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

Overview

dokai-logo

Build and push Generic badge

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

Overview of images

Three types of images differ by tag postfix:

  • base: Python with ML and CV packages, CUDA (11.4.2), cuDNN (8.2.4), FFmpeg (4.4) with NVENC support
  • pytorch: PyTorch (1.10.0-rc1), torchvision (0.10.1), torchaudio (0.9.1) and torch based libraries
  • tensor-stream: Tensor Stream for real-time video streams decoding on GPU

Example

Pull an image

docker pull ghcr.io/osai-ai/dokai:21.09-pytorch

Docker Hub mirror

docker pull osaiai/dokai:21.09-pytorch

Check available GPUs inside container

docker run --rm \
    --gpus=all \
    ghcr.io/osai-ai/dokai:21.09-pytorch \
    nvidia-smi

Example of using dokai image for DL pipeline you can find here.

Versions

base

dokai:20.09-base

ghcr.io/osai-ai/dokai:20.09-base

FFmpeg (release/4.3), nv-codec-headers (sdk/9.1)
Python (3.6.9)

pip==20.2.3
setuptools==50.3.0
packaging==20.4
numpy==1.19.2
opencv-python==4.4.0.42
scipy==1.5.2
matplotlib==3.3.2
pandas==1.1.2
notebook==6.1.4
scikit-learn==0.23.2
scikit-image==0.17.2
albumentations==0.4.6
Cython==0.29.21
Pillow==7.2.0
trafaret-config==2.0.2
pyzmq==19.0.2
librosa==0.8.0
psutil==5.7.2
dataclasses==0.7

dokai:20.10-base

ghcr.io/osai-ai/dokai:20.10-base

FFmpeg (release/4.3), nv-codec-headers (sdk/9.1)
Python (3.6.9)

pip==20.2.4
setuptools==50.3.2
packaging==20.4
numpy==1.19.2
opencv-python==4.4.0.44
scipy==1.5.3
matplotlib==3.3.2
pandas==1.1.3
notebook==6.1.4
scikit-learn==0.23.2
scikit-image==0.17.2
albumentations==0.5.0
Cython==0.29.21
Pillow==8.0.0
trafaret-config==2.0.2
pyzmq==19.0.2
librosa==0.8.0
psutil==5.7.2
dataclasses==0.7
pydantic==1.6.1
requests==2.24.0

dokai:20.12-base

ghcr.io/osai-ai/dokai:20.12-base

CUDA (11.1), cuDNN (8.0.5)
FFmpeg (release/4.3), nv-codec-headers (sdk/9.1)
Python (3.8.5)

pip==20.3.3
setuptools==51.0.0
packaging==20.8
numpy==1.19.4
opencv-python==4.4.0.46
scipy==1.5.4
matplotlib==3.3.3
pandas==1.1.5
notebook==6.1.5
scikit-learn==0.23.2
scikit-image==0.18.0
albumentations==0.5.2
Cython==0.29.21
Pillow==8.0.1
trafaret-config==2.0.2
pyzmq==20.0.0
librosa==0.8.0
psutil==5.8.0
pydantic==1.7.3
requests==2.25.1

dokai:21.01-base

ghcr.io/osai-ai/dokai:21.01-base

CUDA (11.1.1), cuDNN (8.0.5)
FFmpeg (release/4.3), nv-codec-headers (sdk/10.0)
Python (3.8.5)

pip==20.3.3
setuptools==51.3.3
packaging==20.8
numpy==1.19.5
opencv-python==4.5.1.48
scipy==1.6.0
matplotlib==3.3.3
pandas==1.2.0
notebook==6.2.0
scikit-learn==0.24.1
scikit-image==0.18.1
albumentations==0.5.2
Cython==0.29.21
Pillow==8.1.0
trafaret-config==2.0.2
pyzmq==21.0.1
librosa==0.8.0
psutil==5.8.0
pydantic==1.7.3
requests==2.25.1

dokai:21.02-base

ghcr.io/osai-ai/dokai:21.02-base

CUDA (11.2.1), cuDNN (8.1.0)
FFmpeg (release/4.3), nv-codec-headers (sdk/10.0)
Python (3.8.5)

pip==21.0.1
setuptools==53.0.0
packaging==20.9
numpy==1.20.1
opencv-python==4.5.1.48
scipy==1.6.1
matplotlib==3.3.4
pandas==1.2.2
scikit-learn==0.24.1
scikit-image==0.18.1
Pillow==8.1.0
librosa==0.8.0
albumentations==0.5.2
pyzmq==22.0.3
Cython==0.29.22
numba==0.52.0
requests==2.25.1
psutil==5.8.0
trafaret-config==2.0.2
pydantic==1.7.3
PyYAML==5.4.1
notebook==6.2.0
ipywidgets==7.6.3
tqdm==4.57.0
pytest==6.2.2
mypy==0.812
flake8==3.8.4

dokai:21.03-base

ghcr.io/osai-ai/dokai:21.03-base

CUDA (11.2.2), cuDNN (8.1.1)
FFmpeg (release/4.4), nv-codec-headers (sdk/10.0)
Python (3.8.5)

pip==21.0.1
setuptools==54.2.0
packaging==20.9
numpy==1.20.1
opencv-python==4.5.1.48
scipy==1.6.1
matplotlib==3.3.4
pandas==1.2.3
scikit-learn==0.24.1
scikit-image==0.18.1
Pillow==8.1.2
librosa==0.8.0
albumentations==0.5.2
pyzmq==22.0.3
Cython==0.29.22
numba==0.53.0
requests==2.25.1
psutil==5.8.0
trafaret-config==2.0.2
pydantic==1.8.1
PyYAML==5.4.1
notebook==6.3.0
ipywidgets==7.6.3
tqdm==4.59.0
pytest==6.2.2
mypy==0.812
flake8==3.9.0

dokai:21.05-base

ghcr.io/osai-ai/dokai:21.05-base

CUDA (11.3), cuDNN (8.2.0)
FFmpeg (release/4.4), nv-codec-headers (sdk/10.0)
Python (3.8.5)

pip==21.1.1
setuptools==56.2.0
packaging==20.9
numpy==1.20.3
opencv-python==4.5.2.52
scipy==1.6.3
matplotlib==3.4.2
pandas==1.2.4
scikit-learn==0.24.2
scikit-image==0.18.1
Pillow==8.2.0
librosa==0.8.0
albumentations==0.5.2
pyzmq==22.0.3
Cython==0.29.23
numba==0.53.1
requests==2.25.1
psutil==5.8.0
trafaret-config==2.0.2
pydantic==1.8.1
PyYAML==5.4.1
notebook==6.3.0
ipywidgets==7.6.3
tqdm==4.60.0
pytest==6.2.4
mypy==0.812
flake8==3.9.2

dokai:21.07-base

ghcr.io/osai-ai/dokai:21.07-base

CUDA (11.3.1), cuDNN (8.2.0)
FFmpeg (release/4.4), nv-codec-headers (sdk/10.0)
Python (3.8.10)

