In this article, we will introduce one of the core elements describing the mathematics of neural networks: tensors.

Although typically, you won’t work directly with tensors (usually they operate under the hood), it is important to understand what’s going on behind the scenes. In addition, you may often wish to examine tensors so that you can look directly at the data, or look at the arrays of weights and biases, so it’s important to be able to work with tensors.

Note: This article assumes you are familiar with how neural networks work. To review those basics, see the article The Magic of Neural Networks: History and Concepts. It also assumes you have some familiarity with Python’s object oriented programming.

Theoretically, we could use pure Python to implement neural networks.

• We could use Python lists to represent data in the network;
• We could use other lists representing weights and biases in the network; and
• We could use nested for loops to perform the operations of multiplying the inputs by the connection weights.

There are a few issues with this, however: Python, especially the list data type, performs rather slowly. Also, the code would not be very readable with nested for loops.

Instead, the libraries that implement neural networks in software packages such as PyTorch use tensors, and they run much more quickly than pure Python. Also, as you will see, tensors allow much more readable descriptions of networks and their data.

Tensors

Tensors are essentially arrays of values. Since neural networks are essentially arrays of neurons, tensors are a natural fit for describing them. They can be used for describing the data, describing the network connection weights, and other things.

A one-dimensional tensor is known as a vector. Here is an example:

Vectors can also be written horizontally. Here’s the same vector written horizontally:

Switching a vector from vertical to horizontal, or vice versa, is called transposing, and is sometimes needed depending on the math specifics. We will not go into detail on this in this article (see here for more).

Vectors are typically used to represent data in the network. For example, each individual element in a vector can represent the input value for each individual input neuron in the network.

2D Tensor Matrix

A two-dimensional tensor is known as a matrix. Here’s an example:

For a fully connected network, where each neuron in one layer connects to every neuron in the next layer, a matrix is typically used to represent all the connection weights. If there are m neurons connected to n neurons you would need an n x m matrix to describe all the connection weights.

Here’s an example of two neurons connected to three neurons. Here is the network, with connection weights included:

And here is the connection weights matrix:

Why We Use Tensors

Before we finish introducing tensors, let’s use what we’ve seen so far to see why they’re so important to use when modeling neural networks.

Let’s introduce a two-element vector of data and run it through the network we just showed.

Info: Recall neurons add together their weighted inputs, then run the result through an activation function.

In this example, we are ignoring the activation function to keep things simple for the demonstration.

Here is our data vector:

Here’s a diagram depicting the operation:

Let’s calculate the operation (the neuron computations) by hand:

The final result is a 3 element vector:

If you have learned about matrices in grade school and remember doing matrix multiplication, you may note that what we just calculated is identical to matrix multiplication:

Note: Recall matrix multiplication involves multiplying first matrix rows by second matrix columns element-wise, then adding elements together.

This is why tensors are so important for neural networks: tensor math precisely describes neural network operation.

As an added benefit, the equation above showing matrix multiplication is so much more a succinct description than nested for loops would be.

If we introduce the nomenclature of bold lower case for a vector and bold upper case for a matrix, then the operation of vector data running through a neural network weight matrix is described by this very compact equation:

We will see later that matrix multiplication within PyTorch is a similarly compact code equation.

Higher Dimensional Tensors

A three-dimensional (3D) tensor is known simply as a tensor. As you can see, the term tensor generically refers to any dimensional array of numbers. It’s just one-dimensional and two-dimensional tensors that have the unique names “vector” and “matrix” respectively.

You might not think that there is a need for three-dimensional and larger tensors, but that’s not quite true.

A grayscale image is clearly a two-dimensional tensor, in other words, a matrix. But a color image is actually three two-dimensional arrays, one each for red, green, and blue color channels. So a color image is essentially a three-dimensional tensor.

In addition, typically we process data in mini-batches. So if we’re processing a mini-batch of color images we have the three-dimensional aspect already noted, plus one more dimension of the list of images in the mini-batch. So a mini-batch of color images can be represented by a four-dimensional tensor.

Tensors in Neural Network Libraries

One Python library that is well suited to working with arrays is NumPy. In fact, NumPy is used by some users for implementing neural networks. One example is the scikit-learn machine learning library which works with NumPy.

