Does not work `torchaudio.transforms.MelSpectrogram`, no MKL

Collection of Docker images for ML/DL and video processing projects.

Overview of `cpu.*` images

Several types of images are presented:

core: Python (3.11.9)
ffmpeg: FFmpeg (6.1)
base: Python ML and CV packages listed here
pytorch: PyTorch (2.1.2), torchvision (0.16.2), torchaudio (2.1.2) and torch based libraries listed here

Overview of `gpu.*` images

Several types of images are presented:

core: CUDA (12.2.2), cuDNN (8.9.6), Python (3.11.9)
ffmpeg: FFmpeg (6.1) with NVENC/NVDEC support
base: Python ML and CV packages listed here
pytorch: TensorRT (9.2.0), PyTorch (2.1.2), torchvision (0.16.2), torchaudio (2.1.2) and torch based libraries listed here
video: VideoProcessingFramework (2.0.0) and Tensor Stream (0.5.0) for full HW acceleration of video decoding

Supported NVIDIA architectures: Pascal (sm_60, sm_61), Volta (sm_70), Turing (sm_75), Ampere (sm_80, sm_86), Ada Lovelace (sm_89), Hopper (sm_90).
You can identify the architecture of your GPU here.

Overview of `gpu.*.opt` images

There are also optimized GPU images based on nvidia-runtime images, which have the same packages and libraries installed as stated above. To install them add .opt suffix at the end of the docker image name, e.g. dokai:gpu.pytorch.opt.

Difference between `nvidia-devel` and `nvidia-runtime` images

Nvidia's CUDA images come in three flavors and are available through the NVIDIA public hub repository.

base: starting from CUDA 9.0, contains the bare minimum (libcudart) to deploy a pre-built CUDA application. Use this image if you want to select which CUDA packages you want to install manually.
runtime: extends the base image by adding all the shared libraries from the CUDA toolkit. Use this image if you have a pre-built application using multiple CUDA libraries.
devel: extends the runtime image by adding the compiler toolchain, the debugging tools, the headers, and the static libraries. Use this image to compile a CUDA application from sources.

Runtime and devel are the ones that are mostly used and the differences between them are the image sizes (~3Gb), presence of compilers, and debugging tools.

Overview of `*.rootless` images

Initially, all Dokai images were root-based, meaning that they had root privileges inside of the docker container. According to the Docker best-practices it is better to run docker containers without sudo rights with explicitly set user. Because of that *.rootless images were added to duplicate all existing images, but without sudo privileges. The name of the user is set to dokai and it has 1000 set as UID/GID.

Sooner or later these images will fully replace root-based ones as they are more secure.

Examples

Pull an image from the GitHub Container registry

docker pull ghcr.io/osai-ai/dokai:24.06-gpu.pytorch

Docker Hub mirror

docker pull osaiai/dokai:24.06-gpu.pytorch

Check available GPUs inside the container

docker run --rm \
    --gpus=all \
    ghcr.io/osai-ai/dokai:24.06-gpu.pytorch \
    nvidia-smi

In case of `*.rootless` images there may permission problems happen within docker container:

If the source code is copied to the container with the command COPY, make sure to change the owner of the copied files and folders with additional flag, for example:

FROM dokai:gpu.video.opt.rootless

WORKDIR /home/$UNAME/workdir
COPY --chown=$UID:$GID ./ ./

...

If the source code is mounted as a volume with the flag -v make sure to mount it to the correct user home directory path, and --user flag is passed, for example like this:

docker run --rm -it \
    --user=$(id -u):$(id -g) \
	-v $(shell pwd):/home/dokai/workdir \
	ghcr.io/osai-ai/dokai:24.06-gpu.pytorch.opt.rootless \
	bash

Or make sure that the mounted volume has enough permission for the dokai:1000:1000 user to work.

The result of above commands strongly depends on the local setup, although it should work in most cases.

