HacWare is an award winning security training platform that allows organizations to build hyper custom security training solutions in minutes. It reduces training labor costs by 40%.

HacWare

Hiring the right candidate for an organization can be a daunting task, as finding the perfect fit is never guaranteed despite a thorough hiring process. There is no mathematical equation for assessing a candidate's qualifications. Evaluating a candidate primarily hinges on two factors: " " and " ". Technical skills are relatively easier to measure than finding a cultural fit for the team, especially while evaluating during the few hours of the interview process. Technical Ability Cultural Fit The Guiding Principles Despite the effort invested, there's no surety the selected candidate is technically equipped and the perfect fit for the team, leading to a leap of faith. To mitigate hiring risks for a full-time job, we have formalized several rules. One significant rule emphasizes: Prefer candidates with full-time experience over long-term freelancers. Freelancers and Challenges Drawing from our experiences working with long-term freelancers, we have identified some common quirks among many individuals. If you're a freelancer considering this shift, we've pinpointed traits that should be addressed for a successful transition. Approach to Problem Solving 🧩 Freelancers often approach problem-solving differently from full-time employees. Through their extensive experience in various freelance projects, they might start embracing the " mindset. This approach prioritizes immediate functionality and might disregard the best practices for task completion, potentially impacting the project's overall health, team coordination, and successful goal achievement. If It's Working, It's Done" Collaboration Struggles 🖇️ Freelancers' prolonged solo work impacts their team collaboration skills, making it challenging for them to coordinate within the team and other teams in the organization. This lack of team experience often results in difficulties when attempting to synchronize efforts within the organization. No Strings Attached Mindset 🔄 Freelancers often cultivate a habit of transiting from one project to another, nurturing a constant " " within themselves. Organizations looking for stability invest in candidates with the anticipation of a more enduring commitment and are caught off guard by departures within a short span of 2 to 3 months at times. Urge for Change Short-Sightedness ⚙️ The idea of "Be your own man" and doing everything on your own may seem powerful, but it's even more important to recognize the significance of being part of something bigger and creating things that fit flawlessly with others. The nature of freelancing sometimes requires a shorter-term focus on immediate tasks or projects and does not realize " ". Companies prefer candidates who can take a more holistic and long-term view, contributing to the broader vision and goals of the company, rather than focusing solely on short-term tasks. Forward-thinkers who foresee future needs are the architects behind groundbreaking products. Your work will be a cog in the machine instead of the machine itself The Performance Metrics 📈 High-performance applications significantly influence user satisfaction, productivity, and competitive advantage in today's market. The Freelancer's " " approach for delivering tasks quickly, with minimal effort and time, often leads to subpar unoptimized output. These modules and products adversely affect product performance, leading to customer dissatisfaction, impeding growth, and negatively impacting customer retention. quantity over quality Conclusion " " Pleasure in the job puts perfection in the work. -- Aristotle The purpose of the discussion is not to devalue the role of freelancers, rather it is important to acknowledge their significant contributions to the thriving tech industry. While freelancers bring so much to the table, their alignment with organizational objectives often poses challenges. Recognizing the strengths and understanding the potential discord between freelancing and long-term goals is essential for refining hiring strategies and ensuring an ideal match for both employees and organizations. Freelancers, too, should introspect on these common issues discussed and address them, regardless of pursuing a full-time job or continuing in their freelancing career. More From the Author Learn How to Break Free at Dark Truth about AWS Deep Learning AMIs: The Silent Roadblock to Your Success! How Your AWS Deep Learning AMI is Holding You Back Explore the full article: Cost-Effective Deep Learning with Custom AMIs and Spot Instances: Discover Cutting-Edge Insights! Deep Learning with AWS Graviton2 and Nvidia Tensor T4g Follow the detailed instructions on How to Compile and Fine-Tune FFmpeg for Optimal Performance: Tailoring your multimedia toolkit for best performance! Install FFmpeg from Source Check the complete guide at GPU Acceleration for FFmpeg: A Step-By-Step Guide with Nvidia GPU on AWS! Enable Harware Accelaration with FFmpeg See for yourself at CPU vs GPU Benchmark for video Transcoding on AWS: Debunking the CPU-GPU myth! Challenging the Video Encoding Cost-Speed Myth Explore the insights at Crafting the Team for Sustainable Success: Are "Rockstars" the Ultimate Solution for a Thriving Team? Beyond Rockstars Discover more about Builders at How "Builders" Transform Team's Productivity: Navigating Vision & Ideas to Reality! Bridging Dreams and Reality Explore insights: Mental Well-being in Tech: Cultivating a Healthier Workplace for Tech Professionals The Dark Side of High-Tech Success! Discover the details at The Future of Efficient Backend Development: Efficient Backend Development with Outerbase! Building a Robust Backend with Outerbase Also published . here

Why Businesses Don't Employ Freelancers

How We Enhanced Remotion Performance through GPU-Accelerated Server-Side Rendering Running with hardware acceleration in headless mode can be more challenging than it appears. We embarked on this journey with , which is an excellent framework that enables developers to "Make Videos Programmatically.” On our way, explore various Nvidia driver versions, including those from Nvidia's website and Ubuntu's official repositories. Google Chrome Remotion We collaborated with the vibrant Open-source community to find the answer for using . After encountering some setbacks on our way, we were able to make it work for Remotion eventually. We consolidated our findings in the Remotion Docs at and simplified the instructions for Remotion developers to make the most out of it. Although the whole research was done for , the same process will work for any headless application that requires GPU power, especially in headless mode. Remotion GPU with Remotion for server-side rendering Using the GPU in the Cloud Remotion Hardware Acceleration in Browser Rendering Hardware Acceleration is to your browser what " " is for a gasoline car. It uses the power of specialized hardware like GPUs to make your web pages load faster, and videos play smoothly. Hardware acceleration improves the part where the web page is displayed and shown to you, known as rendering. This results in a smoother and snappier browsing experience. Nitrogen Fuel DLAMI from Marketplace? One may wonder why we didn't opt for the AWS Deep Learning AMI or DLAMI. But we needed fine-grained control over the GPU drivers and utilities and the outdated DLAMIs despite all the bloatware was not the answer. Such tasks are often challenging to achieve with pre-packaged AMIs, as discussed in " .” Tailoring the environment to our exact requirements also helped us troubleshoot issues effectively. Why Deep Learning AMI is Holding You Back We experimented with multiple packages to identify the key ones needed for Google Chrome to recognize the GPU. Despite these efforts, GPU recognition remained elusive, and in some instances, GPU drivers were crashing even at initialization. The long and arduous journey has brought us to the end of the tunnel, yet the darkness persists, and the solution remains as elusive as ever. As we decided to call it a day, We stumbled upon on 's Github issues, which did not give us any hope but did give us a new perspective and drive to achieve our goal. this thread Puppeteer The Fresh Start It was time to return to the drawing board and start afresh. We started with a clean base Ubuntu AMI on AWS EC2 Instance. After that, we downloaded and installed the Nvidia Tesla T4G drivers from Nvidia's official website. Once it was confirmed that the GPU was working properly using , we installed Google Chrome and tested it for hardware acceleration. To our relief, Google Chrome finally gave a nod to work with Hardware Acceleration. nvidia-smi Launching an EC2 Instance Launching an EC2 instance can be achieved in various ways, with the AWS Web Console and AWS CLI being two notable options. Regardless of the method, the important parameters are instance type and AMI selection. At the time of writing, the AMI ID for our chosen instance is . For launching the EC2 instance via , the following command can be used after replacing the placeholders with actual values. g4dn.xlarge ami-053b0d53c279acc90 aws-cli aws ec2 run-instances \
  --instance-initiated-shutdown-behavior "terminate" \
  --image-id "ami-053b0d53c279acc90" \
  --instance-type "g4dn.xlarge" \
  --key-name "<KEY_NAME>" \
  --subnet-id "<SUBNET_ID>" \
  --security-group-ids "<SECURITY_GROUP_ID>" \
  --iam-instance-profile Arn="<ARN>" \
  --block-device-mappings '[
  {
    "DeviceName": "/dev/sda1",
    "Ebs": {
      "VolumeSize": 8,
      "VolumeType": "gp3"
    }
  }
]' Ubuntu and Kernel Upgrade Ubuntu 22.04 AMI on AWS comes with Linux Kernel (specifically ). At this point, we have two options either we stick with Kernel or Upgrade to ( ), we decided to upgrade the Kernel. This decision is crucial as the Nvidia driver compiled for one version will not work for the other. ami-053b0d53c279acc90 v5 5.19.0-1025-aws v5 v6 v6.2.0-1013-aws After launching the instance, SSH into the instance and initiate the Ubuntu packages upgrade process. To update all the installed packages, including Linux Kernel, execute the following one-liner. sudo bash -c "apt update && export DEBIAN_FRONTEND=noninteractive && export NEEDRESTART_MODE=a && apt upgrade -y && reboot" We can also split , and . But the reason behind doing it like this is to install these updates in non-interactive mode. apt update apt upgrade -y reboot After the execution, the system will reboot with Linux Kernel Version 6. We can confirm the kernel update with . Rebooting is essential when upgrading the Ubuntu system because it loads the new Kernel on the next boot, and Nvidia drivers will subsequently be built for the updated version. uname -r Nvidia GPU Driver Installation The process of installing GPU drivers is straightforward when we have the correct driver for the GPU and the corresponding compatible version. Before starting the installation, we need to install a couple of packages: and . The former is a bundle with a variety of compilation tools required to compile the Nvidia driver. The latter, while not mandatory, is required by Google Chrome. Therefore, it's a good idea to install it beforehand to enable support for Vulkan ICD loader for Nvidia, should we require it in the future. build-essential libvulkan1 sudo apt install -y build-essential libvulkan1 After build tools are installed, we can proceed to download and install the GPU drivers using the following commands: DRIVER_URL="https://us.download.nvidia.com/tesla/535.104.12/NVIDIA-Linux-x86_64-535.104.12.run"
DRIVER_NAME="NVIDIA-Linux-driver.run"
wget -O "$DRIVER_NAME" "$DRIVER_URL"
sudo sh "$DRIVER_NAME" --disable-nouveau --silent
rm "$DRIVER_NAME" Now we can run to confirm the GPU driver installation. The output will look similar to this: nvidia-smi Configuring the Startup Service We learned this the hard way: running once is needed for the proper initialization of and . Google Chrome and Chromium fail to initialize without this preliminary setup. To automate this process, we will create a service that runs at boot time using the following commands: nvidia-smi EGL ANGLE EGL nvidia-smi echo '[Unit]
Description=Run nvidia-smi at system startup

