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Add .circleci/config.yml#18

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levkk merged 10 commits intomasterfromcircleci-project-setup
Apr 18, 2022
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26 changes: 26 additions & 0 deletions.circleci/config.yml
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# Use the latest 2.1 version of CircleCI pipeline process engine.
# See: https://circleci.com/docs/2.0/configuration-reference
version: 2.1

# Define a job to be invoked later in a workflow.
# See: https://circleci.com/docs/2.0/configuration-reference/#jobs
jobs:
red-wine-quality:
# Specify the execution environment. You can specify an image from Dockerhub or use one of our Convenience Images from CircleCI's Developer Hub.
# See: https://circleci.com/docs/2.0/configuration-reference/#docker-machine-macos-windows-executor
docker:
- image: levkk/postgresml:latest
# Add steps to the job
# See: https://circleci.com/docs/2.0/configuration-reference/#steps
steps:
- checkout
- run:
name: "Train and predict"
command: "bash docker/entrypoint.sh psql -U root -p 5432 -h 127.0.0.1 -f sql/test.sql"

# Invoke jobs via workflows
# See: https://circleci.com/docs/2.0/configuration-reference/#workflows
workflows:
end-to-end-tests:
jobs:
- red-wine-quality
2 changes: 2 additions & 0 deletionsREADME.md
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Expand Up@@ -2,6 +2,8 @@

![PostgresML](./logo-small.png)

[![PostgresML](https://circleci.com/gh/postgresml/postgresml/tree/master.svg?style=svg)](https://circleci.com/gh/postgresml/postgresml/tree/master)

PostgresML is an end-to-end machine learning system. Using only SQL, it allows to train models and run online predictions, alongside normal queries, directly using the data in your databases.

## Why
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2 changes: 1 addition & 1 deletionsql/install.sql
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Expand Up@@ -6,7 +6,7 @@ CREATE EXTENSION IF NOT EXISTS plpython3u;
---
--- Create schema for models.
---
DROP SCHEMA pgml CASCADE;
DROP SCHEMAIF EXISTSpgml CASCADE;
CREATE SCHEMA IF NOT EXISTS pgml;

CREATE OR REPLACE FUNCTION pgml.auto_updated_at(tbl regclass)
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4 changes: 2 additions & 2 deletionssql/test.sql
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Expand Up@@ -8,12 +8,12 @@ SELECT pgml.version();

\timing

SELECT pgml.train('Red Wine Scores', 'regression', 'wine_quality_red', 'quality');
SELECT* FROMpgml.train('Red Wine Scores', 'regression', 'wine_quality_red', 'quality');
SELECT pgml.predict('Red Wine Scores', '{7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
SELECT pgml.predict('Red Wine Scores', '{6.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
SELECT pgml.predict('Red Wine Scores', '{5.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
SELECT pgml.predict('Red Wine Scores', '{3.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');

SELECT pgml.train('Red Wine Categories', 'classification', 'wine_quality_red', 'quality', 'svm');
SELECT* FROMpgml.train('Red Wine Categories', 'classification', 'wine_quality_red', 'quality', 'svm');
SELECT pgml.predict('Red Wine Categories', '{7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');

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