Package versions

CPU images

dokai:24.06-cpu.core

ghcr.io/osai-ai/dokai:24.06-cpu.core

Image based on python:3.11.9-slim-bookworm which includes:

Debian Bookworm Operational System;
Python 3.11.9;
etc..

On top of that packages are installed, here's a brief description and purpose of each:

Utilities:

nano, vim, tmux are text editors and terminal multiplexers used for editing files and managing terminal sessions.
unzip is a utility used to decompress ZIP archives, commonly used for extracting files from compressed archives.
git is a distributed version control system used for tracking changes in source code during software development.
wget, curl are command-line tools used for downloading files from the internet, commonly used in scripts and automation tasks.
htop is interactive system monitoring tool used for displaying system resource usage.
sysstat collects and reports system performance statistics, including CPU, memory, and disk usage.

dokai:24.06-cpu.ffmpeg

ghcr.io/osai-ai/dokai:24.06-cpu.ffmpeg

Image based on dokai:24.06-cpu.core:

Additionally, installed:

FFmpeg (n6.1)

dokai:24.06-cpu.base

ghcr.io/osai-ai/dokai:24.06-cpu.base

Image based on dokai:24.06-cpu.ffmpeg:

Additionally, installed:

Python ML and CV packages: requirements.txt

dokai:24.06-cpu.pytorch

ghcr.io/osai-ai/dokai:24.06-cpu.pytorch

Image based on dokai:24.06-cpu.base:

Additionally, installed:

Pytorch, torch-audio, torch-vision;
Pytorch-related packages: requirements.txt

GPU images built from source

dokai:24.06-gpu.core

ghcr.io/osai-ai/dokai:24.06-gpu.core

Image based on nvidia/cuda:12.2.2-cudnn8-devel-ubuntu22.04 which includes:

Ubuntu 22.04 Operational System;
CUDA (12.2.2);
cuDNN (8.9.6);
NVCC;
etc..

On top of that packages are installed, here's a brief description and purpose of each:

Python (3.11.9):

python3.11 is a programming language used for scripting and software development.
python3.11-dev contains header files and development tools for building Python extensions.
python3.11-distutils contains utilities for distributing and installing Python packages.
python3.11-tk contains Tkinter, a GUI toolkit for Python.

Builders, configurators and compilers:

build-essential package contains essential tools required for building software on Ubuntu, including compilers (gcc, g++, etc.) and build-related tools.
Yasm and nasm are assemblers used for compiling assembly language code into machine-readable format, commonly used in building software.
Ninja is a small build system that is fast and efficient, used as an alternative to GNU Make in some projects.
cmake is a cross-platform build system generator used to control the software compilation process using simple platform-independent configuration files.
pkgconf is a package configuration system used to help locate and configure libraries needed for building software.
gfortran is a GNU Fortran compiler used for compiling Fortran code.
autoconf and libtool are tools for generating configure scripts and managing software builds on various platforms.

Utilities:

nano, vim, tmux are text editors and terminal multiplexers used for editing files and managing terminal sessions.
unzip is a utility used to decompress ZIP archives, commonly used for extracting files from compressed archives.
git is a distributed version control system used for tracking changes in source code during software development.
wget, curl are command-line tools used for downloading files from the internet, commonly used in scripts and automation tasks.
htop and nvtop are interactive system monitoring tools used for displaying system resource usage.
sysstat collects and reports system performance statistics, including CPU, memory, and disk usage.

