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Integrating Ansible and Docker for a CI/CD Pipeline Using Jenkins

  • By Gcore
  • April 12, 2023
  • 10 min read
Integrating Ansible and Docker for a CI/CD Pipeline Using Jenkins

In this guide, we will use Ansible as a Deployment tool in a Continuous Integration/Continuous Deployment process using Jenkins Job.

In the world of CI/CD process, Jenkins is a popular tool for provisioning development/production environments as well as application deployment through pipeline flow. Still, sometimes, it gets overwhelming to maintain the application’s status, and script reusability becomes harder as the project grows.

To overcome this limitation, Ansible plays an integral part as a shell script executor, which enables Jenkins to execute the workflow of a process.

Let us begin the guide by installing Ansible on our Control node.

Install and Configure Ansible

Installing Ansible:
Here we are using CentOS 8 as our Ansible Control Node. To install Ansible, we are going to use python2-pip, and to do so, first, we have to install python2. Use the below-mentioned command to do so:

# sudo yum update# sudo yum install python2

After Python is installed on the system, use pip2 command to install Ansible on the Control Node:

# sudo pip2 install ansible# sudo pip2 install docker

It might take a minute or two to complete the installation, so sit tight. Once the installation is complete, verify:

# ansible --version ansible 2.9.4  config file = None  configured module search path = [u'/root/.ansible/plugins/modules', u'/usr/share/ansible/plugins/modules']  ansible python module location = /usr/lib/python2.7/site-packages/ansible  executable location = /usr/bin/ansible  python version = 2.7.16 (default, Nov 17 2019, 00:07:27) [GCC 8.3.1 20190507 (Red Hat 8.3.1-4)]

Through the above command, we notice that the config file path is missing, which we will create and configure later. For now, let’s move to the next section.

Configuring Ansible Control Node User:
The first thing we are going to do is create a user named ansadmin, as it is considered the best practice. So let’s create a user, by using the command adduser, which will create a new user to our system:

# useradd ansadmin

Now, use the passwd command to update the ansadmin user’s password. Make sure that you use a strong password.

# passwd ansadminChanging password for user ansadmin.New password: Retype new password: passwd: all authentication tokens updated successfully.

Copy the password for user ansadmin and save it somewhere safe.

Once we have created the user, it’s time to grant sudo access to it, so it doesn’t ask for a password when we log in as root. To do so, follow the below-mentioned steps:

# nano /etc/sudoers

Go to the end of the file and paste the below-mentioned line as it is:

...ansadmin ALL=(ALL)       NOPASSWD: ALL...

Before moving forward, we have one last thing to do. By default, SSH password authentication is disabled in our instance. To enable it, follow the below-mentioned steps:

# nano /etc/ssh/sshd_config

Find PasswordAuthentication, uncomment it and replace no with yes, as shown below:

...PasswordAuthentication yes...

You will see why we are doing this in the next few steps. To reflect changes, reload the ssh service:

# service sshd reload

Now, log in as an ansadmin user on your Control Node and generate ssh key, which we will use to connect with our remote or managed host. To generate the private and public key, follow the below-mentioned commands:

# su - ansadmin

Use ssh-keygen command to generate key:

# ssh-keygenEnter file in which to save the key (/home/ansadmin/.ssh/id_rsa): ansible-CN   Enter passphrase (empty for no passphrase): Enter same passphrase again: Your identification has been saved in ansible-CN.Your public key has been saved in ansible-CN.pub.The key fingerprint is:SHA256:6G0xzIrIsmsBwCakACI8CVr8AOuRR8v5F1p2+CsB6EY ansadmin@ansible-hostThe key's randomart image is:+---[RSA 3072]----+|&+o.             ||OO* +   .        ||Bo.E . = .       ||o = o =++        ||.. o o.oS.       || o.. o.o.o.      ||. + . o.o.       || +     ..        ||+.               |+----[SHA256]-----+

Usually, keys are generated in the .ssh/ directory. In our case, you can find keys at /home/ansadmin/.ssh/. Now let us configure our Managed Host for Ansible.

Configuring Ansible Managed Host User:
First, we will create a user on our managed host, so log in to your host and create a user with the same name and password.

As our managed host is an Ubuntu machine, therefore here we have to use the adduser command. Please make sure that the password for the username ansadmin is the same for Control and Managed Host.

# adduser ansadmin# su - ansadmin

Other than this, it is also an excellent thing to cross-check if password authentication is enabled on the Managed Host as we need to copy the ssh public key from the control node to the Managed Host.

Switch to Control Node machine; to copy the public key to our Managed Host machine, we will use the command ssh-copy-id:

$ su - ansadmin$ ssh-copy-id -i .ssh/ansible-CN.pub ansadmin@managed-host-ip-here

For the first time, it will ask for the password. Enter the password for ansadmin, and you are done. Now, if you wish, you can disable Password Authentication on both machines.

Setting Ansible Inventory:
Ansible allows us to manage multiple nodes or hosts at the same time. The default location for the inventory resides in /etc/ansible/hosts. In this file, we can define groups and sub-groups.

If you remember, earlier, the hosts’ file was not created automatically for our Ansible. So let’s create one:

# cd /etc/ansible# touch hosts && nano hosts

Add the following lines in your hosts’ file and save it:

[docker_group]docker_host ansible_host=your-managed-host-ip ansible_user=ansadmin ansible_ssh_private_key_file=/home/ansadmin/.ssh/ansible-CN ansible_python_interpreter=/usr/bin/python3ansible_CN ansible_connection=local

Make sure that you replace your-managed-host-ip with your host IP address.

