DEV Community: Arman Chand

Volumes in Docker

Arman Chand — Wed, 03 Apr 2024 12:03:36 +0000

Problem Statement

It is a very common requirement to persist the data in a Docker container beyond the lifetime of the container. However, the file system
of a Docker container is deleted/removed when the container dies.

Solution

There are 2 different ways how docker solves this problem.

Volumes
Bind Directory on a host as a Mount

Volumes

Volumes aims to solve the same problem by providing a way to store data on the host file system, separate from the container's file system,
so that the data can persist even if the container is deleted and recreated.

Volumes can be created and managed using the docker volume command. You can create a new volume using the following command:

docker volume create <volume_name>

Once a volume is created, you can mount it to a container using the -v or --mount option when running a docker run command.

For example:

docker run -it -v <volume_name>:/data <image_name> /bin/bash

This command will mount the volume to the /data directory in the container. Any data written to the /data directory
inside the container will be persisted in the volume on the host file system.

Bind Directory on a host as a Mount

Bind mounts also aims to solve the same problem but in a complete different way.

Using this way, user can mount a directory from the host file system into a container. Bind mounts have the same behavior as volumes, but
are specified using a host path instead of a volume name.

For example,

docker run -it -v <host_path>:<container_path> <image_name> /bin/bash

Key Differences between Volumes and Bind Directory on a host as a Mount

Volumes are managed, created, mounted and deleted using the Docker API. However, Volumes are more flexible than bind mounts, as
they can be managed and backed up separately from the host file system, and can be moved between containers and hosts.

In a nutshell, Bind Directory on a host as a Mount are appropriate for simple use cases where you need to mount a directory from the host file system into
a container, while volumes are better suited for more complex use cases where you need more control over the data being persisted
in the container.

Welcome to world of Containerization

Arman Chand — Wed, 03 Apr 2024 03:20:07 +0000

What is a container ?

A container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another. A Docker container image is a lightweight, standalone, executable package of software that includes everything needed to run an application: code, runtime, system tools, system libraries and settings.

Ok, let me make it easy !!!

A container is a bundle of Application, Application libraries required to run your application and the minimum system dependencies.

Containers vs Virtual Machine

Containers and virtual machines are both technologies used to isolate applications and their dependencies, but they have some key differences:

1. Resource Utilization: Containers share the host operating system kernel, making them lighter and faster than VMs. VMs have a full-fledged OS and hypervisor, making them more resource-intensive.

2. Portability: Containers are designed to be portable and can run on any system with a compatible host operating system. VMs are less portable as they need a compatible hypervisor to run.

3. Security: VMs provide a higher level of security as each VM has its own operating system and can be isolated from the host and other VMs. Containers provide less isolation, as they share the host operating system.

Management: Managing containers is typically easier than managing VMs, as containers are designed to be lightweight and fast-moving.

Why are containers light weight ?

Containers are lightweight because they use a technology called containerization, which allows them to share the host operating system's kernel and libraries, while still providing isolation for the application and its dependencies. This results in a smaller footprint compared to traditional virtual machines, as the containers do not need to include a full operating system. Additionally, Docker containers are designed to be minimal, only including what is necessary for the application to run, further reducing their size.

Let's try to understand this with an example:

Below is the screenshot of official ubuntu base image which you can use for your container. It's just ~ 22 MB, isn't it very small ? on a contrary if you look at official ubuntu VM image it will be close to ~ 2.3 GB. So the container base image is almost 100 times less than VM image.

To provide a better picture of files and folders that containers base images have and files and folders that containers use from host operating system (not 100 percent accurate -> varies from base image to base image). Refer below.

Files and Folders in containers base images

    /bin: contains binary executable files, such as the ls, cp, and ps commands.

    /sbin: contains system binary executable files, such as the init and shutdown commands.

    /etc: contains configuration files for various system services.

    /lib: contains library files that are used by the binary executables.

    /usr: contains user-related files and utilities, such as applications, libraries, and documentation.

    /var: contains variable data, such as log files, spool files, and temporary files.

