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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: about 2 months ago
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Collection of Docker images for ML/DL and video processing projects
- Host: GitHub
- URL: https://github.com/osai-ai/dokai
- Owner: osai-ai
- License: mit
- Created: 2020-09-18T07:59:36.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-05-22T10:37:10.000Z (4 months ago)
- Last Synced: 2024-05-22T11:33:00.396Z (4 months ago)
- Topics: cuda, deep-learning, docker, docker-image, ffmpeg, opencv, python, pytorch, tensorrt, video-processing
- Language: Makefile
- Homepage:
- Size: 682 KB
- Stars: 99
- Watchers: 6
- Forks: 14
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
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.
**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
### 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`
### 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).