docker build -t opencv-play . docker run -v/home/user/.Xauthority:/home/user/.Xauthority -v/tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=unix$DISPLAY -u user -v `pwd`:/home/user -p8008:8008-t -i opencv-play jupyter notebook --ip='*'--no-browser --port=8008#Inside the container...
最后想提一点,这种部署方式除了部署时灵活方便,另外一个额外的好处就是使用jupyter时也方便,在jupyter使用时最常见的问题有两个,一个是需要经常使用set_env去设置CUDA_VISIBLE_DEVICES,另一个是用完了得把notebook关掉,不然jupyter进程会一直占用GPU。 ♚ 作者:丁果,对django、pyqt、opencv、tornado感兴趣。 GitHub:h...
RUN pip install numpy scipy matplotlib pillow RUN pip install opencv-python RUN pip install ipython==5.5.0 RUN pip install jupyter # Modify Jupter run arguments WORKDIR /docker-opencv-python RUN mkdir -p /root/.jupyter RUN cp -f jupyter_config.py /root/.jupyter/ RUN mkdir -p /root/volum...
ml-workspace 是一个机器学习工作环境,内置了 jupyter notebook, juputer-lab, vscode server, vnc , file browser, ssh 等工具,并已经安装好了各种机器学习需要的库如 pandas/numpy/matplotlib, scikit-learn, pytorch, tensorflow,可以说是机器学习、数据分析与挖掘开发一条龙服务。
我们需要具有基本ML库和JupyterNotebooks的图像来处理实验。我们将所有必需的库存储在app/requirements.txt文件中: numpy==1.19.5 pandas==1.2.2 scikit-learn==0.24.1 matplotlib==3.3.4 jupyter==1.0.0 opencv-python==4.5.1.48 tensorflow-cpu==2.4.01234567复制代码类型:[html] ...
Numpy Jupyter notebook on Docker¶Machine Learning and Data Analytics are becoming quite popular for main stream data processing. In this article we learn how to run numpy programs on Jupyter which is served from inside a docker container....
启动命令: 代码语言:javascript 复制 CMD["bash","-c","source /etc/bash.bashrc && jupyter lab --ip 0.0.0.0 --no-browser --allow-root"] PS:希望构建这样一个比较完整的深度学习环境之后,以后可以省点事,真的挺烦。
OpenCV 3.4.1 Jupyter Notebook Numpy, Scipy, Scikit Learn, Scikit Image, Pandas, Matplotlib, Pillow Caffe Java JDK PyCocoTools (MS COCO dev kit) TODO: GPU/CUDA 这个需要自己加上 waleedka/modern-deep-learning waleedka/modern-deep-learning, https://hub.docker.com/r/waleedka/modern-deep-learni...
本文分享在在Docker中运行Tensorflow和进行源码编译的方法和步骤,包括:编译、构建docker镜像、创建和运行Docker容器。部署完的容器可以通过Jupyter Notebook进行访问。 1、运行Tensorflow容器 快速运行 docker run --name tensorflow -it -p 9888:8888 gcr.io/tensorflow/tensorflow ...
目录下docker run -it --rm -v$HOME:/tf -p 8888:8888 tensorflow/tensorflow:nightly-py3-jupyter# 例三:jupyter# 将 container 的 8888 端口映射到本机 127.0.0.1 的 666 端口,# http://localhost:666/docker run -p 127.0.0.1:666:8888 jupyter/scipy-notebook:17aba6048f44...