This should make previewing documentation easier.
Also updated the Makefile to now copy the theme dir into the _build/website/ dir. Make connect and Make push work.
Specifically, Ubuntu Precise's cgroup-lite script uses mount -n
to mount the cgroup filesystems so they don't appear in mtab, so
detection always fails unless the admin updates mtab with /proc/mounts.
/proc/mounts is valid on just about every Linux machine in existence and
as a bonus is much easier to parse.
I also removed the regex in favor of a more accurate parser that should
also support monolitic cgroup mounts (e.g. mount -t cgroup none /cgroup).
On Gentoo, the memory cgroup is mounted at /sys/fs/cgroup/memory, but the mount line looks like the following:
memory on /sys/fs/cgroup/memory type cgroup (rw,nosuid,nodev,noexec,relatime,memory)
(note that the first word on the line is "memory", not "cgroup", but the other essentials are there, namely the type of cgroup and the memory mount option)
This is especially to fix the current docker on kernels such as gentoo-sources, where the "flavor" is the string "gentoo", and that obviously fails to be converted to an integer.
* Addded quick install on ubuntu as the 1st install option
* Grouped other binary installs under "binary installs"
* Removed duplicate binary ubuntu installs (linked to the docs)
* Improved "build from source" instructions
Ensure the docker daemon creates a file containing its PID under
/var/run/docker.pid.
The daemon takes care of removing the pid file when it receives either
SIGTERM, SIGINT or SIGKILL.
The daemon also refuses to start when the pidfile is found. An
explanation message is shown to the user when this happens.
This change is required to make docker easier to manage by tools like
checkproc which rely on this information.
ip from iproute2 replaces the legacy route tool which is often not
installed by default on recent Linux distributions.
The same patch has been done in network.go and is re-used here.
The raw mode is actually only needed when you attach to a container.
Having it enabled all the time can be a pain, e.g: if docker crashes
your terminal will end up in a broken state.
Since we are currently missing a real API for the docker daemon to
negotiate this kind of options, this changeset actually enable the raw
mode on the login (because it outputs a password), run and attach
commands.
This "optional raw mode" is implemented by passing a more complicated
interface than io.Writer as the stdout argument of each command. This
interface (DockerConn) exposes a method which allows the command to set
the terminal in raw mode or not.
Finally, the code added by this changeset will be deprecated by a real
API for the docker daemon.
Broken link was from python_web_app to nonexistent "base commands page"; updated to point to next item in examples menu, running_ssh_service screencast.
At least, for me, 'map' means that there are two values and one is "mapped" to
another.
In this case, just one value is provided (container's port), the other value is
automatically obtained (host's port) and the actual mapping can be seen using
``docker port`` command.
Not only is this a more common idiom, it'll make finding bugs easier,
and it'll make porting to Go 1.1 easier.
Go 1.1 will not require the final return or panic because it has a
notion of terminating statements.
Instead of allocating all possible IPs in advance, generate them as
needed.
A loop will cycle through all possible IPs in sequential order,
allocating them as needed and marking them as in use. Once the loop
exhausts all IPs, it will wrap back to the beginning. IPs that are
already in use will be skipped. When an IP is released, it will be
cleared and be available for allocation again.
Two decisions went into this design:
1) Minimize memory footprint by only allocating IPs that are actually
in use
2) Minimize reuse of released IP addresses to avoid sending traffic to
the wrong containers
As a side effect, the functions for IP/Mask<->int conversion have been
rewritten to never be able to fail in order to reduce the amount of
error returns.
Fixes gh-231
It was broken because the terminal is in raw mode.
This changeset adds code in the login commmand to do a little bit of
interpretation on the user input (something usually done by the terminal
emulator itself).
As per FIXME, CmdStream could have deadlocked if a command printed
enough on stderr. This commit fixes that, but still keeps all of
the stderr output in memory.
This sets up an idiomatic Go workspace in /opt/go with the source
shared from the host directory in
/opt/go/src/github.com/dotcloud/docker and docker installed into
/opt/go
Current Go tip (+74e65f07a0c8) and likely Go 1.1 does not build docker since net.TCPAddr struct has an additional field now for IPv6:
type TCPAddr struct {
IP IP
Port int
Zone string // IPv6 scoped addressing zone
}
Initializing the struct with named fields resolves this problem.
chooper@chimay:~/projects/docker/bin$ ./docker images
NAME ID CREATED PARENT
chooper@chimay:~/projects/docker/bin$ ./docker run -a base echo "hello world"
Downloading from http://s3.amazonaws.com/docker.io/images/base
Unpacking to base
######################################################################## 100.0%
base:e9cb4ad9173245ac
hello world
chooper@chimay:~/projects/docker/bin$ ./docker run -a base echo "hello world"
hello world
chooper@chimay:~/projects/docker/bin$ ./docker run -a nosuchimage echo "hello world"
Downloading from http://s3.amazonaws.com/docker.io/images/nosuchimage
Unpacking to nosuchimage
######################################################################## 100.0%
Error: Error downloading image: nosuchimage
chooper@chimay:~/projects/docker/bin$
2013-03-12 02:58:39 +00:00
244 changed files with 26601 additions and 4360 deletions
It encapsulates heterogeneous payloads in Standard Containers, and runs them on any server with strong guarantees of isolation and repeatability.
Docker is an open-source engine which automates the deployment of applications as highly portable, self-sufficient containers.
Is is a great building block for automating distributed systems: large-scale web deployments, database clusters, continuous deployment systems, private PaaS, service-oriented architectures, etc.
Docker containers are both *hardware-agnostic* and *platform-agnostic*. This means that they can run anywhere, from your
laptop to the largest EC2 compute instance and everything in between - and they don't require that you use a particular
language, framework or packaging system. That makes them great building blocks for deploying and scaling web apps, databases
and backend services without depending on a particular stack or provider.
Docker is an open-source implementation of the deployment engine which powers [dotCloud](http://dotcloud.com), a popular Platform-as-a-Service.
It benefits directly from the experience accumulated over several years of large-scale operation and support of hundreds of thousands
of applications and databases.
* *Heterogeneous payloads*: any combination of binaries, libraries, configuration files, scripts, virtualenvs, jars, gems, tarballs, you name it. No more juggling between domain-specific tools. Docker can deploy and run them all.
* *Any server*: docker can run on any x64 machine with a modern linux kernel - whether it's a laptop, a bare metal server or a VM. This makes it perfect for multi-cloud deployments.
## Better than VMs
* *Isolation*: docker isolates processes from each other and from the underlying host, using lightweight containers.
A common method for distributing applications and sandbox their execution is to use virtual machines, or VMs. Typical VM formats
are VMWare's vmdk, Oracle Virtualbox's vdi, and Amazon EC2's ami. In theory these formats should allow every developer to
automatically package their application into a "machine" for easy distribution and deployment. In practice, that almost never
happens, for a few reasons:
* *Repeatability*: because containers are isolated in their own filesystem, they behave the same regardless of where, when, and alongside what they run.
* *Size*: VMs are very large which makes them impractical to store and transfer.
* *Performance*: running VMs consumes significant CPU and memory, which makes them impractical in many scenarios, for example local development of multi-tier applications, and
large-scale deployment of cpu and memory-intensive applications on large numbers of machines.
* *Portability*: competing VM environments don't play well with each other. Although conversion tools do exist, they are limited and add even more overhead.
* *Hardware-centric*: VMs were designed with machine operators in mind, not software developers. As a result, they offer very limited tooling for what developers need most:
building, testing and running their software. For example, VMs offer no facilities for application versioning, monitoring, configuration, logging or service discovery.
By contrast, Docker relies on a different sandboxing method known as *containerization*. Unlike traditional virtualization,
containerization takes place at the kernel level. Most modern operating system kernels now support the primitives necessary
for containerization, including Linux with [openvz](http://openvz.org), [vserver](http://linux-vserver.org) and more recently [lxc](http://lxc.sourceforge.net),
Solaris with [zones](http://docs.oracle.com/cd/E26502_01/html/E29024/preface-1.html#scrolltoc) and FreeBSD with [Jails](http://www.freebsd.org/doc/handbook/jails.html).
Docker builds on top of these low-level primitives to offer developers a portable format and runtime environment that solves
all 4 problems. Docker containers are small (and their transfer can be optimized with layers), they have basically zero memory and cpu overhead,
the are completely portable and are designed from the ground up with an application-centric design.
The best part: because docker operates at the OS level, it can still be run inside a VM!
## Plays well with others
Docker does not require that you buy into a particular programming language, framework, packaging system or configuration language.
Is your application a unix process? Does it use files, tcp connections, environment variables, standard unix streams and command-line
arguments as inputs and outputs? Then docker can run it.
Can your application's build be expressed a sequence of such commands? Then docker can build it.
Notable features
-----------------
## Escape dependency hell
* Filesystem isolation: each process container runs in a completely separate root filesystem.
A common problem for developers is the difficulty of managing all their application's dependencies in a simple and automated way.
* Resource isolation: system resources like cpu and memory can be allocated differently to each process container, using cgroups.
This is usually difficult for several reasons:
*Network isolation: each process container runs in its own network namespace, with a virtual interface and IP address of its own.
**Cross-platform dependencies*. Modern applications often depend on a combination of system libraries and binaries, language-specific packages, framework-specific modules,
internal components developed for another project, etc. These dependencies live in different "worlds" and require different tools - these tools typically don't work
well with each other, requiring awkward custom integrations.
* Copy-on-write: root filesystems are created using copy-on-write, which makes deployment extremeley fast, memory-cheap and disk-cheap.
* Logging: the standard streams (stdout/stderr/stdin) of each process container is collected and logged for real-time or batch retrieval.
* Change management: changes to a container's filesystem can be committed into a new image and re-used to create more containers. No templating or manual configuration required.
* Interactive shell: docker can allocate a pseudo-tty and attach to the standard input of any container, for example to run a throaway interactive shell.
* Conflicting dependencies. Different applications may depend on different versions of the same dependency. Packaging tools handle these situations with various degrees of ease -
but they all handle them in different and incompatible ways, which again forces the developer to do extra work.
* Custom dependencies. A developer may need to prepare a custom version of his application's dependency. Some packaging systems can handle custom versions of a dependency,
others can't - and all of them handle it differently.
Docker solves dependency hell by giving the developer a simple way to express *all* his application's dependencies in one place,
and streamline the process of assembling them. If this makes you think of [XKCD 927](http://xkcd.com/927/), don't worry. Docker doesn't
*replace* your favorite packaging systems. It simply orchestrates their use in a simple and repeatable way. How does it do that? With layers.
Docker defines a build as running a sequence unix commands, one after the other, in the same container. Build commands modify the contents of the container
(usually by installing new files on the filesystem), the next command modifies it some more, etc. Since each build command inherits the result of the previous
commands, the *order* in which the commands are executed expresses *dependencies*.
Here's a typical docker build process:
```bash
from ubuntu:12.10
run apt-get update
run apt-get install python
run apt-get install python-pip
run pip install django
run apt-get install curl
run curl http://github.com/shykes/helloflask/helloflask/master.tar.gz | tar -zxv
run cd master && pip install -r requirements.txt
```
Note that Docker doesn't care *how* dependencies are built - as long as they can be built by running a unix command in a container.
Install instructions
==================
Quick install on Ubuntu 12.04 and 12.10
---------------------------------------
```bash
curl get.docker.io | sh -x
```
Binary installs
----------------
Docker supports the following binary installation methods.
Note that some methods are community contributions and not yet officially supported.
* [Ubuntu 12.04 and 12.10 (officially supported)](http://docs.docker.io/en/latest/installation/ubuntulinux/)
Run the `go install` command (above) to recompile docker.
What is a Standard Container?
-----------------------------
=============================
Docker defines a unit of software delivery called a Standard Container. The goal of a Standard Container is to encapsulate a software component and all its dependencies in
a format that is self-describing and portable, so that any compliant runtime can run it without extra dependency, regardless of the underlying machine and the contents of the container.
a format that is self-describing and portable, so that any compliant runtime can run it without extra dependencies, regardless of the underlying machine and the contents of the container.
The spec for Standard Containers is currently work in progress, but it is very straightforward. It mostly defines 1) an image format, 2) a set of standard operations, and 3) an execution environment.
The spec for Standard Containers is currently a work in progress, but it is very straightforward. It mostly defines 1) an image format, 2) a set of standard operations, and 3) an execution environment.
A great analogy for this is the shipping container. Just like Standard Containers are a fundamental unit of software delivery, shipping containers (http://bricks.argz.com/ins/7823-1/12) are a fundamental unit of physical delivery.
@@ -101,7 +302,7 @@ Just like shipping containers, Standard Containers define a set of STANDARD OPER
### 2. CONTENT-AGNOSTIC
Just like shipping containers, Standard Containers are CONTENT-AGNOSTIC: all standard operations have the same effect regardless of the contents. A shipping container will be stacked in exactly the same way whether it contains Vietnamese powder coffe or spare Maserati parts. Similarly, Standard Containers are started or uploaded in the same way whether they contain a postgres database, a php application with its dependencies and application server, or Java build artifacts.
Just like shipping containers, Standard Containers are CONTENT-AGNOSTIC: all standard operations have the same effect regardless of the contents. A shipping container will be stacked in exactly the same way whether it contains Vietnamese powder coffee or spare Maserati parts. Similarly, Standard Containers are started or uploaded in the same way whether they contain a postgres database, a php application with its dependencies and application server, or Java build artifacts.
Data volumes (issue #111) are a much-requested feature which trigger much discussion and debate. Below is the current authoritative spec for implementing data volumes.
This spec will be deprecated once the feature is fully implemented.
Discussion, requests, trolls, demands, offerings, threats and other forms of supplications concerning this spec should be addressed to Solomon here: https://github.com/dotcloud/docker/issues/111
### 1. Creating data volumes
At container creation, parts of a container's filesystem can be mounted as separate data volumes. Volumes are defined with the -v flag.
For example:
```bash
$ docker run -v /var/lib/postgres -v /var/log postgres /usr/bin/postgres
```
In this example, a new container is created from the 'postgres' image. At the same time, docker creates 2 new data volumes: one will be mapped to the container at /var/lib/postgres, the other at /var/log.
2 important notes:
1) Volumes don't have top-level names. At no point does the user provide a name, or is a name given to him. Volumes are identified by the path at which they are mounted inside their container.
2) The user doesn't choose the source of the volume. Docker only mounts volumes it created itself, in the same way that it only runs containers that it created itself. That is by design.
### 2. Sharing data volumes
Instead of creating its own volumes, a container can share another container's volumes. For example:
```bash
$ docker run --volumes-from $OTHER_CONTAINER_ID postgres /usr/local/bin/postgres-backup
```
In this example, a new container is created from the 'postgres' example. At the same time, docker will *re-use* the 2 data volumes created in the previous example. One volume will be mounted on the /var/lib/postgres of *both* containers, and the other will be mounted on the /var/log of both containers.
### 3. Under the hood
Docker stores volumes in /var/lib/docker/volumes. Each volume receives a globally unique ID at creation, and is stored at /var/lib/docker/volumes/ID.
At creation, volumes are attached to a single container - the source of truth for this mapping will be the container's configuration.
Mounting a volume consists of calling "mount --bind" from the volume's directory to the appropriate sub-directory of the container mountpoint. This may be done by Docker itself, or farmed out to lxc (which supports mount-binding) if possible.
### 4. Backups, transfers and other volume operations
Volumes sometimes need to be backed up, transfered between hosts, synchronized, etc. These operations typically are application-specific or site-specific, eg. rsync vs. S3 upload vs. replication vs...
Rather than attempting to implement all these scenarios directly, Docker will allow for custom implementations using an extension mechanism.
### 5. Custom volume handlers
Docker allows for arbitrary code to be executed against a container's volumes, to implement any custom action: backup, transfer, synchronization across hosts, etc.
Here's an example:
```bash
$ DB=$(docker run -d -v /var/lib/postgres -v /var/log postgres /usr/bin/postgres)
$ BACKUP_JOB=$(docker run -d --volumes-from $DB shykes/backuper /usr/local/bin/backup-postgres --s3creds=$S3CREDS)
$ docker wait$BACKUP_JOB
```
Congratulations, you just implemented a custom volume handler, using Docker's built-in ability to 1) execute arbitrary code and 2) share volumes between containers.
docker-build is a script to build docker images from source. It will be deprecated once the 'build' feature is incorporated into docker itself (See https://github.com/dotcloud/docker/issues/278)
Author: Solomon Hykes <solomon@dotcloud.com>
## Install
docker-builder requires:
1) A reasonably recent Python setup (tested on 2.7.2).
2) A running docker daemon at version 0.1.4 or more recent (http://www.docker.io/gettingstarted)
## Usage
First create a valid Changefile, which defines a sequence of changes to apply to a base image.
$ cat Changefile
# Start build from a know base image
from base:ubuntu-12.10
# Update ubuntu sources
run echo 'deb http://archive.ubuntu.com/ubuntu quantal main universe multiverse' > /etc/apt/sources.list
run apt-get update
# Install system packages
run DEBIAN_FRONTEND=noninteractive apt-get install -y -q git
run DEBIAN_FRONTEND=noninteractive apt-get install -y -q curl
run DEBIAN_FRONTEND=noninteractive apt-get install -y -q golang
# Insert files from the host (./myscript must be present in the current directory)
copy myscript /usr/local/bin/myscript
Run docker-build, and pass the contents of your Changefile as standard input.
$ IMG=$(./docker-build < Changefile)
This will take a while: for each line of the changefile, docker-build will:
1. Create a new container to execute the given command or insert the given file
2. Wait for the container to complete execution
3. Commit the resulting changes as a new image
4. Use the resulting image as the input of the next step
If all the steps succeed, the result will be an image containing the combined results of each build step.
You can trace back those build steps by inspecting the image's history:
$ docker history $IMG
ID CREATED CREATED BY
1e9e2045de86 A few seconds ago /bin/sh -c cat > /usr/local/bin/myscript; chmod +x /usr/local/bin/git
77db140aa62a A few seconds ago /bin/sh -c DEBIAN_FRONTEND=noninteractive apt-get install -y -q golang
77db140aa62a A few seconds ago /bin/sh -c DEBIAN_FRONTEND=noninteractive apt-get install -y -q curl
77db140aa62a A few seconds ago /bin/sh -c DEBIAN_FRONTEND=noninteractive apt-get install -y -q git
83e85d155451 A few seconds ago /bin/sh -c apt-get update
bfd53b36d9d3 A few seconds ago /bin/sh -c echo 'deb http://archive.ubuntu.com/ubuntu quantal main universe multiverse' > /etc/apt/sources.list
base 2 weeks ago /bin/bash
27cf78414709 2 weeks ago
Note that your build started from 'base', as instructed by your Changefile. But that base image itself seems to have been built in 2 steps - hence the extra step in the history.
You can use this build technique to create any image you want: a database, a web application, or anything else that can be build by a sequence of unix commands - in other words, anything else.
return docker(["commit"] + (["-author", author] if author else []) + (["-run", json.dumps(run)] if run is not None else []) + [run_id]).read().rstrip()
def insert(base, src, dst, author=None):
print "COPY {} to {} in {}".format(src, dst, base)
:description:An introduction to docker and standard containers?
:keywords:containers, lxc, concepts, explanation
Building blocks
===============
.._images:
Images
------
An original container image. These are stored on disk and are comparable with what you normally expect from a stopped virtual machine image. Images are stored (and retrieved from) repository
Images are stored on your local file system under /var/lib/docker/graph
.._containers:
Containers
----------
A container is a local version of an image. It can be running or stopped, The equivalent would be a virtual machine instance.
Containers are stored on your local file system under /var/lib/docker/containers
:description:An introduction to docker and standard containers?
:keywords:containers, lxc, concepts, explanation
.._introduction:
Introduction
============
Docker - The Linux container runtime
------------------------------------
Docker complements LXC with a high-level API which operates at the process level. It runs unix processes with strong guarantees of isolation and repeatability across servers.
Docker is a great building block for automating distributed systems: large-scale web deployments, database clusters, continuous deployment systems, private PaaS, service-oriented architectures, etc.
-**Heterogeneous payloads** Any combination of binaries, libraries, configuration files, scripts, virtualenvs, jars, gems, tarballs, you name it. No more juggling between domain-specific tools. Docker can deploy and run them all.
-**Any server** Docker can run on any x64 machine with a modern linux kernel - whether it's a laptop, a bare metal server or a VM. This makes it perfect for multi-cloud deployments.
-**Isolation** docker isolates processes from each other and from the underlying host, using lightweight containers.
-**Repeatability** Because containers are isolated in their own filesystem, they behave the same regardless of where, when, and alongside what they run.
Docker defines a unit of software delivery called a Standard Container. The goal of a Standard Container is to encapsulate a software component and all its dependencies in
a format that is self-describing and portable, so that any compliant runtime can run it without extra dependency, regardless of the underlying machine and the contents of the container.
The spec for Standard Containers is currently work in progress, but it is very straightforward. It mostly defines 1) an image format, 2) a set of standard operations, and 3) an execution environment.
A great analogy for this is the shipping container. Just like Standard Containers are a fundamental unit of software delivery, shipping containers (http://bricks.argz.com/ins/7823-1/12) are a fundamental unit of physical delivery.
Standard operations
~~~~~~~~~~~~~~~~~~~
Just like shipping containers, Standard Containers define a set of STANDARD OPERATIONS. Shipping containers can be lifted, stacked, locked, loaded, unloaded and labelled. Similarly, standard containers can be started, stopped, copied, snapshotted, downloaded, uploaded and tagged.
Content-agnostic
~~~~~~~~~~~~~~~~~~~
Just like shipping containers, Standard Containers are CONTENT-AGNOSTIC: all standard operations have the same effect regardless of the contents. A shipping container will be stacked in exactly the same way whether it contains Vietnamese powder coffee or spare Maserati parts. Similarly, Standard Containers are started or uploaded in the same way whether they contain a postgres database, a php application with its dependencies and application server, or Java build artifacts.
Infrastructure-agnostic
~~~~~~~~~~~~~~~~~~~~~~~~~~
Both types of containers are INFRASTRUCTURE-AGNOSTIC: they can be transported to thousands of facilities around the world, and manipulated by a wide variety of equipment. A shipping container can be packed in a factory in Ukraine, transported by truck to the nearest routing center, stacked onto a train, loaded into a German boat by an Australian-built crane, stored in a warehouse at a US facility, etc. Similarly, a standard container can be bundled on my laptop, uploaded to S3, downloaded, run and snapshotted by a build server at Equinix in Virginia, uploaded to 10 staging servers in a home-made Openstack cluster, then sent to 30 production instances across 3 EC2 regions.
Designed for automation
~~~~~~~~~~~~~~~~~~~~~~~~~~
Because they offer the same standard operations regardless of content and infrastructure, Standard Containers, just like their physical counterpart, are extremely well-suited for automation. In fact, you could say automation is their secret weapon.
Many things that once required time-consuming and error-prone human effort can now be programmed. Before shipping containers, a bag of powder coffee was hauled, dragged, dropped, rolled and stacked by 10 different people in 10 different locations by the time it reached its destination. 1 out of 50 disappeared. 1 out of 20 was damaged. The process was slow, inefficient and cost a fortune - and was entirely different depending on the facility and the type of goods.
Similarly, before Standard Containers, by the time a software component ran in production, it had been individually built, configured, bundled, documented, patched, vendored, templated, tweaked and instrumented by 10 different people on 10 different computers. Builds failed, libraries conflicted, mirrors crashed, post-it notes were lost, logs were misplaced, cluster updates were half-broken. The process was slow, inefficient and cost a fortune - and was entirely different depending on the language and infrastructure provider.
Industrial-grade delivery
~~~~~~~~~~~~~~~~~~~~~~~~~~
There are 17 million shipping containers in existence, packed with every physical good imaginable. Every single one of them can be loaded on the same boats, by the same cranes, in the same facilities, and sent anywhere in the World with incredible efficiency. It is embarrassing to think that a 30 ton shipment of coffee can safely travel half-way across the World in *less time* than it takes a software team to deliver its code from one datacenter to another sitting 10 miles away.
With Standard Containers we can put an end to that embarrassment, by making INDUSTRIAL-GRADE DELIVERY of software a reality.
:description:A simple hello world daemon example with Docker
:keywords:docker, example, hello world, daemon
.._hello_world_daemon:
Hello World Daemon
==================
..include:: example_header.inc
The most boring daemon ever written.
This example assumes you have Docker installed and with the base image already imported ``docker pull base``.