pip==21.1.3
setuptools==57.0.0
packaging==20.9
numpy==1.21.0
opencv-python==4.5.2.54
scipy==1.7.0
matplotlib==3.4.2
pandas==1.2.5
scikit-learn==0.24.2
scikit-image==0.18.2
Pillow==8.2.0
librosa==0.8.1
albumentations==1.0.0
pyzmq==22.1.0
Cython==0.29.23
numba==0.53.1
requests==2.25.1
psutil==5.8.0
trafaret-config==2.0.2
pydantic==1.8.2
PyYAML==5.4.1
notebook==6.4.0
ipywidgets==7.6.3
tqdm==4.61.1
pytest==6.2.4
mypy==0.910
flake8==3.9.2

dokai:21.08-base

ghcr.io/osai-ai/dokai:21.08-base

CUDA (11.4.1), cuDNN (8.2.2)
FFmpeg (release/4.4), nv-codec-headers (sdk/11.0)
Python (3.8.10)

pip==21.2.3
setuptools==57.4.0
packaging==21.0
numpy==1.21.1
opencv-python==4.5.3.56
scipy==1.7.1
matplotlib==3.4.2
pandas==1.3.1
scikit-learn==0.24.2
scikit-image==0.18.2
Pillow==8.3.1
librosa==0.8.1
albumentations==1.0.3
pyzmq==22.2.1
Cython==0.29.24
numba==0.53.1
requests==2.26.0
psutil==5.8.0
pydantic==1.8.2
PyYAML==5.4.1
notebook==6.4.3
ipywidgets==7.6.3
tqdm==4.62.0
pytest==6.2.4
mypy==0.910
flake8==3.9.2

dokai:21.09-base

ghcr.io/osai-ai/dokai:21.09-base

CUDA (11.4.2), cuDNN (8.2.4)
FFmpeg (release/4.4), nv-codec-headers (sdk/11.0)
Python (3.8.10)

pip==21.2.4
setuptools==58.1.0
packaging==21.0
numpy==1.21.2
opencv-python==4.5.3.56
scipy==1.7.1
matplotlib==3.4.3
pandas==1.3.3
scikit-learn==1.0
scikit-image==0.18.3
Pillow==8.3.2
librosa==0.8.1
albumentations==1.0.3
pyzmq==22.3.0
Cython==0.29.24
numba==0.53.1
requests==2.26.0
psutil==5.8.0
pydantic==1.8.2
PyYAML==5.4.1
notebook==6.4.4
ipywidgets==7.6.5
tqdm==4.62.3
pytest==6.2.5
mypy==0.910
flake8==3.9.2

pytorch

dokai:20.09-pytorch

ghcr.io/osai-ai/dokai:20.09-pytorch

additionally to dokai:20.09-base:

torch==1.6.0
torchvision==0.7.0
pytorch-argus==0.1.2
timm==0.2.1
apex (master)

dokai:20.10-pytorch

ghcr.io/osai-ai/dokai:20.10-pytorch

additionally to dokai:20.10-base:

torch==1.6.0
torchvision==0.7.0
pytorch-argus==0.1.2
timm==0.2.1
apex (master)

dokai:20.12-pytorch

ghcr.io/osai-ai/dokai:20.12-pytorch

additionally to dokai:20.12-base:

torch==1.7.1 (source, v1.7.1 tag)
torchvision==0.8.2 (source, v0.8.2 tag)
pytorch-argus==0.2.0
timm==0.3.2
kornia==0.4.1
apex (source, master branch)

dokai:21.01-pytorch

ghcr.io/osai-ai/dokai:21.01-pytorch

additionally to dokai:21.01-base:

torch==1.8.0a0+4aea007 (source, master branch)
torchvision==0.8.2 (source, v0.8.2 tag)
pytorch-argus==0.2.0
timm==0.3.4
kornia==0.4.1
apex (source, master branch)

dokai:21.02-pytorch

ghcr.io/osai-ai/dokai:21.02-pytorch

additionally to dokai:21.02-base:

torch==1.9.0a0+c2b9283 (source, master branch)
torchvision==0.8.2 (source, v0.8.2 tag)
pytorch-argus==0.2.0
timm==0.4.4 (source, master branch)
kornia==0.4.1
pretrainedmodels==0.7.4
efficientnet-pytorch==0.7.0
segmentation-models-pytorch==0.1.3
apex (source, master branch)

dokai:21.03-pytorch

ghcr.io/osai-ai/dokai:21.03-pytorch

additionally to dokai:21.03-base:

torch==1.8.0 (source, v1.8.0 tag)
torchvision==0.9.0 (source, v0.9.0 tag)
torchaudio==0.8.0 (source, v0.8.0 tag)
pytorch-argus==0.2.1
timm==0.4.5
kornia==0.5.0
pretrainedmodels==0.7.4
efficientnet-pytorch==0.7.0
segmentation-models-pytorch==0.1.3
apex (source, master branch)

dokai:21.05-pytorch

ghcr.io/osai-ai/dokai:21.05-pytorch

additionally to dokai:21.05-base:

torch==1.8.1 (source, v1.8.1 tag)
torchvision==0.9.1 (source, v0.9.1 tag)
torchaudio==0.8.1 (source, v0.8.1 tag)
pytorch-argus==0.2.1
timm==0.4.8 (source, master branch)
kornia==0.5.1
pretrainedmodels==0.7.4
efficientnet-pytorch==0.7.1
segmentation-models-pytorch==0.1.3
apex (source, master branch)

dokai:21.07-pytorch

ghcr.io/osai-ai/dokai:21.07-pytorch

additionally to dokai:21.07-base:

torch==1.9.0 (source, v1.9.0 tag)
torchvision==0.10.0 (source, v0.10.0 tag)
torchaudio==0.9.0 (source, v0.9.0 tag)
pytorch-argus==0.2.1
pretrainedmodels==0.7.4
efficientnet-pytorch==0.7.1
timm==0.4.12
segmentation-models-pytorch==0.1.3
kornia==0.5.5
apex (source, master branch)

dokai:21.08-pytorch

ghcr.io/osai-ai/dokai:21.08-pytorch

additionally to dokai:21.08-base:

MAGMA (2.6.1)

torch==1.10.0a0+git5b8389e (source, master branch)
torchvision==0.10.0 (source, v0.10.0 tag)
torchaudio==0.9.0 (source, v0.9.0 tag)
pytorch-ignite==0.4.6
pytorch-argus==0.2.1
pretrainedmodels==0.7.4
efficientnet-pytorch==0.7.1
timm==0.4.12
segmentation-models-pytorch==0.2.0
kornia==0.5.8
apex (source, master branch)

dokai:21.09-pytorch

ghcr.io/osai-ai/dokai:21.09-pytorch

additionally to dokai:21.09-base:

