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Notice: The classical python "Docker Registry" is deprecated, in favor of a new golang implementation. This here is kept for historical purpose, and will not receive any significant work/love any more. You should head to the landing page of the new registry or the "Distribution" github project instead.
As the documentation evolves with different registry versions, be sure that before reading any further you:
The stable, released version is the 0.9.1 tag.
Please also have a quick look at the FAQ before reporting bugs.
The fastest way to get running:
docker run -p 5000:5000 registry
That will use the official image from the Docker hub.
Here is a slightly more complex example that launches a registry on port 5000, using an Amazon S3 bucket to store images with a custom path, and enables the search endpoint:
docker run \
-e SETTINGS_FLAVOR=s3 \
-e AWS_BUCKET=mybucket \
-e STORAGE_PATH=/registry \
-e AWS_KEY=myawskey \
-e AWS_SECRET=myawssecret \
-e SEARCH_BACKEND=sqlalchemy \
-p 5000:5000 \
registry
By default, the registry will use the config_sample.yml configuration to start.
Individual configuration options from that file may be overridden using environment variables. Example: docker run -e STORAGE_PATH=/registry.
You may also use different "flavors" from that file (see below).
Finally, you can use your own configuration file (see below).
The registry can be instructed to use a specific flavor from a configuration file.
This mechanism lets you define different running "mode" (eg: "development", "production" or anything else).
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 bucketceph-s3: stores data in a Ceph cluster via a Ceph Object Gateway, using the S3 APIazureblob: stores data in an Microsoft Azure Blob Storage ((docs))dev: 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.
To specify which flavor you want to run, set the SETTINGS_FLAVOR
environment variable: export SETTINGS_FLAVOR=dev
The default flavor is dev.
NOTE: it's possible to load environment variables from within the config file
with a simple syntax: _env:VARIABLENAME[:DEFAULT]. Check this syntax
in action in the example below...
common: &common
standalone: true
loglevel: info
search_backend: "_env:SEARCH_BACKEND:"
sqlalchemy_index_database:
"_env:SQLALCHEMY_INDEX_DATABASE:sqlite:////tmp/docker-registry.db"
prod:
<<: *common
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:
<<: *common
loglevel: debug
storage: local
storage_path: /home/myself/docker
test:
<<: *common
storage: local
storage_path: /tmp/tmpdockertmp
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
debug: boolean, make the /_ping endpoint output more useful information, such as library versions and host information.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 (such as Ceph), in one of the boto config files'
[Credentials] section, set boto_host, boto_port as appropriate for the
service you are using. Alternatively, set boto_host and boto_port in the config file.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).
The Docker Registry can optionally index repository information in a
database for the GET /v1/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
In this case, the module is imported, and an instance of its Index
class is used as the search backend.
Use SQLAlchemy as the search backend.
sqlalchemy_index_database: The database URL passed through to
create_engine.Example:
common:
search_backend: sqlalchemy
sqlalchemy_index_database: sqlite:////tmp/docker-registry.db
On initialization, the SQLAlchemyIndex class checks the database
version. If the database doesn't exist yet (or does exist, but lacks
a version table), the SQLAlchemyIndex creates the database and
required tables.
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
Beware that mirroring only works for the public registry. You can not create a mirror for a private registry.
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 speed 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 passwordstorage 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 drivers:
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 S3s3_use_sigv4: boolean, true for USE_SIGV4 (boto_host needs to be set or use_sigv4 will be ignored by boto.)boto_bucket: string, the bucket name for non-Amazon S3-compliant object storeboto_host: string, host for non-Amazon S3-compliant object storeboto_port: for non-Amazon S3-compliant object storeboto_debug: for non-Amazon S3-compliant object storeboto_calling_format: string, the fully qualified class name of the boto calling format to use when accessing S3 or a non-Amazon S3-compliant object storestorage_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
Start from a copy of config_sample.yml.
Then, start your registry with a mount point to expose your new configuration inside the container (-v /home/me/myfolder:/registry-conf), and point to it using the DOCKER_REGISTRY_CONFIG environment variable:
sudo docker run -p 5000:5000 -v /home/me/myfolder:/registry-conf -e DOCKER_REGISTRY_CONFIG=/registry-conf/mysuperconfig.yml registry
For more features and advanced options, have a look at the advanced features documentation
For more backend drivers, please read drivers.md
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