Let’s break down the basic INI format:

  • docker_group – Heading in brackets is your designated group name.
  • docker_host & ansible_CN – The first hostname is docker_host, which points to our Managed Host. While the second hostname is ansible_CN, which is pointing towards our localhost, to be used in Ad-Hoc commands and Playbooks.
  • ansible_host – Here, you need to specify the IP address of our Managed Host.
  • ansible_user – We mentioned our Ansible user here.
  • ansible_ssh_private_key_file – Add the location of your private key.
  • ansible_python_interpreter – You can specify which Python version you want to use; by default, it will be Python2.
  • ansible_connection – This variable helps Ansible to understand that we are connecting the local machine. It also helps to avoid the SSH error.

It is time to test our Ansible Inventory, which can be done through the following command. Here we are going to use a simple Ansible module PING:

# ansible all -m pingansible_CN | SUCCESS => {    "ansible_facts": {        "discovered_interpreter_python": "/usr/libexec/platform-python"    },     "changed": false,     "ping": "pong"}docker_host | SUCCESS => {    "ansible_facts": {        "discovered_interpreter_python": "/usr/bin/python3"    },     "changed": false,     "ping": "pong"}

It looks like the Ansible system can now communicate with our Managed Host as well as with the localhost.

Install Docker:

We need a Docker ready system to manage our process; for this, we have to install Docker on both systems. So follow the below-mentioned steps:

For CentOS (Control Node):
Run the following command on your Control Node:

# sudo yum install -y yum-utils device-mapper-persistent-data lvm2 # sudo yum-config-manager --add-repo \  https://download.docker.com/linux/centos/docker-ce.repo # sudo yum install docker-ce docker-ce-cli containerd.io

In case you encounter the below-mentioned error during installation:

Error:  Problem: package docker-ce-3:19.03.5-3.el7.x86_64 requires containerd.io >= 1.2.2-3, but none of the providers can be installed

Next, run the following command:

# sudo yum install docker-ce docker-ce-cli containerd.io --nobest

For Ubuntu OS (Managed Host):
Run the following command on your Managed Host, which is a Ubuntu-based machine:

$ sudo apt-get remove docker docker-engine docker.io containerd runc $ sudo apt-get update && sudo apt-get install apt-transport-https ca-certificates curl gnupg-agent software-properties-common$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -$ sudo apt-get update && sudo apt-get install docker-ce docker-ce-cli containerd.io

That’s it for this section. Next, we are going to cover how to integrate Ansible with Jenkins.

Integrating Ansible with Jenkins:

In this section, we will integrate Ansible with Jenkins. Fire up your Jenkins, go to Dashboard > Manage Jenkins > Manage Plugins > Available and then search for Publish Over SSH as shown in the image below:

Now, go to Configure System and find Publish over SSH; under this section, go to SSH Servers and click on the Add button. Here we are going to add our Docker Server as well as Ansible Server, as shown in the image:

SSH server setting for Docker:

SSH server setting for Ansible:

In the Hostname field, add your IP address or domain name of Docker and Ansible server. Before saving the setting, make sure that you test the connection before saving the configuration, by clicking on the Test Configuration button as shown in the image below:

Create Jenkins Job

The next step is to create Jenkins jobs. The sole propose of this Job is to build, test, and upload the artifact to our Ansible Server. Here we are going to create Job as a Maven Project, as shown in the image below:

Next in Job setting page, go to the Source Code Management section and add your Maven project repo URL, as shown in the image below:

Find the Build section, and in Root POM field enter your pom.xml file name. Additionally in the Goals and options field enter clean install package:

After successful build completion, your goal is to send the war file to the specified directory to your Ansible server with the right permissions so that it doesn’t give us the writing permission by assigning ansadmin to the directory.

Right now, we don’t have such a directory, so let us create one. Follow the below-mentioned steps:

# sudo su# mkdir /opt/docker# chown ansadmin:ansadmin /opt/docker -R# ls -al /opt/docker/total 0drwxr-xr-x. 2 ansadmin ansadmin  6 Jan 31 16:57 .drwxr-xr-x. 4 root     root     38 Jan 31 17:10 ..

Directory /opt/docker will be used as our workspace, where Jenkins will upload the artifacts to Ansible Server.

Now, go to the Post-build Actions section and from the drop-down menu, select Send build artifacts over SSH, as shown in the image below:

Make sure that in the Remote Directory field, you enter the pattern //opt//docker as it doesn’t support special characters. Apart from this, for now, we are going to leave the Exec Command field empty so that we can test whether our existing configuration works or not.

Now Build the project, and you will see the following output in your Jenkins’s console output:

Go to your Ansible Server terminal and see if the artifact was sent with right user privileges:

# ls -al /opt/docker/total 4drwxr-xr-x. 2 ansadmin ansadmin   24 Feb  3 10:54 .drwxr-xr-x. 4 root     root       38 Jan 31 17:10 ..-rw-rw-r--. 1 ansadmin ansadmin 2531 Feb  3 10:54 webapp.war

It looks like our webapp.war file was transferred successfully. In the following step, we will create an Ansible Playbook and Dockerfile.

