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Docker-compose setup for experimental commons, small commons, or local development of the Gen3 stack. Production use should use cloud-automation.


This setup uses Docker containers for Postgres, IndexD, Fence, Peregrine, Sheepdog, Windmill (data-portal), and nginx. Images for the CDIS microservices and nginx will be pulled from (master), while Postgres (9.5) images will be pulled from Docker Hub. Nginx will be used as a reverse proxy to each of the services. Below you will find information about migrating existing and setting up new compose services, some tips, basic information about using data commons, and useful links. You can quickly find commonly used commands in our cheat sheet. Config file formats were copied from cloud-automation and stored in the Secrets directory and modified for local use with Docker Compose. Setup scripts for some of the containers are kept in the scripts directory.

Release History and Migration Instructions

2019/03 release

The 2019/03 release includes changes necessary for running the latest versions of the gen3 services as of March 2019. This release may fail to run earlier versions of gen3.

  • Changes

    • add arborist and pidgin services
    • move secrets to Secrets/ folder which git ignores (via the .gitignore file), apis_configs/ is renamed to a templates/ folder
    • bump to Postgres 9.6
    • do not publish Postgres port to host by default - to avoid port conflicts on the host
  • Migrate an existing commons to the new setup

    • move the current secrets to ./Secrets: mv ./apis_configs Secrets
    • git pull
    • docker-compose pull - pull the latest gen3 Docker images
    • bash ./
    • edit the postgres service in docker-compose.yaml to stay on version 9.5 - a 9.6 server cannot read data saved by a 9.5 server. If you want to erase the data currently in the commons, and proceed with Postgres 9.6, then docker-compose down -v clears the old data.
    • Set the settings in Secrets/fence-config.yaml - be sure to set the client_secret and client_id fields under OPENID_CONNECT.
    • ready to go: docker-compose up -d

Some Database Info

Database setup only has to occur the very first time you set up your local gen3 Docker Compose environment, as this docker-compose environment is configured to create a persistent volume for Postgres. The environment configuration is set up to automatically run setup scripts for the postgres container and set up the following:

  1. 5 databases
    • metadata (Used by metadata-service)
    • metadata_db (Used by sheepdog and peregrine)
    • fence_db
    • indexd_db
    • arborist_db
  2. 6 users with passwords and superuser access
    • metadata_user
    • fence_user
    • peregrine_user
    • sheepdog_user
    • indexd_user
    • arborist_user

Configure the Postgres database container to publish the db service port to the host machine by un-commenting the ports block under the postgres service in docker-compose.yml, then running docker-compose up -d postgres:

    # uncomment this to make postgres available from the container host - ex:
    #    psql -h localhost -d fence -U fence_user
      - 5432:5432

The container host can connect to the database after the port is published - ex:

psql -h localhost -U fence_user -d fence_db



  • openssl
  • Docker and Docker Compose

Docker and Docker Compose Setup

If you've never used Docker before, it may be helpful to read some of the Docker documentation to familiarize yourself with containers. You can also read an overview of what Docker Compose is here if you want some extra background information.

The official Docker installation page can be found here. The official Docker Compose installation page can be found here. For Windows and Mac, Docker Compose is included into Docker Desktop. If you are using Linux, then the official Docker installation does not come with Docker Compose; you will need to install Docker Engine before installing Docker Compose. Go through the steps of installing Docker Compose for your platform, then proceed to set up credentials. Note, that Docker Desktop is set to use 2 GB runtime memory by default. Make sure to increase the size of the memory to 6 GB as described here.

Docker ElasticSearch

If you are running on AWS EC2 instance (Amazon Linux), consider setup Docker ElasticSearch prerequisites. The following are known to be required to set on Docker host:

grep vm.max_map_count /etc/sysctl.conf

Setting up Credentials

Setup credentials for Fence, a custom root CA and SSL certs with the provided script by running either:

bash ./

This script will create a Secrets folder that holds various secrets and configuration files. The script by default generates an SSL certificate to access the gen3 stack at https://localhost. If you are running this in a remote server with an actual domain, you can run bash YOUR_DOMAIN. This will create SSL cert signed by the custom CA so that the microservices can talk to each other without bypassing SSL verification. If you are setting this up on AWS, ensure that you use an Elastic IP address BEFORE you set up and use that as your domain. On an EC2 instance, for example, this would be your This will save a lot of time and avoid editing the individual files to set up the hostname(fence-config.yaml, peregrine_creds.json, and sheepdog_creds.json) when the machine is rebooted. This is because each of the microservices can be configured to run on separate machines and thus have their respective configuration files. You will still need to bypass SSL verification when you hit the services from the browser. If you have real certs for your domain, you can copy to Secrets/TLS/service.key and Secrets/TLS/service.crt to overwrite our dev certs.