    /root: is the home directory of the root user.

Files and Folders that containers use from host operating system

    The host's file system: Docker containers can access the host file system using bind mounts, which allow the container to read and write files in the host file system.

    Networking stack: The host's networking stack is used to provide network connectivity to the container. Docker containers can be connected to the host's network directly or through a virtual network.

    System calls: The host's kernel handles system calls from the container, which is how the container accesses the host's resources, such as CPU, memory, and I/O.

    Namespaces: Docker containers use Linux namespaces to create isolated environments for the container's processes. Namespaces provide isolation for resources such as the file system, process ID, and network.

    Control groups (cgroups): Docker containers use cgroups to limit and control the amount of resources, such as CPU, memory, and I/O, that a container can access.

It's important to note that while a container uses resources from the host operating system, it is still isolated from the host and other containers, so changes to the container do not affect the host or other containers.

Note: There are multiple ways to reduce your VM image size as well, but I am just talking about the default for easy comparision and understanding.

so, in a nutshell, container base images are typically smaller compared to VM images because they are designed to be minimalist and only contain the necessary components for running a specific application or service. VMs, on the other hand, emulate an entire operating system, including all its libraries, utilities, and system files, resulting in a much larger size.

I hope it is now very clear why containers are light weight in nature.

Docker

What is Docker ?

Docker is a containerization platform that provides easy way to containerize your applications, which means, using Docker you can build container images, run the images to create containers and also push these containers to container regestries such as DockerHub, Quay.io and so on.

In simple words, you can understand as containerization is a concept or technology and Docker Implements Containerization.

Docker Architecture ?

The above picture, clearly indicates that Docker Deamon is brain of Docker. If Docker Deamon is killed, stops working for some reasons, Docker is brain dead :p (sarcasm intended).

Docker LifeCycle

We can use the above Image as reference to understand the lifecycle of Docker.

There are three important things,

docker build -> builds docker images from Dockerfile
docker run -> runs container from docker images
docker push -> push the container image to public/private regestries to share the docker images.

Understanding the terminology (Inspired from Docker Docs)

Docker daemon

The Docker daemon (dockerd) listens for Docker API requests and manages Docker objects such as images, containers, networks, and volumes. A daemon can also communicate with other daemons to manage Docker services.

Docker client

The Docker client (docker) is the primary way that many Docker users interact with Docker. When you use commands such as docker run, the client sends these commands to dockerd, which carries them out. The docker command uses the Docker API. The Docker client can communicate with more than one daemon.

Docker Desktop

Docker Desktop is an easy-to-install application for your Mac, Windows or Linux environment that enables you to build and share containerized applications and microservices. Docker Desktop includes the Docker daemon (dockerd), the Docker client (docker), Docker Compose, Docker Content Trust, Kubernetes, and Credential Helper. For more information, see Docker Desktop.

Docker registries

A Docker registry stores Docker images. Docker Hub is a public registry that anyone can use, and Docker is configured to look for images on Docker Hub by default. You can even run your own private registry.

When you use the docker pull or docker run commands, the required images are pulled from your configured registry. When you use the docker push command, your image is pushed to your configured registry.
Docker objects

When you use Docker, you are creating and using images, containers, networks, volumes, plugins, and other objects. This section is a brief overview of some of those objects.

Dockerfile

Dockerfile is a file where you provide the steps to build your Docker Image.

Images

An image is a read-only template with instructions for creating a Docker container. Often, an image is based on another image, with some additional customization. For example, you may build an image which is based on the ubuntu image, but installs the Apache web server and your application, as well as the configuration details needed to make your application run.

You might create your own images or you might only use those created by others and published in a registry. To build your own image, you create a Dockerfile with a simple syntax for defining the steps needed to create the image and run it. Each instruction in a Dockerfile creates a layer in the image. When you change the Dockerfile and rebuild the image, only those layers which have changed are rebuilt. This is part of what makes images so lightweight, small, and fast, when compared to other virtualization technologies.

