| --- |
| layout: tryit |
| title: Try PredictionIO |
| --- |
| |
| .codepicnic |
| .tutorial |
| |
| div class="jcarousel-wrapper" |
| ul class="jcarousel-pagination" |
| div class="jcarousel" |
| ul |
| li |
| h2 Introduction |
| p To see how PredictionIO works, you can follow this quick tutorial to: |
| p Start the PredictionIO server |
| p Build, train, and deploy a predictive model with pre-imported data. |
| p Query prediction results. |
| p #{link_to 'Start', '#', class: 'button-primary', id: 'tryit-start'} |
| |
| li |
| h2 Start |
| p Start PredictionIO and it's dependencies with: |
| p <code>$ pio-start-all</code> |
| h2 Check Status |
| p At any time you can run: |
| p <code>$ pio status</code> |
| p Which checks the status of PredictionIO and it's dependencies. |
| |
| li |
| h2 Build |
| p All engine commands need to be run from within the engine directory: |
| p <code>$ cd ~/PredictionIO/MyEngine</code> |
| |
| p Build the engine with: |
| p <code>$ pio build</code> |
| li |
| h2 Train and Deploy |
| |
| h3 Train the engine |
| p #{link_to 'Sample data', 'http://grouplens.org/datasets/movielens/'} is already imported, you can train the model the data by running: |
| p <code>$ pio train</code> |
| |
| h3 Deploy the trained engine |
| p Deploy the trained engine as a background process: |
| p <code>$ pio deploy &</code> |
| |
| li |
| h2 Querying Results |
| p You can query results using cURL with after you see "Ready to serve": |
| p <code>$ curl -H "Content-Type: application/json" -d '{ "items": ["296"], "num": 5 }' http://localhost:8000/queries.json</code> |
| p This will return 5 recommended movies for movie 296 (Pulp Fiction). |
| |
| li |
| h2 Next Steps |
| p #{link_to 'Install', 'http://predictionio.apache.org/install/'} PredictionIO on your own computer! |
| h3 Support |
| p Get support on our #{link_to 'Google Group', 'https://groups.google.com/forum/#!forum/predictionio-user'} or on Twitter #{link_to '@PredictionIO', 'https://twitter.com/predictionio/'} |
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