If you are using MacOS, you may run into an error with the default MacOS OpenSSL config not including the configuration for v3_ca certificate generation. OpenSSL should create the jwt_private_key.pem and jwt_public_key.pem in the Secrets/fenceJwtKeys/{dateTtimeZ} folder. If you do not see them, control whether your version of OpenSSL is correct. You can refer to the solution on this Github issue on a related issue on Jetstack's cert-manager.

Support for multi-tenant fence (configure another fence as an IDP for this fence) is available and can be edited in the fence-config.yaml. If this is not the case, we recommend removing the relevant section.

Setting up Google OAuth Client-Id for Fence

This Docker Compose setup requires Google API Credentials in order for Fence microservice to complete its authentication. To set up Google API Credentials, go to the Credentials page of the Google Developer Console and click the 'Create Credentials' button. Follow the prompts to create a new OAuth Client ID for a Web Application. Add https://localhost/user/login/google/login/ OR https://YOUR_REMOTE_MACHINE_DOMAIN/user/login/google/login/ to your Authorized redirect URIs in the Credentials and click 'Create'. Then copy your client ID and client secret and use them to fill in the 'google.client_secret' and 'google.client_id' fields in the Secrets/fence-config.yaml file. See image below for an example on a sample Google account.

Redirection Set up

If you have Google API credentials set up already that you would like to use with the local gen3 Docker Compose setup, simply add https://localhost/user/login/google/login/ OR https://YOUR_REMOTE_MACHINE_DOMAIN/user/login/google/login/ to your Authorized redirect URIs in your credentials and copy your client ID and client secret from your credentials to the 'client_secret' and 'client_id' fields in the Secrets/fence-config.yaml under OPENID_CONNECT and google.

Setting up Users

To set up user privileges for the services, please edit the Secrets/user.yaml file, following the example format shown in this file. In particular, you should change all occurrences of to the email you intend to log in with, so that you can create administrative nodes later on.

Fence container will automatically sync this file to the fence_db database on startup. If you wish to update user privileges while the containers are running (without restarting the container), just edit the Secrets/user.yaml file and then run

docker exec -it fence-service fence-create sync --arborist http://arborist-service --yaml user.yaml

This command will enter Fence container to run the fence-create sync command, which will update your user privileges. If you are logged in to your commons on a browser, you may need to log out and log back in again or clear your cookies in order to see the changes.

Start running your local Gen3 Docker Compose environment

If your Gen3 Data Commons does not host any data, yet, we recommend commenting out the kibana-service section in the docker-compose.yaml and the guppy section in the nginx.conf file. After having setup the first program/project and uploaded the first data, we recommend enabling these sections.

Now that you are done with the setup, all Docker Compose features should be available. If you are a non-root user you may need to add yourself to the 'docker' group: sudo usermod -aG docker your-user, and the log out and log back in. Here are some useful commands:

The basic command of Docker Compose is

docker-compose up

which can be useful for debugging errors. To detach output from the containers, run

docker-compose up -d

When doing this, the logs for each service can be accessed using

docker logs

To stop the services use

docker-compose down

As the Docker images are pulled from, they do not update automatically. To update your Docker images, run

docker-compose pull
docker image prune -f

These commands may take a while, and they also may fail. If they do fail, simply rerun them, or just update/remove images one at a time manually. Sheepdog and Peregrine services download the dictionary schema at startup, and the portal service runs a series of pre-launch compilations that depend on Sheepdog and Peregrine, so it may take several minutes for the portal to finally come up at https://localhost

Following the portal logs is one way to monitor its startup progress:

docker logs -f portal-service

When you see that bundle.js and index.html were successfully built in the logs, you should be able to log into https://localhost and see the data commons. You are now ready to setup the first program and project.

Update tips

You should of course git pull compose-services if you have not done so for a while. You also need to docker-compose pull new images from Quay--this will not happen automatically. If your git pull pulled new commits, and you already have a Secrets folder, you may also need to delete your old Secrets and rerun (see Setting up Credentials) to recreate it.

Dev Tips

You can quickly find commonly used commands for compose services in our cheat sheet.

When developing, you can have local repositories of the services you are working on and use volumes to mount your local repository files onto the containers to override the containers' code (which is built from GitHub using Then, you can restart a single container with

docker-compose restart [CONTAINER_NAME]

after you update some code in order to see changes without having to rebuild all the microservices. Keep in mind that running docker-compose restart does not apply changes you make in the docker-compose file. Look up the Docker documentation for more information about volumes.

Running Docker Compose on a Remote Machine

To run Docker Compose on a remote machine, modify the BASE_URL field in fence-config.yaml, and the hostname field in peregrine_creds.json and sheepdog_creds.json in the Secrets directory.

