Saravanan666/pypi-org huggingface dataset:
Package: dockerized
dockerized 🏗❤️Easily Docker-ize your build/development environment and seamlessly run commands inside it.dockerizedis a tool for seamlessly executing commands in a container. It takes care of the details so you can run a command in a container as if it was running on your machine - just prepend any command withdockerized execto have it run in the container.DocumentationSeehttps://benzaita.github.io/dockerized-cli/index.htmlGetting StartedInstalldockerized:$pipinstalldockerizedInitialize your environment:$dockerizedinit
$echoFROMpython:3.9>.dockerized/Dockerfile.dockerizedor use an example:$dockerizedinit--fromhttps://github.com/benzaita/dockerized-example-python.gitThen run a command inside that environment:$dockerizedexecpython--version
...
Python3.9.0Or drop into an interactive shell inside the environment:$dockerizedshell# python --versionPython3.9.0
Package: docker-jobber
Docker JobberDocker Jobber (jobber) is a command line interface (CLI) application for managing machine learning workflows using Docker.Please see thehomepagefor documentation and examplesInstallationYou'll needDockerof course, then install thejobbercommand using pip (Docker Jobber requires Python3):pip install docker-jobberVersion History0.3.9 (2019-04-09)CredentialsMerge all config files on directory pathFixed: successful build pushes to defined registriesDefault registry configuration name changed to 'default-registry'0.3.8 (2019-04-09)Really fix PyYAML security hole0.3.7 (2019-04-09)Fix PyYAML security hole0.3.6 (2019-04-09)Initial release
Package: dockerlab
DockerLab用Docker搭建你的工作区。Read this inEnglish.环境要求Docker特点易于使用：只需几个命令即可构建和启动 Docker 容器。可定制：可以自定义要安装在容器中的 Docker 镜像和 Python 包。可重现：可以在不同的计算机上构建相同的 Docker 镜像。告别“这在我的机器上可以运行”的问题。方便：Dockerlab提供了一些方便的设置，包括工具和功能：工具：oh-my-zsh，docker within docker，tmux，lightvim解决权限问题：与主机相同的 uid 和 gid。将 $HOME 目录从容器反向挂载到主机轻松共享~/.ssh和~/.gitconfig即使删除容器，配置文件也不会丢失，例如：zsh历史记录、配置文件、模型检查点等解决端口映射问题：与主机共享网络预构建镜像：Dockerlab提供了几个基本的预构建镜像，可快速设置工作区环境。快速开始1. 安装dockerlab用pip安装dockerlab：pipinstalldockerlab或者从源码安装最新版本：pipinstallgit+https://github.com/hughplay/dockerlab.git2. 设置dockerlab项目创建一个新的dockerlab项目：dockerlabnew<project_name>或者为现有项目初始化一个dockerlab环境：dockerlabinit.这将设置一些文件和目录，其结构如下：.