However, the PyTorch implementation of tensors is more powerful than NumPy arrays. PyTorch tensors are designed with neural networks in mind. PyTorch tensors have these advantages:

1. PyTorch tensors include gradient calculations integrated into them.
2. PyTorch tensors also support GPU calculations, substantially speeding up neural network calculations.

However, if you are used to working with NumPy, you should feel fairly at home with PyTorch tensors. Though the commands to create PyTorch tensors are slightly different, they will feel fairly familiar. For the rest of this article, we will focus exclusively on PyTorch tensors.

Tensors in PyTorch: Creating Them, and Doing Math

OK, let’s finally do some coding!

First, make sure that you have PyTorch available, either by installing on your system or by accessing it through online Jupyter notebook servers.

Reference: See PyTorch’s website for instructions on how to install it on your own system.

See this Finxter article for a review of available online Jupyter notebook services:

Recommended Tutorial: Top 4 Jupyter Notebook Alternatives for Machine Learning

For this article, we will use the online Jupyter notebook service provided by Google called Colab. PyTorch is already installed in Colab; we simply have to import it as a module to use it:

import torch

There are a number of ways of creating tensors in PyTorch.

Typically you would be creating tensors by importing data from data sets available through PyTorch, or by converting your own data into tensors.

For now, since we simply want to demonstrate the use of tensors we will use basic commands to create very simple tensors.

You can create a tensor from a list:

t_list = torch.tensor([[1,2], [3,4]])

t_list

Output:

tensor([[1, 2], [3, 4]])

Note that when we evaluate the tensor variable, the output is labeled to indicate it as a tensor. This means that it is a PyTorch tensor object, so an object within PyTorch that performs just like math tensors, plus has various features provided by PyTorch (such as supporting gradient calculations, and supporting GPU processing).

You can create tensors filled with zeros, filled with ones, or filled with random numbers:

t_zeros = torch.zeros(2,3)

t_zeros

Output:

tensor([[0., 0., 0.], [0., 0., 0.]])

t_ones = torch.ones(3,2)

t_ones

Output:

tensor([[1., 1.], [1., 1.], [1., 1.]])

t_rand = torch.rand(3,2,4)

t_rand

Output:

tensor([[[0.9661, 0.3915, 0.0263, 0.2753], [0.7866, 0.0503, 0.3963, 0.1334]], [[0.4085, 0.1816, 0.2827, 0.3428], [0.9923, 0.4543, 0.0872, 0.0771]], [[0.2451, 0.6048, 0.8686, 0.8148], [0.7930, 0.4150, 0.6125, 0.3401]]])

An important attribute to be familiar with to understand the shape of a tensor is the appropriately named shape attribute:

t_rand.shape

# Output: torch.Size([3, 2, 4])

This shows you that tensor “t_rand” is a three-dimensional tensor composed of three elements of two rows by four columns.

Note: The dimensions of a tensor is referred to as its rank. A one-dimensional tensor, or vector, is a rank-1 tensor; a two-dimensional tensor, or matrix, is a rank-2 tensor; a three-dimensional tensor is a rank-3 tensor, and so on.

Let’s do some math with tensors – let’s add two tensors together:

Note the tensors are added together element-wise. Now here it is in PyTorch:

t_first = torch.tensor([[1,2], [3,4]])

t_second = torch.tensor([[5,6],[7,8]])

t_sum = t_first + t_second

t_sum

Output:

tensor([[ 6, 8], [10, 12]])

Let’s add a scalar, that is, an independent number (or a rank-0 tensor!) to a tensor:

t_add3 = t_first + 3

t_add3

Output:

tensor([[4, 5], [6, 7]])

Note that the scalar is added to each element of the tensor. The same applies when multiplying a scalar by a tensor:

t_times3 = t_first * 3

t_times3

Output:

tensor([[ 3, 6], [ 9, 12]])

The same kind of thing applies to raising a tensor to a power, that is the power operation is applied element-wise:

t_squared = t_first ** 2

t_squared

Output:

tensor([[ 1, 4], [ 9, 16]])

Recall that after summing weighted inputs, the neuron processes the result through an activation function. Note that the same performance applies here as well: when a vector is processed through an activation function, the operation is applied to the vector element-wise.