MAGMA (2.6.1)

torch==1.10.0-rc1 (source, v1.10.0-rc1 tag)
torchvision==0.10.1 (source, v0.10.1 tag)
torchaudio==0.9.1 (source, v0.9.1 tag)
pytorch-ignite==0.4.6
pytorch-argus==0.2.1
pretrainedmodels==0.7.4
efficientnet-pytorch==0.7.1
timm==0.4.12
segmentation-models-pytorch==0.2.0
kornia==0.5.11
apex (source, master branch)

tensor-stream

dokai:20.09-tensor-stream

ghcr.io/osai-ai/dokai:20.09-tensor-stream

additionally to dokai:20.09-pytorch:

tensor-stream==0.4.6 (dev)

dokai:20.10-tensor-stream

ghcr.io/osai-ai/dokai:20.10-tensor-stream

additionally to dokai:20.10-pytorch:

tensor-stream==0.4.6 (dev)

dokai:20.12-tensor-stream

ghcr.io/osai-ai/dokai:20.12-tensor-stream

additionally to dokai:20.12-pytorch:

tensor-stream==0.4.6 (source, dev branch)

dokai:21.01-tensor-stream

ghcr.io/osai-ai/dokai:21.01-tensor-stream

additionally to dokai:21.01-pytorch:

tensor-stream==0.4.6 (source, dev branch)

dokai:21.02-tensor-stream

ghcr.io/osai-ai/dokai:21.02-tensor-stream

additionally to dokai:21.02-pytorch:

tensor-stream==0.4.6 (source, dev branch)

dokai:21.03-tensor-stream

ghcr.io/osai-ai/dokai:21.03-tensor-stream

additionally to dokai:21.03-pytorch:

tensor-stream==0.4.6 (source, dev branch)

dokai:21.05-tensor-stream

ghcr.io/osai-ai/dokai:21.05-tensor-stream

additionally to dokai:21.05-pytorch:

tensor-stream==0.4.6 (source, dev branch)

dokai:21.07-tensor-stream

ghcr.io/osai-ai/dokai:21.07-tensor-stream

additionally to dokai:21.07-pytorch:

tensor-stream==0.4.6 (source, dev branch)

dokai:21.08-tensor-stream

ghcr.io/osai-ai/dokai:21.08-tensor-stream

additionally to dokai:21.08-pytorch:

tensor-stream==0.4.6 (source, dev branch)

dokai:21.09-tensor-stream

ghcr.io/osai-ai/dokai:21.09-tensor-stream

additionally to dokai:21.09-pytorch:

tensor-stream==0.4.6 (source, dev branch)

You might also like...
This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.
This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.

Self Driving Car An autonomous car (also known as a driverless car, self-driving car, and robotic car) is a vehicle that is capable of sensing its env

Doods2 - API for detecting objects in images and video streams using Tensorflow

DOODS2 - Return of DOODS Dedicated Open Object Detection Service - Yes, it's a b

Search Youtube Video and Get Video info
Search Youtube Video and Get Video info

PyYouTube Get Video Data from YouTube link Installation pip install PyYouTube How to use it ? Get Videos Data from pyyoutube import Data yt = Data("ht

We present a framework for training multi-modal deep learning models on unlabelled video data by forcing the network to learn invariances to transformations applied to both the audio and video streams.

Multi-Modal Self-Supervision using GDT and StiCa This is an official pytorch implementation of papers: Multi-modal Self-Supervision from Generalized D

Video lie detector using xgboost - A video lie detector using OpenFace and xgboost

video_lie_detector_using_xgboost a video lie detector using OpenFace and xgboost

 MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images
MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images

Main repo for ECCV 2020 paper MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images. visual.cs.brown.edu/matryodshka

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker
Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker This repository contai

[NeurIPS 2020] Blind Video Temporal Consistency via Deep Video Prior
[NeurIPS 2020] Blind Video Temporal Consistency via Deep Video Prior

pytorch-deep-video-prior (DVP) Official PyTorch implementation for NeurIPS 2020 paper: Blind Video Temporal Consistency via Deep Video Prior TensorFlo

Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.
Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.

vid2vid Project | YouTube(short) | YouTube(full) | arXiv | Paper(full) Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic vid

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

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

    I used docker pulled from ghcr.io/osai-ai/dokai:21.05-pytorch.

    The following code gives an error:

    python -c 'import torchaudio; import torch; a = torch.randn(2, 4663744); torchaudio.transforms.MelSpectrogram(44100)(a)'

    /usr/local/lib/python3.8/dist-packages/torchaudio-0.8.0a0+e4e171a-py3.8-linux-x86_64.egg/torchaudio/functional/functional.py:357: UserWarning: At least one mel filterbank has all zero values. The value for `n_mels` (128) may be set too high. Or, the value for `n_freqs` (201) may be set too low.
      warnings.warn(
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 889, in _call_impl
        result = self.forward(*input, **kwargs)
      File "/usr/local/lib/python3.8/dist-packages/torchaudio-0.8.0a0+e4e171a-py3.8-linux-x86_64.egg/torchaudio/transforms.py", line 480, in forward
        specgram = self.spectrogram(waveform)
      File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 889, in _call_impl
        result = self.forward(*input, **kwargs)
      File "/usr/local/lib/python3.8/dist-packages/torchaudio-0.8.0a0+e4e171a-py3.8-linux-x86_64.egg/torchaudio/transforms.py", line 96, in forward
        return F.spectrogram(
      File "/usr/local/lib/python3.8/dist-packages/torchaudio-0.8.0a0+e4e171a-py3.8-linux-x86_64.egg/torchaudio/functional/functional.py", line 91, in spectrogram
        spec_f = torch.stft(
      File "/usr/local/lib/python3.8/dist-packages/torch/functional.py", line 580, in stft
        return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore
    RuntimeError: fft: ATen not compiled with MKL support
    

    and this check python -c 'import torch; a = torch.randn(10); print(a.to_mkldnn().layout)' works correctly.

    opened by Ayagoz 2
  • Expired link to nv-codec-headers repo

    Expired link to nv-codec-headers repo

    Hi, git.videolan.org is experiencing some issues again, it looks like the certificate for the domain is expired or something like that (but it was alive just a week ago!). Also, they are migrating to code.videolan.org, however nv-codec-headers is not there yet.

    The current link does not work: https://github.com/osai-ai/dokai/blob/6f99608b70881de43740bc84c34f42249f4f65aa/docker/Dockerfile.base#L43

    Temporary workaround: https://github.com/FFmpeg/nv-codec-headers.git

    opened by NikolasEnt 1
Releases(v22.11)
  • v22.11(Nov 22, 2022)

    Updates

    • TensorRT 8.5.1
    • torch 1.14.0a0+git71fe069 (source, close to v1.13.0 after commit "ada lovelace (arch 8.9) support #87436")
    • torchvision 0.14.0 (from source, v0.14.0 tag)
    • torchaudio 0.13.0 (from source, v0.13.0 tag)
    • Update other PyPI packages
    • Ada Lovelace architecture support
    • PyTorch image models benchmark link

    Images

    base

    Python with ML and CV packages, CUDA (11.8.0), cuDNN (8.6.0), FFmpeg (4.4) with NVENC/NVDEC support ghcr.io/osai-ai/dokai:22.11-base

    dokai:22.11-base

    Supported NVIDIA architectures: Pascal (sm_60, sm_61), Volta (sm_70), Turing (sm_75), Ampere (sm_80, sm_86), Ada Lovelace (sm_89).