Creating Dockerfile and Ansible Playbook:

To create a Docker Image with the webapp.war file, first, we will create a DockerFile. Follow the below-mentioned steps:

First, log in to your Ansible Server and go to directory /opt/docker and create a file named as Dockerfile:

# cd /opt/docker/# touch Dockerfile

Now open the Dockerfile in your preferred editor, and copy the below-mentioned lines and save it:

FROM tomcat:8.5.50-jdk8-openjdkMAINTAINER Your-Name-HereCOPY ./webapp.war /usr/local/tomcat/webapps

Here instructions are to pull a Tomcat image with tag 8.5.50-jdk8-openjdk and copying the webapp.war file to Tomcat default webapp directory., which is /usr/local/tomcat/webapps

With the help of this Dockerfile, we will create a Docker container. So let us create the Ansible Playbook, which will enable us to automate the Docker image build process and later run the Docker container out of it.

We are creating a Ansible Playbook, which does two tasks for us:

  1. Pull Tomcat’s latest version and build an image using webapp.war file.
  2. Run the built image on the desired host.

For this, we are going to create a new YAML format file for your Ansible Playbook:

# nano simple-ansible.yaml

Now copy the below-mentioned line into your simple-ansible.yaml file:

---#Simple Ansible Playbook to build and run a Docker containers - name: Playbook to build and run Docker  hosts: all  become: true  gather_facts: false   tasks:    - name: Build a Docker image using webapp.war file      docker_image:        name: simple-docker-image        build:          path: /opt/docker          pull: false        source: build     - name: Run Docker container using simple-docker-image      docker_container:        name: simple-docker-container        image: simple-docker-image:latest        state: started        recreate: yes        detach: true        ports:          - "8888:8080"

You can get more help here: docker_image and docker_container. Now, as our Playbook is created, we can run a test to see if it works as planned:

# cd /opt/docker# ansible-playbook simple-ansible-playbook.yaml --limit ansible_CN

Here we have used the --limit flag, which means it will only run on our Ansible Server (Control Node). You might see the following output, in your terminal window:

PLAY [Playbook to build and run Docker] *************************************************************************** TASK [Build Docker image using webapp.war file] ***************************************************************************changed: [ansible_CN] TASK [Run Docker image using simple-docker-image]***************************************************************************changed: [ansible_CN] PLAY RECAP ***************************************************************************ansible_CN                 : ok=2    changed=2    unreachable=0    failed=0    skipped=0    rescued=0    ignored=0

Look’s like Playbook ran sccessfully and no error was detected during the Ansible Playbook check, so now we can move to Jenkins to complete our CI/CD process using Ansible.

Run Ansible Playbook using Jenkins

In this step, we would execute our Ansible Playbook (i.e., simple-ansible-playbook.yaml) file, and to do so let us go back to the Project Configuration page in Jenkins and find Post-build Actions there.

In this section, copy the below-mentioned command in the Exec command field:

sudo ansible-playbook --limit ansible_CN /opt/docker/simple-ansible-playbook.yaml;

Now, let us try to build the project and see the Jenkins Job’s console output:

In the output, you can see that our Ansible playbook ran successfully. Let us verify if at Ansible Server the image is created and the container is running:

For Docker Image list:

# docker images REPOSITORY            TAG                 IMAGE ID            CREATED             SIZEsimple-docker-image   latest              d47875d99095        32 seconds ago      507MBtomcat                latest              5692d26ea179        15 hours ago        507MB

For Docker Container list:

# docker psCONTAINER ID        IMAGE                        COMMAND             CREATED             STATUS              PORTS                    NAMES5a824d0a43d5        simple-docker-image:latest   "catalina.sh run"   15 seconds ago      Up 14 seconds       0.0.0.0:8888->8080/tcp   simple-docker-container

It looks like Jenkins was able to run the Ansible Playbook successfully. Next, we are going to push Docker Image to Docker Hub.

Pushing Docker Image to Docker Hub Using Ansible

We are going to use Docker Hub public repository for this guide; in case you want to work on a live project, then you should consider using the Docker Hub private registry.

For this step, you have to create a Docker Hub account if you haven’t had one yet.

Our end goal for this step is to publish the Docker Image to Docker Hub using Ansible Playbook. So go to your Ansible Control Node and follow the below-mentioned steps:

# 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: your-docker-hub-userPassword: WARNING! Your password will be stored unencrypted in /root/.docker/config.json.Configure a credential helper to remove this warning. Seehttps://docs.docker.com/engine/reference/commandline/login/#credentials-store Login Succeeded

Make sure that you enter the right username and password.

Now it’s time to create a new Ansible Playbook which will build and push the Docker image to your Docker Hub account. Note that this image will be publicly available, so be cautious.

# nano build-push.yaml

Create a new Ansible Playbook, which will build a Docker image and push it to our Docker Hub account:

---#Simple Ansible Playbook to build and push Docker image to Registry - name: Playbook to build and run Docker  hosts: ansible_CN  become: true  gather_facts: false   tasks:    - name: Delete existing Docker images from the Control Node      shell: docker rmi $(docker images -q) -f       ignore_errors: yes     - name: Push Docker image to Registry      docker_image:        name: simple-docker-image        build:          path: /opt/docker          pull: true        state: present        tag: "latest"        force_tag: yes        repository: gauravsadawarte/simple-docker-image:latest        push: yes        source: build

Let us run the playbook now and see what we get:

# ansible-playbook --limit ansible_CN build-push.yaml PLAY [Playbook to build and run Docker] ***************************************************************************************** TASK [Push Docker image to Registry] *****************************************************************************************changed: [ansible_CN] PLAY RECAP *****************************************************************************************ansible_CN                 : ok=1    changed=1    unreachable=0    failed=0    skipped=0    rescued=0    ignored=0

Go to your Docker Hub account and see if the image was pushed successfully, as shown in the image below:

Next, let us modify our simple-ansible-playbook.yaml playbook, which we created earlier, as from here on, we are going to pull the Docker image from Docker Hub Account and create a container out of it.

