chown-Rwww-data:www-datadata/php
# Or, if previous fails, for instance because `www-data` does not exist:chmod-R777data/php
This is necessary to make sure that you can upload datasets, tasks and runs. Note that the dataset data is meant to be public anyway, so a 777 should not be problematic. This step won't be necessary anymore once the backend stores its files on MinIO.
dockercompose--profileallup-d# all servicesdockercomposeup-d# only the databasedockercompose--profilefrontendup-d# Frontend, rest-api, elasticsearch and database
dockerlogsopenml-php-rest-api-f# tail the logs of the php rest apidockerexec-itopenml-php-rest-api/bin/bash# go into the php rest api container./scripts/connect_db.sql# access the database
[!TIP]
If you change any port, make sure to change it for all services!
When you spin up the docker-compose, you'll get these endpoints:
- Frontend: localhost:8000
- Database: localhost:3306, filled with test data.
- ElasticSearch: localhost:9200 or localhost:8000/es, filled with test data.
- Rest API: localhost:8080
- Minio: console at localhost:9001, filled with test data.
The email-server is used for emails from the frontend. For example, if you create a new user, an
email is send to the user. All outgoing emails are rerouted to catchall@example.com. You can see
the messages in config/email-server/messages. Note that some of the urls in the emails need to
be slightly altered to use them in the test setup: change https to http.
If you want to do local development on containers that are part of the docker-compose, you want those containers to change based on your code. You should have the relevant code somewhere on your system, you only need to tell the docker-compose where to find it. You can do so by setting environment variables.
Create a .env file inside this directory, and set:
PHP_CODE_DIR=/path/to/OpenML# Root of https://github.com/openml/OpenML on your computerPHP_CODE_VAR_WWW_OPENML=/var/www/openml# Always set this to /var/www/openml. Leave empty if you leave PHP_CODE_DIR empty
Make sure to create openml_OS/config/BASE_CONFIG.php in your local $PHP_CODE_DIR. The correct configuration can be found in config/php.env. Run docker compose with profile rest-api.
ARFF_TO_PQ_CODE_DIR=/path/to/minio-data# Root of https://github.com/openml-labs/minio-data on your computerARFF_TO_PQ_APP=/app# Always set this to /app. Leave empty if you leave ARFF_TO_PQ_CODE_DIR empty
CROISSANT_CODE_DIR=/path/to/openml-croissant/python# Python directory of https://github.com/openml/openml-croissant on your computerCROISSANT_APP=/app# Always set this to /app. Leave empty if you leave CROISSANT_CODE_DIR empty
FRONTEND_CODE_DIR=/path/to/openml.org# Python directory of https://github.com/openml/openml.org on your computerFRONTEND_APP=/app# Always set this to /app. Leave empty if you leave FRONTEND_CODE_DIR empty
importopenmlfromopenml.tasksimportTaskTypefromopenml.datasets.functionsimportcreate_datasetimportpandasaspdimportnumpyasnpdf=pd.DataFrame(np.random.randint(0,100,size=(100,4)),columns=list('ABCD'))df["class"]=["test"ifnp.random.randint(0,1)==0else"test2"for_inrange(100)]df["class"]=df["class"].astype("category")dataset=create_dataset(name="test_dataset",description="test",creator="I",contributor=None,collection_date="now",language="en",attributes="auto",ignore_attribute=None,citation="citation",licence="BSD (from scikit-learn)",default_target_attribute="class",data=df,version_label="test",original_data_url="https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html",paper_url="url",)dataset.publish()# Meanwhile you can admire your newly created dataset at http://localhost:8000/search?type=data&id=[dataset.id]# Wait a minute until dataset is activemy_task=openml.tasks.create_task(task_type=TaskType.SUPERVISED_CLASSIFICATION,dataset_id=dataset.id,target_name="class",evaluation_measure="predictive_accuracy",estimation_procedure_id=1,)my_task.publish()# wait a minute, so that the dataset and tasks are both processed by the evaluation engine.# the evaluation engine runs every minute.# Meanwhile you can check out the newly created task at localhost:8000/search?type=task&id=[my_task.id]my_task=openml.tasks.get_task(my_task.task_id)fromsklearnimportcompose,ensemble,impute,neighbors,preprocessing,pipeline,treeclf=tree.DecisionTreeClassifier()run=openml.runs.run_model_on_task(clf,my_task)run.publish()# wait a minute, so the the run is processed by the evaluation enginerun=openml.runs.get_run(run.id,ignore_cache=True)run.evaluations# Expected: {'average_cost': 0.0, 'f_measure': 1.0, 'kappa': 1.0, 'mean_absolute_error': 0.0, 'mean_prior_absolute_error': 0.0, 'number_of_instances': 100.0, 'precision': 1.0, 'predictive_accuracy': 1.0, 'prior_entropy': 0.0, 'recall': 1.0, 'root_mean_prior_squared_error': 0.0, 'root_mean_squared_error': 0.0, 'total_cost': 0.0}
If you want to develop a service that depends on any of the services in this docker-compose, just bring up this docker-compose and point your service to the correct endpoints.