├──docker/
│├──Dockerfile
│└──misc/
├──docker-compose.yml
├──docker.py
└──.gitignore3. 自定义工作环境通常情况下，只需要修改docker/Dockerfile文件来自定义自己的工作环境。可能关心的文件:docker/Dockerfile: 用于构建docker镜像的dockerfile。docker-compose.yml: 用于构建docker容器的docker-compose文件。docker.py: 用于构建和启动容器的python脚本。dockerlab准备了几个模板和预构建的docker镜像。可以通过运行以下命令将默认的docker/Dockerfile替换为需要的模板:dockerlabuse<template_name>可用的模板可以通过dockerlab ls列出，每个模板的详细信息可以在dockerlab/templates找到。默认情况下，生成的docker/Dockerfile将使用预构建的docker镜像（如果存在）。也可以通过运行以下命令获取完整的dockerfile：dockerlabuse<template_name>--full4. 构建和启动容器pythondocker.pystartd当你第一次执行上述命令时，它将要求你输入与容器相关的信息，并将它们存储在.env中。提示和输出示例如下：# promptsGiveaprojectname[dockerlab]:dockerlab
Coderoottobemountedat/project[.]:
Dataroottobemountedat/data[data]:`/home/hongxin/code/dockerlab/data`doesnotexistinyourmachine.Create?[yes]:
Logroottobemountedat/log[log]:`/home/hongxin/code/dockerlab/log`doesnotexistinyourmachine.Create?[yes]:
directorytobemountedtohongxin[./docker/misc/container_home]:`/home/hongxin/code/dockerlab/container_home`doesnotexistinyourmachine.Create?[yes]:# outputYoursetting(.env):UID:1000GID:1000USER_NAME:hongxinPROJECT:dockerlabCODE_ROOT:.DATA_ROOT:/home/hongxin/code/dockerlab/dataLOG_ROOT:/home/hongxin/code/dockerlab/logCONTAINER_HOME:/home/hongxin/code/dockerlab/container_homeCOMPOSE_PROJECT_NAME:dockerlab_hongxin5. 进入容器开启旅程pythondocker.py本地Build镜像dockerbuild\-fdockerlab/templates/workspace_protein/Dockerfile\-tdeepbase/dockerlab:workspace_protein\.许可证Dockerlab使用MIT许可证。
Package: docker-lets
letsA modular framework for arbitrary action,letsenables tasks of varying complexity to be chained together with a simple and consistent interface.Each module accepts input and options, executes some functionality, and returns some output.[input] | lets <module> [options]In this manner, modules can bechainedtogether, allowing completely unrelated functionality to work together seamlessly. Modules can be as simple as base64 encoding or reasonably complex with docker integration.To learn more, take a look at thedocsor explore themodules.Requirementsdockerpython >= 3.5python3-pipInstallletsis built on top ofdocker, so make sure it is installed. You may need to log out and back in for this to take effect.$ curl -fsSL https://get.docker.com | sudo sh
$ sudo usermod -aG docker $USERInstalllets$ pip3 install docker-letsInstallletsextensions$ git clone https://github.com/johneiser/lets_pentest
$ pip3 install ./lets_pentestActivateletstab-completionfor bash.$ lets support/autocomplete bash >> ~/.profile
$ source ~/.profile
$ lets sample/my[TAB][TAB]
sample/mydockermodule   sample/mymoduleUsageQuickstart:$ echo SGVsbG8gd29ybGQhCg== | lets decode/base64
Hello world!For further details, refer to thedocsandmodules.License
Package: docker_links
Parse Docker links into values useful for Django settings.Free software: BSD licenseDocumentation:https://docker_links.readthedocs.org.UsageDocker links provide environment variables for processes that look like this:DB_PORT_5432_TCP=tcp://172.17.0.5:5432That’s nice, but not quite right for, say, use in a Django settings file.So this provides a function that reads an environment variable, and updates it
with overrides you specify:BROKER_URL = docker_links.docker_link('AMQP_PORT_6379_TCP', overrides={
    "scheme": "redis",
    "path": "/0",
})History0.1.0 (2014-10-26)First release on PyPI.
Package: docker-links-python
## Docker Links Environment Parser (Python version)This is a Python rewrite of [docker-links](https://github.com/brotchie/docker-links) for nodejs by @brotchie.[Docker](http://www.docker.io/) has a feature where you can [link containers together by name](http://docs.docker.io/en/latest/use/working_with_links_names/). For example, you start a redis-server in a docker container and expose the default redis port 6379:$ docker run -p 6379 -d -name redis vagrant/redis-serverYou then start another containiner running a Django web service that needs to access this redis server:$ docker run --link redis:db -d vagrant/djangoDocker will internally hook up these the two containers and pass host and port information to the Django web service via environment variables:DB_NAME=/romantic_lumiere/dbDB_PORT=tcp://172.17.0.5:6379DB_PORT_6379_TCP=tcp://172.17.0.5:6379DB_PORT_6379_TCP_ADDR=172.17.0.5DB_PORT_6379_TCP_PORT=6379DB_PORT_6379_TCP_PROTO=tcpThis library provides a helper `parse_links` that will parse these environment variables into easily navigable Python dictionaries.### InstallTODO...### Example UsageConsider a container that accesses three external services on two other containers. The first container exposes redis on port 6379 and postgres on 6500. The second container exposes redis on port 6379.DB_NAME=/romantic_lumiere/dbDB_PORT=tcp://172.17.0.5:6379DB_PORT_6379_TCP=tcp://172.17.0.5:6379DB_PORT_6379_TCP_ADDR=172.17.0.5DB_PORT_6379_TCP_PORT=6379DB_PORT_6379_TCP_PROTO=tcpDB_PORT_6500_TCP=tcp://172.17.0.5:6500DB_PORT_6500_TCP_ADDR=172.17.0.5DB_PORT_6500_TCP_PORT=6500DB_PORT_6500_TCP_PROTO=tcpDB_REDIS_NAME=/romantic_lumiere/db_redisDB_REDIS_PORT=tcp://172.17.0.2:6379DB_REDIS_PORT_6379_TCP=tcp://172.17.0.2:6379DB_REDIS_PORT_6379_TCP_ADDR=172.17.0.2DB_REDIS_PORT_6379_TCP_PORT=6379DB_REDIS_PORT_6379_TCP_PROTO=tcpParse with `docker-links`:```python>> import docker_links>> import os>> links = docker_links.parse_links(os.environ)```Links is then the following object:```python>> print json.dumps(links, indent=4){"db": {"name": "romantic_lumiere/db","proto": "tcp","url": "tcp://172.17.0.5:6379","hostname": "172.17.0.5","tcp": {"6379": {"url": "tcp://172.17.0.5:6379","hostname": "172.17.0.5"},"6500": {"url": "tcp://172.17.0.5:6500","hostname": "172.17.0.5"}},"port": 6379},"db_redis": {"name": "romantic_lumiere/db_redis","proto": "tcp","url": "tcp://172.17.0.2:6379","hostname": "172.17.0.2","tcp": {"6379": {"url": "tcp://172.17.0.2:6379","hostname": "172.17.0.2"}},"port": 6379}}```
Package: docker-lite-python
docker-lite-pythonA simple, Python-based Docker interface built on top of theDocker SDK for Python.
Intended to programmatically spin up, pass communications to, and ultimately tear down single Docker containers.
Requires a local instance of Docker.Installation|Quick Start|Reference|ExamplesInstallation:pip install docker-lite-pythonQuick Start:$ sudo python3
>>>from docker_light_python import DockerLite
>>>dl = DockerLite()start an Alpine container and keep it running>>>dl.run_container('alpine:latest', 'alpine-container', 'sleep infinity')exec into the running container>>>dl.exec_into_running_container('alpine-container', 'echo "Hello World!"')
ExecResult(exit_code=0, output=b'Hello World!\n')tear down that container!>>>dl.kill_container('alpine-container')
0Reference:MethodsArgsOverviewbuild_image()*path_to_dir*: string: The path to the build directory.Build a Docker image from a local Dockerfile.*resulting_image_name*: stringEnforces best practice of explicitly naming images.list_containers()*all*: bool: default=FalseList running containers by default.run_container()*image_name*: stringRun a Docker container, optionally with a command.*resulting_container_name*: stringEnforces best practice of explicitly naming containers.*command*: string: The command to run.Optional.get_container_by_name()*existing_container_name*: stringGet a Docker container by name.exec_into_running_container()*existing_container_name*: stringRun a command in an active container.*command*: string: The command to execute in the running Docker container.list_images()NoneList all images in the local Docker instance.remove_unused_images()NoneEquivalent of docker images pruneremove_all_images()NoneForce removal of all images. Purge system.kill_container()*existing_container_name*: stringShut down and delete a container.NOTE: kill_container() technically just stops the containers, as they are self-removing.Examples:from docker_lite import DockerLite

dl = DockerLite()build image, 'my-image,' from a Dockerfile in the local directorydl.build_image('./', 'my-image')list all containers. Default is to list running containerscontainers = dl.list_containers(all=True)run a Docker container called 'my-container' based on a Docker image called 'my-image'my_container = dl.run_container('my-image', 'my-container')run a terminal command in a running Docker container called 'my-container'. Be creativeoutput = dl.exec_into_running_container('my-container', 'echo "Hello World!"')get a container called 'my-container' by its unique namecontainer = dl.get_container('my-container')Python Docker SDK documentation on container objects herekill a container called 'my-container' by its unique namedl.kill_container('my-container')
Package: docker-load-graph
docker-load-graphHave you ever been working with a docker stack, and been interested in how your containers fair over time? Me too.This project exists to solve that problem.What does it do?Very simply this project usesplotextto display the current status
of keydockercontainer metrics in real time on the command line.How do I use it?I've uploaded this project to the PYPA repository for pip to access. You can simply install this usingpython3.7>withpip install docker-load-graphTo execute this project run:docker-load-graphIt is just that simple.