Earlier, we pointed out that matrix multiplication is an important part of neural network calculations.

There are two ways to do this in PyTorch: you can use the matmul function:

t_matmul1 = torch.matmul(t_first, t_second)

t_matmul1

Output:

tensor([[19, 22], [43, 50]])

Or you can use the matrix multiplication symbol “@“:

t_matmul2 = t_first @ t_second

t_matmul2



Output:

tensor([[19, 22], [43, 50]])

Recall previously, we showed running an input signal through a neural network, where a vector of input signals was multiplied by a matrix of connection weights.

Here is that in PyTorch:

x = torch.tensor([[7],[8]])

x

Output:

tensor([[7], [8]])

W = torch.tensor([[1,4], [2,5], [3,6]])

W

Output:

tensor([[1, 4], [2, 5], [3, 6]])

y = W @ x

y



Output:

tensor([[39], [54], [69]])

Note how compact and readable that is instead of doing nested for loops.

Other math can be done with tensors as well, but we have covered most situations that are relevant to neural networks. If you find you need to do additional math with your tensors, check PyTorch documentation or do a web search.

Indexing and Slicing Tensors

Slicing allows you to examine subsets of your data and better understand how the dataset is constructed. You may find you will use this a lot.

Indexing Slicing PyTorch vs NumPy vs Python Lists

Indexing and slicing tensors work the same way it does with NumPy arrays. Note that the syntax is different from Python lists. With Python lists, a separate pair of brackets are used for each level of nested lists. Instead, with Pytorch one pair of brackets contains all dimensions, separated by commas.

Let’s find the item in tensor “t_rand” that is 2nd element, first row, third column. First here is “t_rand” again:

t_rand

Output:

tensor([[[0.9661, 0.3915, 0.0263, 0.2753], [0.7866, 0.0503, 0.3963, 0.1334]], [[0.4085, 0.1816, 0.2827, 0.3428], [0.9923, 0.4543, 0.0872, 0.0771]], [[0.2451, 0.6048, 0.8686, 0.8148], [0.7930, 0.4150, 0.6125, 0.3401]]])

And here is the item at the 2nd element, first row, and third column (don’t forget indexing starts at zero):

t_rand[1, 0, 2]

# Output: tensor(0.2827)

Let’s look at the slice second element, first row, second through third columns:

t_rand[1, 0, 1:3]

# tensor([0.1816, 0.2827])

Let’s look at the entire 3rd column:

t_rand[:, :, 2]

Output:

tensor([[0.0263, 0.3963], [0.2827, 0.0872], [0.8686, 0.6125]])

Important Slicing Tip: In the above, we use the standard Python convention that a blank before a “:” means “start from the beginning”, and a blank after a “:” means “go all the way to the end”. So a “:” alone means “include everything from beginning to end”.

A likely use for slicing would be to look at a full array (i.e. a matrix) within a set of arrays, i.e. one image out of a set of images.

Let’s pretend our “t_rand” tensor is a list of images. We may wish to sample just a few “images” to get an idea of what they are like.

Let’s examine the first “image” in our tensor (“list of images”):

t_rand[0]

Output:

tensor([[0.9661, 0.3915, 0.0263, 0.2753], [0.7866, 0.0503, 0.3963, 0.1334]])

And here is the last array (“image”) in tensor “t_rand”:

t_rand[-1]

Output:

tensor([[0.2451, 0.6048, 0.8686, 0.8148], [0.7930, 0.4150, 0.6125, 0.3401]])

Using small tensors to demonstrate indexing can be instructive, but let’s see it in action for real. Let’s examine some real datasets with real images.

