JupyterHub

I'm trying to get a reverse proxy (nginx) to work with juypterhub. I want the service available at myurl.com/jhub/. I placed the following in the config: location /jhub/ { proxy_pass http://127.0.0.1:8000/; proxy_set_header X-Real-IP $remote_addr; proxy_set_header Host $host; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-NginX-Proxy true; #WebSocket support proxy_http_version 1.1; proxy_set_header Upgrade $http_upgrade; proxy_set_header Connection "upgrade"; proxy_read_timeout 86400; } However, when I try the url after restarting the proxy jupyterhub is not

提取建筑-iObjects Python with JupyterHub for K8s 镜像构建-iObjects Python with JupyterHub for K8s import os import time from iobjectspy import open_datasource from iobjectspy.ai.recognition import extract_region --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) <ipython-input-1-a1944dbfe13b> in <module>() 3 4 from iobjectspy import open_datasource ----> 5 from iobjectspy.ai.recognition import extract_region /opt/conda/lib/python3.6/site-packages/iobjectspy/ai/recognition.pyc in <module>() ModuleNotFoundError: No module

If I want to download all of the files and folder hierarchy from Jupyter Notebook as shown in the picture, do you know if there is anyway to do that by simple click other than go to every single file in every folder to open the file and click download hundreds of times? Note: This Jupyter Notebook is created by the online course teacher, so it's not opened from my local Acaconda app but the online course webpage instead. Downloading is for future memory refreshing whenever needed. Jason import os import tarfile def recursive_files(dir_name='.', ignore=None): for dir_name,subdirs,files in os

10_栅格数据裁剪-iObjects Python with JupyterHub for K8s 使用Python对影像数据集进行裁剪操作，并输出为tif格式的文件，保存到指定的目录。 更多参考： 镜像构建-iObjects Python with JupyterHub for K8s 导入引用库： from iobjectspy import (clip_raster, Rectangle) import os import sys 设置输出路径： # 设置示例数据路径 example_data_dir = '/home/jovyan/data/smdata/' # 设置结果输出路径 out_dir = os.path.join(example_data_dir, 'out') 数据操作函数： if not os.path.exists(out_dir): os.makedirs(out_dir) def progress_func(step_event): sys.stdout.write('%s,%s %d %%\n' % (step_event.title, step_event.message, step_event.percent)) def clip_image_test(): """裁剪影像数据集""" clip_region = Rectangle(875.5,

数据库记录遍历-iObjects Python with JupyterHub for K8s 使用Python对iObjects的空间数据库进行遍历，可以进一步加入处理操作。 更多参考： 镜像构建-iObjects Python with JupyterHub for K8s from iobjectspy import (density_based_clustering, Unit) import os import sys # 设置示例数据路径 #example_data_dir = '' example_data_dir = '/home/jovyan/data/smdata/' # 设置结果输出路径 out_dir = os.path.join(example_data_dir, 'out') if not os.path.exists(out_dir): os.makedirs(out_dir) def progress_func(step_event): sys.stdout.write('%s,%s \n' % (step_event.title, step_event.message)) def dbscan_test(): """ 使用成都市某日17时到20时的出租车上车点数据taxi_up_webMercator，进行DBSCAN密度聚类

We are currently building a jupyterhub environment in a docker container and struggling with shared folders. Our goal is to set up a shared folder wich is writable for all users. In each user's home folder there is a symbolic link ~/shared to /opt/shared . The target folder has the permissions 777 and is owned by the group jupyter . Each user is member of the group jupyter . However, if one user creates a file in the shared folder logged in with his account into the web app, the permissions are set to 644 . Therefore, no other user can edit the file. I've tried to set the umask to 000 in the

Well I figure eight hours is enough time trying to fix this on my own, so I'll just ask folks: I am running tensorflow-gpu 1.1.0 just fine in my virtual environment named 'tensorflow' outside of jupyterhub and Jupyter notebook. That is, I can import tensorflow and run scripts with it using the gpu. When I'm inside my tensorflow virtualenv and using jupyterhub, Jupyter can not seem to 'see' tensorflow. I get the following error: ImportError: libcublas.so.8.0: cannot open shared object file: No such file or directory 1) This is a common seen error message indicating tensorflow install problems,

注意事项:需要注意的是, sqlite安装完成之后需要进行重新编译python, 需要注意的是，sqlite安装完成之后需要进行重新编译python 1.下载并源码编译python3 #wget https://www.python.org/ftp/python/3.4.5/Python-3.4.5.tar.xz # xz -d Python-3.4.5.tar.xz # tar xf Python-3.4.5.tar -C /usr/local/src/ # cd /usr/local/src/Python-3.4.5/ # ./configure –prefix=/usr/local/python34 # make -j8 && make install 注意事项: #由于pip和ipython需要ssl支持： yum install openssl openssl-devel -y 此时就可以在/usr/local/python3.4/bin/下面看到编译生成的各个可执行文件了，不过比较好的是，python3以后就默认支持了easy_install,pip等工具了！ 2.安装ipython以及notebook等相关插件 #export PATH=$PATH:/usr/local/python34/bin/ #pip3.4 install ipython #pip3.4