---#Simple Ansible Playbook to pull Docker Image from the registry and run a Docker containers - import_playbook: build-push.yaml - name: Playbook to build and run Docker  hosts: docker_host  gather_facts: false   tasks:    - name: Run Docker container using simple-docker-image      docker_container:        name: simple-docker-container        image: gauravsadawarte/simple-docker-image:latest        state: started        recreate: yes        detach: true        pull: yes        ports:          - "8888:8080"

Note that we have used the import_playbook statement at the top of the existing playbook, which means that we want to run the build-push.yaml playbook first along with our main playbook, and this way, we don’t have to run multiple playbooks manually.

Let us break the whole process into steps:

  1. With the help of build-push.yaml playbook, we are asking Ansible to build an image with the artifacts sent by Jenkins to our Control Node, and later push the built image (i.e., simple-docker-image) to our Docker Hub’s account or any other private registry like AWS ECR or Google’s Container Registry.
  2. In the simple-ansible-playbook.yaml file, we have imported the build-push.yaml file, which is going to run prior to any statement present within the simple-ansible-playbook.yaml file.
  3. Once build-push.yaml playbook is executed, Ansible will launch a container into our Managed Docker Host by pulling our image from our defined registry.

Now, it’s time to build our job. So in the next step, we will deploy the artifact to our Control Node, where Ansible Playbook will build an image, push to Docker Hub and run the container in Managed Host. Let us get started!

Jenkins Jobs to Deploy Docker Container Using Ansible

To begin, go to JenkinstoDockerUsingAnsible configure page and change the Exec command in the Post-build Actions section.

Copy the below-mentioned command and add it as shown in the image below:

sudo ansible-playbook /opt/docker/simple-ansible-playbook.yaml;

Save the configuration and start the build; you will see the following output:

Now go to your Control Node and verify if our images were built:

# docker imagesREPOSITORY                            TAG                   IMAGE ID            CREATED             SIZEgauravsadawarte/simple-docker-image   latest                9ccd91b55796        2 minutes ago       529MBsimple-docker-image                   latest                9ccd91b55796        2 minutes ago       529MBtomcat                                8.5.50-jdk8-openjdk   b56d8850aed5        5 days ago          529MB

It looks like Ansible Playbook was successfully executed on our Control Node. It’s time to verify if Ansible was able to launch containers on our Managed Host or not.

Go to your Managed Host and enter the following command:

# docker psCONTAINER ID        IMAGE                                        COMMAND                  CREATED             STATUS              PORTS                               NAMES6f5e18c20a68        gauravsadawarte/simple-docker-image:latest   "catalina.sh run"        4 minutes ago       Up 4 minutes        0.0.0.0:8888->8080/tcp              simple-docker-container

Now visit the following URL http://your-ip-addr:8888/webapp/ in your browser. Note that, Tomcat Server may take some time before you can see the output showing your project is successfully setup.

And you are done!

You successfully managed to deploy your application using Jenkins, Ansible, and Docker. Now, whenever someone from your team pushes code to the repository, Jenkins will build the artifact and send it to Ansible, from there Ansible will be responsible for publishing the application to the desired machine.

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Some cloud providers also offer NVMe storage, which can provide even greater speed for highly demanding workloads.Disk size: Choose the right size based on the amount of data you need to store and process. Over-allocating storage space might seem like a safe bet, but it can also increase costs unnecessarily. You can always resize disks later, so avoid over-allocating them upfront.IOPS and throughput: Some workloads require high input/output operations per second (IOPS). If this is a priority for your workload (e.g., databases), make sure that your VM configuration includes high IOPS storage options.﹟5 Weigh up your network requirementsWhen working with cloud-based VMs, network performance is a critical consideration. High-speed and low-latency networking can make a difference for applications such as online gaming, video conferencing, and real-time analytics.Bandwidth: Check whether the VM configuration offers the necessary bandwidth for your workload. For applications that handle large data transfers, such as cloud backup or file servers, make sure that the network interface provides high throughput.Network latency: Low latency is crucial for applications where real-time performance is key (e.g., trading systems, gaming). Choose VMs with low-latency networking options to minimize delays and improve the user experience.Network isolation and security: Check if your VM configuration provides the necessary network isolation and security features, especially when handling sensitive data or operating in multi-tenant environments.﹟6 Factor in cost considerationsWhile it’s essential that your VM has the right configuration, cost is always an important factor to consider. Cloud providers typically charge based on the resources allocated, so optimizing for cost efficiency can significantly impact your budget.Consider whether a pay-as-you-go or reserved model (which offers discounted rates in exchange for a long-term commitment) fits your usage pattern. The reserved option can provide significant savings if your workload runs continuously. You can also use monitoring tools to track your VM’s performance and resource usage over time. This data will help you make informed decisions about scaling up or down so you’re not paying for unused resources.﹟7 Evaluate security featuresSecurity is a primary concern when selecting a VM configuration, especially for workloads handling sensitive data. Consider the following:Built-in security: Look for VMs that offer integrated security features such as DDoS protection, web application firewall (WAF), and encryption.Compliance: Check that the VM configuration meets industry standards and regulations, such as GDPR, ISO 27001, and PCI DSS.Network security: Evaluate the VM's network isolation capabilities and the availability of cloud firewalls to manage incoming and outgoing traffic.﹟8 Consider geographic locationThe geographic location of your VM can impact latency and compliance. Therefore, it’s a good idea to choose VM locations that are geographically close to your end users to minimize latency and improve performance. In addition, it’s essential to select VM locations that comply with local data sovereignty laws and regulations.﹟9 Assess backup and recovery optionsBackup and recovery are critical for maintaining data integrity and availability. Look for VMs that offer automated backup solutions so that data is regularly saved. You should also evaluate disaster recovery capabilities, including the ability to quickly restore data and applications in case of failure.﹟10 Test and iterateFinally, once you've chosen a VM configuration, testing its performance under real-world conditions is essential. Most cloud providers offer performance monitoring tools that allow you to assess how well your VM is meeting your workload requirements.If you notice any performance bottlenecks, be prepared to adjust the configuration. This could involve increasing CPU cores, adding more memory, or upgrading storage. Regular testing and fine-tuning means that your VM is always optimized.Choosing a virtual machine that suits your requirementsSelecting the best virtual machine configuration is a key step toward optimizing your workloads efficiently, cost-effectively, and without unnecessary performance bottlenecks. By understanding your workload’s needs, considering factors like CPU, memory, storage, and network performance, and continuously monitoring resource usage, you can make informed decisions that lead to better outcomes and savings.Whether you're running a small application or large-scale enterprise software, the right VM configuration can significantly improve performance and cost. Gcore offers a wide range of virtual machine options that can meet your unique requirements. Our virtual machines are designed to meet diverse workload requirements, providing dedicated vCPUs, high-speed storage, and low-latency networking across 30+ global regions. You can scale compute resources on demand, benefit from free egress traffic, and enjoy flexible pricing models by paying only for the resources in use, maximizing the value of your cloud investments.Contact us to discuss your VM needs