Package: dockerma
DockerMA facilitates building multi-arch containers with minimal fuss.What does it do?dockermaaims to be a drop-in replacement fordocker buildanddocker pushto handle building what is often
referred to as “multi-arch” images. A “multi-arch” image is actually a manifest, listing which actual image to use for
a given architecture. The way to create these are still somewhat cumbersome, and considered experimental, especially if
you are using a base image that is already “multi-arch”.dockerma buildworks by reading yourDockerfileand looking for a specially formatted comment that selects which
architectures you want to build. It will then create a manipulated version of yourDockerfile, to inject some tools
for cross-building, and then build architecture specific images. If you have selectedamd64andarm, and buildmyapp:v1.0, the build will producemyapp:v1.0-amd64andmyapp:v1.0-arm.dockerma pushwill take the architecture specific images that was built previously, and push them to the remote
registry. It will then create a manifest, listing each of the images for the wanted architectures, and push the manifest
under the requested tag. This will create a “multi-arch” image.Supported architectures is based on what is available in the tooling used. The cross-building tools are fromhttps://github.com/monsonnl/qemu-wrap-build-files, and at this writing only supportsamd64,armandarm64.
If you wantdockermato support other architectures, you need to persuadeqemu-wrap-build-filesto support it
first.How do I use it?Sincedockermaaims to be a drop-in replacement, using it should be fairly simple.The first step is to add thedockermacomment to yourDockerfile:# dockerma archs:arm,amd64,arm64:When you previously useddocker build-tmyapp:latest-tmyapp:v1.0 .to buildlatestandv1.0tags for your
current architecture, you will now simply replacedockerwithdockerma:dockerma build -t myapp:latest -t myapp:v1.0 .Similary, where you useddocker push-tmyapp:latest, you now usedockerma:dockerma push myapp:latestWhat are the requirements?You need to enable experimental features indocker. This can be done by settingDOCKER_CLI_EXPERIMENTALtoenabled, or changing your docker configuration. Consult the docker documentation for details.Your build host must support runningqemu-<arch>-static.Your base images needs to already support multi-arch (and in particular, the architectures you want to support).
Many official images already do.Are there any downsides to this approach?All images will have some additional files from the cross-build tooling, located in/cross-build.Releasing a new versionTag the new version:git tag-av1.2.3Push the tag to Github:git push origin v1.2.3The new version is published to PyPI and GithubChanges since last version9cf5dbf: Use github token with push rights
Package: docker-machine-py
# docker-machine-py[![Build Status](https://travis-ci.org/jgrowl/docker-machine-py.svg?branch=master)](https://travis-ci.org/jgrowl/docker-machine-py)A Python library for the Docker Machine CLI. It does everything the docker-machine command does, but from within Python– create machines, check status, get ip, run ssh commands, regenerate certificates, etc.## InstallationThe latest stable version is always available on PyPi.pip install docker-py## Documentation[![Documentation Status](https://readthedocs.org/projects/docker-machine-py/badge/?version=latest)](https://readthedocs.org/projects/docker-machine-py/?badge=latest)[Read the full documentation here.](http://docker-machine-py.readthedocs.org/en/latest/)## Usageimport docker_machinemachine = Machine('my_digitalocean_machine')if not machine.exists():machine.create('digitalocean', access_token='my_digitalocean_access_token')status = machine.status()machine.rm(force=True)### Customizing the clientdocker_machine.CLIENT = docker_machine.cli.client.Client(storage_path=None, storage_path=None, tls_ca_cert=None,tls_ca_key=None, tls_client_cert=None, tls_client_key=None, github_api_token=None, native_ssh=False,bugsnag_api_token=None)## Using the lower level wrapperIf you need more control or wish to interact with docker-machine exactly as you would from cli you can use the clientdirectly.import docker_machineclient = Client()
Package: docker-minecraft
docker-minecraft-cliAboutThis is a CLI application aiming to allow you to set up and run a Minecraft server in Docker with just one command.WARNING: This application is currently inbeta, not all features will work or are implemented yet.InstallationThe recommended way to install is usingpipx:pipxinstalldocker-minecraftAlternatively, you can usepip:pipinstalldocker-minecraftLicenseThis project is licensed under theGNU GPL v3. Seethe licensefor more information.
