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4 Easy Steps to Set Up a Private Docker Registry on Ubuntu

  • By Gcore
  • March 21, 2023
  • 7 min read
4 Easy Steps to Set Up a Private Docker Registry on Ubuntu

Once you start working with the docker, you will eventually find that you want a bit more control over the images you want to deploy your container on. Here Docker Registry plays an important role, it helps you to centralize your container images and also reduce the build time for you and your team. Benefits of registries don’t stop here, you can integrate it with your continuous integration/continuous deployment (CI/CD) pipelines, by automating the image push process to a private docker registry, which helps to update the production or development environment on the go.

Docker does provide a free publicly available registry known as Docker Hub, that hosts custom build Docker images. But this is not ideal when you are working on proprietary software or web application, as it contains all the necessary code to run an application. Hence here comes the Private Docker Registry to rescue.

This tutorial will help you to set up and secure your own private Docker Registry. Below are the mentioned prerequisites before we begin 4 step guide:

  1. We need 2 Ubuntu 18.04 servers with sudo privileges. First will act as a client server, and second will be a private Docker Registry.
  2. Both systems should have Docker and Docker Compose.
  3. Static IP to point your domain.
  4. A domain name or a sub-domain (whichever you prefer) that point/resolve registry server.
  5. SSL for the private registry server. We will use Let’s Encrypt with Nginx.

So let’s begin the guide…

Step 1: Setting Up the Docker Registry

The Docker command line is perfect for managing 2 or 3 containers. But if we speak of deploying a full application deployment, which often requires few other components running parallely, then it can be a bit overwhelming.

With Docker Compose, we can create a .yml file that helps us to set up each container’s configuration and establish communications between them. Now, the Docker registry as an application consists of multiple components, so we are going to use Docker Compose to manage configuration.

Just in case if private Docker Registry Server doesn’t have docker-compose yet, then follow the below-mentioned steps.

Installing Docker Compose on Linux Systems

First, you need to download the Docker Compose binary from the Compose repository, then make the binary executable:

$ sudo curl -L "https://github.com/docker/compose/releases/download/1.25.0/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose \&& sudo chmod +x /usr/local/bin/docker-compose

Now test the installation:

$ docker-compose --versiondocker-compose version 1.25.0, build 4667896b

In case your installation fails, then you might need to create an extra symbolic link:

$ sudo ln -s /usr/local/bin/docker-compose /usr/bin/docker-compose

Note that the above-mentioned steps are strictly for Linux based systems. If you are on another OS, follow the instructions mentioned on this link to install docker-compose.

Setting Up Private Docker Registry

On the server follow the below-mentioned steps to create your own private Docker Registry. First, we are going to create a directory, move into it and then create a sub-directory to store our data:

$ mkdir /your/preferred/path/my-pdr && cd $_$ mkdir main

Create the docker-compose.yml configuration file for our registry in the my-pdr directory, and open it in a preferred text editor:

$ touch docker-compose.yml && nano docker-compose.yml

Add the below described basic configuration for Docker registry:

version: '3.7' services: registry:   restart: always   image: registry:2   ports:   - "5000:5000"   Environment:     REGISTRY_AUTH: htpasswd     REGISTRY_AUTH_HTPASSWD_REALM: Registry     REGISTRY_AUTH_HTPASSWD_PATH: /a2auth/registry.password     REGISTRY_HTTP_SECRET: SomeRandomStringToUse     REGISTRY_STORAGE_FILESYSTEM_ROOTDIRECTORY: /main     REGISTRY_STORAGE_DELETE_ENABLED: ‘true’   volumes:     - ./main:/main     - ./a2auth:/a2auth

Let’s break-down the above configuration,

Restart Policy:

We need to ensure that if our system is forcefully stopped or a planned system reboot is required, then registry server restarts as the system boots. Hence we are going to set restart:always.

Image Section:

In the image section, you have to use Docker’s official image https://hub.docker.com/_/registry with tag 2. You can use any other or latest image as per your requirement, but for the sake of this tutorial, we are going to stick with registry:2 image.

Port Section:

In the port section, we had used the default port number as used in registry image which is 5000, it tells Docker to map the port 5000 on the server to port 5000 in the running container. In case if 5000 port is already occupied, then you can use any other port as per your wish.