Dumping config files and logs (MacOS/Linux)

If you are encountering difficulties while setting up Docker Compose and need help from the Gen3 team, you can use the script to create a zip file of your configuration and current logs, which you can share to get help.


Note that if docker-compose is not running, the logs will be empty.

The following configuration files will be included:

  • docker-compose.yml
  • user.yaml
  • any file ending with "settings" or "config"

Credentials files are NOT included and lines containing "password", "secret" or "key" are removed from other files. If your files contain other kinds of sensitive credentials, make sure to remove them before running the script.

Environment Details

The sandbox ecosystem deployed thus architecturally looks as shown below: Sandbox

All the microservices communicate with the Postgres Container based on the configuration specified above. Once the services are up and running, the environment can be visualized using the windmill microservice running on port 80 by typing the URL of the machine on which the containers are deployed. Please see example screenshot below as an example:

Launch Portal

Upon clicking 'Login from Google' and providing Google Credentials (if the same Google Account is used where the developer credentials came from), the system redirects the user to their landing page as shown below:

Logged Into Portal

Revproxy-service cannot start

If revproxy-service cannot start an error will occur. It may be useful to

docker-compose down
docker-compose up -d

If the error still occurs, make sure that apache2 and revproxy-service do not share the same port. You can change the port for revproxy-service and any other service in the docker-compose.yaml file. For revproxy you would also need to change the port in the nginx.conf here.

Using the Data Commons

For some general information about Gen3 Data Commons and how they work (such as how to access and submit data), visit the official site. The section below will go over some useful technical aspects of Gen3.

Smoke test

The script queries the health-check endpoints of each service launched by docker-compose.yml.

bash localhost

Programs and Projects

In a Gen3 Data Commons, programs and projects are two administrative nodes in the graph database that serve as the most upstream nodes. A program must be created first, followed by a project. Any subsequent data submission and data access, along with control of access to data, is done through the project scope.

Before you create a program and a project or submit any data, you need to grant yourself permissions. First, you will need to grant yourself access to create a program and second, you need to grant yourself access to see the program. You can create the program before or after having access to see it. For this, you will need to edit the Secrets/user.yaml file following the docs shown here.

Make sure to update user privileges:

docker exec -it fence-service fence-create sync --arborist http://arborist-service --yaml user.yaml

To create a program, visit the URL where your Gen3 Commons is hosted and append /_root. If you are running the Docker Compose setup locally, then this will be localhost/_root. Otherwise, this will be whatever you set the hostname field to in the creds files for the services with /_root added to the end. Here, you can choose to either use form submission or upload a file. I will go through the process of using form submission here, as it will show you what your file would need to look like if you were using file upload. Choose form submission, search for "program" in the drop-down list and then fill in the "dbgap_accession_number" and "name" fields. As an example, you can use "123" as "dbgap accession number" and "Program1" as "name". Click 'Upload submission json from form' and then 'Submit'. If the message is green ("succeeded:200"), that indicates success, while a grey message indicates failure. More details can be viewed by clicking on the "DETAILS" button. If you don't see the green message, you can control the sheepdog logs for possible errors and check the Sheepdog database (/datadictionary), where programs and projects are stored. If you see your program in the data dictionary, neglect the fact that at this time the green message does not appear and continue to create a project.

To create a project, visit the URL where your Gen3 Commons is hosted and append the name of the program you want to create the project under. For example, if you are running the Docker Compose setup locally and would like to create a project under the program "Program1", the URL you will visit will be localhost/Program1. You will see the same options to use form submission or upload a file. This time, search for "project" in the drop-down list and then fill in the fields. As an example, you can use "P1" as "code", "phs1" as "dbgap_accession_number", and "project1" as "name". If you use different entries, make a note of the dbgap_accession_number for later. Click 'Upload submission json from form' and then 'Submit'. Again, a green message indicates success while a grey message indicates failure, and more details can be viewed by clicking on the "DETAILS" button. You can control in the /datadictionary whether the program and project have been correctly stored.

After that, you're ready to start submitting data for that project! Please note that Data Submission refers to metadata regarding the file(s) (Image, Sequencing files, etc.) that are to be uploaded. Please refer to the Gen3 website for additional details.

Controlling access to data

Access to data and admin privileges in Gen3 is controlled using Fence through the user.yaml file found in the Secrets directory. We use users.policies for individual access and groups for group access. Please refer to the user.yaml_guide to add/subtract users and policies. Make sure to update user privileges with

docker exec -it fence-service fence-create sync --arborist http://arborist-service --yaml user.yaml

or review how to apply the changes made in the user.yaml file to the database in the section Setting up Users.