Package: docker-mirror-packages-repo
docker mirror packages repoAllows to simulate upstream centos / ubuntu / opensuse
package repository servers by using a docker container
which contains a local package mirror.The scripts in this project allow to build a local package
mirror using rsync. When ready the rpm/deb packages get
copied into a docker image which has a small web script
that mimics the mirrorlist function of the original
upstream package repository servers.There is a helper script docker_mirror which can start
a mirror docker image as container. The --add-hosts
option prints the line for "docker run" so that calls
to the original upstream package server get diverted
to the ip address locally running docker container.Effectively all"yum install"or"zypper install"or"apt-get install"commands run locally in the docker
network. No internet access required. Without changing
repo files. Without special proxy host setups. This is
faster, allows for reproducible install tests, and it
can be used in a disconnected environment.
Package: dockermon
# dockermon - Docker Monitor Utilitydockermonlistens for events from docker daemon using the docker/events[HTTP API][api].[api]:https://docs.docker.com/reference/api/docker_remote_api_v1.19/# Usage## Library
You can usedockermonas a library and then callwatch(callback), you’re
callback function will be called with new event (dict).# Command Line
The other option is to usedockermonas a command line tool and specify a
program to call with every new event. The program will be launched and the event
encode in JSON will be send to the program standard input. For example:python -m dockermon “jq –unbuffered .”If not program is specified, events will be printed to standard output as JSON
objects, one per line.# Hacking
“one script no external dependencies” is a design goal, let’s not break it :)We use [tox](https://tox.readthedocs.org/en/latest/) to test. Please make sure
your code passes on all supported platforms (seetox.ini) before sending a
pull request.# Bugs and Projecthttps://github.com/CyberInt/dockermon
Package: dockernel
DockernelMakes it possible to utilize arbitrary docker images as Jupyter kernels.Installation, prerequisitesYou will need Docker (obviously). For detailed instructions on how to install
it, seeGet Dockerpage.To install Dockernel, use Pip.pip install dockernelMake sure that thejupyterinstallation you wish to use with dockerized
kernels is in the same environment asdockernel. Keep in mind, that kernels
installed with Dockernel in one version may not necessarily work with a
different one.UsageFirst, create a docker image that will host your kernel. This will require a
proper dockerfile. An example can be seenhere.To build your image, usedocker build. E.g. to build the example mentioned
above:sudo docker build --tag my_kernel - < example_dockerfileAfter that, use Dockernel to install the docker image as a Jupyter kernel:dockernel install my_kernel --name dockerized_kernel... and you should be ready to go! Fire upjupyter notebookand you should
seedockerized_kernelunder "New" menu, or in the "Notebook" section of the
Launcher tab injupyter lab.Issues or questions?Post a new issue in theDockernell Issue Tracker at
GitHub.