INSTALL DOCKER

A very detailed instructions to install Docker are provide in the below link

https://docs.docker.com/get-docker/

For Demo,

You can create an Ubuntu EC2 Instance on AWS and run the below commands to install docker.

sudo apt update
sudo apt install docker.io -y

Start Docker and Grant Access

A very common mistake that many beginners do is, After they install docker using the sudo access, they miss the step to Start the Docker daemon and grant acess to the user they want to use to interact with docker and run docker commands.

Always ensure the docker daemon is up and running.

A easy way to verify your Docker installation is by running the below command

docker run hello-world

If the output says:

docker: Got permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock: Post "http://%2Fvar%2Frun%2Fdocker.sock/v1.24/containers/create": dial unix /var/run/docker.sock: connect: permission denied.
See 'docker run --help'.

This can mean two things,

Docker deamon is not running.
Your user does not have access to run docker commands.

Start Docker daemon

You use the below command to verify if the docker daemon is actually started and Active

sudo systemctl status docker

If you notice that the docker daemon is not running, you can start the daemon using the below command

sudo systemctl start docker

Grant Access to your user to run docker commands

To grant access to your user to run the docker command, you should add the user to the Docker Linux group. Docker group is create by default when docker is installed.

sudo usermod -aG docker ubuntu

In the above command ubuntu is the name of the user, you can change the username appropriately.

NOTE: : You need to logout and login back for the changes to be reflected.

Docker is Installed, up and running 🥳🥳

Use the same command again, to verify that docker is up and running.

docker run hello-world

Output should look like:

....
....
Hello from Docker!
This message shows that your installation appears to be working correctly.
...
...

Great Job, Now start with the examples folder to write your first Dockerfile and move to the next examples. Happy Learning :)

Clone this repository and move to example folder

git clone https://github.com/iam-veeramalla/Docker-Zero-to-Hero
cd  examples

Login to Docker [Create an account with https://hub.docker.com/]

docker login

Login with your Docker ID to push and pull images from Docker Hub. If you don't have a Docker ID, head over to https://hub.docker.com to create one.
Username: abhishekf5
Password:
WARNING! Your password will be stored unencrypted in /home/ubuntu/.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store

Login Succeeded

Build your first Docker Image

You need to change the username accoringly in the below command

docker build -t abhishekf5/my-first-docker-image:latest .

Output of the above command

    Sending build context to Docker daemon  992.8kB
    Step 1/6 : FROM ubuntu:latest
    latest: Pulling from library/ubuntu
    677076032cca: Pull complete
    Digest: sha256:9a0bdde4188b896a372804be2384015e90e3f84906b750c1a53539b585fbbe7f
    Status: Downloaded newer image for ubuntu:latest
     ---> 58db3edaf2be
    Step 2/6 : WORKDIR /app
     ---> Running in 630f5e4db7d3
    Removing intermediate container 630f5e4db7d3
     ---> 6b1d9f654263
    Step 3/6 : COPY . /app
     ---> 984edffabc23
    Step 4/6 : RUN apt-get update && apt-get install -y python3 python3-pip
     ---> Running in a558acdc9b03
    Step 5/6 : ENV NAME World
     ---> Running in 733207001f2e
    Removing intermediate container 733207001f2e
     ---> 94128cf6be21
    Step 6/6 : CMD ["python3", "app.py"]
     ---> Running in 5d60ad3a59ff
    Removing intermediate container 5d60ad3a59ff
     ---> 960d37536dcd
    Successfully built 960d37536dcd
    Successfully tagged abhishekf5/my-first-docker-image:latest

Verify Docker Image is created

docker images

Output

REPOSITORY                         TAG       IMAGE ID       CREATED          SIZE
abhishekf5/my-first-docker-image   latest    960d37536dcd   26 seconds ago   467MB
ubuntu                             latest    58db3edaf2be   13 days ago      77.8MB
hello-world                        latest    feb5d9fea6a5   16 months ago    13.3kB