[Service]
ExecStart=/usr/bin/nvidia-smi
Type=oneshot
RemainAfterExit=yes

[Install]
WantedBy=multi-user.target' | sudo tee /etc/systemd/system/nvidia-smi.service
sudo systemctl enable nvidia-smi.service
sudo systemctl start nvidia-smi.service Testing the Installation To test hardware acceleration with Google Chrome or Chromium, we can follow these steps. It's worth noting that Google Chrome has more dependencies, making it an easier choice if we plan to use the Node.js library, Puppeteer. On the other hand, Chromium is a lightweight option with a smaller footprint of dependencies, but we can still use Puppeteer with Chromium by installing a few extra dependencies. Installing Chromium Installation Installing Chromium is straightforward, as it is readily available with Ubuntu's official software repositories. We can install it using with the following command. apt sudo apt install -y chromium-browser Testing Chromium is fully prepared to handle Hardware Acceleration tasks. We can verify its readiness by launching Chromium with the specified parameters. chromium-browser --headless --use-gl=angle \
   --use-angle=gl-egl --use-cmd-decoder=passthrough \
   --print-to-pdf=output.pdf 'chrome://gpu' Installing Puppeteer Dependencies To use Puppeteer with Chromium instead of Google Chrome, there are a few extra dependencies that should be installed. sudo apt install ca-certificates fonts-liberation \
    libasound2 libatk-bridge2.0-0 libatk1.0-0 libc6 \
    libcairo2 libcups2 libdbus-1-3 libexpat1 libfontconfig1 \
    libgbm1 libgcc1 libglib2.0-0 libgtk-3-0 libnspr4 libnss3 \
    libpango-1.0-0 libpangocairo-1.0-0 libstdc++6 libx11-6 \
    libx11-xcb1 libxcb1 libxcomposite1 libxcursor1 libxdamage1 \
    libxext6 libxfixes3 libxi6 libxrandr2 libxrender1 libxss1 \
    libxtst6 lsb-release wget xdg-utils Installing Google Chrome Installation Google Chrome can be installed from multiple options, but we will use the Google Chrome official repository for Debian. We need to add it to Ubuntu's repo list along with the public signing key. curl -fsSL https://dl.google.com/linux/linux_signing_key.pub | sudo gpg --dearmor -o /usr/share/keyrings/googlechrom-keyring.gpg
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/googlechrom-keyring.gpg] http://dl.google.com/linux/chrome/deb/ stable main" | sudo tee /etc/apt/sources.list.d/google-chrome.list
sudo apt update
sudo apt install -y google-chrome-stable Testing Google Chrome is ready for our Hardware Acceleration tasks. We can test it by launching Google Chrome with the following parameters. google-chrome-stable --headless --use-gl=angle \
    --use-angle=gl-egl --use-cmd-decoder=passthrough \
    --print-to-pdf=output.pdf 'chrome://gpu' The above commands will generate , and transfer it to your local machine to check the status of Hardware Acceleration in Google Chrome or Chromium. If the process goes smoothly, the resultant will look like this. output.pdf PDF Working with Puppeteer To verify GPU acceleration using the Node.js library Puppeteer, follow these steps: Installation Install Node.js, as this clean Ubuntu AMI does not include any unnecessary packages. Use the following commands: curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key | sudo gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg
NODE_MAJOR=18
echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_$NODE_MAJOR.x nodistro main" | sudo tee /etc/apt/sources.list.d/nodesource.list
sudo apt update
sudo apt install nodejs Testing After installing Node.js, we can install Puppeteer by running in our code directory. Here's a sample file for checking hardware acceleration: npm i puppeteer index.js const puppeteer = require('puppeteer');
(async () => {
  const browser = await puppeteer.launch({
    headless: true, 
    args: ['--use-gl=angle', '--use-angle=gl-egl'], 
  });
  const page = await browser.newPage();
  await page.goto('chrome://gpu');
  await page.waitForTimeout(2000);
  await page.screenshot({ path: 'output.png' });
  await browser.close();
})(); Run the script using . This will generate an file containing hardware acceleration information. node index.js output.png Note from the Creator has been immensely helpful in helping the “ ” community adopt GPU-accelerated rendering.He's researched and written about how to obtain, configure and run EC2 containers and headless Chrome correctly in order to take advantage of graphics acceleration.This area has been especially hard to crack, and without we would not have been able to unlock huge speed boosts. Mirza Remotion Mirza We are super grateful for that! -- Jonny Burger Creator / Remotion.dev Conclusion With hardware acceleration now readily available in headless mode, we can harness the power of GPUs for faster and more complex rendering tasks. Our next steps involve creating a custom AMI from this instance and streamlining the process using AWS Image Builder Pipelines for efficiency. Additionally, we plan to extend hardware capabilities to Docker containers, further extending our options. Resources Remotion - Make videos programmatically: https://remotion.dev Remotion - Using the GPU in the cloud: https://www.remotion.dev/docs/miscellaneous/cloud-gpu Cannot enable GPU acceleration: github.com/puppeteer/puppeteer/issues/3637 : Recommend a workflow for using the GPU on a AWS instance https://github.com/remotion-dev/remotion/issues/2889 Getting Started with Headless Chrome: developer.chrome.com/blog/headless-chrome Why Your AWS Deep Learning AMI is Holding You Back and How to Fix: mirzabilal.com/why-your-aws-deep-learning-a.. Deep Learning with “AWS Graviton2 + NVIDIA Tensor T4G” for as low as free* with CUDA 12.2: mirzabilal.com/deep-learning-with-aws-gravi.. How to install FFmpeg with Harware Accelaration on AWS: mirzabilal.com/how-to-install-ffmpeg-with-h.. CPU vs GPU for Video Transcoding - Challenging the Cost-Speed Myth: https://mirzabilal.com/cpu-vs-gpu-for-video-transcoding-challenging-the-cost-speed-myth Also published . here

