Submissions orchestrator and processors for the RAMP bundle

pip install ramp-engine==0.5.1



Fresh local test

Add environment variables

export DATABOARD_DB_NAME='databoard'
export DATABOARD_DB_USER='mrramp'
export DATABOARD_DB_PASSWORD=<fill this>
export DATABOARD_DB_URL_TEST=postgresql://$DATABOARD_DB_USER:$DATABOARD_DB_PASSWORD@localhost/databoard_test

export DATABOARD_MAIL_USERNAME=<fill this>
export DATABOARD_MAIL_PASSWORD=<fill this>
export DATABOARD_MAIL_SENDER=<fill this>
export DATABOARD_ADMIN_MAILS=[<fill this>]
export DATABOARD_SECRET_KEY=<fill this with a random string>

Alternatively, you can hard-code these variables into after copying to

Set up db

You only have to do this once after each time you restart your computer.

conda install postgresql
mkdir postgres_dbs
initdb postgres_dbs
pg_ctl -D postgres_dbs -l postgres_dbs/logfile start
createuser --pwprompt mrramp

mrramp should be the user specified in $DATABOARD_DB_USER. It will prompt you for password which should be the same specified in $DATABOARD_DB_PASSWORD. Create db by

createdb --owner=mrramp databoard_test


git clone
cd ramp-workflow
pip install -r requirements.txt
git checkout titanic
python develop
cd ..
git clone
cd ramp-board
git checkout migrate
cp databoard/ databoard/
python develop
make test


make test-all

The deployment directory is /tmp/databoard. You can change it in

cd /tmp/databoard
fab serve

On the backend


Adding a new problems

cd <frontend>/ramp-kits
git clone<problem>
cd <problem>
jupyter nbconvert --to html <problem>_starting_kit.ipynb

## Dependencies

Install dependencies with `pip install -Ur requirements.txt`
(You might want to create a virtualenv beforehand)

pip install --upgrade

## Configuration

**Copy `databoard/` to `databoard/`**. If you need special settings, you can modify `databoard/`.
**Do not commit `databoard/`**, since it might contain passwords...

## Set up the database

To run the test you will need to set environment variable DATABOARD_TEST to True:

    export DATABOARD_TEST=True

otherwise set it to False:

    export DATABOARD_TEST=False

You can use different database system:

**local sqlite**

For a local install (on a unix system) you can do:

    export DATABOARD_DB_URL_TEST=sqlite:////tmp/databoard_test.db
    export DATABOARD_DB_URL=sqlite:////tmp/databoard_test.db

**Postgres databases**: one for test and one for dev.

1. Install postgres and create two databases (`createdb <db_name>`)

For example you do in the postgres terminal: `createdb databoard`

with conda:

conda install postgresql
conda install -c anaconda psycopg2=2.6.2

un ubuntu:

sudo apt-get install postgresql postgresql-contrib

make a dir postgres_dbs containing all the dbs (test and prod eventually) and cd there
then go up and execute
initdb postgres_dbs
start the server by
pg_ctl -D postgres_dbs -l postgres_dbs/logfile start
Create user by
createuser --pwprompt mrramp
mrramp should be the user specified in DATABOARD_DB_USER
it will prompt you for password
pwd should be the same specified in DATABOARD_DB_PASSWORD
Create db by
createdb --owner=mrramp databoard_test

2. Set up environment variables:

    - `DATABOARD_DB_URL`: `SQLALCHEMY_DATABASE_URI` for the dev database, which should be something like `postgresql://<db_user>:<db_password>@localhost/<db_name>`
    - `DATABOARD_DB_URL_TEST`: `SQLALCHEMY_DATABASE_URI` for the test database


    If you do in the postgres "psql" command line:

    CREATE USER databoard WITH password 'password';
    CREATE DATABASE databoard_test WITH OWNER databoard;

    Then you need to do:

    export DATABOARD_DB_URL=postgresql://databoard:password@localhost/databoard_test

    The general strucure is:

    export DATABOARD_DB_URL=postgresql://$USER:<db_password>@localhost/<db_name>

Then you can setup or upgrade the database with:

    `python db upgrade`

### Migrations

Run: `python db migrate`. It creates a migration file in `migrations/versions/`
Add `import databoard` on top of the migration file
Run: `python db upgrade` to apply the migration
**Don't forget to add and commit migrations files**

### Get the prod database  

A dump of the prod database is saved everyday on the scienceFS backup disk. You can use this dump to populate your test db. You need access to the scienceFS backup disk and the prod database  credentials.  