Environment Section:

In the environment section, we had set a couple of environment variables in the Docker Registry container, we are going to break this into two parts as mentioned below:

Storage:

  1. REGISTRY_STORAGE_FILESYSTEM_ROOTDIRECTORY with the path /main, as application detects the variable during startup, it will start saving data to the defined directory, i.e. /main.
  2. REGISTRY_STORAGE_DELETE_ENABLED it is set to true, otherwise, Docker Registry container will not support deleting images.

Authentication:

You can use a basic authentication mechanism to manage the access to your private Docker Registry. For this, you can create an authentication file with htpasswd and add users to it.

You can install a htpasswd package by running the following command:

$ sudo apt install apache2-utils

Create a directory to store the credentials:

$ mkdir /your/preferred/path/my-pdr/a2auth && cd $_

To create the first user, you can use below command. It will prompt you to enter the password, as you enter the password make a sure you copy it somewhere safe. As this will be used to login into your private registry:

$ htpasswd -Bc registry.password yourusername

You can use flag -B to specify bcrypt, which is more secure than default encryption and -c to create a new user.

  1. REGISTRYAUTH, by this we have specified htpasswd, which is the authentication schema we used.
  2. REGISTRYAUTHHTPASSWDPATH, it points to the authentication file which we had created for the user.
  3. REGISTRYAUTHHTPASSWDREALM, it implies the name of the htpasswd realm.
  4. REGISTRYHTTPSECRET, you can use any long-string format random string into the variable, or you can generate one from your system itself by running this command: cat /dev/urandom | tr -dc 'a-zA-Z0-9' | head -c 32

Volume Section:

You had also mentioned volumes section in the configuration file, which helps Docker to map the /main directory inside the container to /main on registry server. So by the end of the day, all data which is sent to registry container will get stored in /your/preferred/path/my-pdr on the registry server.

Now it’s time to put our configuration to test. You can do this by running the following command:

$ docker-compose up

You will see the below mentioned output:

Creating network "registry_default" with the default driverPulling registry (registry:2)...2: Pulling from library/registryc87736221ed0: Pull complete1cc8e0bb44df: Pull complete54d33bcb37f5: Pull completee8afc091c171: Pull completeb4541f6d3db6: Pull completeDigest: sha256:8004747f1e8cd820a148fb7499d71a76d45ff66bac6a29129bfdbfdc0154d146Status: Downloaded newer image for registry:2Creating registry_registry_1 ... doneAttaching to registry_registry_1registry_1  | time="2019-12-31T09:21:37.028548595Z" level=info msg="redis not configured" go.version=go1.11.2 instance.id=3a07596d-d918-4b9f-ac80-e0b3f9ae1e1a service=registry version=v2.7.1 registry_1  | time="2019-12-31T09:21:37.028661776Z" level=info msg="Starting upload purge in 55m0s" go.version=go1.11.2 instance.id=3a07596d-d918-4b9f-ac80-e0b3f9ae1e1a service=registry version=v2.7.1 registry_1  | time="2019-12-31T09:21:37.040850986Z" level=info msg="using inmemory blob descriptor cache" go.version=go1.11.2 instance.id=3a07596d-d918-4b9f-ac80-e0b3f9ae1e1a service=registry version=v2.7.1 registry_1  | time="2019-12-31T09:21:37.041642152Z" level=info msg="listening on [::]:5000" go.version=go1.11.2 instance.id=3a07596d-d918-4b9f-ac80-e0b3f9ae1e1a service=registry version=v2.7.1

Voila… The output indicates that our container is starting and running on port 5000. For now, hit the CTRL+C to shut down your private Docker Registry.

Step 2: Configuring Nginx for Port Forwarding

Now you have to set up port forwarding via Nginx to container’s port which is running on port 5000. Once this step is complete, you can access the private registry at your defined domain or subdomain.

This guide is assuming you already have set up Nginx with Let’s Encrypt. Now you need to create a server configuration file –

$ cd /etc/nginx/conf.d && nano yourdomainname.com.conf

Here you have to forward traffic to port 5000, on which your existing Docker Registry container is going to run. You need to append the additional information from the server to registry for each request and response in the header.

The Server configuration file should look something like below. Note that you need to run certbot to install the SSL for the domain.

http {  upstream docker-registry {    server registry:5000;  }map $upstream_http_docker_distribution_api_version $docker_distribution_api_version {    '' 'registry/2.0';}server {   listen 80;   listen [::]:80;       server_name yourdomainname.com;   return 301 https://$host$request_uri;} server {   listen 443 ssl http2;   listen [::]:443 ssl http2;         server_name yourdomainname.com;       ssl_certificate      /etc/letsencrypt/live/yourdomainname.com/fullchain.pem;       ssl_certificate_key  /etc/letsencrypt/live/yourdomainname.com/privkey.pem;        location /v2/ {       if ($http_user_agent ~ "^(docker\/1\.(3|4|5(?!\.[0-9]-dev))|Go ).*$" ) {         return 404;       }      add_header 'Docker-Distribution-Api-Version' $docker_distribution_api_version always;       proxy_pass                          http://docker-registry;       proxy_set_header  Host              $http_host;       proxy_set_header  X-Real-IP         $remote_addr;       proxy_set_header  X-Forwarded-For   $proxy_add_x_forwarded_for;       proxy_set_header  X-Forwarded-Proto $scheme;       proxy_read_timeout                  900;       }}

The $http_user_agent block verifies that whether Docker version of the client is above 1.5 or not because here we are using version 2.0 of the registry. UserAgent ensures that it is not a Go application. You can find more information on Nginx header configuration here.

Now it’s time to test the server configuration file:

$ sudo nginx -t

You will see the following output:

nginx: the configuration file /etc/nginx/nginx.conf syntax is oknginx: configuration file /etc/nginx/nginx.conf test is successful

In case your test fails, then you can go through the Nginx error log which usually resides in /var/log/nginx/ direcorty, it will help you to understand what went wrong.

Next, you need to change the Nginx’s default file upload limit which happens to be the only 1MB. As Docker splits large image uploads into separate layers, sometimes they can get as big as 1GB, so you need to ensure that the registry can handle large file uploads. To do so, you need to tweak client_max_body_size limit in /etc/nginx/nginx.conf file.

Open the file and find http section:

... http {	client_max_body_size 3000M;	...}...

Again, test your changes and restart the Nginx.

$ sudo nginx -tnginx: the configuration file /etc/nginx/nginx.conf syntax is oknginx: configuration file /etc/nginx/nginx.conf test is successful $ sudo systemctl restart nginx

Here you can see that the test is successful and you had changed the max upload size to 3GB.

To confirm that Nginx is forwarding traffic to port 5000, open a browser window and enter your URL:

https://yourdomainname.com/v2/

You will see a prompt in your browser, as the authentication process is in the play. Enter the username and password you created earlier, and you will see an empty JSON object:

{}

Whereas, if you go to your terminal, you will find the output similar to the following:

registry_1  | time="2019-12-31T09:21:37.041642152Z" level=info msg="response completed" go.version=go1.7.6 http.request.host=cornellappdev.com http.request.id=a8f5984e-15e3-4946-9c40-d71f8557652f http.request.method=GET http.request.remoteaddr=128.84.125.58 http.request.uri="/v2/" http.request.useragent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_2) AppleWebKit/604.4.7 (KHTML, like Gecko) Version/11.0.2 Safari/604.4.7" http.response.contenttype="application/json; charset=utf-8" http.response.duration=2.125995ms http.response.status=200 http.response.written=2 instance.id=3093e5ab-5715-42bc-808e-73f310848860 version=v2.6.2registry_1  | 172.17.0.2 - - [31/Dec/2019:09:21:38+0000] "GET /v2/ HTTP/1.0" 200 2 "" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_2) AppleWebKit/604.4.7 (KHTML, like Gecko) Version/11.0.2 Safari/604.4.7"

200 Response code in the last line indicates that the container handled the request successfully.

Step 3: Publishing Image to Private Docker Registry

For the sake of this tutorial, we will create a simple image based on the alpine image from Docker Hub.

Going to your client-server, run the following command:

$ docker pull alpineedge: Pulling from library/alpined95bb1b66adb: Pull complete Digest: sha256:2e8c50cbe65693cdf3e6c3822f23ee3e07a7d92fd891d0a5ed9710aedd05ee19Status: Downloaded newer image for alpinedocker.io/library/alpine $ docker run -it --name test-alpine alpine /bin/sh

Flag -it gives you interactive shell access into the container. Through which you acan create a file to test the publishing process:

root@dc59bfcd63b3:/# touch file-from-client-server.txt

Exit from the Docker container:

root@dc59bfcd63b3:/# exit

By doing so, you have created a new image, based on the image already running plus the changes you performed. Now you have to commit the changes:

$ docker commit $(docker ps -lq) your-test-imagesha256:83b7ae6c35a534d81ea0ad13fce007c2c6da39dfaf13a35faacdb358d7e12eb6

You can verify if the above command runs successfully, by entering the following command:

