should make things more reproducible
This pipline can be used to run analyses in a structured way, and stores configurations and results in a database.
the user writes a minimal plan file which contains only the following information:
- "how do I get the data?", by providing a dataloader
- "what to do with the data?", by providing a scikit pipeline
- "how to process the result?", by providing result handlers.
- "where to additionally store results?" by providing storage handlers.
Please have a look at the examples for more information.
We provide a
dbispipeline-link tool that can be used to link datasets to
the data directory. This ensures that datasets are linked in a consistent way
even on different machines. The general process is as follows:
Either in the
dbispipeline.inior as an argument in the cli call, one can define where in general datasets are stored on the local machine. For example, many datasets are available on
/storage/nas3/datasets/text. In this case, this would be the value in the configuration:
#dbispipeline.ini: [project] dataset_dir = /storage/nas3/datasets
In a file
data/links.yaml, one can define specific datasets that are used by the software. Thereby, the first path segment will be cut off (not sure why). For example, the following yaml file:
--- datasets: - music/acousticbrainz - music/billboard - music/millionsongdataset
would assume that a physical directory exists at
/storage/nas3/datasets/music/billboardand after calling the script
dbispipeline-linkwithout parameters using the above configuration, the following symlinks will be created:
data/acousticbrainz -> /storage/nas3/datasets/music/acousticbrainz data/billboard -> /storage/nas3/datasets/music/billboard data/millionsongdataset -> /storage/nas3/datasets/music/millionsongdataset
The value of
dataset_dirfrom the config can be overwritten in the cli script by using the
- python >= 3.6
- a PostgreSQL database
- an email server if you want to use notification emails
- Install dbispipeline in your python. We recommend using pipenv to keep your
pipenv install dbispipelineThis call will install a virtual environment as well as all dependencies.
- Write your plan(s). See the example plan files for guidance.
pipenv run dp <yourplanfile.py>
The framework look in multiple directories for its configuration files.
/usr/local/etc/dbispipeline.iniused for system wide default.
$HOME/.config/dbispipeline.iniused for user specific configurations.
./dbispipeline.inifor project specific configurations.
And example configuration file looks like this:
[database] # url to your postgres database host = your.personal.database # your database user name user = user # port of your postgres database, default = 5432 # port = 5432 # password of your database user password = <secure-password> # database to use database = pipelineresults # table to be used result_table = my_super_awesome_results [project] # this will be stored in the database name = dbispipeline-test # this is used to store backups of the execution # it is possible to override this by setting the DBISPIPELINE_BACKUP_DIR # environment variable # the default is the temp dir of the os if this option is not set. backup_dir = tmp # this is used to linke the used datasets spcified in data/links.yaml # it is possible to override this by setting the DBISPIPELINE_DATASET_DIR # environment variable dataset_dir = /storage/nas/datasets [mail] # email address to use as sender sender = firstname.lastname@example.org # recipient. This should probably be set on a home-directory-basis. recipient = email@example.com # smtp server address to use smtp_server = smtp.yourserver.com # use smtp authentication, default = no # authenticate = no # username for smtp authentication, required if authenticate = yes # username = foo # password for smtp authentication, required if authenticate = yes # password = bar # port to use for smtp server connection, default = 465 # port = 465
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