Libraries:

libsm6, libxext6, libxrender1, libgl1-mesa-glx libraries for X Window System used for rendering graphics and managing graphical applications.
libtcmalloc-minimal4 is a memory allocation library, providing optimized memory management functions.
libx264-dev is a library for encoding H.264 video streams.
libsndfile1 is a library for reading and writing audio files.
libssl-dev is a development package for OpenSSL, providing cryptographic functions.
libpng-dev and libjpeg-dev are development libraries for handling PNG and JPEG image formats, respectively.
libmp3lame-dev is a development library for encoding and decoding MP3 audio files.
liblapack-dev is a library for numerical linear algebra functions.
libopenblas-dev is an optimized BLAS (Basic Linear Algebra Subprograms) library.

dokai:24.06-gpu.ffmpeg

ghcr.io/osai-ai/dokai:24.06-gpu.ffmpeg

Image based on dokai:24.06-gpu.core:

Additionally, installed:

FFmpeg (n6.1)
nv-codec-headers (n12.1.14.0)

dokai:24.06-gpu.base

ghcr.io/osai-ai/dokai:24.06-gpu.base

Image based on dokai:24.06-gpu.ffmpeg:

Additionally, installed:

Python ML and CV packages: requirements.txt

dokai:24.06-gpu.pytorch

ghcr.io/osai-ai/dokai:24.06-gpu.pytorch

Image based on dokai:24.06-gpu.base.

Additionally, installed:

TensorRT (9.2.0)
MAGMA (2.7.1)
Pytorch, torch-audio, torch-vision;
Pytorch-related packages: requirements.txt

dokai:24.06-gpu.video

ghcr.io/osai-ai/dokai:24.06-gpu.video

Image based on dokai:24.06-gpu.pytorch.

Additionally, installed:

VideoProcessingFramework==2.0.0 (source, v2.0.0)
tensor-stream==0.5.0 (source, 0.5.0)

GPU images optimized

Optimized set is composed of the previous one but based on lighter NVIDIA runtime base image.

dokai:24.06-gpu.core.opt

ghcr.io/osai-ai/dokai:24.06-gpu.core.opt

Image based on nvidia/cuda:12.2.2-cudnn8-runtime-ubuntu22.04 and includes the same additionally installed packages as dokai:24.06-gpu.core.

dokai:24.06-gpu.ffmpeg.opt

ghcr.io/osai-ai/dokai:24.06-gpu.ffmpeg.opt

Image based on dokai:24.06-gpu.core.opt and includes the same additionally installed packages as dokai:24.06-gpu.ffmpeg.

dokai:24.06-gpu.base.opt

ghcr.io/osai-ai/dokai:24.06-gpu.base.opt

Image based on dokai:24.06-gpu.ffmpeg.opt and includes the same additionally installed packages as dokai:24.06-gpu.base.

dokai:24.06-gpu.pytorch.opt

ghcr.io/osai-ai/dokai:24.06-gpu.pytorch.opt

Image based on dokai:24.06-gpu.base.opt and includes the same additionally installed packages as dokai:24.06-gpu.pytorch.

dokai:24.06-gpu.video.opt

ghcr.io/osai-ai/dokai:24.06-gpu.video.opt

Image based on dokai:24.06-gpu.pytorch.opt and includes the same additionally installed packages as dokai:24.06-gpu.video.

Performance comparison

`dokai:23.12-pytorch` vs `dokai:24.04-gpu.pytorch`

Size comparison

Image	cpu.*[.rootless]	gpu.*[.rootless]	gpu.*.opt[.rootless]
core	0.49Gb	11Gb	4.5Gb
ffmpeg	0.74Gb	11Gb	4.6Gb
base	2.0Gb	12Gb	5.6Gb
pytorch	-	17Gb	12Gb
video	-	17Gb	12Gb

Versions for past releases can be found in release descriptions.

osai-ai / dokai Goto Github PK

dokai's Introduction

Overview of cpu.* images

Overview of gpu.* images

Overview of gpu.*.opt images

Overview of *.rootless images

Examples

In case of *.rootless images there may permission problems happen within docker container:

Package versions

CPU images

GPU images built from source

GPU images optimized

Performance comparison

Size comparison

dokai's People

Contributors

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Watchers

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dokai's Issues

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Overview of `cpu.*` images

Overview of `gpu.*` images

Overview of `gpu.*.opt` images

Overview of `*.rootless` images

In case of `*.rootless` images there may permission problems happen within docker container: