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20210414: added OpenCV 3.4.14 and 4.5.2
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‎Makefile‎

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@@ -3,7 +3,7 @@ SHELL := /bin/bash
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.PHONY: all build_all actual_build build_prep
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# Release to match data of Dockerfile and follow YYYYMMDD pattern
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CTO_RELEASE=20210211
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CTO_RELEASE=20210414
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# Maximize build speed
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CTO_NUMPROC := $(shell nproc --all)
@@ -45,8 +45,8 @@ DNN_ARCH_CUDA10=6.0,6.1,7.0,7.5
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DNN_ARCH_CUDA11=6.0,6.1,7.0,7.5,8.0,8.6
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# According to https://opencv.org/releases/
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STABLE_OPENCV3=3.4.13
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STABLE_OPENCV4=4.5.1
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STABLE_OPENCV3=3.4.14
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STABLE_OPENCV4=4.5.2
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# TF2 at minimum CUDA 10.1
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# TF2 is not going to support CUDA11 until 2.4.0, so not building those yet
@@ -78,25 +78,30 @@ TF1_NUMPY='numpy<1.19.0'
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TF2_NUMPY='numpy<1.20.0'
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# PyTorch (from pip) using instructions from https://pytorch.org/
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PT_CPU="torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html"
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# and https://pytorch.org/get-started/previous-versions/
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# 1.7.1 last version supported by 9.2
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PT_CPU="torch==1.8.0+cpu torchvision==0.9.0+cpu torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html"
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PT_CUDA9="torch==1.7.1+cu92 torchvision==0.8.2+cu92 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html"
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PT_CUDA10="torch torchvision"
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PT_CUDA11="torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio===0.7.2 -f https://download.pytorch.org/whl/torch_stable.html"
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PT_CUDA10="torch torchvision torchaudio"
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PT_CUDA11="torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html"
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##########
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##### CUDA _ Tensorflow _ OpenCV
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CTO_BUILDALL =cuda_tensorflow_opencv-${STABLE_CUDA9}_${STABLE_TF1}_${STABLE_OPENCV3}
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CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA9}_${STABLE_TF1}_${STABLE_OPENCV4}
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CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA9}_${STABLE_TF2}_${STABLE_OPENCV3}
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CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA9}_${STABLE_TF2}_${STABLE_OPENCV4}
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CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA10}_${STABLE_TF1}_${STABLE_OPENCV3}
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CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA10}_${STABLE_TF1}_${STABLE_OPENCV4}
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CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA10}_${STABLE_TF2}_${STABLE_OPENCV3}
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CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA10}_${STABLE_TF2}_${STABLE_OPENCV4}
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# Ubuntu 20.04 comes with Python 3.8, so unable to build TF1 support
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#CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA11}_${STABLE_TF1}_${STABLE_OPENCV3}
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#CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA11}_${STABLE_TF1}_${STABLE_OPENCV4}
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CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA11}_${STABLE_TF2}_${STABLE_OPENCV3}
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CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA11}_${STABLE_TF2}_${STABLE_OPENCV4}
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## 20210414: No real interest from users in the cuda_ version of the built containers, not generating those anymore
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#CTO_BUILDALL =cuda_tensorflow_opencv-${STABLE_CUDA9}_${STABLE_TF1}_${STABLE_OPENCV3}
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#CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA9}_${STABLE_TF1}_${STABLE_OPENCV4}
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#CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA9}_${STABLE_TF2}_${STABLE_OPENCV3}
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#CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA9}_${STABLE_TF2}_${STABLE_OPENCV4}
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#CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA10}_${STABLE_TF1}_${STABLE_OPENCV3}
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#CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA10}_${STABLE_TF1}_${STABLE_OPENCV4}
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#CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA10}_${STABLE_TF2}_${STABLE_OPENCV3}
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#CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA10}_${STABLE_TF2}_${STABLE_OPENCV4}
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## Ubuntu 20.04 comes with Python 3.8, so unable to build TF1 support
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##CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA11}_${STABLE_TF1}_${STABLE_OPENCV3}
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##CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA11}_${STABLE_TF1}_${STABLE_OPENCV4}
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#CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA11}_${STABLE_TF2}_${STABLE_OPENCV3}
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#CTO_BUILDALL+=cuda_tensorflow_opencv-${STABLE_CUDA11}_${STABLE_TF2}_${STABLE_OPENCV4}
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##### CuDNN _ Tensorflow _ OpenCV
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DTO_BUILDALL =cudnn_tensorflow_opencv-${STABLE_CUDA9}_${STABLE_TF1}_${STABLE_OPENCV3}
@@ -126,7 +131,7 @@ all:
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@echo ""
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@echo "** Available Docker images to be built (make targets):"
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@echo " tensorflow_opencv: "; echo -n " "; echo ${TO_BUILDALL} | sed -e 's/ /\n /g'
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@echo " cuda_tensorflow_opencv: "; echo -n " "; echo ${CTO_BUILDALL} | sed -e 's/ /\n /g'
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# @echo " cuda_tensorflow_opencv: "; echo -n " "; echo ${CTO_BUILDALL} | sed -e 's/ /\n /g'
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@echo " cudnn_tensorflow_opencv: "; echo -n " "; echo ${DTO_BUILDALL} | sed -e 's/ /\n /g'
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@echo ""
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@echo "** To build all, use: make build_all"
@@ -136,23 +141,23 @@ all:
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## special command to build all targets
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build_all:
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@make ${TO_BUILDALL}
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@make ${CTO_BUILDALL}
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# @make ${CTO_BUILDALL}
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@make ${DTO_BUILDALL}
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tensorflow_opencv:
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@make ${TO_BUILDALL}
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cuda_tensorflow_opencv:
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@make ${CTO_BUILDALL}
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#cuda_tensorflow_opencv:
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# @make ${CTO_BUILDALL}
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cudnn_tensorflow_opencv:
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@make ${DTO_BUILDALL}
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${TO_BUILDALL}:
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@CUDX="" CUDX_COMP="" BTARG="$@" make build_prep
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${CTO_BUILDALL}:
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@CUDX="cuda" CUDX_COMP="" BTARG="$@" make build_prep
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#${CTO_BUILDALL}:
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# @CUDX="cuda" CUDX_COMP="" BTARG="$@" make build_prep
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${DTO_BUILDALL}:
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@CUDX="cudnn" CUDX_COMP="-DWITH_CUDNN=ON -DOPENCV_DNN_CUDA=ON" BTARG="$@" make build_prep

‎README.md‎

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# DockerFile with Nvidia GPU support for TensorFlow and OpenCV
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Revision: 20210211
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Revision: 20210414
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<!-- vscode-markdown-toc -->
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* 1. [About](#About)
@@ -41,17 +41,13 @@ Version history:
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- `20200803`: added PyTorch. Removal of `cudnn_` version for CUDA 9.2 with TensorFlow 2.3.0 (minimum needed was 10.1)
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- `20201204`: added support for Python 3.7 for TensorFlow 1 builds and Python 3.8 for Tensorflow 2 builds (makes use of the [`deadsnakes/ppa`](https://launchpad.net/~deadsnakes/+archive/ubuntu/ppa) and changes the default `python3`). Warning: only `pip3` installed packages will work, not `apt-get` installed ones (Python 3.6 is still the default for Ubuntu 18.04)
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- `20210211`: added support for CUDA 11 (using Nvidia's Ubuntu 20.04 based container). CUDA 9.2 and 10.2 versions are still created from Nvidia's Ubuntu 18.04 based containers.
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- `20210414`: updated for OpenCV 3.4.14 and 4.5.2, removed `cuda_` from build target (and built containers)
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`tensorflow_opencv`:
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- Builds containers with TensorFlow and OpenCV. Also install, Jupyter, Keras, numpy, pandas, PyTorch and X11 support.
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- Can be used on systems without a Nvidia GPU, and the `runDocker.sh` script will setup proper X11 passthrough
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- for MacOS X11 passthrough, install the latest XQuartz server and activate the `Security -> Allow connections from network clients` (must logout for it to take effect)
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`cuda_tensorflow_opencv`:
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- Builds an Nvidia GPU optimized version of OpenCV. Also install, Jupyter, Keras, numpy, pandas, PyTorch and X11 support.
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- Note that TensorFlow need CUDNN for GPU support.
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- Requires a Linux system with nvidia-docker (v2) and the Nvidia drivers installed to run. See https://github.com/NVIDIA/nvidia-docker for setup details
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`cudnn_tensorflow_opencv`:
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- Builds an Nvidia GPU optimized version of TensorFlow and OpenCV. Also install, Jupyter, Keras, numpy, pandas, PyTorch and X11 support.
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- As of the 20200615 version, both OpenCV and TensorFlow are compiled within the container.
@@ -61,7 +57,7 @@ Version history:
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`jetsonnano-cuda_tensorflow_opencv` (see the `JetsonNano` directory):
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- Builds a Nvidia Jetson Nano `cuda_tensorflow_opencv` container image based on Nvidia's provided `l4t-base` container and adapted from the `Makefile` and `Dockerfile` used for the other builds.
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**Docker Images built from this repository are publicly available at https://hub.docker.com/r/datamachines/tensorflow_opencv / https://hub.docker.com/r/datamachines/cuda_tensorflow_opencv / https://hub.docker.com/r/datamachines/cudnn_tensorflow_opencv / https://hub.docker.com/r/datamachines/jetsonnano-cuda_tensorflow_opencv .**
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**Docker Images built from this repository are publicly available at https://hub.docker.com/r/datamachines/tensorflow_opencv / https://hub.docker.com/r/datamachines/cudnn_tensorflow_opencv / https://hub.docker.com/r/datamachines/jetsonnano-cuda_tensorflow_opencv .**
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The [Builds-DockerHub.md](https://github.com/datamachines/cuda_tensorflow_opencv/blob/master/Builds-DockerHub.md) file is a quick way of seeing the list of pre-built container images. When available, a "BuiidInfo" will give the end user a deeper look of the capabilities of said container and installed version. In particular the compiled GPU architecture (see https://en.wikipedia.org/wiki/CUDA#GPUs_supported ).
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This is useful for you to decide if you would benefit from re-compiling some container(s) for your specific hardware.
@@ -84,7 +80,6 @@ The tag for any image built will contain the `datamachines/` organization additi
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Use the provided `Makefile` by running `make` to get a list of targets to build:
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- `make build_all` will build all container images
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- `make tensorflow_opencv` to build all the `tensorflow_opencv` container images
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- `make cuda_tensorflow_opencv` will build all the `cuda_tensorflow_opencv` container images
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- `make cudnn_tensorflow_opencv` will build all the `cudnn_tensorflow_opencv` container images
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- use a direct tag to build a specific version (from the list provided by the call to `make`); for example `make cudnn_tensorflow_opencv-10.2_2.2.0_4.3.0`, will build the `datamachines/cudnn_tensorflow_opencv:10.2_2.2.0_4.3.0-20200615` container image (if such a built is available, see the `Docker Image tag ending` and the list of `Available Docker images to be built` for accurate values).
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