Run your First Docker Container

docker run -it abhishekf5/my-first-docker-image

Output

Hello World

Push the Image to DockerHub and share it with the world

docker push abhishekf5/my-first-docker-image

Output

Using default tag: latest
The push refers to repository [docker.io/abhishekf5/my-first-docker-image]
896818320e80: Pushed
b8088c305a52: Pushed
69dd4ccec1a0: Pushed
c5ff2d88f679: Mounted from library/ubuntu
latest: digest: sha256:6e49841ad9e720a7baedcd41f9b666fcd7b583151d0763fe78101bb8221b1d88 size: 1157

You must be feeling like a champ already

Lastly, before you leave, don't forget to key in your feedback in the Comments section below and show some love by dropping a LIKE on this article

Predicting Logistics Quality using MindsDB

Arman Chand — Tue, 18 Oct 2022 09:52:58 +0000

Introduction

By using MindsDB, you can train models easily and instantly with the help of simple SQL statements, and even predict outcomes using the best-in-class machine learning algorithms. It acts as a layer over the existing tables to provide the best AI capabilities on top.

To make it easy for users to manage their existing data infrastructure along with MindsDB, MindsDB offers a wide selection of integrations with almost all the available databases and many machine learning frameworks. Additionally, MindsDB offers its service in two ways, Local Deployments (Using Docker or PIP) as well as MindsDB Cloud, both of which have a free tier for all users.

In this tutorial we will be Predicting Logistics Quality based on its feature set using MindsDB Cloud.

Importing Data to MindsDB Cloud

In this tutorial, we will use this dataset from Kaggle to train our Predictor model. You can also get free datasets from Kaggle, Datahub, and Google Dataset Search.

It's time to get this dataset added to MindsDB Cloud.

Step 1: We need to first sign in to the MindsDB Cloud console or start registering for a new account.

Step 2: A MindsDB Cloud Editor appears once you sign in. The Query Editor is at the top, where you can write queries and then press the Run (Shift+Enter) button above it to execute those queries. At the bottom, you can see the results of your queries, and finally, you have a Learning Hub on your right, which serves as a learning aid.

Step 3: Now find the Add Data button from the top right corner and tap on it. Then switch the tab at the top to Files instead of Databases and click on the Import File button.

Step 4: Once you have selected the dataset file you just downloaded from the above link, click the Import File section, browse and select it. Provide a name for the table in the Table name field, and click Save and continue to proceed.

Step 5: As soon as the dataset is imported successfully, we will be redirected to the MindsDB Cloud Editor. We will now see two basic SQL queries listed in the Query Editor Let's run them one by one and see what happens.
The first query should show a list of available tables. Check to see if there is a table in the list that matches the name we provided when importing the dataset.

SHOW TABLES FROM files;

The second query lets you check whether we have the right data records present inside the table that we just imported.

SELECT * FROM files.logistics LIMIT 10;

We are now ready with the data table and can now proceed to the next section.

Training a Predictor Model

You can train a Predictor Model with MindsDB as easily as writing a SQL query and executing it. Here are the steps we can follow for creating and training the model.
Step 1: MindsDB offers the CREATE PREDICTOR statement. The syntax of this statement is as follows.

CREATE PREDICTOR mindsdb.predictor_name       (Your Predictor Name)
FROM database_name                            (Your Database Name)
(SELECT columns FROM table_name LIMIT 10000)  (Your Table Name)
PREDICT target_parameter;                     (Your Target Parameter)

An actual query with real field_names instead of the placeholders will look like the one below.

CREATE PREDICTOR mindsdb.logisticService
FROM Logistic                      
(SELECT * FROM logisitcs LIMIT 10000) 
PREDICT logisitcsQualityofLogisticsServices;

Step 2: The model might take some time to complete its training based on the size of the training data provided.

While we wait, we can check the status of the model with the command below. If the query returns complete, then the model is ready to do the predictions. But, if it returns generating or training, it is advised to wait until the status is complete.

SELECT status
FROM mindsdb.predictors
WHERE name='Name_of_the_Predictor_Model';

The real query will be something like this.