Unlocking GPU-Accelerated Server-Side Rendering in Remotion With Chrome, Chromium & Puppeteer

How to Create and Setup a Custom Deep Learning AMI and Reduce Costs With “Spot Instances” The world we live in today heavily relies on artificial intelligence. From vacuum bots to sales support, from self-driving cars to disease detection, from finding the content you want to consume to translating from a foreign language to your native one. is behind every great product out there, and the need for an efficient, cost-effective, and scalable deep learning architecture has never been more critical. AI The G5g instances powered by Amazon’s own Graviton2 processor and also feature NVIDIA T4G Tensor Core GPUs are a cost-effective alternative to Intel’s and AMD’s powered instances for deploying deep learning applications. The Dilemma AWS offers robust, powerful, cost-effective architecture for running artificial intelligence and deep learning tasks. One of the advantages is the option to use spot instances, which are far more cost-effective at times and up to 70% cheaper than on-demand instances. For example, the spot pricing history for the instance in various “us-east” zones ranged from to per hour for the past three months. These rates are tempting, but at the time of writing, no official Deep learning AMI is available for the Amazon instances family. Setting up the environment can be cumbersome: finding drivers, the correct dev toolchain, and a pre-compiled PyTorch module supporting the latest DL toolkit. g5g.xlarge $0.1720 $0.1519 Amazon Linux 2023 G5g Spot price history for g5g.xlarge for the last three months. Navigating the Challenge — A How-To Guide This aims to bridge the gap by offering comprehensive step-by-step instructions suitable for newcomers and seasoned data scientists. The goal is to enable you to leverage these state-of-the-art technologies at a meager cost without the hassle of finding the right driver and packages for the G5g family. Eventually, we will compile all the individual steps into a single script that will further streamline the process. 1. Launching an Instance For setting up an instance, we’ll use instance. The idea behind using a more powerful instance is to accelerate compilation time. We will launch the build instance with the AWS Command Line Interface (aws cli). g5g.4xlarge First, set the following environment variables: : Specifies the AWS region, e.g., ‘us-east-1’. REGION : Your security group ID(s). SECURITY_GROUPS : The name of your SSH key pair. KEY_PAIR : The ID of your subnet. SUBNET If you have any confusion about these variables. You can refer to the and documentation. security group, keypair, subnets Once you have these values, you can set these variables like this. export REGION='us-east-1'
export SECURITY_GROUPS='YourFirstSecurityGroupIdsHere'
export KEY_PAIR='YourSSHKeyNameHere'
export SUBNET='YourSubnetHere' Next, we need to find the latest so you will get the latest AMI every time you run this script. The following command will fetch the AMI ID and store it as . Amazon Linux 2023 AMI ID AMI_ID Let’s launch the instance using the AMI ID we retrieved earlier by executing: aws ec2 run-instances \
--image-id $AMI_ID \
--instance-type g5g.4xlarge \
--key-name $KEY_PAIR \
--subnet-id $SUBNET \
--security-group-ids $SECURITY_GROUPS \
--region $REGION \
--block-device-mappings '[{"DeviceName":"/dev/xvda","Ebs":{"VolumeSize":20,"VolumeType":"gp3"}}]' \
--tag-specifications 'ResourceType=instance,Tags=[{Key=Name,Value=AMI-Builder}]' This command initiates a instance with the Latest Amazon Linux 2023 AMI ID. It also configures the instance to use the specified security groups, key pair, and subnet we provided in environment variables. We’ve also attached 20 GB of storage to the root device for downloading different libraries and PyTorch compilation. g5g.4xlarge 2. Installing System Updates and Required Packages Setting up any machine, be it local or in the cloud, it is always a good practice to keep it updated. This part will install all the updates and tools used in compilation or running AI tasks. But before going to Gung Ho, We recommend taking an overview of the guide first and checking the complete script at the end of this tutorial, which should save you from lots of trouble. First, let’s define some essential environment variables. CUDA_HOME=/usr/local/cuda
HOME_DIR=/home/ec2-user Now, we’ll create a function called that carries out a series of tasks. install_utils install_utils() {
    # Update all system packages to their latest versions
    dnf -y update

    # Install development tools, which include compilers and other utilities
    dnf -y groupinstall "Development Tools"

    # Install the packages that are specifically required for our setup
    dnf install -y openssl-devel cmake3 rust cargo
    dnf install -y amazon-efs-utils htop iotop yasm nasm jq python3-pip python-devel cronie cronie-anacron

    # Add necessary paths to the .bashrc file
    echo "PATH=$CUDA_HOME/bin:\$PATH" | sudo tee -a $HOME_DIR/.bashrc
    echo "LD_LIBRARY_PATH=$CUDA_HOME/lib64:\$LD_LIBRARY_PATH" | sudo tee -a $HOME_DIR/.bashrc

    # Configure shared libraries
    echo "/usr/local/lib" | sudo tee /etc/ld.so.conf.d/usr-local-lib.conf
    echo "/usr/local/lib64" | sudo tee -a /etc/ld.so.conf.d/usr-local-lib.conf
} By running this function, you will have an updated OS and development tools needed in later steps. install_utils 3. Install Latest NVIDIA Drivers, CUDA 12.2 Toolkit, and Cuda Deep Neural Network library: In this step, we will install the NVIDIA GPU driver, the Latest CUDA 12.2 toolkit, and CUDA Deep Neural Network (CuDNN) libraries. This part uses the latest driver and toolkit released on August 29, 2023. If you read it later, you can update the URLs for the latest driver and libraries; everything else will be the same. Steps to find the latest driver, toolkit, and library are also mentioned below. Install NVIDIA GPU Driver To download and install the NVIDIA Tesla T4G driver, execute wget us.download.nvidia.com/tesla/535.104.05/NVI..sh NVIDIA-Linux-aarch64-535.104.05.run --disable-nouveau --silent If everything goes smoothly; you should have a working NVIDIA driver by now, which can be checked by running the NVIDIA system management interface command in the terminal. nvidia-smi nvidia-smi — NVIDIA System Management Interface The latest drivers for NVIDIA Tesla T4G can be found by selecting the following options. here For guidance on selecting the correct driver, refer to the options above. Install CUDA Toolkit The next step involves downloading and installing the CUDA 12.2 toolkit, which can be done by running the following bash commands wget https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux_sbsa.run
sh cuda_12.2.2_535.104.05_linux_sbsa.run --silent --override \
--toolkit --samples --toolkitpath=/usr/local/cuda-12.2 \
--samplespath=$CUDA_HOME --no-opengl-libs To find the latest version, visit and use the following selection. NVIDIA’s developer page Follow the options above to choose the right CUDA Toolkit for your setup. Install NVIDIA CUDA® Deep Neural Network Library (cuDNN): Lastly, we’ll install the CuDNN library for “Server Base System Architecture (SBSA).” wget https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa/cudnn-linux-sbsa-8.9.4.25_cuda12-archive.tar.xz
tar -xf cudnn-linux-sbsa-8.9.4.25_cuda12-archive.tar.xz
cp -P cudnn-linux-sbsa-8.9.4.25_cuda12-archive/include/* $CUDA_HOME/include/
cp -P cudnn-linux-sbsa-8.9.4.25_cuda12-archive/lib/* $CUDA_HOME/lib64/
chmod a+r $CUDA_HOME/lib64/* The latest cuDNN can be downloaded from . here List of available cuDNN sbsa libraries for CUDA 11 and CUDA 12. By combining all three, we will have the following function, which we will use in the final script as well. setup_gpu() {
    wget https://us.download.nvidia.com/tesla/535.104.05/NVIDIA-Linux-aarch64-535.104.05.run
    sh NVIDIA-Linux-aarch64-535.104.05.run --disable-nouveau --silent

