Cloud

AWS

AWS BEDROCK

ssh root@yourserverip #you can also do it with ssh keys

GCP

Azure

Other Clouds

Hetzner

• https://www.hetzner.com/cloud/
  1. Using the cheapest x2 shared vCPU intel (2x Skylake @2ghz)
  2. Using the next tier x4 shared vCPU Intel Intel Xeon Processor (Skylake)
  • [Hetzner_101.sh]
  • Selfhosting_101

ssh root@yourserverip #you can also do it with ssh keys
#you will be prompted to change the password (and will have to type it manuallys)

sudo apt update && sudo apt upgrade -y
#lscpu
#cat /proc/cpuinfo
#nproc

sudo apt install sysbench -y
sysbench cpu --threads=2 run #https://github.com/akopytov/sysbench#general-command-line-options
#sysbench cpu --threads=4 run 

sudo apt install speedtest-cli  # For Debian/Ubuntu
speedtest-cli --simple

curl -O https://raw.githubusercontent.com/JAlcocerT/Linux/main/Z_Linux_Installations_101/Selfhosting_101.sh
#nano Selfhosting_101.sh #MAKE SURE YOU UNDERSTAND WHAT YOU WILL BE RUNNING

chmod +x Selfhosting_101.sh
sudo ./Selfhosting_101.sh
#docker --version && docker-compose --version

Hetzner Value for Money

Comparing server costs to SelfHosting with a mini PC or SBC

docker pull python:3.8

git clone https://github.com/JAlcocerT/Py_Trip_Planner/
cd Py_Trip_Planner
time docker build -t pytripplanner .

This took ~77s for the 2 cores vCPU and 48s for the x4 one

FAQ

Other Benchmarks

• https://jalcocert.github.io/RPi/posts/minipc-vs-pi/

• https://jalcocert.github.io/RPi/posts/pi-vs-orange/

• Rpi 4 2GB - 4 threads 113k total number of events

• Orange Pi 5 8GB - 4 cores 100k total number of events & 8 cores 134.k

Install phoronix in ubuntu or arch.

sysbench cpu --threads=4 run

Intel Core i5-1135G7 ~ 41.14s & 91.1k total events 4 cores /

AMD 4600G

AMD 5600G

• Phoronix: ~11.6s
• sysbench: 4 threads total number of events 194.4k / 1 thread 48.5 / all (6x2) cores 334.3k

FFMPEG Benchmark in Linux

Benchmarking with FFmpeg on Linux can help you assess the performance of your system in handling video and audio processing tasks. Here’s a guide on how to perform a benchmark using FFmpeg:

1. Install FFmpeg: If you haven’t already installed FFmpeg on your Linux system, you can do so by using the package manager of your distribution. For example, on Ubuntu or Debian-based systems, you can install FFmpeg with the command:

sudo apt-get update && sudo apt-get install ffmpeg

2. Select or Prepare Media for Testing: Choose a video file as your benchmarking material. It’s a good idea to use a high-resolution video to really test the capabilities of your system.

3. Perform the Benchmark: There are various ways to benchmark FFmpeg, focusing on different aspects such as encoding speed, decoding speed, and filtering. Here are a few examples:

• Encoding Speed: Measure how quickly FFmpeg can encode a video. The following command encodes a video to the H.264 codec without saving the output, effectively testing the encoding speed:

ffmpeg -i input.mp4 -c:v libx264 -f null -

Replace input.mp4 with your video file. The -f null - part tells FFmpeg not to save the output.

• Decoding Speed: This measures how quickly FFmpeg can decode a video. Use the following command:

ffmpeg -i input.mp4 -f null -

 This command decodes the video and discards the output, focusing the test on the decoding process.

• Filtering Performance: To benchmark the performance of specific filters, you can use a command like this:

ffmpeg -i input.mp4 -vf "filtername=parameter" -f null -

Replace filtername=parameter with the filter you wish to test. This measures how quickly FFmpeg can process the video with the specified filter.

4. Interpreting Results: FFmpeg will display the processing time and frame rate at the end of the benchmark. A higher frame rate and lower processing time indicate better performance.

5. Compare Different Settings or Systems: You can repeat the benchmarks with different codecs, filters, or on different machines to compare performance. For example, encoding with H.265 (HEVC) might be slower but more efficient than H.264, and comparing the results can provide insights into the trade-offs.

6. Advanced Benchmarking: For more detailed analysis, you can use the -benchmark option to get more detailed timing information, or -benchmark_all to get even more comprehensive data.

GCP Free Tier Deploy - CLI

• Google Free Tier (as of now): https://cloud.google.com/free/docs/free-cloud-features#compute
  • e2-micro and us-east1 is important!

gcloud compute instances create instance-2 \
    --project=your_project_name \
    --zone=us-east1-b \
    --machine-type=e2-micro \
    --network-interface=network-tier=PREMIUM,stack-type=IPV4_ONLY,subnet=default \
    --no-restart-on-failure \
    --maintenance-policy=TERMINATE \
    --provisioning-model=SPOT \
    --instance-termination-action=STOP \
    --service-account=your_service_account@developer.gserviceaccount.com \
    --scopes=https://www.googleapis.com/auth/devstorage.read_only,https://www.googleapis.com/auth/logging.write,https://www.googleapis.com/auth/monitoring.write,https://www.googleapis.com/auth/servicecontrol,https://www.googleapis.com/auth/service.management.readonly,https://www.googleapis.com/auth/trace.append \
    --tags=http-server,https-server \
    --create-disk=auto-delete=yes,boot=yes,device-name=instance-2,image=projects/debian-cloud/global/images/debian-11-bullseye-v20231212,mode=rw,size=10,type=projects/projectrpi-398008/zones/us-east1-b/diskTypes/pd-standard \
    --no-shielded-secure-boot \
    --shielded-vtpm \
    --shielded-integrity-monitoring \
    --labels=goog-ec-src=vm_add-gcloud \
    --reservation-affinity=any