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As the documentation evolves with different registry versions, be sure that before reading any further you do:
The stable, released version is currently the 0.7.3 tag.
The fastest way to get running:
docker run -p 5000:5000 registry
That will use the official image from the Docker index.
Here is another example that will launch a container on port 5000, and store images in an Amazon S3 bucket:
docker run \
-e SETTINGS_FLAVOR=s3 \
-e AWS_BUCKET=acme-docker \
-e STORAGE_PATH=/registry \
-e AWS_KEY=AKIAHSHB43HS3J92MXZ \
-e AWS_SECRET=xdDowwlK7TJajV1Y7EoOZrmuPEJlHYcNP2k4j49T \
-e SEARCH_BACKEND=sqlalchemy \
-p 5000:5000 \
registry
See config_sample.yml for all available environment variables.
The Docker Registry comes with a sample configuration file,
config_sample.yml. Copy this to config.yml to provide a basic
configuration:
cp config/config_sample.yml config/config.yml
Docker Registry can run in several flavors. This enables you to run it in development mode, production mode or your own predefined mode.
In the config_sample.yml file, you'll see several sample flavors:
common: used by all other flavors as base settingslocal: stores data on the local filesystems3: stores data in an AWS S3 bucketdev: basic configuration using the local flavortest: used by unit testsprod: production configuration (basically a synonym for the s3 flavor)gcs: stores data in Google cloud storageswift: stores data in OpenStack Swiftglance: stores data in OpenStack Glance, with a fallback to local storageglance-swift: stores data in OpenStack Glance, with a fallback to Swiftelliptics: stores data in Elliptics key/value storageYou can define your own flavors by adding a new top-level yaml key.
You can specify which flavor to run by setting SETTINGS_FLAVOR in your
environment: export SETTINGS_FLAVOR=dev
The default flavor is dev.
NOTE: it's possible to load environment variables from the config file
with a simple syntax: _env:VARIABLENAME[:DEFAULT]. Check this syntax
in action in the example below...
common:
loglevel: info
search_backend: "_env:SEARCH_BACKEND:"
sqlalchemy_index_database:
"_env:SQLALCHEMY_INDEX_DATABASE:sqlite:////tmp/docker-registry.db"
prod:
loglevel: warn
storage: s3
s3_access_key: _env:AWS_S3_ACCESS_KEY
s3_secret_key: _env:AWS_S3_SECRET_KEY
s3_bucket: _env:AWS_S3_BUCKET
boto_bucket: _env:AWS_S3_BUCKET
storage_path: /srv/docker
smtp_host: localhost
from_addr: docker@myself.com
to_addr: my@myself.com
dev:
loglevel: debug
storage: local
storage_path: /home/myself/docker
test:
storage: local
storage_path: /tmp/tmpdockertmp
Specify the config file to be used by setting DOCKER_REGISTRY_CONFIG in your
environment: export DOCKER_REGISTRY_CONFIG=config.yml
The default location of the config file is config.yml, located in
the config subdirectory. If DOCKER_REGISTRY_CONFIG is a relative
path, that path is expanded relative to the config subdirectory.
When building an image using the Dockerfile or using an image from the
Docker index, the default config is
config_sample.yml.
It is also possible to mount the configuration file into the docker image
sudo docker run -p 5000:5000 -v /home/user/registry-conf:/registry-conf -e DOCKER_REGISTRY_CONFIG=/registry-conf/config.yml registry
When using the config_sample.yml, you can pass all options through as environment variables. See config_sample.yml for the mapping.
loglevel: string, level of debugging. Any of python's
logging module levels:
debug, info, warn, error or critical
storage_redirect: Redirect resource requested if storage engine supports
this, e.g. S3 will redirect signed URLs, this can be used to offload the
server.boto_host/boto_port: If you are using storage: s3 the
standard boto config file locations
(/etc/boto.cfg, ~/.boto) will be used. If you are using a
non-Amazon S3-compliant object store, in one of the boto config files'
[Credentials] section, set boto_host, boto_port as appropriate for the
service you are using.bugsnag: The bugsnag API key (note that if you don't use the official docker container, you need to install the registry with bugsnag enabled: pip install docker-registry[bugsnag])standalone: boolean, run the server in stand-alone mode. This means that
the Index service on index.docker.io will not be used for anything. This
implies disable_token_auth.
index_endpoint: string, configures the hostname of the Index endpoint.
This is used to verify passwords of users that log in. It defaults to
https://index.docker.io. You should probably leave this to its default.
disable_token_auth: boolean, disable checking of tokens with the Docker
index. You should provide your own method of authentication (such as Basic
auth).
privileged_key: allows you to make direct requests to the registry by using
an RSA key pair. The value is the path to a file containing the public key.
If it is not set, privileged access is disabled.openssl
You will need to install the python-rsa package (pip install rsa) in addition to using openssl.
Generating the public key using openssl will lead to producing a key in a format not supported by
the RSA library the registry is using.
Generate private key:
openssl genrsa -out private.pem 2048
Associated public key :
pyrsa-priv2pub -i private.pem -o public.pem
The Docker Registry can optionally index repository information in a
database for the GET /v1/search [endpoint][search-endpoint]. You
can configure the backend with a configuration like:
The search_backend setting selects the search backend to use. If
search_backend is empty, no index is built, and the search endpoint always
returns empty results.
search_backend: The name of the search backend engine to use.
Currently supported backends are:
sqlalchemyIf search_backend is neither empty nor one of the supported backends, it
should point to a module.
Example:
common:
search_backend: foo.registry.index.xapian
sqlalchemy_index_database: The database URLExample:
common:
search_backend: sqlalchemy
sqlalchemy_index_database: sqlite:////tmp/docker-registry.db
In this case, the module is imported, and an instance of it's Index
class is used as the search backend.
All mirror options are placed in a mirroring section.
mirroring:source:source_index:tags_cache_ttl:Example:
common:
mirroring:
source: https://registry-1.docker.io
source_index: https://index.docker.io
tags_cache_ttl: 172800 # 2 days
It's possible to add an LRU cache to access small files. In this case you need
to spawn a redis-server configured in
LRU mode. The config file "config_sample.yml"
shows an example to enable the LRU cache using the config directive cache_lru.
Once this feature is enabled, all small files (tags, meta-data) will be cached in Redis. When using a remote storage backend (like Amazon S3), it will speeds things up dramatically since it will reduce roundtrips to S3.
All config settings are placed in a cache or cache_lru section.
cache/cache_lru:host: Host address of serverport: Port server listens onpassword: Authentication passwordSettings these options makes the Registry send an email on each code Exception:
email_exceptions:smtp_host: hostname to connect to using SMTPsmtp_port: port number to connect to using SMTPsmtp_login: username to use when connecting to authenticated SMTPsmtp_password: password to use when connecting to authenticated SMTPsmtp_secure: boolean, true for TLS to using SMTP. this could be a path
to the TLS key file for client authentication.from_addr: email address to use when sending emailto_addr: email address to send exceptions toExample:
test:
email_exceptions:
smtp_host: localhost
storage selects the storage engine to use. The registry ships with two storage engine by default (file and s3).
If you want to find other (community provided) storages: pip search docker-registry-driver
To use and install one of these alternate storages:
pip install docker-registry-driver-NAMEstorage to NAME
Currently, we are aware of the following storage driver:
storage_path: Path on the filesystem where to store dataExample:
local:
storage: file
storage_path: /mnt/registry
If you use any type of local store along with a registry running within a docker
remember to use a data volume for the storage_path. Please read the documentation
for data volumes for more information.
Example:
docker run -p 5000 -v /tmp/registry:/tmp/registry registry
AWS Simple Storage Service options
s3_access_key: string, S3 access keys3_secret_key: string, S3 secret keys3_bucket: string, S3 bucket names3_region: S3 region where the bucket is locateds3_encrypt: boolean, if true, the container will be encrypted on the
server-side by S3 and will be stored in an encrypted form while at rest
in S3.s3_secure: boolean, true for HTTPS to S3boto_bucket: string, the bucket namestorage_path: string, the sub "folder" where image data will be stored.Example:
prod:
storage: s3
s3_region: us-west-1
s3_bucket: acme-docker
storage_path: /registry
s3_access_key: AKIAHSHB43HS3J92MXZ
s3_secret_key: xdDowwlK7TJajV1Y7EoOZrmuPEJlHYcNP2k4j49T
docker run -p 5000:5000 registry
or
docker run \
-e SETTINGS_FLAVOR=s3 \
-e AWS_BUCKET=acme-docker \
-e STORAGE_PATH=/registry \
-e AWS_KEY=AKIAHSHB43HS3J92MXZ \
-e AWS_SECRET=xdDowwlK7TJajV1Y7EoOZrmuPEJlHYcNP2k4j49T \
-e SEARCH_BACKEND=sqlalchemy \
-p 5000:5000 \
registry
NOTE: The container will try to allocate the port 5000. If the port
is already taken, find out which container is already using it by running docker ps
Install the system requirements for building a Python library:
sudo apt-get install build-essential python-dev libevent-dev python-pip liblzma-dev
Then install the Registry app:
sudo pip install docker-registry
If you need extra requirements, like bugsnag, specify them:
sudo pip install docker-registry[bugsnag]
(or clone the repository and pip install .)
Install the required dependencies:
sudo yum install python-devel libevent-devel python-pip gcc xz-devel
NOTE: On RHEL and CentOS you will need the EPEL repostitories enabled. Fedora should not require the additional repositories.
Then install the Registry app:
sudo python-pip install docker-registry[bugsnag]
(or clone the repository and pip install .)
gunicorn --access-logfile - --debug -k gevent -b 0.0.0.0:5000 -w 1 docker_registry.wsgi:application
The first time someone tries to push to your registry, it will prompt them for a username, password, and email.
The recommended setting to run the Registry in a prod environment is gunicorn behind a nginx server which supports chunked transfer-encoding (nginx >= 1.3.9).
You could use for instance supervisord to spawn the registry with 8 workers using this command:
gunicorn -k gevent --max-requests 100 --graceful-timeout 3600 -t 3600 -b localhost:5000 -w 8 docker_registry.wsgi:application
Here is an nginx configuration file example., which applies to versions < 1.3.9 which are compiled with the HttpChunkinModule.
This is another example nginx configuration file that applies to versions of nginx greater than 1.3.9 that have support for the chunked_transfer_encoding directive.
And you might want to add Basic auth on Nginx to protect it (if you're not using it on your local network):
Enable mod_proxy using a2enmod proxy_http, then use this snippet forward
requests to the Docker Registry:
ProxyPreserveHost On
ProxyRequests Off
ProxyPass / http://localhost:5000/
ProxyPassReverse / http://localhost:5000/
The central Registry runs on the dotCloud platform:
cd docker-registry/
dotcloud create myregistry
dotcloud push
Read CONTRIBUTE.md
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