We will use the base image to run a simple hello world daemon that will just print hello world to standard
out every second. It will continue to do this until we stop it.
**Steps:**
..code-block::bash
CONTAINER_ID=$(docker run -d base /bin/sh -c "while true; do echo hello world; sleep 1; done")
We are going to run a simple hello world daemon in a new container made from the base image.
-**"docker run -d "** run a command in a new container. We pass "-d" so it runs as a daemon.
-**"base"** is the image we want to run the command inside of.
-**"/bin/sh -c"** is the command we want to run in the container
-**"while true; do echo hello world; sleep 1; done"** is the mini script we want to run, that will just print hello world once a second until we stop it.
-**$CONTAINER_ID** the output of the run command will return a container id, we can use in future commands to see what is going on with this process.
..code-block::bash
docker logs $CONTAINER_ID
Check the logs make sure it is working correctly.
-**"docker logs**" This will return the logs for a container
-**$CONTAINER_ID** The Id of the container we want the logs for.
..code-block::bash
docker attach $CONTAINER_ID
Attach to the container to see the results in realtime.
-**"docker attach**" This will allow us to attach to a background process to see what is going on.
-**$CONTAINER_ID** The Id of the container we want to attach too.
..code-block::bash
docker ps
Check the process list to make sure it is running.
-**"docker ps"** this shows all running process managed by docker
..code-block::bash
docker stop $CONTAINER_ID
Stop the container, since we don't need it anymore.
-**"docker stop"** This stops a container
-**$CONTAINER_ID** The Id of the container we want to stop.
:description:Building your own python web app using docker
:keywords:docker, example, python, web app
.._python_web_app:
Building a python web app
=========================
..include:: example_header.inc
The goal of this example is to show you how you can author your own docker images using a parent image, making changes to it, and then saving the results as a new image. We will do that by making a simple hello flask web application image.
**Steps:**
..code-block::bash
docker pull shykes/pybuilder
We are downloading the "shykes/pybuilder" docker image
We set a URL variable that points to a tarball of a simple helloflask web app
..code-block::bash
BUILD_JOB=$(docker run -d -t shykes/pybuilder:latest /usr/local/bin/buildapp $URL)
Inside of the "shykes/pybuilder" image there is a command called buildapp, we are running that command and passing the $URL variable from step 2 to it, and running the whole thing inside of a new container. BUILD_JOB will be set with the new container_id.
..code-block::bash
docker attach $BUILD_JOB
[...]
We attach to the new container to see what is going on. Ctrl-C to disconnect
Save the changed we just made in the container to a new image called "_/builds/github.com/hykes/helloflask/master" and save the image id in the BUILD_IMG variable name.
..code-block::bash
WEB_WORKER=$(docker run -d -p 5000$BUILD_IMG /usr/local/bin/runapp)
-**"docker run -d "** run a command in a new container. We pass "-d" so it runs as a daemon.
-**"-p 5000"** the web app is going to listen on this port, so it must be mapped from the container to the host system.
-**"$BUILD_IMG"** is the image we want to run the command inside of.
-**/usr/local/bin/runapp** is the command which starts the web app.
Use the new image we just created and create a new container with network port 5000, and return the container id and store in the WEB_WORKER variable.
..code-block::bash
docker logs $WEB_WORKER
* Running on http://0.0.0.0:5000/
view the logs for the new container using the WEB_WORKER variable, and if everything worked as planned you should see the line "Running on http://0.0.0.0:5000/" in the log output.
..code-block::bash
WEB_PORT=$(docker port $WEB_WORKER 5000)
lookup the public-facing port which is NAT-ed store the private port used by the container and store it inside of the WEB_PORT variable.
..code-block::bash
curl http://`hostname`:$WEB_PORT
Hello world!
access the web app using curl. If everything worked as planned you should see the line "Hello world!" inside of your console.
:description:An overview on how to run the docker examples
:keywords:docker, examples, how to
.._running_examples:
Running The Examples
--------------------
All the examples assume your machine is running the docker daemon. To run the docker daemon in the background, simply type:
..code-block::bash
sudo docker -d &
Now you can run docker in client mode: all commands will be forwarded to the docker daemon, so the client
can run from any account.
..code-block::bash
# now you can run docker commands from any account.
docker help
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