    wget https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux_sbsa.run
    sh cuda_12.2.2_535.104.05_linux_sbsa.run --silent --override --toolkit --samples --toolkitpath=/usr/local/cuda-12.2 --samplespath=$CUDA_HOME --no-opengl-libs

    wget https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa/cudnn-linux-sbsa-8.9.4.25_cuda12-archive.tar.xz
    tar -xf cudnn-linux-sbsa-8.9.4.25_cuda12-archive.tar.xz
    cp -P cudnn-linux-sbsa-8.9.4.25_cuda12-archive/include/* $CUDA_HOME/include/
    cp -P cudnn-linux-sbsa-8.9.4.25_cuda12-archive/lib/* $CUDA_HOME/lib64/
    chmod a+r $CUDA_HOME/lib64/*
    ldconfig
} 4. Compiling and Installing CUDA 12.2 enabled PyTorch Next, we will compile and install PyTotch from source with the latest CUDA support for ARM-based ec2 instances, along with all the necessary Python packages. # Download and install ccache for faster compilation
wget https://github.com/ccache/ccache/releases/download/v4.8.3/ccache-4.8.3.tar.xz
tar -xf ccache-4.8.3.tar.xz
pushd ccache-4.8.3
cmake .
make -j $CPUS
make install
popd
# Install NumPy, a dependency for PyTorch
dnf install -y numpy
# Install Python typing extensions for better type-checking
sudo -u ec2-user pip3 install typing-extensions
# Clone PyTorch repository and install from source
git clone --recursive https://github.com/pytorch/pytorch.git
pushd pytorch
python3 setup.py install
popd
# Refresh the dynamic linker run-time bindings
ldconfig
# Install additional Python libraries for PyTorch
sudo -u ec2-user pip3 install sympy filelock fsspec networkx 5. Test Your Installation After you’ve gone through the installation process, you’ll want to ensure that PyTorch and CUDA are working as expected. Run the following command to test the setup. python3 -c "import torch; print('Using device: ', torch.device('cuda' if torch.cuda.is_available() else 'cpu'))"; If the device returns ‘Cuda,’ then congratulations, you’ve successfully installed PyTorch with the latest CUDA support! Your custom Deep Learning AMI will require only volume, in contrast to the or more typically available on the AWS Marketplace, resulting in less storage and faster EC2 Instance launch time. 25 GB 50 GB Complete script for effortless Setup 🪄 Ready for some magic? Before getting started, ensure that your AWS CLI is properly configured. If you haven’t done this, refer to the to get up to speed. You will also need to gather the IDs for your and and the name of your . AWS documentation security group subnet key pair Once you have completed the necessary preparations, run the provided script. This will launch a g5g.4xlarge instance pre-loaded with user data, which initiates the installation process upon launch. The entire setup process should take approximately an hour to complete. However, you can monitor the progress as it goes. To begin, SSH into your newly launched instance. ssh -i "your-key-pair.pem" ec2-user@your-instance-ip Then, run the following command to monitor the installation in real-time: tail -f /home/ec2-user/install.log The complete script can be downloaded from and goes as follows. Github https://gist.github.com/bilalmughal/0500f27454a508bd3552fcf03e3adadb After everything is done, you should get the following greetings. Using AWS Management Console You can also use the AWS Management console for this process as well. All you need to do is “Launch an instance” from the ec2 console and then select the right AMI, Architecture, and instance type, along with other networking and security configurations you will do for launching any other instance. Don’t forget to increase the volume size to 20 GB as well. After selecting the right AMI, architecture, instance type, storage, and other options, configure your instance’s User Data by adding custom setup commands that will run during launch. To add User Data, go to the ‘Advanced Details’ section during the ‘Configure Instance’ stage, input the desired text or file, and paste the script from the GitHub repository between the ‘EOF’ markers into the User Data text area. Remember, this User Data script is what automates your deep learning setup, so don’t skip this step! Wrapping Up And there you have it! A one-stop solution to make your deep learning setup on an Amazon EC2 Graviton2 ARM-based instance much easier. After following these steps, you can create an AMI (Amazon Machine Image) and use it for deep-learning tasks. You should also try out spot instances for your interruptible artificial intelligence inferences, as it could save you a lot on operational costs! With this guide, we made configuration and setup hassle-free so you can dive straight into the work that matters most to you. If you find this script as helpful as we do, we would love to hear about the exciting projects it’s helping you accomplish. Feel free to share your success stories and any ingenious modifications you’ve made. Happy coding! 💡 Pro Tip: Max Power, Min Price — The G5G Magic Equation! If increasing the CPU power or adding more memory doesn’t significantly improve the performance of your application, you can stick with the to save some money! g5g.xlarge Did you know the , , and have the same GPU power? g5g.xlarge g5g.2xlarge g5g.4xlarge g5g.8xlarge G5g Instance specification details. About the Author and Our Journey at Jumpshare I have been part of the tech industry for 18 years, serving different roles and devising different engineering solutions throughout. The ever-changing landscape of the tech world and the challenges it brings excite me, especially in the area of cloud computing and machine learning. At Jumpshare, where I hold the position of VP of Engineering, we have successfully turned these challenges into opportunities. We’re passionate about implementing techniques like this to make our machine-learning inference tasks more cost-effective. By leveraging the power of AWS Graviton2 and NVIDIA Tensor T4G instances, we’ve drastically reduced operational costs without compromising performance. This guide is yet another effort to express our commitment to sharing our experience and insights with the community, as we strongly believe that democratizing technology and saving costs on infrastructure can unlock doors to innovation. We’re always open to hearing about your own experiences and improvements on the journey towards cost-effective, high-performance deep learning. Resources : Learn How to Break Free at . Dark Truth about AWS Deep Learning AMIs The Silent Roadblock to Your Success! https://mirzabilal.com/why-your-aws-deep-learning-ami-is-holding-you-back-and-how-to-fix : Read this article and more at . Deep Learning with AWS Graviton2 and Nvidia Tensor T4g Discover Cutting-Edge Insights! https://mirzabilal.com/deep-learning-with-aws-graviton2-nvidia-tensor-t4g-for-as-low-as-free-with-cuda-12-2-56d8457a6f6d : Follow the detailed instructions on . Building FFmpeg from Source Master the Compilation Process! https://mirzabilal.com/how-to-install-ffmpeg-on-linux-from-source : See for yourself at CPU vs GPU Benchmark for video Transcoding on AWS Debunking the CPU-GPU myth! https://mirzabilal.com/cpu-vs-gpu-for-video-transcoding-challenging-the-cost-speed-myth : Get the guide at . Unlock GPU Acceleration for FFmpeg Harness Hardware Power on AWS! https://mirzabilal.com/how-to-install-ffmpeg-with-harware-accelaration-on-aws : Visit . Finding and Downloading Nvidia GPU Drivers Get Started with Your GPU Journey! NVIDIA : . Download CUDA Toolkit https://developer.nvidia.com/cuda-toolkit : . Download CuDNN https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa : . Explore AWS Instance Prices and Specs https://instances.vantage.sh/?selected=g5g..x|g5g.x Also published . here

Creating Cost-Effective Deep Learning with Custom AMIs and Spot Instances on AWS

Video is not just a story; it is the storyteller. It weaves narratives, captures moments, and connects us in ways words alone cannot. The ever-increasing demand for video content in marketing, streaming and OTT platforms, social media, and mobile video consumption has increased the demand for bandwidth and server capacity. processing has revolutionized the media industry, enhancing accessibility to video content beyond previous boundaries. The continuously increasing appetite for multimedia content and the growing prominence of deep learning have driven industry leaders like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform to offer GPU-enabled instances tailored for parallel processing excellence. Cloud media Installing FFmpeg on CPU for Optimal Performance FFmpeg is an indispensable multimedia innovation champion, consistently delivering eminence in audio-video processing and transcoding. If you are new to FFmpeg or someone who wants to make the most out of FFmpeg and set it up for optimal performance on CPU, you can refer to, , where we discussed FFmpeg and provided a step-by-step guide to make the most out of FFmpeg on CPU. How to install FFmpeg on Linux from Source Now, we will take a step further and look into how FFmpeg can leverage the power of hardware acceleration, which can significantly reduce processing time and help deliver content to users faster than ever before. This How-to guide will show you how to set up FFmpeg for multimedia transcoding and other multimedia task with hardware acceleration. Problem Traditional CPU-based video processing on cloud servers can be time-consuming and inefficient. As videos increase in resolution and size, the processing demand grows exponentially. Even with multi-core CPU configurations, the computational overhead can cause extended processing times, leading to delays in workflows and increased costs. Given cloud infrastructure providers charge per instance-hour, the longer your server runs, the more expensive your bill becomes. Failing to embrace GPU-powered machines means underutilizing their potential, especially since GPUs are specifically designed for parallel computational tasks. Thus, there is a pressing need to harness the GPU's power to optimize FFmpeg's performance and make the most out of it. The Remedy Know your Hardware The first step is to figure out the GPU for your required architecture. For the scope of this guide, we will focus on and available with AWS Graviton2 and Intel-based instances on AWS, but you can follow this guide for any Nvidia GPU by installing the desired driver yourself or by following and getting the appropriate driver from the . Nvidia Tesla T4 Nvidia Tesla T4G G4g G4dn this guide Nvidia driver download center Update and Install System Utilities The following snippet updates your existing libraries and other utilities. It uses some variables that are defined in the final script. echo "Installing utilities..."
dnf -y update
dnf -y groupinstall "Development Tools"
dnf install -y openssl-devel cmake3 amazon-efs-utils htop iotop yasm nasm jq Setup Nvidia GPU, CUDA, and CUDNN As the scope of this article is to demonstrate a working GPU-powered FFmpeg on AWS, the following will check whether this is the Graviton2-powered instance which is based on architecture or an Intel-based instance. Once selected, script will install the appropriate drivers for the architecture. G4g ARM/AARCH64 G4dn if [ "$(uname -m)" = "aarch64" ]; then
    echo "System is running on ARM / AArch64"
    DRIVE_URL="https://us.download.nvidia.com/tesla/535.104.05/NVIDIA-Linux-aarch64-535.104.05.run"
    CUDA_SDK_URL="https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux_sbsa.run"
    CUDNN_ARCHIVE_URL="https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa/cudnn-linux-sbsa-8.9.5.29_cuda12-archive.tar.xz"
else
    DRIVE_URL="https://us.download.nvidia.com/tesla/535.104.05/NVIDIA-Linux-x86_64-535.104.05.run"
    CUDA_SDK_URL="https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux.run"
    CUDNN_ARCHIVE_URL="https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-8.9.5.29_cuda12-archive.tar.xz"
fi
echo "Setting up GPU..."
DRIVER_NAME="NVIDIA-Linux-driver.run"
wget -O "$DRIVER_NAME" "$DRIVE_URL"
TMPDIR=$LOCAL_TMP sh "$DRIVER_NAME" --disable-nouveau --silent

CUDA_SDK="cuda-linux.run"
wget -O "$CUDA_SDK" "$CUDA_SDK_URL"
TMPDIR=$LOCAL_TMP sh "$CUDA_SDK" --silent --override --toolkit --samples --toolkitpath=$USR_LOCAL_PREFIX/cuda-12.2 --samplespath=$CUDA_HOME --no-opengl-libs

CUDNN_ARCHIVE="cudnn-linux.tar.xz"
EXTRACT_PATH="$SRC_DIR/cudnn-extracted"
mkdir -p "$EXTRACT_PATH"

