| import sys | |
| import re | |
| import binascii | |
| pattern = b"\x4E\x45\x54\x53\x43\x41\x50\x45\x32\x2E\x30" | |
| regex = re.compile(pattern) | |
| rep = sys.argv[2] | |
| with open(sys.argv[1], 'r+b') as f: | |
| content = f.read() |
| ####### | |
| ## These results are still open to the public. See | |
| ## https://blog.ktz.me/the-best-media-server-cpu-in-the-world/ | |
| ## for analysis of them. | |
| # https://github.com/ironicbadger/quicksync_calc | |
| # zoidberg - dell 7040 sff pc | |
| CPU TEST FILE BITRATE TIME AVG_FPS AVG_SPEED AVG_WATTS | |
| i5-6600T h264_1080p_cpu ribblehead_1080p_h264 18952 kb/s 116.352s 29.88 1.04x N/A |
| #!/usr/bin/env bash | |
| set -e | |
| [[ -n $HYPRLAND_DEBUG_CONF ]] && exit 0 | |
| USAGE="\ | |
| Import environment variables | |
| Usgae: $0 <command> | |
| Commands: |
On M1 machines, Docker for Mac is running a lightweight linux ARM VM, then running containers within that, so containers are essentially running natively. Don't be fooled by the fact the UI or binary CLI tools (e.g. docker) might require Rosetta.
Within that VM is an emulation layer called QEmu. This can be used by docker to run Intel containers. This does not use Rosetta at all, and has a roughly 5-6X performance penalty. (If you just upgraded your CPU this may result in a similar performance to your old machine!)
Many images in public registries are multi-architecture. For instance at the time of writing on Docker Hub the php:8.0-cli image has the following digests:
| #!/usr/bin/env python3 | |
| import argparse | |
| from dataclasses import dataclass | |
| import time | |
| from typing import ClassVar | |
| from datetime import datetime | |
| import boto3 |
- Use HTML tags to define the table to get the best layout result
- Use either backticks (```) or the HTML
preelement with attributelang - Keep a blank line before and after a code block for correct formatting and syntax highlighting
| import random | |
| from enum import Enum, IntEnum | |
| class BRAIN(str, Enum): | |
| SMALL = "small" | |
| MEDIUM = "medium" | |
| GALAXY = "galaxy" | |
| def __str__(self) -> str: |
| git clone https://github.com/xorg62/tty-clock | |
| cd tty-clock | |
| sudo dnf install ncurses ncurses-devel -y | |
| make | |
| chmod +x tty-clock | |
| sudo mv tty-clock /usr/local/bin/tty-clock |
Original guide with a standard build is here.
With this guide, I'm adding more instructions to enable support for NVIDIA CUVID and NVIDIA NPP for enhanced encode and decode performance.
Warning:
If all you require is NVENC's enablement, you do NOT need the CUDA SDK.
The nv-codec-headers (below) is ALL you require.