Define the following environment variables:  

    export DATABOARD_DB_NAME='databoard'
    export DATABOARD_DB_USER='<prod_db_user>'     # Ask Balazs
    export DATABOARD_DB_PASSWORD='<prod_db_pwd>'  # Ask Balazs
    export SCIENCEFS_LOGIN='balazs.kegl'          # You need the private key or the password...
    export SCIENCEFS_ID='<scienceFS_key>'         # Path and name of the scienceFS private key
    export mount_path='mount_backup'              # Path where to mount the scienceFS disk to get backups
    export DATABOARD_PATH='/tmp'                  # Root path to your databoard app. By default, on your local computer it is /tmp, so that the app is in /tmp/datacamp/databoard. For prod and test servers it is '/mnt/ramp_data'.                     
    export prod_db_dump='<blabla.dump>'           # db dump to be used (just write the dump name without the path to it

Run script: ``bash tools/``

## Set up datarun  

If you want to use datarun (e.g. for local tests), you need to define the 3 environment variables (with your datarun credentials):  

    export DATARUN_URL='uuuu'
    export DATARUN_USERNAME='vvvv'
    export DATARUN_PASSWORD='wwww'

### How to use datarun to train test submissions?

See datarun documentation (especially "notes for databoard users"):
- [pdf here](
- [html here](

## Deploy

### Prepare starting kit bundle, data, and test submissions for local test

 - cd problems/<problem_name>
 - starting_kit/<event_name>
 - starting_kit/public_train.csv (+ other data if needed)
 - starting_kit/*.py for the initial submissions in the sandbox
 - starting_kit/
 - data/raw/* all the data which prepare_data() will use + the directory structure, usually data/private
 - for local tests (optional), create deposited_submissions/<test_team>/<test_submission> and put the test submission files there
 - run
zip -r starting_kit
   This is a bit tricky: if the public_train is created in prepare_data, after deployment (fab add_problem, fab add_event), zip should be re-run just in case.
 - run
fab publish_problem:<problem_name>,target=local/test/production
   which will copy everything on the local test, production or test server

### Add workflow element types and workflows (in case the ramp needs a new one)

 - code the workflow element type in databoard/specific/workflows
 - add it to
 - workflow element type example
fab add_workflow_element_type:classifier,code
 - workflow example:
fab add_workflow:feature_extractor_classifier_calibrator_workflow,feature_extractor,classifier,calibrator

### Add score types (in case the ramp needs a new one)

 - code the score type in databoard/specific/score_types
 - add it to
 - score type example
fab add_score_type:test_score_type,True,0.0,inf

### Add problem

 - code the problem in databoard/specific/problems
 - add it to
 - problem example
fab add_problem:variable_stars

### Add event

 - code the event in databoard/specific/events
 - add it to
 - event example
fab add_event:variable_stars

## Backup

rsync -rultv root@ db/
rsync -rultv root@ ./

### Test ramp locally

set the environment variable `DATABOARD_TEST` to `True` (`export DATABOARD_TEST=True`)
fab test_setup
fab serve
 - goto and test the interface

### Publish on the server

fab publish_software:target=production
fab publish_software:target=test

### If code is redeployed

pip install -Ur requirements.txt
python develop

### Server

 - old:
fab serve:80 > server_logs/server16.txt 2>&1
 - new:
sudo service apache2 restart

 - inspect server log file:
tail -n1000 -f /var/log/apache2/error.log

sed -i "s#os.environ.get('DATABOARD_DB_URL')#'$DATABOARD_DB_URL'#g" /home/datacamp/code/databoard/

### Mac bug

Postgres and anaconda are somehow clashing. Add this to ~/.bash_profile:

On MacOS 10.12, maybe you need this:

export DYLD_FALLBACK_LIBRARY_PATH=$HOME/anaconda/lib/:/usr/local/Cellar/openssl/1.0.2k/lib/:/usr/lib/:$DYLD_FALLBACK_LIBRARY_PATH

It seems now that the above stuff doesn't work. Instead, postgres should be installed through conda:

conda install postgresql
conda install -c anaconda psycopg2=2.6.2

### Example sequence of adding ramps

#### Titanic

fab add_score_type:auc,"0",0.0,1.0 fab add_workflow:feature_extractor_classifier_workflow,feature_extractor,classifier fab add_problem:titanic fab add_event:titanic fab sign_up_team:titanic,kegl

#### Pollenating insects 2

fab add_score_type:f1_above,"0",0.0,1.0 fab add_problem:pollenating_insects_2,force=True fab add_event:pollenating_insects_2_paillasse,force=True fab sign_up_team:pollenating_insects_2_paillasse,kegl fab sign_up_team:pollenating_insects_2_paillasse,mcherti

#### Pollenating insects

fab add_workflow_element_type:image_preprocessor,code fab add_workflow_element_type:batch_classifier,code fab add_workflow:batch_classifier_workflow,image_preprocessor,batch_classifier fab add_problem:pollenating_insects fab add_event:pollenating_insects_M1XMAP583_M2HECXMAP542_201617 fab sign_up_team:pollenating_insects_M1XMAP583_M2HECXMAP542_201617,kegl fab sign_up_team:pollenating_insects_M1XMAP583_M2HECXMAP542_201617,mcherti

#### HEP tracking

fab add_score_type:clustering_efficiency,"0",0.0,1 fab add_workflow_element_type:clusterer,code fab add_workflow:clusterer_workflow,clusterer fab add_problem:HEP_tracking fab add_event:HEP_tracking fab sign_up_team:HEP_tracking,kegl

#### drug_spectra

fab add_score_type:error_mare_mixed,"1",0.0,inf fab add_score_type:error_mixed,"1",0.0,1.0 fab add_score_type:mare_mixed,"1",0.0,inf fab add_score_type:mare,"1",0.0,inf fab add_workflow_element_type:feature_extractor_reg,code fab add_workflow_element_type:feature_extractor_clf,code fab add_workflow:feature_extractor_classifier_regressor_workflow,feature_extractor_clf,classifier,feature_extractor_reg,regressor fab add_problem:drug_spectra fab add_event:drug_spectra fab sign_up_team:drug_spectra,kegl

#### air passengers

fab add_workflow_element_type:external_data,data fab add_workflow:feature_extractor_regressor_with_external_data_workflow,feature_extractor,regressor,external_data fab add_problem:air_passengers fab add_event:air_passengers_dssp4 fab sign_up_team:air_passengers_dssp4,kegl fab sign_up_team:air_passengers_dssp4,agramfort

#### sea ice

fab add_workflow_element_type:ts_feature_extractor,code fab add_workflow:ts_feature_extractor_regressor_workflow,ts_feature_extractor,regressor fab add_problem:sea_ice fab add_event:sea_ice_colorado fab sign_up_team:sea_ice_colorado,kegl

#### el nino (the first two lines are unnecessary if sea ice is already there)

fab add_workflow_element_type:ts_feature_extractor,code fab add_workflow:ts_feature_extractor_regressor_workflow,ts_feature_extractor,regressor fab add_problem:el_nino fab add_event:el_nino fab sign_up_team:el_nino,kegl

#### epidemium2 cancer mortality

fab add_workflow_element_type:feature_extractor,code fab add_workflow_element_type:regressor,code fab add_workflow:feature_extractor_regressor_workflow,feature_extractor,regressor fab add_problem:epidemium2_cancer_mortality fab add_event:epidemium2_cancer_mortality fab sign_up_team:epidemium2_cancer_mortality,kegl

### Batch sign up users
 - make a file users_to_add.csv with header
 firstname  lastname  email name  hidden_notes
 - make passwords for them:
fab generate_passwords:users_to_add.csv,users_to_add.csv.w_pwd
 - add them to the ramp (it's a bit messy now when a user is already there with a mail but different username; should be handled)
fab add_users_from_file:users_to_add.csv,users_to_add.csv.w_pwd
 - sign them up to an event:
fab sign_up_event_users_from_file:users_to_add.csv,<event>
 - send them mails with their passwords:
fab send_password_mails:users_to_add.csv.w_pwd

### App performance

#### Profiling
fab profile:port,profiling_output_file
By default `port=None` (for local profiling) and `profiling_output_file=profiler.log`
#### Database performance
To report in the logging system queries that takes too long, define an environment variable `DATABOARD_DB_PERF` (equals to 'True' for instance).
#### Stress Tests with [Locust](
1. Modify `` to specify the current ramp url to be tested and the databoard path (or to add some tasks)
2. Define two environment variables `DATABOARD_USERNAME` and `DATABOARD_PASSWORD` to login during tests.
3. Set `WTF_CSRF_ENABLED` to `False` in `databoard/`
4. Run `locust -f`
5. Go to and enter the number of users to simulate

### Current deployment on stratuslab (openstack)

#### 1. Production server

- Production server deployed on prod_ramp

- Two disks are mounted to the VM:
    * sciencefs disk, which is used for backup. It is mounted to /mnt/datacamp.
    * persistent volume (prod_ramp), where databoard code and submission files are stored. It is mounted to /mnt/ramp_data.

- Databoard code is in /mnt/ramp_data/code/databoard

- Databoard submission files,, ... are in /mnt/ramp_data/datacamp/databoard

- Backup are made every day around midnight and are saved (on the sciencefs disk) in /mnt/datacamp/backup. It is made using cron and bash scripts in /mnt/ramp_data/code/databoard/tools/ +
During RAMP, it might be better to increase the backup frequency. To do this, the crontab file can be edited by running "crontab -e".
Two types of backup:
    * dump of the database
    * rsync of /mnt/ramp_data/datacamp/databoard folder

#### 2. Test server

- Test server deployed on test_ramp

- One disk is mounted to the VM:
    * persistent volume (test_ramp), where databoard code and submission files are stored. It is mounted to /mnt/ramp_data.

- Databoard submission files,, ... are in /mnt/ramp_data/datacamp/databoard

- Databoard code is in /mnt/ramp_data/code/databoard

#### Remount disks on prod server

##### sciencefs disk

export SCIENCEFS_LOGIN='balazs.kegl'

sshfs -o allow_other -o IdentityFile=/root/.ssh/id_rsa_sciencefs -o StrictHostKeyChecking=no "$SCIENCEFS_LOGIN""$SCIENCEFS_LOGIN"/databoard /mnt/datacamp

##### prod_ramp disk

mount dev_file /mnt/ramp_data

where dev_file corresponds to the path of the dev file
of the prod_ramp disk : <>.

Currently it is : /dev/vdb, so the command is :

mount /dev/vdb /mnt/ramp_data

### How to deploy databoard on stratuslab

A databoard server needs:
- an **Ubuntu 14.04 VM** with databoard installed on it
- a **persistent disk** where submission files and data are saved
- the **sciencefs disk** where are saved **backups** of the postgres database and of submission files (**only for a production server**)

Below are the instructions to **start a databoard server using the latest state of the production database**:

#### For a test server

1. Start a VM (Ubuntu 14.04) on openstack (via the openstack interface).
2. Go to `databoard/tools directory` and Make it possible to log in as root: `ssh ubuntu@<VM_IP_ADDRESS> 'bash -s' <`
3. Create a persistent disk and attach it to the VM (via the openstack interface).
4. Create a file `` which contain required environment variables **DO NOT COMMIT THIS FILE**:

export DATABOARD_PATH='/mnt/ramp_data/' #where to mount the persistent disk export DATABOARD_DB_NAME='databoard' export DATABOARD_DB_USER='xxxx' export DATABOARD_DB_PASSWORD='yyyy' export DATABOARD_DB_URL='postgresql://xxxx:yyyy@localhost/databoard' export SCIENCEFS_LOGIN='zzzz' export DATARUN_URL='uuuu' export DATARUN_USERNAME='vvvv' export DATARUN_PASSWORD='wwww'

5. scp to the VM the file `` and the script ``: `scp root@<VM_IP_ADDRESS>:/root/.`
6. ssh to the instance and run `bash {disk_path} {db_dump}` where `disk path` is the path to the attached disk (something like `/dev/vdb`, which can be found on the openstack interface) and database dump from which to create new database (give only the dump file name, this file should be located on the sciencefs disk in `~/databoard/backup` which will be mounted on the VM in `/mnt/datacamp/backup`). This script:
    - installs databoard on the VM. It will clone the project from git (line 112). **Modify this line to clone it with your account if needed**.
    - mounts the sciencefs disk to retrieve backups of the latest state of the db and of associated submission files
    - mounts the persistent disk and copy onto it backups of submission files from the sciencefs disk
    - installs apache, ... and start the application
    - starts celery workers to send jobs to datarun  6. Unmount the sciencefs disk `fusermount -u /mnt/datacamp`

