule-style: double;
}
</style>
</head>
<body>
<h1>PyTorch</h1>
<div class="demo">
PyTorch is defined as an open source machine learning library for Python. It is used for applications such as natural language processing. It is initially developed by Facebook artificial-intelligence research group, and Uber’s Pyro software for probabilistic programming which is built on it.
Originally, PyTorch was developed by Hugh Perkins as a Python wrapper for the LusJIT based on Torch framework. There are two PyTorch variants.
PyTorch redesigns and implements Torch in Python while sharing the same core C libraries for the backend code. PyTorch developers tuned this back-end code to run Python efficiently. They also kept the GPU based hardware acceleration as well as the extensibility features that made Lua-based Torch.
</div>
</body>
</html>

Output

Updated on 31-Dec-2019 10:50:16

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