Package: docker-nuke
docker-nuke, for when you want to blow away every docker object in sightInstallationdocker-nuke is availiable on PyPI and can be pip installed$pipinstalldocker-nukeRunning$docker-nukeStoppingcontainer50bd26e339:SucceededRemovingcontainer50bd26e339:SucceededRemovingcontainer913061d931:SucceededRemovingimage59507b30b4:Failed(retrywith"--force")Removingimage3fd9065eaf:Failed(retrywith"--force")Removingvolume140bb8e5cc:SucceededUse the--forceoption to force remove docker objects:$docker-nuke--forceRemovingimage59507b30b4:SucceededRemovingimage3fd9065eaf:Succeeded
Package: docker-optimizer
docker-optimizerallows collapsing multipleRUNlayers into a
single one. This way, you can use the docker caching for development,
yet get fewer layers for the final container.Installationpipinstalldocker-optimizerUsageInputDockerfile.dev:FROMpython:3.8-slim-buster#============================================================================# Install requirements#============================================================================COPYrequirements.txt/requirements.txtRUNpipinstall-r/requirements.txt#============================================================================# Update the package list#============================================================================RUNapt-getupdate-y#============================================================================# Install certbot#============================================================================RUNapt-getinstall-ycurl&&\curl-LOhttps://dl.eff.org/certbot-auto&&\mvcertbot-auto/usr/local/bin&&\chownroot/usr/local/bin/certbot-auto&&\chmod755/usr/local/bin/certbot-auto&&\certbot-auto--install-only-n#============================================================================# cleanup package list#============================================================================RUNrm-rf/var/lib/apt/lists/*COPYnew-certificate*/usr/local/bin/USER1000ENVLE_AUTO_SUDO=WORKDIR/usr/local/binENTRYPOINT["python","new-certificate.py"]docker-optimizerDockerfile.devDockerfile# compiled by docker-optimizer# https://github.com/bmustiata/docker-optimizerfrompython:3.8-slim-bustercopyrequirements.txt/requirements.txtrunpipinstall-r/requirements.txt&&apt-getupdate-y&&apt-getinstall-ycurl&&curl-LOhttps://dl.eff.org/certbot-auto&&mvcertbot-auto/usr/local/bin&&chownroot/usr/local/bin/certbot-auto&&chmod755/usr/local/bin/certbot-auto&&certbot-auto--install-only-n&&rm-rf/var/lib/apt/lists/*copynew-certificate*/usr/local/bin/user1000env'LE_AUTO_SUDO'''workdir/usr/local/binentrypoint['python','new-certificate.py']
Package: docker-overdose
docker_overdoseDocker-compose alternative with (too much) added functionality.Install it from PyPIpipinstalldocker_overdoseUsagefromdocker_overdose.containermanagerimportContainerManager,ContainersManager# Instantiate the orchestratorcontainers=ContainersManager()# Instantiate a containercontainer=ContainerManager('container-name',image='debian:bookworm-slim',run_options={"command":f"sh -c 'while true; do echo Hello World!; sleep 1; done'"},net_options={"add_if":"enp0s2"},autostart=True)containers.add(container)containers.start()$python-mdocker_overdose#or$docker_overdoseDevelopmentRead theCONTRIBUTING.mdfile.
Package: docker-parser
Docker parserDocker attempts to simplify machine processing of dockerfiles. This moduel is not intended to extend docker SDK or build images, instead it attempts to parse instructions into simply manipulatable python objects, deduce state of environment in which commands are executed and allow modification of resulting dockerfile.Instalationpip3 install docker-parserUsagePrint all executed RUN commandsfromdocker_parserimportparserwithopen("dockerfile")assrc:dockerfile=parser.Parser.loads(src.read())forcmdindockerfile.run():print(cmd.arg)dockerfile:FROM ubuntu:20.04
ENV DEBIAN_FRONTEND=noninteractive
RUN apt update
RUN apt install -y gcc make
COPY ./src /var/tmp/
WORKDIR /var/tmp/src
RUN makeoutput:apt update
apt install -y gcc make
makefor more examples refer toexamplesContributingTo contribute:Increment version inpyproject.tomlCreate merge request topreprodbranchOnce your changes are reviewed and merged, pipeline publishes new version to testpypiAfter verifying that changes don't break rest of the code and don't justify major release, changes will be pushed tomainbranch and pushed to production pypi