How to get the size of a directory in Linux

Understanding how to check directory size in Linux is critical for managing storage space efficiently. Understanding this process is essential whether you’re assessing specific folder space or preventing storage issues.This comprehensive guide covers commands and tools so you can easily calculate and analyze directory sizes in a Linux environment. We will guide you step-by-step through three methods: du, ncdu, and ls -la. They’re all effective and each offers different benefits.What is a Linux directory?A Linux directory is a special type of file that functions as a container for storing files and subdirectories. It plays a key role in organizing the Linux file system by creating a hierarchical structure. This arrangement simplifies file management, making it easier to locate, access, and organize related files. Directories are fundamental components that help ensure smooth system operations by maintaining order and facilitating seamless file access in Linux environments.#1 Get Linux directory size using the du commandUsing the du command, you can easily determine a directory’s size by displaying the disk space used by files and directories. The output can be customized to be presented in human-readable formats like kilobytes (KB), megabytes (MB), or gigabytes (GB).Check the size of a specific directory in LinuxTo get the size of a specific directory, open your terminal and type the following command:du -sh /path/to/directoryIn this command, replace /path/to/directory with the actual path of the directory you want to assess. The -s flag stands for “summary” and will only display the total size of the specified directory. The -h flag makes the output human-readable, showing sizes in a more understandable format.Example: Here, we used the path /home/ubuntu/, where ubuntu is the name of our username directory. We used the du command to retrieve an output of 32K for this directory, indicating a size of 32 KB.Check the size of all directories in LinuxTo get the size of all files and directories within the current directory, use the following command:sudo du -h /path/to/directoryExample: In this instance, we again used the path /home/ubuntu/, with ubuntu representing our username directory. Using the command du -h, we obtained an output listing all files and directories within that particular path.#2 Get Linux directory size using ncduIf you’re looking for a more interactive and feature-rich approach to exploring directory sizes, consider using the ncdu (NCurses Disk Usage) tool. ncdu provides a visual representation of disk usage and allows you to navigate through directories, view size details, and identify large files with ease.For Debian or Ubuntu, use this command:sudo apt-get install ncduOnce installed, run ncdu followed by the path to the directory you want to analyze:ncdu /path/to/directoryThis will launch the ncdu interface, which shows a breakdown of file and subdirectory sizes. Use the arrow keys to navigate and explore various folders, and press q to exit the tool.Example: Here’s a sample output of using the ncdu command to analyze the home directory. Simply enter the ncdu command and press Enter. The displayed output will look something like this:#3 Get Linux directory size using 1s -1aYou can alternatively opt to use the ls command to list the files and directories within a directory. The options -l and -a modify the default behavior of ls as follows:-l (long listing format)Displays the detailed information for each file and directoryShows file permissions, the number of links, owner, group, file size, the timestamp of the last modification, and the file/directory name-a (all files)Instructs ls to include all files, including hidden files and directoriesIncludes hidden files on Linux that typically have names beginning with a . (dot)ls -la lists all files (including hidden ones) in long format, providing detailed information such as permissions, owner, group, size, and last modification time. This command is especially useful when you want to inspect file attributes or see hidden files and directories.Example: When you enter ls -la command and press Enter, you will see an output similar to this:Each line includes:File type and permissions (e.g., drwxr-xr-x):The first character indicates the file type- for a regular filed for a directoryl for a symbolic linkThe next nine characters are permissions in groups of three (rwx):r = readw = writex = executePermissions are shown for three classes of users: owner, group, and others.Number of links (e.g., 2):For regular files, this usually indicates the number of hard linksFor directories, it often reflects subdirectory links (e.g., the . and .. entries)Owner and group (e.g., user group)File size (e.g., 4096 or 1045 bytes)Modification date and time (e.g., Jan 7 09:34)File name (e.g., .bashrc, notes.txt, Documents):Files or directories that begin with a dot (.) are hidden (e.g., .bashrc)ConclusionThat’s it! You can now determine the size of a directory in Linux. Measuring directory sizes is a crucial skill for efficient storage management. Whether you choose the straightforward du command, use the visual advantages of the ncdu tool, or opt for the versatility of ls -la, this expertise enhances your ability to uphold an organized and efficient Linux environment.Looking to deploy Linux in the cloud? With Gcore Edge Cloud, you can choose from a wide range of pre-configured virtual machines suitable for Linux:Affordable shared compute resources starting from €3.2 per monthDeploy across 50+ cloud regions with dedicated servers for low-latency applicationsSecure apps and data with DDoS protection, WAF, and encryption at no additional costGet started today