Generating Test Metadata

The gen3 stack requires metadata submitted to the system to conform to a schema defined by the system's dictionary. The gen3 developers use a tool to generate test data that conforms to a particular dictionary. For example - the following commands generate data files suitable to submit to a gen3 stack running the default genomic dictionary at

export TEST_DATA_PATH="$(pwd)/testData"
mkdir -p "$TEST_DATA_PATH"

docker run -it -v "${TEST_DATA_PATH}:/mnt/data" --rm --name=dsim --entrypoint=data-simulator simulate --url --path /mnt/data --program jnkns --project jenkins --max_samples 10

Changing the data dictionary

For an introduction to the data model and some essential information for modifying a data dictionary, please read this before proceeding.

The data dictionary the commons uses is dictated by either the DICTIONARY_URL or the PATH_TO_SCHEMA_DIR environment variable in both Sheepdog and Peregrine. The default value for DICTIONARY_URL are set to and the default value for PATH_TO_SCHEMA_DIR is set to the datadictionary/gdcdictionary/schemas directory which is downloaded as part of the compose-services repo (from here). Both correspond to the developer test data dictionary, as one is on AWS and one is a local data dictionary setup. To override this default, edit the environment fields in the peregrine-service section of the docker-compose.yml file. This will change the value of the environment variable in both Sheepdog and Peregrine. An example, where the DICTIONARY_URL and PATH_TO_SCHEMA_DIR environment variables is set to the default values, is provided in the docker-compose.yml.

NOTE: Only one of the two environment variables can be active at a time. The data commons will prefer DICTIONARY_URL over PATH_TO_SCHEMA_DIR. To reduce confusion, keep the variable you're not using commented out.

There are 3 nodes that are required for the dev (default) portal--case, experiment, and aliquot. If you remove any one of these, then you will also need to change the APP environment variable in portal-service, in addition to changing the DICTIONARY_URL or PATH_TO_SCHEMA field.

As this is a change to the Docker Compose configuration, you will need to restart the Docker Compose (docker-compose restart) to apply the changes.

Configuring guppy for exploration page

In order to enable guppy for exploration page, the gitops.json, etlMapping.yaml and guppy_config.json need to be configured. There are some examples of configurations located at It is worth to mentioning that the index and type in guppy_config.json need to be matched with the index in etlMapping.json.

When the data dictionary is changed, those files are also configured accordingly so that the exploration page can work.

Run bash ./ to create/re-create ES indices

Enabling data upload to s3

The templates/user.yaml file has been configured to grant data_upload privileges to the user. Connect it to your s3 bucket by configuring access keys and bucket name in fence-config.yaml.

<     aws_access_key_id: 'your-key'
<     aws_secret_access_key: 'your-key'
>     aws_access_key_id: ''
>     aws_secret_access_key: ''
<   your-bucket:
>   bucket1:
< DATA_UPLOAD_BUCKET: 'your-bucket'

Uploaded data file in "Generating..." status

It is important to note that Gen3 Compose-Services use AWS Simple Notification System (SNS) to get notifications when objects are uploaded to a bucket. These notifications are then stored in an AWS Simple Queue System (SQS). The Gen3 job dispatcher service watches the SQS and spins up an indexing job to update indexd with the file information (size, hash). During this process, the UI shows the file status as "Generating..." until indexd is updated.

If one or multiple data files have been submitted to an S3 bucket and you do not want to set up automation through an SNS and SQS, a simple alternative is to index the data files manually after the upload. The upload command creates a "blank" record in indexd, which should be then updated by adding the file's size and hash. This can be done with a PUT request to index, where the base URL is{GUID}. A list of URLS to reach other services from the Gen3 Framework is shown here. Only once the uploaded data file is indexed, graph metadata can be submitted to it.

Useful links

These links show insightful work conducted by other users in the Gen3 community and may be of help to new and experienced users/operators alike of a Gen3 Data Commons. We emphasize that we are not responsible for the content and opinions on the third-party webpages listed below.

  1. Working with on premises data and servers: The gen3 system is optimized to deploy on cloud systems and work with cloud buckets. The Oregon Health & Science University (OHSU) has developed a collection of extensions to enable gen3 to work in a non aws environment. Read this overview for more information.
  2. A group of users shared their experiences with setting up their Gen3 Data Commons on a local desktop using Compose Services in August 2020 in form of three videos: Gen3 Data Commons Setup Part 1, Gen3 Data Commons Setup Part 2, and Data Upload. Please note, that the content in these videos might not reflect the current status of the Compose-Services repository. Referring to the video part 1, the following is outdated: the format of the user.yaml reflects the one shown in the Fence repository and the arborist DB setup is up to date.
  3. A stand-alone data dictionary viewer for schema.json artifacts was published here.