    CUDA (11.8.0), cuDNN (8.6.0) FFmpeg (release/4.4), nv-codec-headers (sdk/11.0) Python (3.10.6) CMake (3.22.1)

    pip==22.3.1 setuptools==65.5.1 packaging==21.3 numpy==1.23.4 opencv-python==4.6.0.66 scipy==1.9.3 matplotlib==3.6.2 pandas==1.5.1 scikit-learn==1.1.3 scikit-image==0.19.3 Pillow==9.3.0 librosa==0.9.2 albumentations==1.3.0 pyzmq==24.0.1 Cython==0.29.32 numba==0.56.4 requests==2.28.1 psutil==5.9.4 pydantic==1.10.2 PyYAML==6.0 notebook==6.5.2 ipywidgets==8.0.2 tqdm==4.64.1 pytest==7.2.0 pytest-cov==4.0.0 mypy==0.991 flake8==5.0.4 pre-commit==2.20.0

    pytorch

    TensorRT (8.5.1) , PyTorch (1.13.0), torchvision (0.14.0), torchaudio (0.13.0) and torch based libraries. ghcr.io/osai-ai/dokai:22.11-pytorch

    dokai:22.11-pytorch

    additionally to dokai:22.11-base:

    TensorRT (8.5.1) MAGMA (2.6.2)

    torch==1.14.0a0+git71fe069 (source, close to v1.13.0 after commit "ada lovelace (arch 8.9) support #87436") torchvision==0.14.0 (source, v0.14.0 tag) torchaudio==0.13.0 (source, v0.13.0 tag) pytorch-ignite==0.4.10 pytorch-argus==1.0.0 pretrainedmodels==0.7.4 efficientnet-pytorch==0.7.1 pytorch-toolbelt==0.5.2 kornia==0.6.8 timm==0.6.11 segmentation-models-pytorch==0.3.0

    tensor-stream

    Tensor Stream for real-time video streams decoding on GPU.
    ghcr.io/osai-ai/dokai:22.11-tensor-stream

    dokai:22.11-tensor-stream

    additionally to dokai:22.11-pytorch:

    tensor-stream==0.4.6 (source, dev branch)

    Source code(tar.gz)
    Source code(zip)
    build_logs.zip(471.12 KB)
  • v22.03(Mar 28, 2022)

    Updates

    • CUDA 11.6.0
    • torch 1.11.0 (from source, v1.11.0 tag)
    • torchvision 0.12.0 (from source, v0.12.0 tag)
    • torchaudio 0.11.0 (from source, v0.11.0 tag)
    • CMake (3.22.2)
    • Update other PyPI packages
    • Update README

    Images

    base

    Python with ML and CV packages, CUDA (11.6.0), FFmpeg (4.4) with NVENC support.

    dokai:22.03-base

    ghcr.io/osai-ai/dokai:22.03-base

    CUDA (11.6.0) FFmpeg (release/4.4), nv-codec-headers (sdk/11.0)
    Python (3.8.10)
    CMake (3.22.2)

    pip==22.0.3
    setuptools==59.5.0
    packaging==21.3
    numpy==1.21.5
    opencv-python==4.5.5.62
    scipy==1.8.0
    matplotlib==3.5.1
    pandas==1.4.1
    scikit-learn==1.0.1
    scikit-image==0.18.3
    Pillow==8.4.0
    librosa==0.8.1
    albumentations==1.1.0
    pyzmq==22.3.0
    Cython==0.29.24
    numba==0.53.1
    requests==2.26.0
    psutil==5.8.0
    pydantic==1.8.2
    PyYAML==6.0
    notebook==6.4.5
    ipywidgets==7.6.5
    tqdm==4.62.3
    pytest==6.2.5
    mypy==0.910
    flake8==4.0.1

    pytorch

    PyTorch, torchvision and torch based libraries.

    dokai:22.03-pytorch

    ghcr.io/osai-ai/dokai:22.03-pytorch

    additionally to dokai:22.03-base:

    MAGMA (2.6.1)

    torch==1.11.0 (source, v1.11.0 tag)
    torchvision==0.12.0 (source, v0.12.0 tag)
    torchaudio==0.11.0 (source, v0.11.0 tag)
    pytorch-ignite==0.4.8
    pytorch-argus==1.0.0
    pretrainedmodels==0.7.4
    efficientnet-pytorch==0.7.1
    timm==0.5.4
    segmentation-models-pytorch==0.2.1
    kornia==0.6.3

    tensor-stream

    Tensor Stream.

    dokai:22.03-tensor-stream

    ghcr.io/osai-ai/dokai:22.03-tensor-stream

    additionally to dokai:22.03-pytorch:

    tensor-stream==0.4.6 (source, dev branch)

    Source code(tar.gz)
    Source code(zip)
  • v21.11(Nov 9, 2021)

    Updates

    • torch 1.10.0 (from source, v1.10.0 tag)
    • torchvision 0.11.1 (from source, v0.11.1 tag)
    • torchaudio 0.10.0 (from source, v0.10.0 tag)
    • CMake (3.21.4)
    • Remove Apex installation
    • Update other PyPI packages

    Images

    base

    Python with ML and CV packages, CUDA (11.4.2), cuDNN (8.2.4), FFmpeg (4.4) with NVENC support.
    ghcr.io/osai-ai/dokai:21.11-base

    dokai:21.11-base

    CUDA (11.4.2), cuDNN (8.2.4)
    FFmpeg (release/4.4), nv-codec-headers (sdk/11.0)
    Python (3.8.10)
    CMake (3.21.4)

    pip==21.3.1
    setuptools==58.5.3
    packaging==21.2
    numpy==1.21.4
    opencv-python==4.5.4.58
    scipy==1.7.2
    matplotlib==3.4.3
    pandas==1.3.4
    scikit-learn==1.0.1
    scikit-image==0.18.3
    Pillow==8.4.0
    librosa==0.8.1
    albumentations==1.1.0
    pyzmq==22.3.0
    Cython==0.29.24
    numba==0.53.1
    requests==2.26.0
    psutil==5.8.0
    pydantic==1.8.2
    PyYAML==6.0
    notebook==6.4.5
    ipywidgets==7.6.5
    tqdm==4.62.3
    pytest==6.2.5
    mypy==0.910
    flake8==4.0.1

    pytorch

    PyTorch, torchvision, Apex and torch based libraries.
    ghcr.io/osai-ai/dokai:21.11-pytorch

    dokai:21.11-pytorch

    additionally to dokai:21.11-base:

    MAGMA (2.6.1)

    torch==1.10.0 (source, v1.10.0 tag)
    torchvision==0.11.1 (source, v0.11.1 tag)
    torchaudio==0.10.0 (source, v0.10.0 tag)
    pytorch-ignite==0.4.7
    pytorch-argus==1.0.0
    pretrainedmodels==0.7.4
    efficientnet-pytorch==0.7.1
    timm==0.4.12
    segmentation-models-pytorch==0.2.0
    kornia==0.6.1

    tensor-stream

    Tensor Stream.
    ghcr.io/osai-ai/dokai:21.11-tensor-stream

    dokai:21.11-tensor-stream

    additionally to dokai:21.11-pytorch:

    tensor-stream==0.4.6 (source, dev branch)

    Source code(tar.gz)
    Source code(zip)
  • v21.09(Sep 30, 2021)

    Updates

    • CUDA 11.4.2, cuDNN 8.2.4
    • Build torch 1.10.0-rc1 (from source, v1.10.0-rc1 tag)
    • FFmpeg with HTTPS support
    • kornia 0.5.11
    • Update other PyPI packages

    Images

    base

    Python with ML and CV packages, CUDA (11.4.2), cuDNN (8.2.4), FFmpeg (4.4) with NVENC support.
    ghcr.io/osai-ai/dokai:21.09-base

    dokai:21.09-base

    CUDA (11.4.2), cuDNN (8.2.4)
    FFmpeg (release/4.4), nv-codec-headers (sdk/11.0)
    Python (3.8.10)

    pip==21.2.4
    setuptools==58.1.0
    packaging==21.0
    numpy==1.21.2
    opencv-python==4.5.3.56
    scipy==1.7.1
    matplotlib==3.4.3
    pandas==1.3.3
    scikit-learn==1.0
    scikit-image==0.18.3
    Pillow==8.3.2
    librosa==0.8.1
    albumentations==1.0.3
    pyzmq==22.3.0
    Cython==0.29.24
    numba==0.53.1
    requests==2.26.0
    psutil==5.8.0
    pydantic==1.8.2
    PyYAML==5.4.1
    notebook==6.4.4
    ipywidgets==7.6.5
    tqdm==4.62.3
    pytest==6.2.5
    mypy==0.910
    flake8==3.9.2

    pytorch

    PyTorch, torchvision, Apex and torch based libraries.
    ghcr.io/osai-ai/dokai:21.09-pytorch

    dokai:21.09-pytorch

    additionally to dokai:21.09-base:

    MAGMA (2.6.1)

    torch==1.10.0-rc1 (source, v1.10.0-rc1 tag)
    torchvision==0.10.1 (source, v0.10.1 tag)
    torchaudio==0.9.1 (source, v0.9.1 tag)
    pytorch-ignite==0.4.6
    pytorch-argus==0.2.1
    pretrainedmodels==0.7.4
    efficientnet-pytorch==0.7.1
    timm==0.4.12
    segmentation-models-pytorch==0.2.0
    kornia==0.5.11
    apex (source, master branch)

    tensor-stream

    Tensor Stream.
    ghcr.io/osai-ai/dokai:21.09-tensor-stream

    dokai:21.09-tensor-stream

    additionally to dokai:21.09-pytorch:

    tensor-stream==0.4.6 (source, dev branch)

    Source code(tar.gz)
    Source code(zip)
  • v21.08(Aug 12, 2021)

    Updates

    • CUDA 11.4.1, cuDNN 8.2.2
    • nv-codec-headers (sdk/11.0)
    • MAGMA 2.6.1
    • Build torch 1.10.0a0+git5b8389e from source (master branch)
    • pytorch-ignite 0.4.6
    • segmentation-models-pytorch 0.2.0
    • kornia 0.5.8
    • Update other PyPI packages

    Images

    base

    Python with ML and CV packages, CUDA (11.4.1), cuDNN (8.2.2), FFmpeg (4.4) with NVENC support.
    ghcr.io/osai-ai/dokai:21.08-base

    dokai:21.08-base

    CUDA (11.4.1), cuDNN (8.2.2)
    FFmpeg (release/4.4), nv-codec-headers (sdk/11.0)
    Python (3.8.10)

    pip==21.2.3
    setuptools==57.4.0
    packaging==21.0
    numpy==1.21.1
    opencv-python==4.5.3.56
    scipy==1.7.1
    matplotlib==3.4.2
    pandas==1.3.1
    scikit-learn==0.24.2
    scikit-image==0.18.2
    Pillow==8.3.1
    librosa==0.8.1
    albumentations==1.0.3
    pyzmq==22.2.1
    Cython==0.29.24
    numba==0.53.1
    requests==2.26.0
    psutil==5.8.0
    pydantic==1.8.2
    PyYAML==5.4.1
    notebook==6.4.3
    ipywidgets==7.6.3
    tqdm==4.62.0
    pytest==6.2.4
    mypy==0.910
    flake8==3.9.2

    pytorch

    PyTorch, torchvision, Apex and torch based libraries.
    ghcr.io/osai-ai/dokai:21.08-pytorch

    dokai:21.08-pytorch

    additionally to dokai:21.08-base:

    MAGMA (2.6.1)

    torch==1.10.0a0+git5b8389e (source, master branch)
    torchvision==0.10.0 (source, v0.10.0 tag)
    torchaudio==0.9.0 (source, v0.9.0 tag)
    pytorch-ignite==0.4.6
    pytorch-argus==0.2.1
    pretrainedmodels==0.7.4
    efficientnet-pytorch==0.7.1
    timm==0.4.12
    segmentation-models-pytorch==0.2.0
    kornia==0.5.8
    apex (source, master branch)

    tensor-stream

    Tensor Stream.
    ghcr.io/osai-ai/dokai:21.08-tensor-stream

    dokai:21.08-tensor-stream

    additionally to dokai:21.08-pytorch:

    tensor-stream==0.4.6 (source, dev branch)

    Source code(tar.gz)
    Source code(zip)
  • v21.07(Jul 2, 2021)

    Updates

    • CUDA 11.3.1
    • Build torch 1.9.0 from source (v1.9.0 tag)
    • torchvision 0.10.0 from source (v0.10.0 tag)
    • torchaudio 0.9.0 from source (v0.9.0 tag)
    • timm 0.4.12
    • kornia 0.5.5
    • Update other PyPI packages