Real Example

We won’t describe the following in detail, except to note that we are importing various libraries that allow us to download and work with a dataset. The last line creates a function that converts tensors into PIL images:

import torch

from torch.utils.data import Dataset

from torchvision import datasets

from torchvision.transforms import ToTensor

import matplotlib.pyplot as plt import torchvision.transforms as T conv_to_PIL = T.ToPILImage()

The following downloads the Caltech 101 dataset, which is a collection of over 8000 images in 101 categories:

caltech101_data = datasets.Caltech101( root="data", download=True, transform=ToTensor()

)

Extracting data/caltech101/101_ObjectCategories.tar.gz to data/caltech101

Extracting data/caltech101/Annotations.tar to data/caltech101

This has created a dataset object which is a container for the data. These objects can be indexed like lists:

len(caltech101_data)

# 8677 type(caltech101_data[0])

# tuple len(caltech101_data[0])

# 2

The above code shows the dataset contains 8677 items. Looking at the first item of the set we can see they are tuples of 2 items each. Here are the kinds of items in the tuples:

type(caltech101_data[0][0])

# torch.Tensor type(caltech101_data[0][1])

# int

The two items in the tuple are the image as a tensor, and an integer code corresponding to the image’s category.

Colab has a convenient function display() which will display images. First, we use the conversion function we created earlier to convert our tensors to a PIL image, then we display the images.

img = conv_to_PIL(caltech101_data[0][0])

display(img)

We can use indexing to sample and display a few other images from the set:

img = conv_to_PIL(caltech101_data[1234][0])

display(img)

img = conv_to_PIL(caltech101_data[4321][0])

display(img)

Summary

We have learned a number of things:

1. What tensors are
2. Why tensors are key mathematical objects for describing and implementing neural networks
3. Creating tensors in PyTorch
4. Doing math with tensors in PyTorch
5. Doing indexing and slicing of tensors in PyTorch, especially to examine images in datasets

We hope you have found this article informative. We wish you happy coding!

---

Programmer Humor

This article provides a lightweight JavaScript plugin to display news tickers on a website. The news ticker is a way of showing content in marquee mode either in horizontal or vertical scroll. It is useful to display content like the latest updates and upcoming events.

It saves the site space real estate by occupying less portion of the screen. It also reduces the user effort of scrolling to see more content by keeping on ticking the content display.

In a way it is an older thing. Couple of decades back we cannot see a website without a scrolling ticker. Over a period its eradicated as a bad UI/UX practice. But it is still widely used in news websites and in particular in stock price display. If you use it wisely, it provides good advantages.

The following examples will remind you of the places that require news tickers on screen.

1. Online news bytes display headlines in a ticker.
2. Stock prices.
3. Online shops that show ‘what is new’ on a ticker board.

This tutorial shows a simple news ticker on a webpage. On hovering the ticker box, it stops the content marquee and releases on mouse out.

It will look like a carousal effect but applied to an element with text content.

View Demo

News ticker features

1. Ultra lightweight; Just 2KB.
2. Plain JavaScript. Standalone and not dependent on any other libraries like JQuery. Of course, if needed you can use it along with JQuery.
3. Fully Responsive.

Usage

You can integrate this news ticker in a web page in three simple steps.

1. Include the JavaScript library file.
2. Ticker content as HTML unordered list in a div with an id.
3. Call startTicker JavaScript function immediately next to ticker-box div.

STEP 1: Download and include the JavaScript library file.

<script src="news-ticker.js"></script>

STEP 2: Ticker content as HTML unordered list in a div with an id.

<div id="ticker-box"> <ul> <li>First ticker item.</li> <li>Second ticker item.</li> <li>Final ticker item.</li> </ul>

</div>

STEP 3: Call startTicker JavaScript function immediately next to ticker-box div.

This step is to call the library function with reference to the ticker box id attribute.

The startTicker() function has an optional parameter to supply the speed and interval between news contents. The default speed is 5 and the default interval is 500 milliseconds.

<script>startTicker('ticker-box');</script>

[OR]

<script>startTicker('ticker-box', {speed:7, delay:1000});</script>

News ticker JavaScript library code

This library contains functions to enable a news ticker on a web page. The startTicker() function iterates the ticker <li> elements and let it slides horizontally.

It applies styles to change the position of the ticker element based on the speed. The extend() function changes the default speed and interval with the specified option.

function applyStyles(obj, styles) { var property; var styleLength = Object.keys(styles).length; for (var i = 0; i < styleLength; i++) { property = Object.keys(styles)[i]; obj.style[property] = styles[property]; }

} function extend(object1, object2) { for (var attrname in object2) { object1[attrname] = object2[attrname]; } return object1;

} function startTicker(id, param) { var tickerBox = document.getElementById(id); var defaultParam = { speed: 5, delay: 500, rotate: true }; var extendedParam = extend(defaultParam, param); applyStyles(tickerBox, { overflow: "hidden", 'min-height': '40px' }); var ul = tickerBox.getElementsByTagName("ul"); var li = ul[0].getElementsByTagName("li"); applyStyles(ul[0], { padding: 0, margin: 0, position: 'relative', 'list-style-type': 'none' }); for (i = 0; i < li.length; i++) { applyStyles(li[i], { position: 'absolute', 'white-space': 'nowrap', display: 'none' }); } var li_index = 0; var trans_width = tickerBox.offsetWidth; var chunk_width = 1; var iterateTickerElement = function(trans_width) { li[li_index].style.left = trans_width + "px"; li[li_index].style.display = ''; var t = setInterval(function() { if (parseInt(li[li_index].style.left) > -li[li_index].offsetWidth) { li[li_index].style.left = parseInt(li[li_index].style.left) - chunk_width + "px"; } else { clearInterval(t); trans_width = tickerBox.offsetWidth; li_index++; if (li_index == li.length && extendedParam.rotate == true) { li_index = 0; iterateTickerElement(trans_width); } else if (li_index < li.length) { setTimeout(function() { iterateTickerElement(trans_width); }, extendedParam.delay); } } }, extendedParam.speed); tickerBox.onmouseover = function() { clearInterval(t); } tickerBox.onmouseout = function() { iterateTickerElement(parseInt(li[li_index].style.left)); } } iterateTickerElement(trans_width);

}

Note:

1. Presently the news ticker is available only in a horizontal direction. For the next release, a vertical direction is planned.
2. Ticker movement can be paused on mouseover.
3. Contact me, if you have any feature requests or for any special customization needs.

View DemoDownload

↑ Back to Top

In this article, we continue building on our previous topic, the Solidity compiler installation:

Previous Topic: Solidity Compiler Installation (NPM)

The previous article was focused on an installation via npm, and in this article, we’ll go through the installation and use of the Solidity compiler via Docker.

Our goal is to get more familiar with the possibilities of this approach, as well as to get introduced to the technology that “runs the show”. This knowledge and experience will enable us to recognize the reasons behind choosing any of the approaches in the future, depending on the real-world needs of our projects.

What is Docker?

Before we go into details about the Docker installation of solc, let’s first get introduced to what Docker is.

Docker is an open platform for developing, shipping, and running applications… Docker provides the ability to package and run an application in a loosely isolated environment called a container… Containers are lightweight and contain everything needed to run the application, so you do not need to rely on what is currently installed on the host.

Source: https://docs.docker.com/get-started/overview/

There are some parts of the description I’ve deliberately left out (separated by the symbol …) because they’re not essential to our understanding of the technology.

Now, let’s dissect the Docker description: the keywords of our interest are platform, isolated environment, and container. Let’s quickly dive into each of those next

Platform

A platform is a software framework that supports a specific function or a goal.

The goal Docker supports is enabling a piece of software (application, service, etc.) to correctly run, regardless of the target environment.

For us, this means running the Solidity compiler, i.e. feeding it with the input source code and producing the output bytecode in the form of .abi and .bin files.

Isolated Environment

By mentioning an isolated environment, we remember the concept of virtualization learned about earlier, meaning that Docker enables our software to run as intended by providing it with the resources in form of software libraries, network access, remote services, and other dependencies.

Container

Docker ensures the resources are provided without additional intervention by arranging them in a package called a container. Containers begin their lifecycle as images that we most commonly download and run.

We can also create a Docker image, but that’s another story.

Running an image creates a live instance of it, a container. Before it can be used, a Docker image has to be prepared, meaning that someone should install and configure all the required resources needed for the software to run.

Preparation of a Docker image falls in the domain of DevOps, i.e. Development and Operations:

“DevOps engineers manage the operations of software development, implementing engineering tools and knowledge of the software development process to streamline software updates and creation.”

Source: https://www.indeed.com/hire/c/info/devops-engineer

Also, read our article:

Recommended Article: Top 20 Skills Every DevOps Engineer Ought to Have

Using Solidity Compiler via Docker

Now that we have introduced Docker in general, we are continuing with the installation of the Solidity compiler via Docker.

