Notice: This repository hosts the classic (stable and recommended) python docker-registry. If you are looking for the next-generation (unstable and experimental) of docker distribution tools (including the new golang registry), you should head over to docker/distribution instead.
About this document
As the documentation evolves with different registry versions, be sure that before reading any further you:
- check which version of the registry you are running
- switch to the corresponding tag to access the README that matches your product version
The stable, released version is the 0.9.1 tag.
Please also have a quick look at the FAQ before reporting bugs.
Table of Contents
- Quick Start
- Configuration mechanism overview
- Configuration flavors
- Available configuration options
- Your own config
- Advanced use
- For developers
The fastest way to get running:
- install docker
- run the registry:
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
Configuration mechanism overview
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).
config_sample.yml file, you'll see several sample flavors:
common: used by all other flavors as base settings
local: stores data on the local filesystem
s3: stores data in an AWS S3 bucket
ceph-s3: stores data in a Ceph cluster via a Ceph Object Gateway, using the S3 API
azureblob: stores data in an Microsoft Azure Blob Storage ((docs))
dev: basic configuration using the
test: used by unit tests
prod: production configuration (basically a synonym for the
gcs: stores data in Google cloud storage
swift: stores data in OpenStack Swift
glance: stores data in OpenStack Glance, with a fallback to local storage
glance-swift: stores data in OpenStack Glance, with a fallback to Swift
elliptics: stores data in Elliptics key/value storage
You can define your own flavors by adding a new top-level yaml key.
To specify which flavor you want to run, set the
The default flavor is
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: email@example.com to_addr: firstname.lastname@example.org dev: <<: *common loglevel: debug storage: local storage_path: /home/myself/docker test: <<: *common storage: local storage_path: /tmp/tmpdockertmp
Available configuration options
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: boolean, make the
/_pingendpoint 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
boto_port: If you are using
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'
boto_portas appropriate for the
service you are using. Alternatively, set
boto_portin 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
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
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:
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:
search_backend is neither empty nor one of the supported backends, it
should point to a module.
common: search_backend: foo.registry.index.xapian
In this case, the module is imported, and an instance of its
class is used as the search backend.
Use SQLAlchemy as the search backend.
sqlalchemy_index_database: The database URL passed through to
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
version table), the
SQLAlchemyIndex creates the database and
All mirror options are placed in a
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
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
host: Host address of server
port: Port server listens on
password: Authentication password
storage selects the storage engine to use. The registry ships with two storage engine by default (
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-NAME
- in the configuration set
- add any other storage dependent configuration option to the conf file
review the storage specific documentation for additional dependency or configuration instructions.
Currently, we are aware of the following storage drivers:
storage_path: Path on the filesystem where to store data
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.
docker run -p 5000 -v /tmp/registry:/tmp/registry registry
AWS Simple Storage Service options
s3_access_key: string, S3 access key
s3_secret_key: string, S3 secret key
s3_bucket: string, S3 bucket name
s3_region: S3 region where the bucket is located
s3_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
s3_secure: boolean, true for HTTPS to S3
s3_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 store
boto_host: string, host for non-Amazon S3-compliant object store
boto_port: for non-Amazon S3-compliant object store
boto_debug: for non-Amazon S3-compliant object store
boto_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 store
storage_path: string, the sub "folder" where image data will be stored.
prod: storage: s3 s3_region: us-west-1 s3_bucket: acme-docker storage_path: /registry s3_access_key: AKIAHSHB43HS3J92MXZ s3_secret_key: xdDowwlK7TJajV1Y7EoOZrmuPEJlHYcNP2k4j49T
Your own config
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