    Images

    base

    Python with ML and CV packages, CUDA (11.3.1), cuDNN (8.2.0), FFmpeg (4.4) with NVENC support.
    ghcr.io/osai-ai/dokai:21.07-base

    dokai:21.07-base

    CUDA (11.3.1), cuDNN (8.2.0)
    FFmpeg (release/4.4), nv-codec-headers (sdk/10.0)
    Python (3.8.10)

    pip==21.1.3
    setuptools==57.0.0
    packaging==20.9
    numpy==1.21.0
    opencv-python==4.5.2.54
    scipy==1.7.0
    matplotlib==3.4.2
    pandas==1.2.5
    scikit-learn==0.24.2
    scikit-image==0.18.2
    Pillow==8.2.0
    librosa==0.8.1
    albumentations==1.0.0
    pyzmq==22.1.0
    Cython==0.29.23
    numba==0.53.1
    requests==2.25.1
    psutil==5.8.0
    trafaret-config==2.0.2
    pydantic==1.8.2
    PyYAML==5.4.1
    notebook==6.4.0
    ipywidgets==7.6.3
    tqdm==4.61.1
    pytest==6.2.4
    mypy==0.910
    flake8==3.9.2

    pytorch

    PyTorch, torchvision, Apex and torch based libraries.
    ghcr.io/osai-ai/dokai:21.07-pytorch

    dokai:21.07-pytorch

    additionally to dokai:21.07-base:

    torch==1.9.0 (source, v1.9.0 tag)
    torchvision==0.10.0 (source, v0.10.0 tag)
    torchaudio==0.9.0 (source, v0.9.0 tag)
    pytorch-argus==0.2.1
    pretrainedmodels==0.7.4
    efficientnet-pytorch==0.7.1
    timm==0.4.12
    segmentation-models-pytorch==0.1.3
    kornia==0.5.5
    apex (source, master branch)

    tensor-stream

    Tensor Stream.
    ghcr.io/osai-ai/dokai:21.07-tensor-stream

    dokai:21.07-tensor-stream

    additionally to dokai:21.07-pytorch:

    tensor-stream==0.4.6 (source, dev branch)

    Source code(tar.gz)
    Source code(zip)
  • v21.05(May 11, 2021)

    Updates

    • CUDA 11.3, cuDNN 8.2.0
    • Build torch 1.8.1 from source (v1.8.1 tag)
    • torchvision 0.9.1 from source (v0.9.1 tag)
    • torchaudio 0.8.1 from source (v0.8.1 tag)
    • timm 0.4.8 from source (master branch)
    • Update other PyPI packages

    Images

    base

    Python with ML and CV packages, CUDA (11.3), cuDNN (8.2.0), FFmpeg (4.4) with NVENC support.
    ghcr.io/osai-ai/dokai:21.05-base

    dokai:21.05-base

    CUDA (11.3), cuDNN (8.2.0)
    FFmpeg (release/4.4), nv-codec-headers (sdk/10.0)
    Python (3.8.5)

    pip==21.1.1
    setuptools==56.2.0
    packaging==20.9
    numpy==1.20.3
    opencv-python==4.5.2.52
    scipy==1.6.3
    matplotlib==3.4.2
    pandas==1.2.4
    scikit-learn==0.24.2
    scikit-image==0.18.1
    Pillow==8.2.0
    librosa==0.8.0
    albumentations==0.5.2
    pyzmq==22.0.3
    Cython==0.29.23
    numba==0.53.1
    requests==2.25.1
    psutil==5.8.0
    trafaret-config==2.0.2
    pydantic==1.8.1
    PyYAML==5.4.1
    notebook==6.3.0
    ipywidgets==7.6.3
    tqdm==4.60.0
    pytest==6.2.4
    mypy==0.812
    flake8==3.9.2

    pytorch

    PyTorch, torchvision, Apex and torch based libraries.
    ghcr.io/osai-ai/dokai:21.05-pytorch

    dokai:21.05-pytorch

    additionally to dokai:21.05-base:

    torch==1.8.1 (source, v1.8.1 tag)
    torchvision==0.9.1 (source, v0.9.1 tag)
    torchaudio==0.8.1 (source, v0.8.1 tag)
    pytorch-argus==0.2.1
    timm==0.4.8 (source, master branch)
    kornia==0.5.1
    pretrainedmodels==0.7.4
    efficientnet-pytorch==0.7.1
    segmentation-models-pytorch==0.1.3
    apex (source, master branch)

    tensor-stream

    Tensor Stream.
    ghcr.io/osai-ai/dokai:21.05-tensor-stream

    dokai:21.05-tensor-stream

    additionally to dokai:21.05-pytorch:

    tensor-stream==0.4.6 (source, dev branch)

    Source code(tar.gz)
    Source code(zip)
  • v21.03(Mar 25, 2021)

    Updates

    • CUDA 11.2.2, cuDNN 8.1.1
    • FFmpeg 4.4
    • Build torch 1.8.0 from source (v1.8.0 tag)
    • torchvision 0.9.0
    • Add PyTorch package: torchaudio 0.8.0
    • timm 0.4.5
    • pytorch-argus 0.2.1
    • Update other PyPI packages
    • Support more GPU architectures for FFmpeg

    Images

    base

    Python with ML and CV packages, CUDA (11.2.2), cuDNN (8.1.1), FFmpeg (4.4) with NVENC support.
    ghcr.io/osai-ai/dokai:21.03-base

    dokai:21.03-base

    ghcr.io/osai-ai/dokai:21.03-base

    CUDA (11.2.2), cuDNN (8.1.1)
    FFmpeg (release/4.4), nv-codec-headers (sdk/10.0)
    Python (3.8.5)

    pip==21.0.1
    setuptools==54.2.0
    packaging==20.9
    numpy==1.20.1
    opencv-python==4.5.1.48
    scipy==1.6.1
    matplotlib==3.3.4
    pandas==1.2.3
    scikit-learn==0.24.1
    scikit-image==0.18.1
    Pillow==8.1.2
    librosa==0.8.0
    albumentations==0.5.2
    pyzmq==22.0.3
    Cython==0.29.22
    numba==0.53.0
    requests==2.25.1
    psutil==5.8.0
    trafaret-config==2.0.2
    pydantic==1.8.1
    PyYAML==5.4.1
    notebook==6.3.0
    ipywidgets==7.6.3
    tqdm==4.59.0
    pytest==6.2.2
    mypy==0.812
    flake8==3.9.0

    pytorch

    PyTorch, torchvision, Apex and torch based libraries.
    ghcr.io/osai-ai/dokai:21.03-pytorch

    dokai:21.03-pytorch

    additionally to dokai:21.03-base:

    torch==1.8.0 (source, v1.8.0 tag)
    torchvision==0.9.0 (source, v0.9.0 tag)
    torchaudio==0.8.0 (source, v0.8.0 tag)
    pytorch-argus==0.2.1
    timm==0.4.5
    kornia==0.5.0
    pretrainedmodels==0.7.4
    efficientnet-pytorch==0.7.0
    segmentation-models-pytorch==0.1.3
    apex (source, master branch)

    tensor-stream

    Tensor Stream.
    ghcr.io/osai-ai/dokai:21.03-tensor-stream

    dokai:21.03-tensor-stream

    additionally to dokai:21.03-pytorch:

    tensor-stream==0.4.6 (source, dev branch)

    Source code(tar.gz)
    Source code(zip)
  • v21.02(Feb 23, 2021)

    New features

    • CUDA 11.2.1, cuDNN 8.1.0
    • Build torch 1.9.0a0+c2b9283 from source (master branch)
    • Install timm 0.4.4 from source (master branch)
    • Add more Python packages: tqdm, PyYAML, pytest, mypy, flake8
    • Add more PyTorch packages: pretrainedmodels, efficientnet-pytorch, segmentation-models-pytorch
    • Update other PyPI packages

