Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/osai-ai/dokai

Collection of Docker images for ML/DL and video processing projects
https://github.com/osai-ai/dokai

cuda deep-learning docker docker-image ffmpeg opencv python pytorch tensorrt video-processing

Last synced: 3 months ago
JSON representation

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

Awesome Lists containing this project

README 

        
![dokai-logo](https://raw.githubusercontent.com/osai-ai/dokai/master/pics/dokai-logo.png)



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](requirements/pip/base.txt)

* `pytorch`: PyTorch (2.1.2), torchvision (0.16.2), torchaudio (2.1.2) and torch based libraries listed [here](requirements/pip/pytorch.txt)



## 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](requirements/pip/base.txt)

* `pytorch`: TensorRT (9.2.0), PyTorch (2.1.2), torchvision (0.16.2), torchaudio (2.1.2) and torch based libraries listed [here](requirements/pip/pytorch.txt)

* `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](https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/).



## 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.



![Comparison of NVIDIA images](pics/nvidia.png)



**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](https://docs.docker.com/build/building/best-practices/#user)

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](https://github.com/osai-ai/dokai/pkgs/container/dokai)

```bash

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

```



Docker Hub [mirror](https://hub.docker.com/r/osaiai/dokai/tags)

```bash

docker pull osaiai/dokai:24.06-gpu.pytorch

```



Check available GPUs inside the container

```bash

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:



```bash

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:



```bash

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



![Dokai images overview](pics/comparison.png)



### CPU images



dokai:24.06-cpu.core




[ghcr.io/osai-ai/dokai:24.06-cpu.core](https://github.com/osai-ai/dokai/pkgs/container/dokai)



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](https://github.com/osai-ai/dokai/pkgs/container/dokai)



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](https://github.com/osai-ai/dokai/pkgs/container/dokai)



Image based on `dokai:24.06-cpu.ffmpeg`:



Additionally, installed:



- Python ML and CV packages: [requirements.txt](requirements/pip/base.txt)






dokai:24.06-cpu.pytorch




[ghcr.io/osai-ai/dokai:24.06-cpu.pytorch](https://github.com/osai-ai/dokai/pkgs/container/dokai)



Image based on `dokai:24.06-cpu.base`:



Additionally, installed:



- Pytorch, torch-audio, torch-vision;

- Pytorch-related packages: [requirements.txt](requirements/pip/pytorch.txt)






### GPU images built from source



dokai:24.06-gpu.core




[ghcr.io/osai-ai/dokai:24.06-gpu.core](https://github.com/osai-ai/dokai/pkgs/container/dokai)



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](https://github.com/osai-ai/dokai/pkgs/container/dokai)



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](https://github.com/osai-ai/dokai/pkgs/container/dokai)



Image based on `dokai:24.06-gpu.ffmpeg`:



Additionally, installed:



- Python ML and CV packages: [requirements.txt](requirements/pip/base.txt)






dokai:24.06-gpu.pytorch




[ghcr.io/osai-ai/dokai:24.06-gpu.pytorch](https://github.com/osai-ai/dokai/pkgs/container/dokai)



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](requirements/pip/pytorch.txt)






dokai:24.06-gpu.video




[ghcr.io/osai-ai/dokai:24.06-gpu.video](https://github.com/osai-ai/dokai/pkgs/container/dokai)



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](https://github.com/osai-ai/dokai/pkgs/container/dokai)



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](https://github.com/osai-ai/dokai/pkgs/container/dokai)



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](https://github.com/osai-ai/dokai/pkgs/container/dokai)



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](https://github.com/osai-ai/dokai/pkgs/container/dokai)



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](https://github.com/osai-ai/dokai/pkgs/container/dokai)



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`




![Comparison of releases](pics/release_comparison.png)






### 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](https://github.com/osai-ai/dokai/releases).