wget -O "$CUDNN_ARCHIVE" "$CUDNN_ARCHIVE_URL"
tar -xJf "$CUDNN_ARCHIVE" -C "$EXTRACT_PATH"
CUDNN_INCLUDE=$(find "$EXTRACT_PATH" -type d -name "include" -print -quit)
CUDNN_LIB=$(find "$EXTRACT_PATH" -type d -name "lib" -print -quit)
cp -P "$CUDNN_INCLUDE"/* $CUDA_HOME/include/
cp -P "$CUDNN_LIB"/* $CUDA_HOME/lib64/
chmod a+r $CUDA_HOME/lib64/*
ldconfig By now, you should have a working system with an Nvidia device driver, which can be checked by running in the terminal. nvidia-smi Install FFmpeg Prerequisites Before diving into FFmpeg's installation, it's crucial to understand and set up its dependencies so FFmpeg can process most general process tasks for different formats and get an understanding of how to expand or limit the scope of FFmpeg for your multimedia task. System Dependencies FFmpeg is a great tool for text processing and text rendering in videos as well. To enable this feature, we will need to install libraries that are used for text manipulation, embedding subtitles, and other fancy operations like embedding the watermark of your brand in the video. These are essential system libraries that FFmpeg requires. dnf install -y freetype-devel fribidi-devel harfbuzz-devel fontconfig-devel bzip2-devel Installing Audio & Video Codecs At its core, FFmpeg functions like a versatile framework, ready to be extended with various plugins and modules. One of the primary ways this extensibility shines is through its support for many audio and video codecs. These codecs are the building blocks that allow FFmpeg to decode and encode media in various formats. Now, we will install different codecs. FFmpeg is like a template that is built in a way anyone can write a plugin like a video codec and that can be used with FFmpeg : Since the target instance is powered with GPU, codec enables FFmpeg to leverage NVIDIA GPU acceleration, significantly speeding up video processing tasks for and / ffnvcodec ffnvcodec av1, H.264, HEVC H.265 : FFmpeg can use for subtitle renderer to handle subtitles in various video formats. LIBASS LIBASS : FFmpeg uses LIBAOM to decode and encode videos, if you want to use software transcoding for . LIBAOM (AV1 Codec Library) AV1 AV1 : These include libraries for various audio and video formats (e.g., for MP3 audio, for the Opus audio codec, and for the Ogg container). In this build we will also enable software encoding of and using and libraries . Other Codecs libmp3lame opus libogg H.264 HEVC/H.265 x264 x265 By ensuring all these prerequisites are installed and correctly set up, FFmpeg can provide its robust set of features, and users can harness the desired power out of this great utility. Installing FFmpeg Finally, we will compile GPU-accelerated FFmpeg from the source, with enabled support of all the codecs and libraries we have compiled or installed in previous steps. wget https://ffmpeg.org/releases/ffmpeg-snapshot.tar.bz2 &&
tar -jxf ffmpeg-snapshot.tar.bz2 &&
pushd ffmpeg &&
PKG_CONFIG_PATH="$USR_LOCAL_PREFIX/lib/pkgconfig:/usr/lib64/pkgconfig:/usr/share/pkgconfig:/usr/lib/pkgconfig:$USR_LOCAL_PREFIX/lib/pkgconfig" \
    ./configure \
    --prefix="$USR_LOCAL_PREFIX" --disable-static --enable-shared \
    --extra-cflags="-I$USR_LOCAL_PREFIX/include $NVIDIA_CFLAGS" \
    --extra-ldflags="-L$USR_LOCAL_PREFIX/lib $NVIDIA_LDFLAGS" \
    --extra-libs='-lpthread -lm' --bindir="$USR_LOCAL_PREFIX/bin" \
    --enable-gpl --enable-libaom --enable-libass --enable-libfdk-aac \
    --enable-libfreetype --enable-libmp3lame --enable-libopus \
    --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 \
    --enable-nonfree --enable-openssl $NVIDIA_FFMPEG_OPTS &&
make -j $CPUS &&
make install Verifying GPU Acceleration Support in FFmpeg Once you've set up FFmpeg with GPU acceleration capabilities, it's a good idea to ensure that it recognizes and can utilize the GPU for tasks like video encoding and decoding. The command below checks for the presence of NVIDIA's NVENC (NVIDIA Video Encoder) in the list of supported codecs within FFmpeg: ffmpeg -hide_banner -codecs | grep nvenc If you see the output codecs with the label (e.g., , , ), it indicates that FFmpeg has been correctly configured to support GPU-accelerated encoding for those specific codecs using NVIDIA's hardware. nvenc h264_nvenc hevc_nvenc av1_nvenc The Complete Script Building upon the various segments we've discussed, here's the consolidated FFmpeg installation script along with the Nvidia GPU installation. This script has been thoroughly tested on and running on AWS instances. It's not just limited to those; its flexibility is its strength. Whether you're on Debian, Red Hat, CentOS, Fedora, openSUSE, or even the sleek Alpine, this script is crafted to serve both and architectures seamlessly. Dive in and experience the universality of our FFmpeg installation script, bringing the power of multimedia to every corner of the Linux world. Amazon Linux 2023 Ubuntu 22.04 G5g ARM/aarch64 x86_64 https://gist.github.com/bilalmughal/cb89936bc947fa727a8ec66e3ddf768a?embedable=true Hands-On: Converting H.264 to H.265 using Hardware Acceleration In this section, we'll take a hands-on approach to show you how to convert an H.264 encoded video to H.265 using the power of NVIDIA's GPU acceleration. This way we can test the installation and experience the power of GPU acceleration in action. To perform this conversion, use the following command: ffmpeg -y -hide_banner -hwaccel cuvid -c:v h264_cuvid -i input.mp4 -vf "scale_cuda=720:480" -c:v hevc_nvenc output.mp4 In this command: and uses GPU-based decoding of the H.264 video. -hwaccel cuvid -c:v h264_cuvid scales the video using GPU acceleration. -vf "scale_cuda=1920:1080" instructs FFmpeg to encode the output video using the H.265 codec with NVIDIA's GPU acceleration. -c:v hevc_nvenc After executing the command, you'll get an encoded video. output.mp4 H.265 1080p Save up to 400% on Video-Transcoding. We conducted a comprehensive cost-performance-centric benchmark on AWS with CPU and GPU-powered instances, and the findings changed our perspective about the narrative “CPU is slow, but GPU is expensive.” The results were astonishing and enabled us to make decisions that enabled us to save up to 400% cost on video-transcoding jobs. We recommend you to check that out as well at CPU vs GPU for Video Transcoding: Challenging the Cost-Speed Myth Conclusion By integrating FFmpeg with GPU hardware acceleration on AWS or any other infra-provider or architecture, you can optimize costs and reduce processing times, but it also future-proofs your workflow for higher-resolution media processing tasks down the line. By compiling from the source, we are also addressing the outdated libraries and generalized package issues, which are not fully optimized for your architecture, as discussed in " " and some aspects are also discussed in the article as well. The evolution of multimedia is ceaseless, but with tools like FFmpeg combined with AWS's GPU prowess and cost-effectiveness, you are well-equipped to handle whatever challenges come next. Whether you're a media company looking to scale or a hobbyist aiming for maximum efficiency, GPU-accelerated FFmpeg on AWS is a game-changer. How to install FFmpeg on Linux from source "Why your AWS Deep learning AMI is Holding you back and how to fix Resources : Follow the detailed instructions on . Building FFmpeg from Source Master the Compilation Process! https://mirzabilal.com/how-to-install-ffmpeg-on-linux-from-source : See for yourself at CPU vs GPU Benchmark for video Transcoding on AWS Debunking the CPU-GPU myth! https://mirzabilal.com/cpu-vs-gpu-for-video-transcoding-challenging-the-cost-speed-myth : Learn How to Break Free at . Dark Truth about AWS Deep Learning AMIs The Silent Roadblock to Your Success! https://mirzabilal.com/why-your-aws-deep-learning-ami-is-holding-you-back-and-how-to-fix : Read this article and more at . Deep Learning with AWS Graviton2 and Nvidia Tensor T4g Discover Cutting-Edge Insights! https://mirzabilal.com/deep-learning-with-aws-graviton2-nvidia-tensor-t4g-for-as-low-as-free-with-cuda-12-2-56d8457a6f6d This guide was originally published at . https://mirzabilal.com/how-to-install-ffmpeg-with-harware-accelaration-on-aws Also published . here

Enabling GPU-Accelerated FFmpeg on AWS for Optimal Video Processing

It was probably 2006 when I first heard about FFmpeg, and I was amazed by its capabilities. FFmpeg is a go-to solution for transcoding and video manipulation, from trimming to burning the subtitles, adding a watermark, and more. Since FFmpeg was first launched, it has come a long way and has become the industry leader for multimedia workload for desktop apps, web, and especially on the backend. In this article, we will discuss why you need to customize FFmpeg to match your requirements and how you can compile FFmpeg from the source along with various essential libraries and codes needed for video processing on different Linux distributions, from Ubuntu to Redhat-based systems. The Problem Installing FFmpeg directly from a Linux distribution's default repositories may seem like a no-brainer, but this method is filled with potential drawbacks. For one, these repositories might not offer the most recent versions of FFmpeg and essential codecs or libraries, possibly exposing users to security risks and preventing them from accessing the latest features. Moreover, these default builds can be burdened with unnecessary codecs and libraries that many users may never use, or you might stranded in a scenario where the package has everything but does not come with the library you need. But the challenges don't stop here; in today's diverse device ecosystem, which spans across ARM and x86_64 architectures, a one-size-fits-all build from the repository might not be optimized for a specific device with a specific instruction set. This is especially concerning for multimedia tasks that demand real-time processing, and you want to make the most out of the hardware you have. Furthermore, for young developers or those new to the Linux landscape, crafting a custom FFmpeg build tailored to their requirements can be a daunting experience. The sea of options, configurations, and dependencies can be overwhelming, leading them to settle for suboptimal, generic builds rather than extracting the true potential of FFmpeg. Why Compile from Source? To overcome discussed problems for newbies to experts, the importance of comprehensive guides and resources to bridge the knowledge gap and empower the next generation of developers to confidently customize their multimedia tools. Following are the notable advantages of using source code as the starting point. : By compiling from the source, you can choose the exact features and codecs you want, leading to a personalized FFmpeg build tailored to your needs. Customization : When you include only the components you need, the resultant build will often be leaner and require less disk space. Smaller Footprint : Official channels might not always have the latest version of FFmpeg. Compiling from source ensures you're working with the most recent codebase. Access to the Latest Code : Building from source code can enable certain optimizations specific to your machine's architecture and instruction set, potentially enhancing performance. Performance Optimizations : Compiling software from a source provides a deeper understanding of the software and its dependencies. Greater Learning Opportunity Installing Dependencies Before we jump into the installation process, it's essential to set up our system with the necessary dependencies. On Ubuntu: sudo apt update
sudo apt install -y build-essential yasm cmake libtool libc6 libc6-dev unzip wget On RedHat-based distributions: sudo yum groupinstall "Development Tools"
sudo yum install -y yasm cmake libtool unzip wget Installing dependencies and codes: Once your build environment is set up, the next critical step is to install the various codecs and libraries that FFmpeg relies upon or that you wish to utilize for specific multimedia tasks. Remember, one of the key benefits of building from source is the ability to customize your FFmpeg installation, ensuring it's streamlined to your needs; here, you can pick and drop or even add codecs or libraries that you wish to use. In the final script, we have provided a comprehensive list of dependencies and libraries, ensuring that you have a broad spectrum of multimedia capabilities at your fingertips. Installing FFmpeg You can obtain the latest source code from FFmpeg's official website or use : git git clone https://git.ffmpeg.org/ffmpeg.git ffmpeg
cd ffmpeg Configuration This step is crucial. The script allows you to select which features and codecs to include. For a basic setup, you can run: ./configure ./configure \
            --prefix="$USR_LOCAL_PREFIX" \
            --disable-static --enable-shared \
            --extra-cflags="-I$USR_LOCAL_PREFIX/include $NVIDIA_CFLAGS" \
            --extra-ldflags="-L$USR_LOCAL_PREFIX/lib $NVIDIA_LDFLAGS" \
            --extra-libs='-lpthread -lm' \
            --bindir="$USR_LOCAL_PREFIX/bin" \
            --enable-gpl --enable-libaom --enable-libass \
            --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame \
            --enable-libopus --enable-libvorbis --enable-libvpx \
            --enable-libx264 --enable-libx265 --enable-nonfree \
            --enable-openssl However, for a more customized setup, use flags like , , , etc. Check for a full list of options. --enable-codec --disable-codec --enable-libx264 ./configure --help Compilation & Installation Once configured, compile and install FFmpeg here: make -j$(nproc)
sudo make install This will use all your CPU cores for the compilation ( ) and then install the software. -j$(nproc) Verification To verify your FFmpeg installation: ffmpeg -version Wrap-up & the magic script: By now, you should have a basic understanding of how to customize FFmpeg. If so, that was our goal. If you have any confusion, leave a comment, and I'll be happy to discuss it. To streamline everything and every step mentioned here, there's a script for you to simply run, and it will handle the entire process. https://gist.github.com/bilalmughal/83de56f470bf5b91a7c4424cce4071ac?embedable=true Conclusion Now that we've gone through the process, I'm curious: Where do you plan to use your custom-tailored FFmpeg? Are you working on an innovative multimedia project, developing a web application, or just experimenting with video processing for personal use? FFmpeg's versatility excels in a multitude of applications, from large-scale video platforms to hobbyist projects. Share your plans or current FFmpeg projects in the comments below. Let's learn from one another and discover even more unique use cases for this powerful tool. Remember, the world of multimedia is vast, and with FFmpeg, the possibilities are endless. Resources My Tech journey and experiences https://mirzabilal.com *Running FFmpeg with Hardware acceleration on GPU: * https://mirzabilal.com/how-to-install-ffmpeg-with-harware-accelaration-on-aws CPU vs GPU Benchmark for video transcoding on AWS: https://mirzabilal.com/cpu-vs-gpu-for-video-transcoding-challenging-the-cost-speed-myth Low Cost High Performance Deep Learning: https://mirzabilal.com/deep-learning-with-aws-graviton2-nvidia-tensor-t4g-for-as-low-as-free-with-cuda-12-2-56d8457a6f6d Also published . here