    Images

    base

    Python with ML and CV packages, CUDA (11.2.1), cuDNN (8.1.0), FFmpeg with NVENC support.
    ghcr.io/osai-ai/dokai:21.02-base

    dokai:21.02-base

    CUDA (11.2.1), cuDNN (8.1.0)
    FFmpeg (release/4.3), nv-codec-headers (sdk/10.0)
    Python (3.8.5)

    pip==21.0.1
    setuptools==53.0.0
    packaging==20.9
    numpy==1.20.1
    opencv-python==4.5.1.48
    scipy==1.6.1
    matplotlib==3.3.4
    pandas==1.2.2
    scikit-learn==0.24.1
    scikit-image==0.18.1
    Pillow==8.1.0
    librosa==0.8.0
    albumentations==0.5.2
    pyzmq==22.0.3
    Cython==0.29.22
    numba==0.52.0
    requests==2.25.1
    psutil==5.8.0
    trafaret-config==2.0.2
    pydantic==1.7.3
    PyYAML==5.4.1
    notebook==6.2.0
    ipywidgets==7.6.3
    tqdm==4.57.0
    pytest==6.2.2
    mypy==0.812
    flake8==3.8.4

    pytorch

    PyTorch, torchvision, Apex and torch based libraries.
    ghcr.io/osai-ai/dokai:21.02-pytorch

    dokai:21.02-pytorch

    additionally to dokai:21.02-base:

    torch==1.9.0a0+c2b9283 (source, master branch)
    torchvision==0.8.2 (source, v0.8.2 tag)
    pytorch-argus==0.2.0
    timm==0.4.4 (source, master branch)
    kornia==0.4.1
    pretrainedmodels==0.7.4
    efficientnet-pytorch==0.7.0
    segmentation-models-pytorch==0.1.3
    apex (source, master branch)

    tensor-stream

    Tensor Stream.
    ghcr.io/osai-ai/dokai:21.02-tensor-stream

    dokai:21.02-tensor-stream

    additionally to dokai:21.02-pytorch:

    tensor-stream==0.4.6 (source, dev branch)

    Source code(tar.gz)
    Source code(zip)
  • v21.01(Jan 21, 2021)

    New features

    • CUDA 11.1.1
    • nv-codec-headers (sdk/10.0)
    • Build torch 1.8.0a0+4aea007 from source (master branch)
    • Update other PyPI packages
    • Docker Hub mirror

    Images

    base

    Python with ML and CV packages, CUDA, FFmpeg with NVENC support.
    ghcr.io/osai-ai/dokai:21.01-base

    dokai:21.01-base

    CUDA (11.1.1), cuDNN (8.0.5)
    FFmpeg (release/4.3), nv-codec-headers (sdk/10.0)
    Python (3.8.5)

    pip==20.3.3
    setuptools==51.3.3
    packaging==20.8
    numpy==1.19.5
    opencv-python==4.5.1.48
    scipy==1.6.0
    matplotlib==3.3.3
    pandas==1.2.0
    notebook==6.2.0
    scikit-learn==0.24.1
    scikit-image==0.18.1
    albumentations==0.5.2
    Cython==0.29.21
    Pillow==8.1.0
    trafaret-config==2.0.2
    pyzmq==21.0.1
    librosa==0.8.0
    psutil==5.8.0
    pydantic==1.7.3
    requests==2.25.1

    pytorch

    PyTorch, torchvision, Apex and torch based libraries.
    ghcr.io/osai-ai/dokai:21.01-pytorch

    dokai:21.01-pytorch

    additionally to dokai:21.01-base:

    torch==1.8.0a0+4aea007 (source, master branch)
    torchvision==0.8.2 (source, v0.8.2 tag)
    pytorch-argus==0.2.0
    timm==0.3.4
    kornia==0.4.1
    apex (source, master branch)

    tensor-stream

    Tensor Stream.
    ghcr.io/osai-ai/dokai:21.01-tensor-stream

    dokai:21.01-tensor-stream

    additionally to dokai:21.01-pytorch:

    tensor-stream==0.4.6 (source, dev branch)

    Source code(tar.gz)
    Source code(zip)
  • v20.12(Dec 24, 2020)

    New features

    • CUDA 11.1, cuDNN 8.0.5, Ubuntu 20.04, Python 3.8.5
    • Build PyTorch and torchvision from source
    • Build CUDA libraries for Ampere architecture (TORCH_CUDA_ARCH_LIST="6.0;6.1;7.0;7.5;8.0;8.6")
    • kornia

    Images

    base

    Python with ML and CV packages, CUDA, FFmpeg with NVENC support.
    ghcr.io/osai-ai/dokai:20.12-base

    dokai:20.12-base

    CUDA (11.1), cuDNN (8.0.5) FFmpeg (release/4.3), nv-codec-headers (sdk/9.1)
    Python (3.8.5)

    pip==20.3.3
    setuptools==51.0.0
    packaging==20.8
    numpy==1.19.4
    opencv-python==4.4.0.46
    scipy==1.5.4
    matplotlib==3.3.3
    pandas==1.1.5
    notebook==6.1.5
    scikit-learn==0.23.2
    scikit-image==0.18.0
    albumentations==0.5.2
    Cython==0.29.21
    Pillow==8.0.1
    trafaret-config==2.0.2
    pyzmq==20.0.0
    librosa==0.8.0
    psutil==5.8.0
    pydantic==1.7.3
    requests==2.25.1

    pytorch

    PyTorch, torchvision, Apex and torch based libraries.
    ghcr.io/osai-ai/dokai:20.12-pytorch

    dokai:20.12-pytorch

    additionally to dokai:20.12-base:

    torch==1.7.1 (source, v1.7.1 tag)
    torchvision==0.8.2 (source, v0.8.2 tag)
    pytorch-argus==0.2.0
    timm==0.3.2
    kornia==0.4.1
    apex (source, master branch)

    tensor-stream

    Tensor Stream.
    ghcr.io/osai-ai/dokai:20.12-tensor-stream

    dokai:20.12-tensor-stream

    additionally to dokai:20.12-pytorch:

    tensor-stream==0.4.6 (source, dev branch)

    Source code(tar.gz)
    Source code(zip)
  • v20.10(Oct 22, 2020)

