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
Official Pytorch implementation of paper "Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images"

Reverse_Engineering_GMs Official Pytorch implementation of paper "Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Gener

100 Dec 18, 2022
Breast cancer is been classified into benign tumour and malignant tumour.

Breast cancer is been classified into benign tumour and malignant tumour. Logistic regression is applied in this model.

1 Feb 04, 2022
GPU Accelerated Non-rigid ICP for surface registration

GPU Accelerated Non-rigid ICP for surface registration Introduction Preivous Non-rigid ICP algorithm is usually implemented on CPU, and needs to solve

Haozhe Wu 144 Jan 04, 2023
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels

Simple and Robust Loss Design for Multi-Label Learning with Missing Labels Official PyTorch Implementation of the paper Simple and Robust Loss Design

Xinyu Huang 28 Oct 27, 2022
Go from graph data to a secure and interactive visual graph app in 15 minutes. Batteries-included self-hosting of graph data apps with Streamlit, Graphistry, RAPIDS, and more!

✔️ Linux ✔️ OS X ❌ Windows (#39) Welcome to graph-app-kit Turn your graph data into a secure and interactive visual graph app in 15 minutes! Why This

Graphistry 107 Jan 02, 2023
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

lokaman chendekar 35 Nov 25, 2022
Torch-based tool for quantizing high-dimensional vectors using additive codebooks

Trainable multi-codebook quantization This repository implements a utility for use with PyTorch, and ideally GPUs, for training an efficient quantizer

Daniel Povey 41 Jan 07, 2023
A PyTorch Implementation of the Luna: Linear Unified Nested Attention

Unofficial PyTorch implementation of Luna: Linear Unified Nested Attention The quadratic computational and memory complexities of the Transformer’s at

Soohwan Kim 32 Nov 07, 2022
StyleGAN - Official TensorFlow Implementation

StyleGAN — Official TensorFlow Implementation Picture: These people are not real – they were produced by our generator that allows control over differ

NVIDIA Research Projects 13.1k Jan 09, 2023
Code image classification of MNIST dataset using different architectures: simple linear NN, autoencoder, and highway network

Deep Learning for image classification pip install -r http://webia.lip6.fr/~baskiotisn/requirements-amal.txt Train an autoencoder python3 train_auto

Hector Kohler 0 Mar 30, 2022
Code for the ICCV2021 paper "Personalized Image Semantic Segmentation"

PSS: Personalized Image Semantic Segmentation Paper PSS: Personalized Image Semantic Segmentation Yu Zhang, Chang-Bin Zhang, Peng-Tao Jiang, Ming-Ming

张宇 15 Jul 09, 2022
Deep Learning Specialization by Andrew Ng, deeplearning.ai.

Deep Learning Specialization on Coursera Master Deep Learning, and Break into AI This is my personal projects for the course. The course covers deep l

Engen 1.5k Jan 07, 2023
Quasi-Dense Similarity Learning for Multiple Object Tracking, CVPR 2021 (Oral)

Quasi-Dense Tracking This is the offical implementation of paper Quasi-Dense Similarity Learning for Multiple Object Tracking. We present a trailer th

ETH VIS Research Group 327 Dec 27, 2022
This is the repo for the paper "Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement".

Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement This is the repository for the paper "Improving the Accuracy-Memory Trad

3 Dec 29, 2022
Keras implementation of the GNM model in paper ’Graph-Based Semi-Supervised Learning with Nonignorable Nonresponses‘

Graph-based joint model with Nonignorable Missingness (GNM) This is a Keras implementation of the GNM model in paper ’Graph-Based Semi-Supervised Lear

Fan Zhou 2 Apr 17, 2022
JFB: Jacobian-Free Backpropagation for Implicit Models

JFB: Jacobian-Free Backpropagation for Implicit Models

Typal Research 28 Dec 11, 2022
Orange Chicken: Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation

Orange Chicken: Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation This repository contains code and data f

Zoey Liu 0 Jan 07, 2022
Code to reproduce the results for Compositional Attention

Compositional-Attention This repository contains the official implementation for the paper Compositional Attention: Disentangling Search and Retrieval

Sarthak Mittal 58 Nov 30, 2022
The deployment framework aims to provide a simple, lightweight, fast integrated, pipelined deployment framework that ensures reliability, high concurrency and scalability of services.

savior是一个能够进行快速集成算法模块并支持高性能部署的轻量开发框架。能够帮助将团队进行快速想法验证(PoC),避免重复的去github上找模型然后复现模型;能够帮助团队将功能进行流程拆解,很方便的提高分布式执行效率;能够有效减少代码冗余,减少不必要负担。

Tao Luo 125 Dec 22, 2022
Official implementation of "Motif-based Graph Self-Supervised Learning forMolecular Property Prediction"

Motif-based Graph Self-Supervised Learning for Molecular Property Prediction Official Pytorch implementation of NeurIPS'21 paper "Motif-based Graph Se

zaixi 71 Dec 20, 2022