Installing FFmpeg: How to Compile and Fine-Tune for Optimal Performance

When evaluating computational power, especially in terms of CPUs and GPUs, there’s a prevailing narrative. A general belief is, that CPUs may take longer to process, but they're cost-effective, whereas GPUs might be faster but operate at a higher cost. How true is this widely accepted notion? To challenge this belief, we conducted a tangible, real-world assessment using instances and FFmpeg for video transcoding benchmarks. And sought to determine the most cost-and-time-efficient option for transcoding videos and audio, hence enabling us to save on our AWS bills. AWS To install FFmpeg from the source on CPU you can follow this , and for installing FFmpeg with hardware acceleration you can . guide check this out The Instances Selection In our tests, we compared various types, covering both GPU and CPU across Intel and AWS's Silicon-based Graviton2 instances. On the GPU side, we picked instances featuring Nvidia Tesla T4 and T4G. Whereas for CPUs, we looked at three instances, two from the same generation and size, an Intel-based , and Graviton2-powered . The third CPU-based instance we chose was , to assess the impact of more vCPU on transcoding. AWS instance c7i.2xlarge c7g.2xlarge c6g.4xlarge GPU Instances: g4dn.xlarge g5g.xlarge CPU Instances: c7g.2xlarge c7i.2xlarge c6g.4xlarge We made thoughtful selections for each of these instances. We aimed to choose those with similar costs to ensure a fair cost-to-performance comparison. Additionally, to explore the performance implications of doubling the CPU count, we extended our benchmarks to the instance. c6g.4xlarge The Process Before analyzing the results, it's important to discuss and understand the types of tests that were conducted. Downscale to 480p: Downscaling is the process when the video is squeezed smaller than its original size. It's useful for platforms or devices that cannot support high-resolution videos or when smaller file sizes are needed. For this test, we downscale the input video to 480p (640 pixels x 480 pixels). Resample at 720p: Resampling does not change the video's resolution but may alter the underlying pixel values. It can be beneficial for modifying encoding settings or applying specific filters. In this case, we resampled the video at its original resolution of 720p (1280 pixels × 720 pixels). Upscale to 1080p: Upscaling is the opposite of downscaling and is used to convert the video to higher resolution. Upscaling generally produces better results than playing or rendering a smaller video and stretching at playtime. In this test, we upscale the video to a higher resolution of 1080p (1920 pixels × 1080 pixels). No Scaling: All the above tests were conducted using a scale filter of FFmpeg but for this test, we did not provide any filter for scaling instead we simply re-encoded the video. The Benchmarking To ensure objectivity, we use the same video file for benchmarking. The input video details are as follows: Container Format: mp4 Duration: 01:34:40.38 Bitrate: 1579 kb/s , Video Codec: h264 (High) @ Video Resolution: 1280x720 23.98 fps , Sample Rate: Audio: aac (LC) 48000 Hz, 5.1 We utilized , a leading open-source software for multimedia processing, to devise our benchmark script. The script contains tests for both CPU and GPU-powered machines, first, it checks whether GPU is available or not. Depending on the result, it executes the appropriate command for video processing. FFmpeg To execute our transcoding tasks, we used the following benchmark script: #!/bin/bash

if lspci | grep -i "NVIDIA Corporation" >/dev/null; then
    echo "System has a GPU"
commands=(
    'ffmpeg -y -hide_banner -hwaccel cuda -hwaccel_output_format cuda -i input.mp4  -vf "scale_cuda=720:480"   -c:a copy -c:v h264_nvenc output.mp4 -benchmark'
    'ffmpeg -y -hide_banner -hwaccel cuda -hwaccel_output_format cuda -i input.mp4  -vf "scale_cuda=1280:720"  -c:a copy -c:v h264_nvenc output.mp4 -benchmark'
    'ffmpeg -y -hide_banner -hwaccel cuda -hwaccel_output_format cuda -i input.mp4  -vf "scale_cuda=1920:1080" -c:a copy -c:v h264_nvenc output.mp4 -benchmark'
    'ffmpeg -y -hide_banner -hwaccel cuda -hwaccel_output_format cuda -i input.mp4                             -c:a copy -c:v h264_nvenc output.mp4 -benchmark'
)
else 
commands=(
    'ffmpeg -y -hide_banner -i input.mp4 -vf "scale=720:480"   -c:a copy -c:v libx264 output.mp4 -benchmark'
    'ffmpeg -y -hide_banner -i input.mp4 -vf "scale=1280:720"  -c:a copy -c:v libx264 output.mp4 -benchmark'
    'ffmpeg -y -hide_banner -i input.mp4 -vf "scale=1920:1080" -c:a copy -c:v libx264 output.mp4 -benchmark'
    'ffmpeg -y -hide_banner -i input.mp4                       -c:a copy -c:v libx264 output.mp4 -benchmark'
)
fi

