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Изворен канал @pythonotes · Post #324 · 23 сеп.

Я нашел самый быстрый способ поднять свой независимый и бесплатный VPN Сразу оговорка, платить придётся только за хостинг. 1️⃣ Покупаем сервер где-то на просторах интернета. Конечно же сервер должен находиться за пределами страны. Например я закупился на https://eurohoster.org/ (не реклама). Проверяйте лимиты по трафику, в идеале - без ограничений. 2️⃣ Ставим docker sudo apt install docker.io Если удобней с DockerCompose то ставим и его sudo apt install docker-compose 3️⃣ Ставим WG-EASY Самый простой способ поднять сервис WireGuard c WebUI это проект wg-easy Код и документация здесь https://github.com/weejewel/wg-easy Запускаем контейнер: https://github.com/weejewel/wg-easy#2-run-wireguard-easy Для тех кто с DockerCompose, забираем файл здесь: https://gist.github.com/paulwinex/be87f79687b96786098ec8fa6a8e251c В обоих случаях потребуется поменять две переменные: WG_HOST - внешний статичный IP вашего сервера PASSWORD - придумайте пароль для WEB UI Остальные параметры указаны ниже на странице github https://github.com/weejewel/wg-easy#options 4️⃣ Ставим клиента Все доступные клиенты здесь https://www.wireguard.com/install/ Есть возможность добавить клиента в Network Manager для управления подключением через UI. Установка зависит от вашей системы, ищите мануалы в сети, их много. https://github.com/max-moser/network-manager-wireguard Скрипт установки для RasperryPi https://gist.github.com/paulwinex/c2c4090f19dbe8bd1253c5744f3f06e1 ЗЫ. Конечно же это не "самый простой" и далеко не единственный способ. А просто тот, который использую я сам. #offtop#linux

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djangoproject

@djangoproject · Post #557 · 24.01.2018 г., 05:45

http://go2.anaconda.com/eR0p1W01000NXe0lq4U2f0C Data Scientist-Tested, IT-Approved Operational Best Practices for Enterprise Data Science We know how hard you work to keep things running smoothly at your enterprise. But when it comes to enterprise data science, do you know how to give your data science team the tools they need while also keeping everything secure and stable? #Anaconda

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djangoproject

@djangoproject · Post #506 · 26.11.2017 г., 21:54

Taming the #Python Visualization Jungle It’s no secret that Python has a ton of plotting libraries—but which ones should you use? And how should you go about choosing them? Many people end up sticking with whatever library they first encountered, even if there are now much better tools for the job. Join #Anaconda Co-Founder and CTO Peter Wang and Senior Solutions Architect James Bednar for a live webinar on Wednesday, November 29, at 12pm CT, as they give you some key starting points and demonstrate how to solve a range of common problems. They’ll take a workflow-oriented approach toward exploring the large ecosystem of Python viz libraries, and show you how to: http://bit.ly/2zpATx7

djangoproject

@djangoproject · Post #445 · 17.09.2017 г., 01:01

https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/ It can be difficult to install a #Python#machine_learning environment on some platforms. Python itself must be installed first and then there are many packages to install, and it can be confusing for beginners. In this tutorial, you will discover how to set up a Python machine learning development environment using #Anaconda.

djangoproject

@djangoproject · Post #465 · 16.10.2017 г., 08:17

https://goo.gl/ucbkhT #Data_Science for #Big_Data with #Anaconda Enterprise Getting Python and R’s most popular data science libraries to work on a computational cluster can be a major challenge. And in a Big Data world, surmounting this challenge is key to leveraging data science within your organization to make smart, data-driven decisions.

djangoproject

@djangoproject · Post #526 · 19.12.2017 г., 20:13

https://goo.gl/XT2vGj Anaconda Enterprise 5 new capabilities include: Integrated #data_science experience for the entire organization Collaboration and reproducibility with JupyterLab and #Anaconda Project One-click data science #deployment Scalable architecture for on-premises and cloud deployments

djangoproject

@djangoproject · Post #513 · 30.11.2017 г., 22:00

#AI#Artificial_Intelligence #AJAX #aiohttp #Anaconda #AngularJS #API #Atom #AWS #asyncio (#Asynchronous) #audio #automated_testing #automation #atexit #BeeWare #Big_Data #bitcoin #blockchain #Bluemix #Brython #button #Celery #client #class #classmethod #concurrency #Coroutine #cron #CSS #curl #data_analysis #data_mining #data_processing #database #Deep_Learning#deep_learning #Debian #decorator #deploy #dict #dispatch #django #django_cms #Django_REST_Framework #dropdownbox #Docker #event #Firefox #Flask #form #functions #Generator #GeoDjango #git #Google #GPU #GUI #Gym #host #HTML #httplib #learn #Image_processing #intelligence #input #Instagram #IOT #iPython #Jupyter #lambda #learn #License #Linux #lists #machine_learning #Magenta #map #Matplotlib #Metaprogramming #Micro_services #Micropython #mind #monitoring #MongoDB #modules #Mozilla #Multipart #multi_touch_apps #multiprocessing #Nodes #NoSQL #numeric_computation #numerical #NumPy #network #neural_network #OAuth #object_serialization #OCR #overloading #package #parallel #pipeline #protocols #PostGIS #pyAudioAnalysis #pycon #Pyflakes #PyInstaller #PyPI #PyQt #PySide #PyTorch #pytest #python #Pyvideo_archives #Qt #Raspberry_Pi #React #Redis #random #request #Regular_Expressions (#re) #REST #RSS #satellite #scikit_learn #SciPy #scrapy #searching #selectbox #Selenium #serialization #server #sessions #single_responsibility_principle #socket #Spark #str #submit #task #telegram #template #TensorFlow #test #text_boxes #text #tuples #unicode #Universe #Unix #unit_test #urllib #upload #uWSGI #Web #WSGI