How to Run Hugging Face Spaces on Gcore Inference at the Edge

Running machine learning models, especially large-scale models like GPT 3 or BERT, requires a lot of computing power and comes with a lot of latency. This makes real-time applications resource-intensive and challenging to deliver. Running ML models at the edge is a lightweight approach offering significant advantages for latency, privacy, and resource optimization.  Gcore Inference at the Edge makes it simple to deploy and manage custom models efficiently, giving you the ability to deploy and scale your favorite Hugging Face models globally in just a few clicks. In this guide, we’ll walk you through how easy it is to harness the power of Gcore’s edge AI infrastructure to deploy a Hugging Face Space model. Whether you’re developing NLP solutions or cutting-edge computer vision applications, deploying at the edge has never been simpler—or more powerful. Step 1: Log In to the Gcore Customer PortalGo to gcore.com and log in to the Gcore Customer Portal. If you don’t yet have an account, go ahead and create one—it’s free. Step 2: Go to Inference at the EdgeIn the Gcore Customer Portal, click Inference at the Edge from the left navigation menu. Then click Deploy custom model. Step 3: Choose a Hugging Face ModelOpen huggingface.com and browse the available models. Select the model you want to deploy. Navigate to the corresponding Hugging Face Space for the model. Click on Files in the Space and locate the Docker option. Copy the Docker image link and startup command from Hugging Face Space. Step 4: Deploy the Model on GcoreReturn to the Gcore Customer Portal deployment page and enter the following details: Model image URL: registry.hf.space/ethux-mistral-pixtral-demo:latest Startup command: python app.py Container port: 7860 Configure the pod as follows: GPU-optimized: 1x L40S vCPUs: 16 RAM: 232GiB For optimal performance, choose any available region for routing placement. Name your deployment and click Deploy.Step 5: Interact with Your ModelOnce the model is up and running, you’ll be provided with an endpoint. You can now interact with the model via this endpoint to test and use your deployed model at the edge.Powerful, Simple AI Deployment with GcoreGcore Inference at the Edge is the future of AI deployment, combining the ease of Hugging Face integration with the robust infrastructure needed for real-time, scalable, and global solutions. By leveraging edge computing, you can optimize model performance and simultaneously futureproof your business in a world that increasingly demands fast, secure, and localized AI applications. Deploying models to the edge allows you to capitalize on real-time insights, improve customer experiences, and outpace your competitors. Whether you’re leading a team of developers or spearheading a new AI initiative, Gcore Inference at the Edge offers the tools you need to innovate at the speed of tomorrow. Explore Gcore Inference at the Edge