for cmd in "${commands[@]}"; do
    echo "--------------------------------------------------------------------------------"
    echo "Executing: $cmd"
    echo "--------------------------------------------------------------------------------"
    # Use the time command to measure how long it takes to run the command
    { time eval "$cmd"; } 2>&1
    rm output.mp4
done | tee output_results.txt The Findings The data from our AWS benchmarks after executing 20 different tests over five different AWS Instances, painted a compelling narrative. The results showed clear differences in the cost-efficiency and performance dynamics among these instances. The data extracted and processed from benchmark results can be listed as: The GPU instances, notably AWS Graviton2 , were not only faster but also more cost-effective across various transcoding operations, compared to CPU-centric instances like . The introduction of the , with its doubled vCPUs, provided insights into how increasing computational power influences performance and cost. Interestingly, even with the added CPUs, despite it being more expensive than GPU-powered instances, it performed significantly worse, and GPU instances continue to offer a better balance between speed and cost. Furthermore, it is important to mention here that the FFmpeg was built to run on multiple cores and was utilizing all CPU cores as can be seen in screenshot during a transcoding task. g5g.xlarge c7g.2xlarge c6g.4xlarge htop Let's create a visualization of benchmark results to compare the time taken and cost when running on different EC2 instances. The Winner? From the previous charts, it's evident that the AWS Graviton2-based emerges as the most efficient choice. Not only does it excel in efficiency, but it also appears to be more cost-effective. To further illustrate its cost advantage, let's juxtapose it with various AWS instances to discern just how economical it truly is. G5g.xlarge The bar chart offers a vivid representation of how stacks up against other AWS EC2 instances in terms of cost. When downscaling to 480p, the is significantly more cost-effective, with the , which is the most expensive across different transcoding tasks, which is a whopping 370.9% more expensive than for downscaling operations. For the resampling at 720p, the disparity grows even more evident, with the being 445% pricier than the . Similarly, when upscaling to 1080p, the cost associated with is 438.9% more than our winner. Finally, for the 'No Scaling' operation, proves to be 446.1% more expensive. G5g.xlarge G5g.xlarge c6g.4xlarge G5g.xlarge c6g.4xlarge G5g.xlarge c6g.4xlarge c6g.4xlarge In stark contrast, the , although being one of the GPU-based instances, presents minimal cost differences when compared with . Its costs are just around 24.8% to 27% for the various operations, showcasing that while GPUs might be fast, their cost benefits, especially in this case, aren't always as pronounced. g4dn.xlarge G5g.xlarge These findings underline the impressive cost efficiency of the featuring , when placed against other popular AWS instances. AWS Graviton2 G5g.xlarge Nvidia Tesla T4G In Conclusion The ever-evolving realm of technology often holds narratives based on past truths, which may not hold relevance today. Our experiment underscores a crucial fact: in video transcoding, modern GPU instances aren't just faster; they also offer a more economical choice. When choosing between a CPU or GPU for cloud-based operations, it's essential to consider both performance and cost. And as demonstrated, sometimes the supposedly "faster and pricier" option can also be the most cost-effective. Resources FFmpeg on CPU: https://mirzabilal.com/how-to-install-ffmpeg-on-linux-from-source FFmpeg on GPU: https://mirzabilal.com/how-to-install-ffmpeg-with-harware-accelaration-on-aws First published . here

CPU vs. GPU for Video Transcoding: Challenging the Cost-Speed Myth

In computer security, a cold boot attack is a type of side-channel attack in which an attacker with physical access to a computer performs a memory dump of a computer’s random access memory by performing a hard reset of the target machine. Security researchers have discovered a new technique for stealing sensitive data on computers and will detail how cold-start attacks can use computer firmware to allow attackers to recover passwords, credit card numbers, and other sensitive information stored in them. As we have known since 2008, cold-start attacks on encryption keys or cold-start attacks can steal the encryption key of a computer from an attacker with physical access to a machine, which remains briefly in memory after a hard reboot. The new cold start attack technique differs from the traditional cold start attack, which was developed in 2008 and allows attackers to steal the shortest information that remains on a computer before it is shut down. The primary functionality of the cold-start attack is to obtain an encryption key from the user’s device before booting by extracting data from RAM or memory. This reduces the probability that an attacker is theoretically able to retrieve the encryption keys from memory before executing the cold boot attacks. When a hard drive is encrypted with full encryption — Cold boot attacks on hard drives: Cold boot attacks may or may not be required for hard drives that are encrypted without full encryption or potentially contain evidence of criminal activity. Cold-start attacks can be used in digital forensics to forensically preserve the data in the memory as criminal evidence, for example as evidence in criminal investigations. In fact, this technique allows encryption keys to be extracted from memory, and cold boot attacks can be used when a hard drive is encrypted with full disk encryption. This reduces the likelihood that the attacker can rescue the encryption key from memory before executing a cold-start attack. If an attacker hijacks the key with cold boots or similar attacks, he has the “key to the kingdom” for a long time, even if the encryption of the phone is disabled. It reduces the likelihood that attackers can retrieve the keys in memory and the likelihood that they will be able to execute the Cold-Boot attack on a phone. Cold-boot restrictions on modern computers make these attacks less effective than they were a decade ago, and there would be no reliable way to decrypt a lost or stolen computer. Here’s the good news: Cold-boot attacks are not a trivial type of exploit. They require special hardware and tools to work means they are not trivial to exploit. Operating systems and chipmakers added mitigation against cold attacks 10 years ago, and F-Secure researchers have found a way to bring them back from the dead. But companies have difficulty finding reliable ways to prevent or block them, even if you get an attacker with the right expertise. The company configures the laptops so that the attacker who uses the cold boot attack does not find anything stolen, such as a hard drive, hard drive, or even a computer. Think of Cold Boot Attacks on Encryption Keys, which were released at the IEEE International Conference on Secure Computing and Security (ICS) 2015 in San Francisco, California, the USA in 2008. This research paper describes how the key to encrypting the encrypted data of a computer (e.g. passwords) can be obtained by a cold-start attack. The paper is titled “That you do not remember: Cold Encrypted Keys, “which describes a method for recovering the information stored in memory and a solution to the problem. In a nutshell, Cold Boot Attacks use the DRAM remanence effect to restore cryptographic keys in memory. If the encryption key is stored in the memory of a computer while the computer is hibernating, there is no chance for attackers to steal valuable information and the data remains on a chip that is kept at a low temperature. This reduces the chances that the attacker can retrieve the key from memory and perform a cold boot attack on a phone. Data recovery is not possible if the attackers physically access the system; they could still perform successful cold boot attacks on computers configured for this purpose, but because the chips are kept at low temperatures, no data is preserved. Cited Sources https://blog.f-secure.com/cold-boot-attacks/ https://gbhackers.com/cold-boot-attacks-hackers-can-unlock-all-the-modern-computers-and-steal-encryption-keys-passwords/ https://privatecore.com/resources-overview/physical-memory-attacks/index.html https://thehackernews.com/2018/09/cold-boot-attack-encryption.html https://www.linuxjournal.com/magazine/cold-boot-attack-tools-linux https://www.darkreading.com/risk/new-cold-boot-attack-gives-hackers-the-keys-to-pcs-macs/d/d-id/1332814 https://www.andreafortuna.org/2020/03/06/cold-boot-attack-in-digital-forensics/ https://www.starlab.io/blog/encrypting-in-a-dangerous-world https://threatpost.com/researchers-heat-up-cold-boot-attack-that-works-on-all-laptops/137466/ https://en.wikipedia.org/wiki/Cold_boot_attack https://www.ethicalhacker.net/features/root/using-cold-boot-attacks-forensic-techniques-penetration-tests/ https://techxplore.com/news/2018-09-firmware-weakness-red-carpet-cold.html https://hackwarenews.com/new-cold-boot-attack-can-unlock-disk-encryption-in-nearly-all-laptops-under-2-minutes/ https://www.zdnet.com/article/new-cold-boot-attack-affects-nearly-all-modern-computers/ https://www.wired.com/story/cold-boot-break-pc-encryption/ Read behind a paywall here .

HACWARE

hacware.com

11-50 employees

Since 2019

COMPANY RANKING

HACWARE

EVERGREEN INDEX #5259

Representing this company? Click here to claim and customize this page!

HacWare WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

HACWARE

hacware.com

11-50 employees

Since 2019

COMPANY RANKING

HACWARE

EVERGREEN INDEX #5259

RELATED COMPANIESRANK

STORIES ABOUT HACWARE

Representing this company? Click here to claim and customize this page!

HacWare WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!