#dockerfile

[saifosys]

Fundamentals of Docker for Beginners - Learn Docker from Scratch - Saifosys.com

[robomad]

Using Docker with Node.js Applications

Learn how to use Docker with Node.js applications. This guide covers setting up Docker, creating Dockerfiles, managing dependencies, and using Docker Compose.

Introduction Docker has revolutionized the way we build, ship, and run applications. By containerizing your Node.js applications, you can ensure consistency across different environments, simplify dependencies management, and streamline deployment processes. This guide will walk you through the essentials of using Docker with Node.js applications, from setting up a Dockerfile to running your…

[simplifyyourday]

🚀 Exciting Docker Adventures Await! 🐳✨

Hey Dev Fam! 🤓 Ever wondered about the magic behind creating Docker images? 🌟 Let's dive into the Dockerfile wonderland and unleash the power of containerization! 🚢💻

📜 Dockerfile 101: 1️⃣ Start with a base image 🏗️ 2️⃣ Add dependencies and customize 🧩 3️⃣ Set the working directory ���� 4️⃣ Copy your app code 📦 5️⃣ Expose ports and define commands 🚪🔧

🔥 Now, let's spice things up! 🔥

💡 TIP: Use multi-stage builds to keep your images lean and mean! 🏋️‍♂️

Ready to rock your Dockerfile game? 🚀 Share your favorite tips and tricks below! Let's build a community of containerization champions! 🏆💬 #DockerMagic #ContainerizationNation #CodeInContainers

📺 Dive deeper into the Docker universe with this awesome tutorial: Dockerfile Magic Tutorial 🎥✨

Remember, the journey is just as exciting as the destination! 🌈 Happy Dockering! 🐋💙

[bitaacademy1234]

Learn Docker and kubernetes in 50+ hrs from Professionals. Join Docker Training @Bitaacademy and get your placement.

[niobiumao3]

.

[transienturl]

I don't always want to think about dev infrastructure but I do naturally hyperfocus on it every so often

[thesysadm]

Complete Real-World DevOps Project | Deploy using K8S from Ansible | Rep...

[myconetted]

pl;ease hire a sysadmin im begigng. you

writing software leads to writing package specification files for software, so it is, so it has been, and so it will be forever.

[kde-official]

Why in gods name do Dockerfiles not support symlinks

And instead of telling you that, they just error out with a cryptic message

[cyber-sec]

NVIDIA AI Flaw Lets Hackers Escape Containers and Control Hosts

A shocking three-line Dockerfile exploit in NVIDIA’s AI container toolkit lets attackers break free from container limits and seize full control of cloud GPUs, risking millions of sensitive AI models and data. This urgent vulnerability targets shared AI infrastructure and demands immediate fixes to stop attackers from running wild.

Source: Wiz Research

Read more: CyberSecBrief

[aqwult]

guess i'll write my own wireguard dockerfile

[hope-and-sleep]

2025年05月09日 (禮拜五) [3/100]

Anyway I spent 3 hours yesterday troubleshooting my brother's homework. It was a learning experience for both of us -- I now know how to write a Dockerfile and deploy a webapp to Microsoft Azure, and also that even if you're the owner of a repository, you might need to actually give yourself the container registry reader role so you can see it. DESPITE THE FACT THAT AS THE OWNER YOU SHOULD HAVE THE PERMISSIONS FOR THAT ALREADY. "You have full control over the resource you have all the permissions already" NO APPARENTLY YOU DO NOT.

Yeah, that took me an hour to figure out.

On the bright side, I found out I passed my interview :D

[govindhtech]

Open Platform For Enterprise AI Avatar Chatbot Creation

How may an AI avatar chatbot be created using the Open Platform For Enterprise AI framework?

I. Flow Diagram

The graph displays the application’s overall flow. The Open Platform For Enterprise AI GenAIExamples repository’s “Avatar Chatbot” serves as the code sample. The “AvatarChatbot” megaservice, the application’s central component, is highlighted in the flowchart diagram. Four distinct microservices Automatic Speech Recognition (ASR), Large Language Model (LLM), Text-to-Speech (TTS), and Animation are coordinated by the megaservice and linked into a Directed Acyclic Graph (DAG).

Every microservice manages a specific avatar chatbot function. For instance:

Software for voice recognition that translates spoken words into text is called Automatic Speech Recognition (ASR).

By comprehending the user’s query, the Large Language Model (LLM) analyzes the transcribed text from ASR and produces the relevant text response.

The text response produced by the LLM is converted into audible speech by a text-to-speech (TTS) service.

The animation service makes sure that the lip movements of the avatar figure correspond with the synchronized speech by combining the audio response from TTS with the user-defined AI avatar picture or video. After then, a video of the avatar conversing with the user is produced.

An audio question and a visual input of an image or video are among the user inputs. A face-animated avatar video is the result. By hearing the audible response and observing the chatbot’s natural speech, users will be able to receive input from the avatar chatbot that is nearly real-time.

Create the “Animation” microservice in the GenAIComps repository

We would need to register a new microservice, such “Animation,” under comps/animation in order to add it:

Register the microservice

@register_microservice( name=”opea_service@animation”, service_type=ServiceType.ANIMATION, endpoint=”/v1/animation”, host=”0.0.0.0″, port=9066, input_datatype=Base64ByteStrDoc, output_datatype=VideoPath, ) @register_statistics(names=[“opea_service@animation”])

It specify the callback function that will be used when this microservice is run following the registration procedure. The “animate” function, which accepts a “Base64ByteStrDoc” object as input audio and creates a “VideoPath” object with the path to the generated avatar video, will be used in the “Animation” case. It send an API request to the “wav2lip” FastAPI’s endpoint from “animation.py” and retrieve the response in JSON format.

Remember to import it in comps/init.py and add the “Base64ByteStrDoc” and “VideoPath” classes in comps/cores/proto/docarray.py!

This link contains the code for the “wav2lip” server API. Incoming audio Base64Str and user-specified avatar picture or video are processed by the post function of this FastAPI, which then outputs an animated video and returns its path.

The functional block for its microservice is created with the aid of the aforementioned procedures. It must create a Dockerfile for the “wav2lip” server API and another for “Animation” to enable the user to launch the “Animation” microservice and build the required dependencies. For instance, the Dockerfile.intel_hpu begins with the PyTorch* installer Docker image for Intel Gaudi and concludes with the execution of a bash script called “entrypoint.”

Create the “AvatarChatbot” Megaservice in GenAIExamples

The megaservice class AvatarChatbotService will be defined initially in the Python file “AvatarChatbot/docker/avatarchatbot.py.” Add “asr,” “llm,” “tts,” and “animation” microservices as nodes in a Directed Acyclic Graph (DAG) using the megaservice orchestrator’s “add” function in the “add_remote_service” function. Then, use the flow_to function to join the edges.

Specify megaservice’s gateway

An interface through which users can access the Megaservice is called a gateway. The Python file GenAIComps/comps/cores/mega/gateway.py contains the definition of the AvatarChatbotGateway class. The host, port, endpoint, input and output datatypes, and megaservice orchestrator are all contained in the AvatarChatbotGateway. Additionally, it provides a handle_request function that plans to send the first microservice the initial input together with parameters and gathers the response from the last microservice.

In order for users to quickly build the AvatarChatbot backend Docker image and launch the “AvatarChatbot” examples, we must lastly create a Dockerfile. Scripts to install required GenAI dependencies and components are included in the Dockerfile.

II. Face Animation Models and Lip Synchronization

GFPGAN + Wav2Lip

A state-of-the-art lip-synchronization method that uses deep learning to precisely match audio and video is Wav2Lip. Included in Wav2Lip are:

A skilled lip-sync discriminator that has been trained and can accurately identify sync in actual videos

A modified LipGAN model to produce a frame-by-frame talking face video

An expert lip-sync discriminator is trained using the LRS2 dataset as part of the pretraining phase. To determine the likelihood that the input video-audio pair is in sync, the lip-sync expert is pre-trained.

A LipGAN-like architecture is employed during Wav2Lip training. A face decoder, a visual encoder, and a speech encoder are all included in the generator. Convolutional layer stacks make up all three. Convolutional blocks also serve as the discriminator. The modified LipGAN is taught similarly to previous GANs: the discriminator is trained to discriminate between frames produced by the generator and the ground-truth frames, and the generator is trained to minimize the adversarial loss depending on the discriminator’s score. In total, a weighted sum of the following loss components is minimized in order to train the generator:

A loss of L1 reconstruction between the ground-truth and produced frames

A breach of synchronization between the lip-sync expert’s input audio and the output video frames

Depending on the discriminator score, an adversarial loss between the generated and ground-truth frames

After inference, it provide the audio speech from the previous TTS block and the video frames with the avatar figure to the Wav2Lip model. The avatar speaks the speech in a lip-synced video that is produced by the trained Wav2Lip model.

Lip synchronization is present in the Wav2Lip-generated movie, although the resolution around the mouth region is reduced. To enhance the face quality in the produced video frames, it might optionally add a GFPGAN model after Wav2Lip. The GFPGAN model uses face restoration to predict a high-quality image from an input facial image that has unknown deterioration. A pretrained face GAN (like Style-GAN2) is used as a prior in this U-Net degradation removal module. A more vibrant and lifelike avatar representation results from prettraining the GFPGAN model to recover high-quality facial information in its output frames.

SadTalker

It provides another cutting-edge model option for facial animation in addition to Wav2Lip. The 3D motion coefficients (head, stance, and expression) of a 3D Morphable Model (3DMM) are produced from audio by SadTalker, a stylized audio-driven talking-head video creation tool. The input image is then sent through a 3D-aware face renderer using these coefficients, which are mapped to 3D key points. A lifelike talking head video is the result.

Intel made it possible to use the Wav2Lip model on Intel Gaudi Al accelerators and the SadTalker and Wav2Lip models on Intel Xeon Scalable processors.

Read more on Govindhtech.com

[codeonedigest]

Create Docker Image of NodeJS JavaScript API Project | Deploy Docker Ima... Full Video Link - https://youtu.be/ah32rkWMPis              Check out this new video on the CodeOneDigest YouTube channel! Learn how to create a Docker image of your Node.js API project and run it in a Docker container. #video #dockerimage #nodejs #api #dockercontainer #codeonedigest@java @awscloud @AWSCloudIndia @YouTube @codeonedigest #awsecs #nodejs #dockerimage  #aws #amazonwebservices #cloudcomputing #awscloud #awstutorial  #nodejsapiproject #nodejsapitutorial #nodejsapitutorialforbeginners #nodejsdockertutorial #nodejsdocker #nodejsdockerimage #nodejsdockercontainer #nodejsdockerdevelopment #nodejsdockerfile #javascriptapiproject #javascriptapitutorial #rundockerimage #createdockerimagestepbystep #createdockerimagefornodejsapplication #deploydockerimage #dockercontainertutorialforbeginners #dockercontainer #dockercontainerization #dockerfile

[hiddendip]

fighting a nightmare combination of shell scripting, dockerfile syntax, github actions reusable workflows and maven; and their individual mental ideas about how quoting and expansion should work

computers were a mistake

[blubberquark]

Nothing encapsulates my misgivings with Docker as much as this recent story. I wanted to deploy a PyGame-CE game as a static executable, and that means compiling CPython and PyGame statically, and then linking the two together. To compile PyGame statically, I need to statically link it to SDL2, but because of SDL2 special features, the SDL2 code can be replaced with a different version at runtime.

I tried, and failed, to do this. I could compile a certain version of CPython, but some of the dependencies of the latest CPython gave me trouble. I could compile PyGame with a simple makefile, but it was more difficult with meson.

Instead of doing this by hand, I started to write a Dockerfile. It's just too easy to get this wrong otherwise, or at least not get it right in a reproducible fashion. Although everything I was doing was just statically compiled, and it should all have worked with a shell script, it didn't work with a shell script in practice, because cmake, meson, and autotools all leak bits and pieces of my host system into the final product. Some things, like libGL, should never be linked into or distributed with my executable.

I also thought that, if I was already working with static compilation, I could just link PyGame-CE against cosmopolitan libc, and have the SDL2 pieces replaced with a dynamically linked libSDL2 for the target platform.

I ran into some trouble. I asked for help online.

The first answer I got was "You should just use PyInstaller for deployment"

The second answer was "You should use Docker for application deployment. Just start with

FROM python:3.11

and go from there"

The others agreed. I couldn't get through to them.

It's the perfect example of Docker users seeing Docker as the solution for everything, even when I was already using Docker (actually Podman).

I think in the long run, Docker has already caused, and will continue to cause, these problems:

Over-reliance on containerisation is slowly making build processes, dependencies, and deployment more brittle than necessary, because it still works in Docker

Over-reliance on containerisation is making the actual build process outside of a container or even in a container based on a different image more painful, as well as multi-stage build processes when dependencies want to be built in their own containers

Container specifications usually don't even take advantage of a known static build environment, for example by hard-coding a makefile, negating the savings in complexity