10 Common Web Performance Mistakes and How to Overcome Them

Web performance mistakes can carry a high price, resulting in websites that yield low conversion rates, high bounce rates, and poor sales. In this article, we dig into the top 10 mistakes you should avoid to boost your website performance.1. Slow or Unreliable Web HostYour site speed begins with your web host, which provides the server infrastructure and resources for your website. This includes the VMs and other infrastructure where your code and media files reside. Three common host-related problems are as follows:Server location: The further away your server is from your users, the slower the site speed and the poorer the experience for your website visitors. (More on this under point 7.)Shared hosting: Shared hosting solutions share server resources among multiple websites, leading to slow load times and spotty connections during peak times due to heavy usage. Shared VMs can also impact your website’s performance due to increased network traffic and resource contention.VPS hosting: Bandwidth limitations can be a significant issue with VPS hosting. A limited bandwidth package can cause your site speed to decrease during high-traffic periods, resulting in a sluggish user experience.Correct for server and VM hosting issues by choosing a provider with servers located closer to your user base and provisioning sufficient computational resources, like Gcore CDN. Use virtual dedicated servers (VDS/VPS) rather than shared hosting to avoid network traffic from other websites affecting your site’s performance. If you already use a VPS, consider upgrading your hosting plan to increase server resources and improve UX. For enterprises, dedicated servers may be more suitable.2. Inefficient Code, Libraries, and FrameworksPoor-quality code and inefficient frameworks can increase the size of web pages, consume too many resources, and slow down page load times. Code quality is often affected by syntax, semantics, and logic errors. Correct these issues by writing clean and simple code.Errors or inefficiencies introduced by developers can impact site performance, such as excessive API calls or memory overuse. Prevent these issues by using TypeScript, console.log, or built-in browser debuggers during development. For bugs in already shipped code, utilize logging and debugging tools like the GNU debugger or WinDbg to identify and resolve problems.Improving code quality also involves minimizing the use of large libraries and frameworks. While frontend frameworks like React, Vue, and Angular.js are popular for accelerating development, they often include extensive JavaScript and prebuilt components that can bloat your website’s codebase. To optimize for speed, carefully analyze your use case to determine if a framework is necessary. If a static page suffices, avoid using a framework altogether. If a framework is needed, select libraries that allow you to link only the required components.3. Unoptimized Code Files and FontsEven high-quality code needs optimization before shipping. Unoptimized JavaScript, HTML, and CSS files can increase page weight and necessitate multiple HTTP requests, especially if JavaScript files are executed individually.To optimize code, two effective techniques are minification and bundling.Minification removes redundant libraries, code, comments, unnecessary characters (e.g., commas and dots), and formatting to reduce your source code’s size. It also shortens variable and function names, further decreasing file size. Tools for minification include UglifyJS for JavaScript, CSSNano for CSS, and HTMLminifier for HTML.Bundling groups multiple files into one, reducing the number of HTTP requests and speeding up site load times. Popular bundling tools include Rollup, Webpack, and Parcel.File compression using GZIP or Brotli can also reduce the weight of HTTP requests and responses before they reach users’ browsers. Enable your chosen compression technique on your server only after checking that your server provider supports it.4. Unoptimized Images and VideosSome websites are slowed down by large media files. Upload only essential media files to your site. For images, compress or resize them using tools like TinyPNG and Compressor.io. Convert images from JPEG, PNG, and GIF to WebP and AVIF formats to maintain quality while reducing file size. This is especially beneficial in industries like e-commerce and travel, where multiple images boost conversion rates. Use dynamic image optimization services like Gcore Image Stack for efficient processing and delivery. For pages with multiple images, use CSS sprites to group them, reducing the number of HTTP requests and speeding up load times.When adding video files, use lite embeds for external links. Standard embed code, like YouTube’s, is heavy and can slow down your pages. Lite embeds load only thumbnail images initially, and the full video loads when users click the thumbnail, improving page speed.5. No Lazy LoadingLazy loading delays the rendering of heavy content like images and JavaScript files until the user needs it, contrasting with “eager” loading, which loads everything at once and slows down site load times. Even with optimized images and code, lazy loading can further enhance site speed through a process called “timing.”Image timing uses the HTML loading attribute in an image tag or frameworks like Angular or React to load images in response to user actions. The browser only requests images when the user interacts with specific features, triggering the download.JavaScript timing controls when certain code loads. If JavaScript doesn’t need to run until the entire page has rendered, use the defer attribute to delay its execution. If JavaScript can load at any time without affecting functionality, load it asynchronously with the async attribute.6. Heavy or Redundant External Widgets and PluginsWidgets and plugins are placed in designated frontend and backend locations to extend website functionality. Examples include Google review widgets that publish product reviews on your website and Facebook plugins that connect your website to your Facebook Page. As your website evolves, more plugins are typically installed, and sometimes website admins forget to remove those that are no longer required.Over time, heavy and unused plugins can consume substantial resources, slowing down your website unnecessarily. Widgets may also contain heavy HTML, CSS, or JavaScript files that hinder web performance.Remove unnecessary plugins and widgets, particularly those that make cURL calls, HTTP requests, or generate excessive database queries. Avoid plugins that load heavy scripts and styles or come from unreliable sources, as they may contain malicious code and degrade website performance.7. Network IssuesYour server’s physical location significantly impacts site speed for end users. For example, if your server is in the UK and your users are in China, they’ll experience high latency due to the distance and DNS resolution time. The greater the distance between the server and the user, the more network hops are required, increasing latency and slowing down site load times.DNS resolution plays a crucial role in this process. Your authoritative DNS provider resolves your domain name to your IP address. If the provider’s server is too far from the user, DNS resolution will be slow, giving visitors a poor first impression.To optimize content delivery and reduce latency, consider integrating a content delivery network (CDN) with your server-side code. A CDN stores copies of your static assets (e.g., container images, JavaScript, CSS, and HTML files) on geographically distributed servers. This distribution ensures that users can access your content from a server closer to their location, significantly improving site speed and performance.8. No CachingWithout caching, your website has to fetch data from the origin server every time a user requests. This increases the load time because the origin server is another physical hop that data has to travel.Caching helps solve this problem by serving pre-saved copies of your website. Copies of your web files are stored on distributed CDN servers, meaning they’re available physically closer to website viewers, resulting in quicker load times.An additional type of caching, DNS caching, temporarily stores DNS records in DNS resolvers. This allows for faster domain name resolution and accelerates the initial connection to a website.9. Excessive RedirectsWebsite redirects send users from one URL to another, often resulting in increased HTTP requests to servers. These additional requests can potentially crash servers or cause resource consumption issues. To prevent this, use tools like Screaming Frog to scan your website for redirects and reduce them to only those that are absolutely necessary. Additionally, limit each redirect to making no more than one request for a .css file and one for a .js file.10. Lack of Mobile OptimizationForgetting to optimize for mobile can harm your website’s performance. Mobile-first websites optimize for speed and UX. Better UX leads to happier customers and increased sales.Optimizing for mobile starts with understanding the CPU, bandwidth, and memory limitations of mobile devices compared to desktops. Sites with excessively heavy files will load slowly on mobiles. Writing mobile-first code, using mobile devices or emulators for building and testing, and enhancing UX for various mobile device types—such as those with larger screens or higher capacity—can go a long way to optimizing for mobile.How Can Gcore Help Prevent These Web Performance Mistakes?If you’re unsure where to start in correcting or preventing web performance mistakes, don’t worry—you don’t have to do it alone. Gcore offers a comprehensive suite of solutions designed to enhance your web performance and deliver the best user experience for your visitors:Powerful VMs: Fast web hosting with a wide range of virtual machines.Managed DNS: Hosting your DNS zones and ensuring quick DNS resolution with our fast Managed DNS.CDN: Accelerate both static and dynamic components of your website for global audiences.With robust infrastructure from Gcore, you can ensure optimal performance and a seamless experience for all your web visitors. Keep your website infrastructure in one place for a simplified website management experience.Need help getting started? Contact us for a personalized consultation and discover how Gcore can supercharge your website performance.Get in touch to boost your website