$ docker imagesREPOSITORY          TAG                 IMAGE ID            CREATED             SIZEyour-test-image     latest              83b7ae6c35a5        7 seconds ago       6MB

You have successfully created the image, but at this point, it only resides in the client server. Now it’s time to push your newly created image to your private Docker Registry. You will be prompted to enter the username and password:

$ docker login https://yourdomainname.com

It is always a good thing to add tags to your image as it certainly helps in the long run and helps you to identify images and push the tagged image to the registry:

$ docker tag v1 yourdomainname.com/your-test-image$ docker push yourdomainname.com/your-test-image

Your output will look similar to the following:

The push refers to a repository [yourdomainname.com/test-image]83b7ae6c35a5: Pusheddec4bff59bf4: Pushed7363c5bcdbce: Pushed982d147b635c: Pushed... 

Step 4: Pulling for Private Docker Registry

You have already pushed the image successfully, now you have to pull an image from the remote server into your client server. If you wish, you can also test it from another machine.

So now you are going to log in with the username password you created earlier:

$ docker login https://yourdomainname.com

Pull the image from your private Docker registry. If successful you will see the following output in your terminal:

$ docker pull yourdomainname.com/your-test-imagev1: Pulling from v2/your-test-imaged95bb1b66adb: Pull complete Digest: sha256:2e8c50cbe65693cdf3e6c3822f23ee3e07a7d92fd891d0a5ed9710aedd05ee19Status: Downloaded newer image for alpine:edgeyourdomainname.com/v2/your-test-image:v1

To confirm that pull was successful, you can always run the container with the following command. You can use --rm flag so that the container will destroy itself once you exit the interactive shell:

$ docker run --rm -it yourdomainname.com/your-test-image /bin/sh

To verify the changes you made to your image, use the ls command:

$ lsbin    dev    etc  file-from-client-server.txt  home   lib    media  mnt    opt    proc   root   run    sbin   srv    sys    tmp    usr    var

You will see the file we created previously with name file-from-client-server.txt. You can now confirm that you have set up a secure private Docker Registry through which anyone with the credentials can push and pull custom images.

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Here’s a quick breakdown to help you decide what works best for you.FactorBare metal serversVirtual machinesPerformanceDedicated resources; ideal for high-performance workloadsShared resources; suitable for moderate or variable workloadsScalabilityOften requires manual scaling; less flexibleHighly elastic; easy to scale up or downCustomizationFull control over hardware, OS, and configurationLimited by hypervisor and provider’s environmentSecurityIsolated by default; no hypervisor layerShared environment with strong isolation protocolsCostHigher upfront cost; dedicated hardwarePay-as-you-go pricing; cost-effective for flexible workloadsBest forHPC, AI/ML, compliance-heavy workloadsStartups, dev/test, fast-scaling applicationsAll about bare metal serversA bare metal server is a single-tenant physical server rented from a cloud provider. Unlike virtual servers, the hardware is not shared with other users, giving you full access to all resources and deeper control over configurations. You get exclusive access and control over the hardware via the cloud provider, which offers the stability and security needed for high-demand applications.The benefits of bare metal serversHere are some of the business advantages of opting for a bare metal server:Maximized performance: Because they are dedicated resources, bare metal servers provide top-tier performance without sharing processing power, memory, or storage with other users. This makes them ideal for resource-intensive applications like high-performance computing (HPC), big data processing, and game hosting.Greater control: Since you have direct access to the hardware, you can customize the server to meet your specific requirements. This is especially important for businesses with complex, specialized needs that require fine-tuned configurations.High security: Bare metal servers offer a higher level of security than their alternatives due to the absence of virtualization. With no shared resources or hypervisor layer, there’s less risk of vulnerabilities that come with multi-tenant environments.Dedicated resources: Because you aren’t sharing the server with other users, all server resources are dedicated to your application so that you consistently get the performance you need.Who should use bare metal servers?Here are examples of instances where bare metal servers are the best option for a business:High-performance computing (HPC)Big data processing and analyticsResource-intensive applications, such as AI/ML workloadsGame and video streaming serversBusinesses requiring enhanced security and complianceAll about virtual machinesA virtual server (or virtual machine) runs on top of a physical server that’s been partitioned by a cloud provider using a hypervisor. This allows multiple VMs to share the same hardware while remaining isolated from each other.Unlike bare metal servers, virtual machines share the underlying hardware with other cloud provider customers. That means you’re using (and paying for) part of one server, providing cost efficiency and flexibility.The benefits of virtual machinesHere are some advantages of using a shared virtual machine:Scalability: Virtual machines are ideal for businesses that need to scale quickly and are starting at a small scale. With cloud-based virtualization, you can adjust your server resources (CPU, memory, storage) on demand to match changing workloads.Cost efficiency: You pay only for the resources you use with VMs, making them cost-effective for companies with fluctuating resource needs, as there is no need to pay for unused capacity.Faster deployment: VMs can be provisioned quickly and easily, which makes them ideal for anyone who wants to deploy new services or applications fast.Who should use virtual machines?VMs are a great fit for the following:Web hosting and application hostingDevelopment and testing environmentsRunning multiple apps with varying demandsStartups and growing businesses requiring scalabilityBusinesses seeking cost-effective, flexible solutionsWhich should you choose?There’s no one-size-fits-all answer. Your choice should depend on the needs of your workload:Choose bare metal if you need dedicated performance, low-latency access to hardware, or tighter control over security and compliance.Choose virtual servers if your priority is flexible scaling, faster deployment, and optimized cost.If your application uses GPU-based inference or AI training, check out our dedicated guide to VM vs. BM for AI workloads.Get started with Gcore BM or VMs todayAt Gcore, we provide both bare metal and virtual machine solutions, offering flexibility, performance, and reliability to meet your business needs. Gcore Bare Metal has the power and reliability needed for demanding workloads, while Gcore Virtual Machines offers customizable configurations, free egress traffic, and flexibility.Compare Gcore BM and VM pricing now