#### For a production server

Follow **instructions 1 to 6** from above.
7. Set up backups of the db and of submission files: use of `crontab` to run `tools/` and `tools/`. To set up it, add these lines to the file opened by running `crontab -e`:

02 0 * * * root bash /mnt/ramp_data/code/databoard/tools/ 22 1 * * * root bash /mnt/ramp_data/code/databoard/tools/

## Set up the backend

Normally, given a large enough frontend server, you are good to go using manual training (fab train_test) on the frontend server. The principle of the RAMP backend is the following:

1. We mount the databoard root directory $DATABOARD_PATH/datacamp/databoard on the backend server, and make the database visible.

2. We launch a job (fab backend_train_test_loop) which loops infinitely with 30s waiting times between each iteration of:
    1. get all new submissions
    2. select the one earliest submission time (FIFO)
    3. set its state to "training"
    4. train/test it, compute contributivities, update leaderboards, set its state to trained/validated/tested/training_error/validating_error/testing_error (same as train_test)

### What to do on the frontend

(make the postgres db visible to outside servers; Mehdi will fill this out)

### What to do at each backend server

1. Create an Ubuntu14.04 virtual machine. We typically use 8 CPUs and 4-64GB RAM, depending on the size of the data used in the RAMP. We assume that you can log into this machine as user 'ubuntu'.

2. Run tools/ If your backend has these packages installed, you can skip this step. If it's a virgin server, you will need to manually upload this script or copy-paste it at command line.

3. Clone the databoard code from github, eg. by executing tools/ It will also install all the requirements in requirements.txt. Feel free to edit this file, add or delete libraries needed/not needed for the training jobs of the RAMP.

4. cd tools and edit It should contain the following environment variables:

export DATABOARD_IP='xx.xx.xx.xx' # the IP of the frontent export DATABOARD_PATH='/mnt/ramp_data/' # databoard path: on the frontend server it is in $DATABOARD_PATH/datacamp/databoard. It will be mounted on this server to the same path. export DATABOARD_DB_NAME='databoard' # the name of the DB at the frontend export DATABOARD_DB_USER='xxxx' # the username of the DB at the frontend export DATABOARD_DB_PASSWORD='yyyy' # the username of the DB at the frontend export DATABOARD_DB_URL='postgresql://'$DATABOARD_DB_USER':'$DATABOARD_DB_PASSWORD'@'$DATABOARD_IP':'$DATABOARD_DB_PORT'/'$DATABOARD_DB_NAME export DATABOARD_IDENTITY_FILE='/home/ubuntu/.ssh/ramp/id_rsa' # the private key to log in to root@$DATABOARD_IP

5. Make sure that you can log in as a user root to the frontend server. Eg, 
    1. mkdir /home/ubuntu/.ssh/ramp
    2. Add
    Host xx.xx.xx.xx # ramp frontend
        User root
        ForwardAgent yes
        IdentityFile /home/ubuntu/.ssh/ramp/id_rsa
    to /home/ubuntu/.ssh/config
    Make sure the $DATABOARD_IDENTITY_FILE variable is set to wherever the private key is (/home/ubuntu/.ssh/ramp/id_rsa in the example)
    3. Copy the private key that allows you to log in to the databoard frontend as root to /home/ubuntu/.ssh/ramp/id_rsa. Don't forget to change its rights to chmod 400

6. Execute This script will install additional libraires, e.g., xgboost and python-netcdf4, which may take long time. You can comment out these installations if they are not needed in your ramp. The script then mounts the frontend directory $DATABOARD_IP:$DATABOARD_PATH/datacamp/databoard to the local directory $DATABOARD_PATH/datacamp/databoard.

7. cd to $DATABOARD_PATH/datacamp/databoard. At this point you can execute any fab statements that you can execute at the frontend server. If you want this server to start training automatically, launch
fab backend_train_test_loop.