SELECT status
FROM mindsdb.predictors
WHERE name='logisticService';

As we got the complete status, we can now do the predictions for logistics quality.

Describing the Predictor Model

It is really very important to understand the details about our Predictor model before directly jumping in to do the predictions.

So, in this section we will try to figure out the details of our model in 3 different ways using the DESCRIBE statement.

By Features
By Model
By Model Ensemble

By Features

This query is designed to return the roles of each column in the table for the model and also mentions the specific encoders used on each of these columns to train the model.

DESCRIBE mindsdb.predictor_name.features;

By Model

This query fetches the list of all the available candidate models that were used during training. The candidate model which has 1 under its selected column is selected to be used in the Predictor model and is supposed to have the best performance value.

DESCRIBE mindsdb.predictor_name.model;

By Model Ensemble

This query is designed to provide us with a JSON output with the list of different parameters that helped to determine the best candidate model for the Predictor.

DESCRIBE mindsdb.predictor_name.ensemble;

It's time to now move on to the interesting part of predicting the logistics quality values.

Querying the Model

It is possible to predict target values using only the SELECT statement in MindsDB Here, we can use a SELECT query to predict the quality of logistics and ask the model to return the predicted quality to us.
It should be noted that the quality of logistics is determined by a combination of several feature values.

We can still use a query like this to predict logistics quality based on a few feature sets, but the accuracy may degrade if some of these values are left out.

SELECT target_value_name, target_value_confidence, target_value_confidence
FROM mindsdb.predictor_name
WHERE feature1=value1 AND feature2=value 2,...;

Our real query will take the values of the feature parameter set like this.

SELECT QualityofLogisticsServices
FROM mindsdb.logisticQuality
WHERE Stateslogisticsenablinginitiatives =3 AND Assessmentofvariableoflogisticsease = 2.67;;

Here the predicted quality (logistics Quality) is 2.12

That's it! We have now successfully predicted the logistics quality using the Predictor Model.

Conclusion

Having finished this tutorial, I wanted to give you a quick recap of what we have learned. Initially, we created a MindsDB Cloud account, imported data, created a table using the cloud GUI, trained a Predictor model, explained its details in three different ways, and finally predicted the Logistics Quality. As this tutorial is over, it would be a great idea to sign up for your own MindsDB account and try it out.

Lastly, before you leave, don't forget to key in your feedback in the Comments section below and show some love by dropping a LIKE on this article

Predicting Employee Attrition & Performance using MindsDB Cloud

Arman Chand — Sat, 15 Oct 2022 10:43:07 +0000

Introduction

With MindsDB, we can begin predicting in SQL right now. In order to predict the Target feature on the basis of the options, we will fetch our dataset and create a model instantly.

This tutorial will teach you how to train a model to predict Employee Attrition based on many variables, such as Age, Attrition , DailyRate, Education, etc. From Kaggle, we will use a IBM Employee Attrition dataset.

Then we will log in to MindsDB Cloud, connect it to our data, train a model based on the dataset in our data, and attempt to predict Employee Attrition & Performance.

Importing Data to MindsDB Cloud

Download the dataset from Kaggle and then merely extract it and save the CSV file for later use.
Now allow us to start with our MindsDB Cloud account to require things additional.

Step 1: Login to your existing MindsDB Cloud account or signup for a brand new one here.

Step 2: Once you've got signed up or logged in to your MindsDB cloud account, you'll be able to realize the MindsDB Cloud Editor displayed for you.
The top panel is for writing the question, all-time low panel is for displaying the results and therefore the right panel lists out a number of the training hub resources to form things easier for the new users.

Step 3: Click on Add Data from the highest right and on consequent screen that seems, turn to Files rather than Databases and so click on Import File.

Step 4: Currently on the Import File dashboard, browse the dataset file from your pc, set a name for the Table and then hit the Save and Continue button. this could simply transfer the dataset file for you and build a Table with the name you provided.

Step 5: Now the GUI returns back to the Editor screen where you can find two generic queries mentioned to either list the tables or query the data in the table that you just uploaded. Let's run both of these queries and check through the results.