Optimize your workload: a guide to selecting the best virtual machine configuration

Virtual machines (VMs) offer the flexibility, scalability, and cost-efficiency that businesses need to optimize workloads. However, choosing the wrong setup can lead to poor performance, wasted resources, and unnecessary costs.In this guide, we’ll walk you through the essential factors to consider when selecting the best virtual machine configuration for your specific workload needs.﹟1 Understand your workload requirementsThe first step in choosing the right virtual machine configuration is understanding the nature of your workload. Workloads can range from light, everyday tasks to resource-intensive applications. When making your decision, consider the following:Compute-intensive workloads: Applications like video rendering, scientific simulations, and data analysis require a higher number of CPU cores. Opt for VMs with multiple processors or CPUs for smoother performance.Memory-intensive workloads: Databases, big data analytics, and high-performance computing (HPC) jobs often need more RAM. Choose a VM configuration that provides sufficient memory to avoid memory bottlenecks.Storage-intensive workloads: If your workload relies heavily on storage, such as file servers or applications requiring frequent read/write operations, prioritize VM configurations that offer high-speed storage options, such as SSDs or NVMe.I/O-intensive workloads: Applications that require frequent network or disk I/O, such as cloud services and distributed applications, benefit from VMs with high-bandwidth and low-latency network interfaces.﹟2 Consider VM size and scalabilityOnce you understand your workload’s requirements, the next step is to choose the right VM size. VM sizes are typically categorized by the amount of CPU, memory, and storage they offer.Start with a baseline: Select a VM configuration that offers a balanced ratio of CPU, RAM, and storage based on your workload type.Scalability: Choose a VM size that allows you to easily scale up or down as your needs change. Many cloud providers offer auto-scaling capabilities that adjust your VM’s resources based on real-time demand, providing flexibility and cost savings.Overprovisioning vs. underprovisioning: Avoid overprovisioning (allocating excessive resources) unless your workload demands peak capacity at all times, as this can lead to unnecessary costs. Similarly, underprovisioning can affect performance, so finding the right balance is essential.﹟3 Evaluate CPU and memory considerationsThe central processing unit (CPU) and memory (RAM) are the heart of a virtual machine. The configuration of both plays a significant role in performance. Workloads that need high processing power, such as video encoding, machine learning, or simulations, will benefit from VMs with multiple CPU cores. However, be mindful of CPU architecture—look for VMs that offer the latest processors (e.g., Intel Xeon, AMD EPYC) for better performance per core.It’s also important that the VM has enough memory to avoid paging, which occurs when the system uses disk space as virtual memory, significantly slowing down performance. Consider a configuration with more RAM and support for faster memory types like DDR4 for memory-heavy applications.﹟4 Assess storage performance and capacityStorage performance and capacity can significantly impact the performance of your virtual machine, especially for applications requiring large data volumes. Key considerations include:Disk type: For faster read/write operations, opt for solid-state drives (SSDs) over traditional hard disk drives (HDDs). 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

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