    New features

    • pydantic
    • requests

    Fix

    • Build Tensor Stream for lower cuDNN versions 3.7+PTX;5.0;6.0;6.1;7.0;7.5

    Images

    base

    Python with ML and CV packages, CUDA, FFmpeg with NVENC support.
    ghcr.io/osai-ai/dokai:20.10-base

    dokai:20.10-base

    FFmpeg (release/4.3), nv-codec-headers (sdk/9.1)
    Python (3.6.9)

    pip==20.2.4
    setuptools==50.3.2
    packaging==20.4
    numpy==1.19.2
    opencv-python==4.4.0.44
    scipy==1.5.3
    matplotlib==3.3.2
    pandas==1.1.3
    notebook==6.1.4
    scikit-learn==0.23.2
    scikit-image==0.17.2
    albumentations==0.5.0
    Cython==0.29.21
    Pillow==8.0.0
    trafaret-config==2.0.2
    pyzmq==19.0.2
    librosa==0.8.0
    psutil==5.7.2
    dataclasses==0.7
    pydantic==1.6.1
    requests==2.24.0

    pytorch

    PyTorch, torchvision, Apex and torch based libraries.
    ghcr.io/osai-ai/dokai:20.10-pytorch

    dokai:20.10-pytorch

    additionally to dokai:20.10-base:

    torch==1.6.0
    torchvision==0.7.0
    pytorch-argus==0.1.2
    timm==0.2.1
    apex (master)

    tensor-stream

    Tensor Stream.
    ghcr.io/osai-ai/dokai:20.10-tensor-stream

    dokai:20.10-tensor-stream

    additionally to dokai:20.10-pytorch:

    tensor-stream==0.4.6 (dev)

    Source code(tar.gz)
    Source code(zip)
  • v20.09(Sep 29, 2020)

    base

    Python with ML and CV packages, CUDA, FFmpeg with NVENC support.
    ghcr.io/osai-ai/dokai:20.09-base

    dokai:20.09-base

    FFmpeg (release/4.3), nv-codec-headers (sdk/9.1)
    Python (3.6.9)

    pip==20.2.3
    setuptools==50.3.0
    packaging==20.4
    numpy==1.19.2
    opencv-python==4.4.0.42
    scipy==1.5.2
    matplotlib==3.3.2
    pandas==1.1.2
    notebook==6.1.4
    scikit-learn==0.23.2
    scikit-image==0.17.2
    albumentations==0.4.6
    Cython==0.29.21
    Pillow==7.2.0
    trafaret-config==2.0.2
    pyzmq==19.0.2
    librosa==0.8.0
    psutil==5.7.2
    dataclasses==0.7

    pytorch

    PyTorch, torchvision, Apex and torch based libraries.
    ghcr.io/osai-ai/dokai:20.09-pytorch

    dokai:20.09-pytorch

    additionally to dokai:20.09-base:

    torch==1.6.0
    torchvision==0.7.0
    pytorch-argus==0.1.2
    timm==0.2.1
    apex (master)

    tensor-stream

    Tensor Stream.
    ghcr.io/osai-ai/dokai:20.09-tensor-stream

    dokai:20.09-tensor-stream

    additionally to dokai:20.09-pytorch:

    tensor-stream==0.4.6 (dev)

    Source code(tar.gz)
    Source code(zip)
Owner
OSAI
OSAI is developing automatic systems that help to analyze a game and provide real-time game data with Computer Vision and AI in Sports.
OSAI
Source code for our paper "Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations"

Source code for our paper "Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations" this repository is maintained by bo

Yuhan Liu 24 Nov 29, 2022
This tutorial aims to learn the basics of deep learning by hands, and master the basics through combination of lectures and exercises

2021-Deep-learning This tutorial aims to learn the basics of deep learning by hands, and master the basics through combination of paper and exercises.

108 Feb 24, 2022
Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.

Subspace Adversarial Training Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However,

15 Sep 02, 2022
Official PyTorch implementation of "Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient".

Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient This repository is the official PyTorch implementation of "Edge Rewiring Go

Shanchao Yang 4 Dec 12, 2022
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

BasicVSR BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond Ported from https://github.com/xinntao/BasicSR Dependencie

Holy Wu 8 Jun 07, 2022
Create and implement a deep learning library from scratch.

In this project, we create and implement a deep learning library from scratch. Table of Contents Deep Leaning Library Table of Contents About The Proj

Rishabh Bali 22 Aug 23, 2022
Defending graph neural networks against adversarial attacks (NeurIPS 2020)

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Authors: Xiang Zhang ( Zitnik Lab @ Harvard 44 Dec 07, 2022

Spatial-Temporal Transformer for Dynamic Scene Graph Generation, ICCV2021

Spatial-Temporal Transformer for Dynamic Scene Graph Generation Pytorch Implementation of our paper Spatial-Temporal Transformer for Dynamic Scene Gra

Yuren Cong 119 Jan 01, 2023
Implementation of character based convolutional neural network

Character Based CNN This repo contains a PyTorch implementation of a character-level convolutional neural network for text classification. The model a

Ahmed BESBES 248 Nov 21, 2022
Pytorch implementation of our paper accepted by NeurIPS 2021 -- Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021) (Link) Overview Prerequisites Linu

Shaojie Li 34 Mar 31, 2022
Neural network for digit classification powered by cuda

cuda_nn_mnist Neural network library for digit classification powered by cuda Resources The library was built to work with MNIST dataset. python-mnist

Nikita Ardashev 1 Dec 20, 2021
A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

Evan 1.3k Jan 02, 2023
使用yolov5训练自己数据集(详细过程)并通过flask部署

使用yolov5训练自己的数据集(详细过程)并通过flask部署 依赖库 torch torchvision numpy opencv-python lxml tqdm flask pillow tensorboard matplotlib pycocotools Windows,请使用 pycoc

HB.com 19 Dec 28, 2022
Official implementation of "StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation" (SIGGRAPH 2021)

StyleCariGAN in PyTorch Official implementation of StyleCariGAN:Caricature Generation via StyleGAN Feature Map Modulation in PyTorch Requirements PyTo

PeterZhouSZ 49 Oct 31, 2022
MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research

MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research.The pipeline is based on nn-UNet an

QIMP team 30 Jan 01, 2023
Fortuitous Forgetting in Connectionist Networks

Fortuitous Forgetting in Connectionist Networks Introduction This repository includes reference code for the paper Fortuitous Forgetting in Connection

Hattie Zhou 14 Nov 26, 2022
Face recognize system

FRS Face_recognize_system This project contains my work that target on solving some problems of FRS: Face detection: Retinaface Face anti-spoofing: Fo

Tran Anh Tuan 4 Nov 18, 2021
Official repository of the paper "A Variational Approximation for Analyzing the Dynamics of Panel Data". Mixed Effect Neural ODE. UAI 2021.

Official repository of the paper (UAI 2021) "A Variational Approximation for Analyzing the Dynamics of Panel Data", Mixed Effect Neural ODE. Panel dat

Jurijs Nazarovs 7 Nov 26, 2022
Code for the paper “The Peril of Popular Deep Learning Uncertainty Estimation Methods”

Uncertainty Estimation Methods Code for the paper “The Peril of Popular Deep Learning Uncertainty Estimation Methods” Reference If you use this code,

EPFL Machine Learning and Optimization Laboratory 4 Apr 05, 2022
Implementation detail for paper "Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet"

Multi-level-colonoscopy-malignant-tissue-detection-with-adversarial-CAC-UNet Implementation detail for our paper "Multi-level colonoscopy malignant ti

CVSM Group - email: <a href=[email protected]"> 84 Nov 22, 2022