We are now ready to create a Predictor model using our attrition table that we just uploaded.

Training a Predictor Model

MindsDB makes it extremely easy to define a Predictor model and train it by using a simple SQL syntax.

Step 1: We will now use the CREATE PREDICTOR syntax to create the Predictor. The syntax will be like this.

CREATE PREDICTOR mindsdb.predictor_name (Your Predictor Name)
FROM database_name                      (Your Database Name)
(SELECT * FROM table_name LIMIT 10000)  (Your Table Name)
PREDICT target_parameter;               (Your Target Parameter)

The query get's executed and return successful in the terminal.

Step 2: The model should take a little while to get created and trained. We can check the status of the model using the syntax below. If the query returns Complete, then the model is ready to use or else wait if it returns Training status.

Describing the Predictor Model

MindsDB provides a DESCRIBE statement that we can use to gain some insights into the Predictor Model. We can find more details about the model in the following three ways.

By Features
By Model
By Model Ensemble

By Features

DESCRIBE mindsdb.predictor_model_name.features;

This statement is used to find out the type of encoders used on each column to train the model and the role of each of the columns for the model. A sample output for our model is posted below.

By Model

DESCRIBE mindsdb.predictor_model_name.model;

MindsDB uses multiple models internally to train the data and then select the most optimized one for the model to do the predictions. This statement simply lists out all the candidate models used to train the data along with other details. The model with 1 in its selected column is the one that is the most optimized and accurate.

By Model Ensemble

DESCRIBE mindsdb.predictor_model_name.ensemble;

With the above statement, we can simply query out a JSON object that lists out the multiple attributes used to select the best candidate model to do the predictions.

Querying the Model

Now that we have a tendency to square measure prepared with our Predictor Model, we will merely execute some straightforward SQL question statements to predict the target Attrition supported by the feature parameters.

We will start by predicting that only 1 feature parameter is supported by ibmAttrition and therefore the question statement should look like this.


SELECT Attrition
FROM mindsdb.ibmAttrition
WHERE BusinessTravel ='Travel_Frequently';
;

This should return the expected Attrition having BusinessTravel value as Travel_Frequently.

Let's now try predicting the Attrition based on multiple feature parameters.

SELECT Attrition
FROM mindsdb.ibm
WHERE Department='Sales' AND Education = 2;;

This should return the expected Attrition having Department values as Sales and Education value as 2;

Conclusion

It's time to wrap up the tutorial. As part of this tutorial, we created a MindsDB Cloud account, uploaded a dataset to the cloud interface, trained a predictor model with the dataset, and predicted the IBM Attrition.

Using MindsDB, you can coach your own predictive models using datasets available on the market online. Don't be afraid to give it a try if you want to make all the predictions you want.

As a final note, please LIKE this page if you learn something new and interesting today and feel free to share your feedback below.

Predicting Life Expectancy using MindsDB Cloud

Arman Chand — Wed, 12 Oct 2022 15:52:09 +0000

Introduction

With MindsDB, we can begin predicting in SQL right now. In order to predict the Life Expectancy on the basis of the options, we will fetch our dataset and create a model instantly.

This tutorial will teach you how to train a model to predict Life Expectancy based on many variables, such as Country, Status, lifeexpectancy, Infantdeath, Adultmortality etc. From Kaggle, we will use a Life Expectancy dataset.

Then we will log in to MindsDB Cloud, connect it to our data, train a model based on the dataset in our data, and attempt to predict mobile costs.

Importing Datasets as Tables in our Database

Step 1: After you've downloaded the Dataset, save the CSV file in a folder Make sure you have MySQL installed on your system, and that it is active. Use MySQL Workbench to test the Database connections and make sure everything is working.

Step 2: If the connection is successful, you can click on OK to save the connection. Then click on the connection and it should open the Localhost dashboard along with the query editor.

Step 3: Now click on Create a new schema in the connected server, provide a name for the schema and then hit Apply, and then click on Finish.

Step 4: After creating the database, select Schemas from the bottom left and then right-click on the schema you just created and select Table Data Import Wizard. A dialog box should appear for you to select the CSV file and click Next.

Click on Next to create a new table and give it a name as you like. Click Next again to click on Finish and then click Next again twice to complete the import. Now the table data import is complete and we are ready to manipulate the data in our table.

Step 5: Let us run a sample SELECT query to see whether we have all of our data in the table.

show databases;

use MindsDB_Demo;

select * from lifeexpectancydata;

This should return a list of rows from the lifeexpectancydata table in the MindsDB_Demo database. We are now ready with the database and the table to perform further operations.

Connecting the Database to MindsDB Cloud

In this tutorial we are using Life Expectancy dataset.Download the dataset from Kaggle https://www.kaggle.com/datasets/kumarajarshi/life-expectancy-who and then extract it and save the CSV file for later use.
Now allow us to start with our MindsDB Cloud account to require things additional.

Step 1: Login to your existing MindsDB Cloud account or signup for a brand new one here.

Step 2: After signing up or logging in, you will see the MindsDB Cloud Editor. A question can be written in the top panel, the results can be displayed in the bottom panel, and a number of training hub resources can be found in the right panel to make things easier for new users.

Step 3: Click on Add Data from the highest right and on consequent screen that seems, select Databases from left panel and so click on Mysql database.

Step 4: In this pre-filled query, we need to provide certain parameters to establish a connection to our local database. In order to that, we need to expose localhost:3306 to the internet so that we can establish a tcp/ip connection to interact with it.

Step 5: We have to now use ngrok to tunnel our localhost to the internet. This command should do the job for you.

ngrok tcp 3306

Make sure you signup for a ngrok account and connect with your account using the auth token you received after signing up so that you are not time-limited for your tunnelling. You can do the following using this command.

ngrok config add-authtoken YourAuthTokenHere

Step 6: We can now fill in the parameters in the query above to connect to the database and then hit the Run(Shift + Enter) button to fire the query. This query should execute successfully. Have a look at the snip below to learn more about each of the parameters.

Step 7: Now let's run a simple SELECT query now to make sure that MindsDB can now access the data in the database.

select * from life.lifeexpectancydata;

This should return all the records from the table lifeexpectancydata.

Training a Predictor Model

MindsDB makes it extremely easy to define a Predictor model and train it by using a simple SQL syntax.

Step 1: We will now use the CREATE PREDICTOR syntax to create the Predictor. The syntax will be like this.

CREATE PREDICTOR mindsdb.predictor_name (Your Predictor Name)
FROM database_name                      (Your Database Name)
(SELECT * FROM table_name LIMIT 10000)  (Your Table Name)
PREDICT target_parameter;               (Your Target Parameter)

The query get's executed and return successful in the terminal.

Step 2: It will take a little while for the model to be created and trained, but we can check its status using the syntax below. If the query returns Complete, the model is ready for use; otherwise, it will return Training.

SELECT status
FROM mindsdb.predictors
WHERE name='name_of_the_predictor_model';

Describing the Predictor Model

MindsDB provides a DESCRIBE statement that we can use to gain some insights into the Predictor Model. We can find more details about the model in the following three ways.

By Features
By Model
By Model Ensemble

By Features

DESCRIBE mindsdb.predictor_model_name.features;

This statement is used to find out the type of encoders used on each column to train the model and the role of each of the columns for the model. A sample output for our model is posted below.

By Model

DESCRIBE mindsdb.predictor_model_name.model;

By Model Ensemble

DESCRIBE mindsdb.predictor_model_name.ensemble;

With the above statement, we can simply query out a JSON object that lists out the multiple attributes used to select the best candidate model to do the predictions.

Querying the Model

The predictor model lifeexpectancy is now ready to predict the Life expectancy percentage for us. MindsDB provides simple SELECT query statements that we can fire to find the predictions based on the specified features.

Let's try predicting the failure rate based on a single feature. The SELECT statement would be as follows.

SELECT Lifeexpectancy
FROM mindsdb.lifeexpectancy 
WHERE Status ='Developing';

This should predict the life expectancy percentage of the people living in Developing countries.

Now let's try predicting the failure rate with more than one parameter. The query should look something like this.

SELECT Lifeexpectancy
FROM mindsdb.lifeexpectancy 
WHERE Status ='Developing' AND Country="Australia";

This should predict the life expectancy percentage of the people living in Developing country and specific to Australia.

Conclusion

Using MindsDB, you can coach your own predictive models using datasets available on the market online. Don't be afraid to give it a try if you want to make all the predictions you want.

As a final note, please LIKE this page if you learn something new and interesting today and feel free to share your feedback below.

Predicting Mobile Phone Price using MindsDB Cloud

Arman Chand — Sun, 09 Oct 2022 07:14:10 +0000

Introduction

With MindsDB, we can begin predicting in SQL right now. In order to predict the Target price on the basis of the options, we will fetch our dataset and create a model instantly.

This tutorial will teach you how to train a model to predict mobile worth based on many variables, such as clock_speed, battery_power, dual_sim, etc. From Kaggle, we will use a Mobile Worth Classification dataset.

Then we will log in to MindsDB Cloud, connect it to our data, train a model based on the dataset in our data, and attempt to predict mobile costs.

Importing Data to MindsDB Cloud

We will want a dataset at first that contains Mobile worth on the idea of multiple parameters. you'll be able to transfer a duplicate of the dataset here.

Download the dataset from Kaggle https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification

and then merely extract it and save the CSV file for later use.
Now allow us to start with our MindsDB Cloud account to require things additional.

Step 1: Login to your existing MindsDB Cloud account or signup for a brand new one here.

Step 3: Click on Add Data from the highest right and on consequent screen that seems, turn to Files rather than Databases and so click on Import File.

We are now ready to create a Predictor model using our Mobile_Price table that we just uploaded.

Training a Predictor Model

MindsDB makes it extremely easy to define a Predictor model and train it by using a simple SQL syntax.

Step 1: We will now use the CREATE PREDICTOR syntax to create the Predictor. The syntax will be like this.

CREATE PREDICTOR mindsdb.predictor_name (Your Predictor Name)
FROM database_name                      (Your Database Name)
(SELECT * FROM table_name LIMIT 10000)  (Your Table Name)
PREDICT target_parameter;               (Your Target Parameter)

The query get's executed and return successful in the terminal.

Describing the Predictor Model

MindsDB provides a DESCRIBE statement that we can use to gain some insights into the Predictor Model. We can find more details about the model in the following three ways.

By Features
By Model
By Model Ensemble

By Features

DESCRIBE mindsdb.predictor_model_name.features;

This statement is used to find out the type of encoders used on each column to train the model and the role of each of the columns for the model. A sample output for our model is posted below.

By Model

DESCRIBE mindsdb.predictor_model_name.model;

By Model Ensemble

DESCRIBE mindsdb.predictor_model_name.ensemble;

With the above statement, we can simply query out a JSON object that lists out the multiple attributes used to select the best candidate model to do the predictions.

Querying the Model

Now that we have a tendency to square measure prepared with our Predictor Model, we will merely execute some straightforward SQL question statements to predict the target price supported by the feature parameters.

We will start by predicting that only 1 feature parameter is supported by price_range and therefore the question statement should look like this.

SELECT price_range
FROM mindsdb.Mobile_Price_Predict
WHERE dual_sim ='0';

This should return the expected price_range having dual_sim value as 0.

Let's now try predicting the price_range based on multiple feature parameters.

SELECT price_range 
FROM mindsdb.Mobile_Price_Predict
WHERE touch_screen ='0' and int_memory = 51;

This should return the expected price_range having touch_screen values as 0 and int_memory value as 51;

Conclusion

Using MindsDB, you can coach your own predictive models using datasets available on the market online. Don't be afraid to give it a try if you want to make all the predictions you want.

As a final note, please LIKE this page if you learn something new and interesting today and feel free to share your feedback below.