First, we have to check if Docker is present on our system by simultaneously checking the Docker version:

$ docker version

bash: /usr/bin/docker: No such file or directory

As our check shows, we have to install Docker on our system before we can use it. The installation process via the Ubuntu repository is made of several steps (https://docs.docker.com/engine/install/ubuntu/):

Step 1: Update the apt package index

$ sudo apt update

…

Reading package lists... Done

Building dependency tree Reading state information... Done

All packages are up to date.

Step 2: Install packages

Installation of additional packages; we need these packages to enable the installation process accessing the repository over the secure HTTPS connection (note the backslash symbol \ for the multiline command):

$ sudo apt install \

ca-certificates \

curl gnupg lsb-release

...

The following additional packages will be installed: gnupg-l10n gnupg-utils gpg-wks-server

Suggested packages: parcimonie xloadimage

The following NEW packages will be installed: ca-certificates curl gnupg gnupg-l10n gnupg-utils gpg-wks-server lsb-release

...

Do you want to continue? [Y/n] y

...

Step 3: Add Docker GPG key

Adding the Docker’s official GPG key:

$ sudo mkdir \

-p /etc/apt/keyrings

$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg \

| sudo gpg – dearmor -o /etc/apt/keyrings/docker.gpg

Info: “GPG, or GNU Privacy Guard, is a public key cryptography implementation. This allows for the secure transmission of information between parties and can be used to verify that the origin of a message is genuine.”

Source: https://www.digitalocean.com/community/tutorials/how-to-use-gpg-to-encrypt-and-sign-messages

Step 4: Set up repository

Setting up the repository by writing to docker.list file.

The echo command evaluates the text inside the $( ), populates it with the command outputs (in parentheses), and sends it via stdin to system utility sudo tee with root privileges, which in turn overwrites the docker.list file and omits the output by redirecting it to /dev/null:

$ echo \ "deb [arch=$(dpkg – print-architecture) \

signed-by=/etc/apt/keyrings/docker.gpg] \

https://download.docker.com/linux/ubuntu \

$(lsb_release -cs) stable" | sudo tee \

/etc/apt/sources.list.d/docker.list > /dev/null

Info: Repositories added by mistake can be removed from Ubuntu 20.04 by selectively deleting them in /etc/apt/sources.list.d/ directory.

Step 5: Update apt package index

Updating the apt package index (once again):

$ sudo apt update

...

Reading package lists... Done

Building dependency tree Reading state information... Done

All packages are up to date.

Step 6: Install Docker

Installing Docker (the latest stable version) and its components:

$ sudo apt-get install docker-ce docker-ce-cli containerd.io docker-compose-plugin

Reading package lists... Done

Building dependency tree Reading state information... Done

The following additional packages will be installed: docker-ce-rootless-extras docker-scan-plugin pigz slirp4netns

Suggested packages: aufs-tools cgroupfs-mount | cgroup-lite

The following NEW packages will be installed: containerd.io docker-ce docker-ce-cli docker-ce-rootless-extras docker-compose-plugin docker-scan-plugin pigz slirp4netns

0 upgraded, 8 newly installed, 0 to remove and 0 not upgraded.

Need to get 108 MB of archives.

After this operation, 449 MB of additional disk space will be used.

Do you want to continue? [Y/n] y

...

Let’s check the Docker version once again:

$ docker version

Client: Docker Engine - Community Version: 20.10.17 API version: 1.41 Go version: go1.17.11 Git commit: 100c701 Built: Mon Jun 6 23:02:57 2022 OS/Arch: linux/amd64 Context: default Experimental: true Server: Docker Engine - Community Engine: Version: 20.10.17 API version: 1.41 (minimum version 1.12) Go version: go1.17.11 Git commit: a89b842 Built: Mon Jun 6 23:01:03 2022 OS/Arch: linux/amd64 Experimental: false containerd: Version: 1.6.7 GitCommit: 0197261a30bf81f1ee8e6a4dd2dea0ef95d67ccb runc: Version: 1.1.3 GitCommit: v1.1.3-0-g6724737 docker-init: Version: 0.19.0 GitCommit: de40ad0

Now that we’re sure that our Docker installation went through and the Docker Engine version we have is 20.20.17 (at the time of writing this article). The next step is getting the Docker image with the Solidity compiler.

Docker images are identified by their release organization, image name (shorter, images), and tag, i.e. label that makes them unique. In general, we can download a Docker image by referencing it with its organization/image:tag marker.

We will download a Docker image of the Solidity compiler by specifying its marker as ethereum/solc:stable for a stable version, and ethereum/solc:nightly for the bleeding edge, potentially unstable version.

We can also specify a distinct version of the Solidity compiler by setting a tag to a specific version, e.g. ethereum/solc:0.5.4.

We will do three things with one Docker command: we’ll download the image, instantiate (run) a container from the image and print the container usage (flag – help):

docker run ethereum/solc:stable – help

Sure enough, we’d like to compile our Solidity files, so we’ll make three preparations (First, Second, Third):

First: Create a local directory containing our Solidity source code (I’ll use 1_Storage.sol from the Remix contracts folder by creating an empty file and pasting the content into it):

$ mkdir ~/solidity_src/ && cd ~/solidity_src/

$ touch 1_Storage.sol

Second: You can write your own contract for testing purposes or just open the 1_Storage.sol with your favorite text editor and paste the contents from 1_Storage.sol example in Remix.

Third: Run a Docker container (we already have the image so the download procedure will be skipped); command flag -v mounts our local ~/solidity_src directory to the container’s path /sources, path ethereum/solc:stable selects the Docker image to run a container, command flag -o sets the output location for the compiled files, --abi and --bin activate the generation of both .abi and .bin files, and the path /sources/1_Storage.sol selects the source file for compilation:

$ docker run -v ~/solidity_src:/sources ethereum/solc:stable -o /sources/output – abi – bin /sources/1_Storage.sol

Compiler run successful. Artifact(s) can be found in directory "/sources/output".

When checking our solidity_src directory, we’ll discover a new directory output, created by the Solidity compiler, containing both .abi and .bin files.

Docker also enables us to use the standard JSON interface, and it is a recommended approach when using the compiler with a toolchain. This interface doesn’t require mounted directories if the JSON input is self-contained, in other words, all the code is already contained in the source files and there are no references to external, imported files:

docker run ethereum/solc:stable – standard-json < input.json > output.json

Since we haven’t done any examples using the JSON interface, we’ll suspend this approach until a later time.

Conclusion

This article introduced us to a Solidity-supporting technology called Docker.

Of course, our main focus is on an ecosystem consisting of Solidity, Ethereum, blockchain technology, etc., but I recognized an opportunity of making a detour and walking us through the process of setting up and using the Solidity compiler via the Docker platform. Therefore, although initially unplanned, we’re also gaining some DevOps skills.

In the first and only chapter (yeah, I’m a bit surprised as well) we’ve set the mining charges by getting to know what Docker is. Then we blew a big piece of rock away by discovering how to install Docker on Ubuntu Linux (and by extension, some other operating systems). I believe this article will prove useful and provide multiple tips and tricks in terms of setting your development environment for Solidity on Ubuntu Linux. Besides that and personally speaking, it was always useful to gain secondary knowledge whenever I learned a specific topic, and I’m sure you’ll have the same experience.

Recommended Tutorial: Solidity Crash Course (by Matija)

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Learn Solidity Course

Solidity is the programming language of the future.

It gives you the rare and sought-after superpower to program against the “Internet Computer”, i.e., against decentralized Blockchains such as Ethereum, Binance Smart Chain, Ethereum Classic, Tron, and Avalanche – to mention just a few Blockchain infrastructures that support Solidity.

In particular, Solidity allows you to create smart contracts, i.e., pieces of code that automatically execute on specific conditions in a completely decentralized environment. For example, smart contracts empower you to create your own decentralized autonomous organizations (DAOs) that run on Blockchains without being subject to centralized control.

NFTs, DeFi, DAOs, and Blockchain-based games are all based on smart contracts.

This course is a simple, low-friction introduction to creating your first smart contract using the Remix IDE on the Ethereum testnet – without fluff, significant upfront costs to purchase ETH, or unnecessary complexity.