How to Choose Between Bare Metal GPUs and Virtual GPUs for AI Workloads

Choosing the right GPU type for your AI project can make a huge difference in cost and business outcomes. The first consideration is often whether you need a bare metal or virtual GPU. With a bare metal GPU, you get a physical server with an entire GPU chip (or chips) installed that is completely dedicated to the workloads you run on the server, whereas a virtual GPU means you share GPU resources with other virtual machines.Read on to discover the key differences between bare metal GPUs and virtual GPUs, including performance and scalability, to help you make an informed decision.The Difference Between Bare Metal and Virtual GPUsThe main difference between bare metal GPUs and virtual GPUs is how they use physical GPU resources. With a bare metal GPU, you get a physical server with an entire GPU chip (or chips) installed that is completely dedicated to the workloads you run on the server. There is no hypervisor layer between the operating system (OS) and the hardware, so applications use the GPU resources directly.With a virtual GPU, you get a virtual machine (VM) and uses one of two types of GPU virtualization, depending on your or a cloud provider’s capabilities:An entire, dedicated GPU used by a VM, also known as a passthrough GPUA shared GPU used by multiple VMs, also known as a vGPUAlthough a passthrough GPU VM gets the entire GPU, applications access it through the layers of a guest OS and hypervisor. Also, unlike a bare metal GPU instance, other critical VM resources that applications use, such as RAM, storage, and networking, are also virtualized.The difference between running applications with bare metal and virtual GPUsThese architectural features affect the following key aspects:Performance and latency: Applications running on a VM with a virtual GPU, especially vGPU, will have lower processing power and higher latency for the same GPU characteristics than those running on bare metal with a physical GPU.Cost: As a result of the above, bare metal GPUs are more expensive than virtual GPUs.Scalability: Virtual GPUs are easier to scale than bare metal GPUs because scaling the latter requires a new physical server. In contrast, a new GPU instance can be provisioned in the cloud in minutes or even seconds.Control over GPU hardware: This can be critical for certain configurations and optimizations. For example, when training massive deep learning models with a billion parameters, total control means the ability to optimize performance optimization—and that can have a big impact on training efficiency for massive datasets.Resource utilization: GPU virtualization can lead to underutilization if the tasks being performed don’t need the full power of the GPU, resulting in wasted resources.Below is a table summarizing the benefits and drawbacks of each approach: Bare metal GPUVirtual GPUPassthrough GPUvGPUBenefitsDedicated GPU resourcesHigh performance for demanding AI workloadsLower costSimple scalabilitySuitable for occasional or variable workloadsLowest costSimple scalabilitySuitable for occasional or variable workloadsDrawbacksHigh cost compared to virtual GPUsLess flexible and scalable than virtual GPUsLow performanceNot suitable for demanding AI workloadsLowest performanceNot suitable for demanding AI workloadsShould You Use Bare Metal or Virtual GPUs?Bare metal GPUs and virtual GPUs are typically used for different types of workloads. Your choice will depend on what AI tasks you’re looking to perform.Bare metal GPUs are better suited for compute-intensive AI workloads that require maximum performance and speed, such as training large language models. They are also a good choice for workloads that must run 24/7 without interruption, such as some production AI inference services. Finally, bare metal GPUs are preferred for real-time AI tasks, such as robotic surgery or high-frequency trading analytics.Virtual GPUs are a more suitable choice for the early stages of AI/ML and iteration on AI models, where flexibility and cost-effectiveness are more important than top performance. Workloads with variable or unpredictable resource requirements can also run on this type of GPU, such as training and fine-tuning small models or AI inference tasks that are not sensitive to latency and performance. Virtual GPUs are also great for occasional, short-term, and collaborative AI/ML projects that don’t require dedicated hardware—for example, an academic collaboration that includes multiple institutions.To choose the right type of GPU, consider these three factors:Performance requirements. Is the raw GPU speed critical for your AI workloads? If so, bare metal GPUs are a superior choice.Scalability and flexibility. Do you need GPUs that can easily scale up and down to handle dynamic workloads? If yes, opt for virtual GPUs.Budget. Depending on the cloud provider, bare metal GPU servers can be more expensive than virtual GPU instances. Virtual GPUs typically offer more flexible pricing, which may be appropriate for occasional or variable workloads.Your final choice between bare metal GPUs and virtual GPUs depends on the specific requirements of the AI/ML project, including performance needs, scalability requirements, workload types, and budget constraints. Evaluating these factors can help determine the most appropriate GPU option.Choose Gcore for Best-in-Class AI GPUsGcore offers bare metal servers with NVIDIA H100, A100, and L40S GPUs. Using the 3.2 Tbps InfiniBand interface, you can combine H100 or A100 servers into scalable GPU clusters for training and tuning massive ML models or for high-performance computing (HPC).If you are looking for a scalable and low-latency solution for global AI inference, explore Gcore Inference at the Edge. It especially benefits latency-sensitive, real-time applications, such as generative AI and object recognition.Discover Gcore bare metal GPUs

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