quickstart.md

Quick Start

1. Install loom following the Install Guide and activate the virtualenv you created during the install process, for example using:

   workon loom
   

2. (optional) Set up a remote ipython notebook server

   ssh -A -L 8888:localhost:8888 <user>@<hostname>
   workon loom
   pip install ipython[all]
   pip install --upgrade pyzmq
   ipython notebook --no-browser --ip=0.0.0.0 --matplotlib=inline
   

   You should now be able to access the ipython notebook server at http:https://localhost:8888

3. There are three basic steps in the loom workflow: preparing and ingesting data, running inference, and querying the results.

   To prepare a dataset for ingestion, loom needs two files: the data in a csv file, and a schema indicating which feature models to use for each column in the csv.

   The taxi example contains both of these, ready to go: example.csv, schema.json.

4. Ingest the data. Loom will read in the csv and the schema, and translate them into its own on-disk representation.

   You must supply a name, which is how loom will refer to this analysis in subsequent steps; we will use the name "quickstart".

   cd $LOOM/examples/taxi
   python -m loom.tasks ingest quickstart schema.json example.csv
   

5. Run inference. This step reads the ingested data from the previous step, and produces indexes that can be queried.

   python -m loom.tasks infer quickstart
   

6. Interactively query loom using the client library. See here for more information about the supported query operations.

   python
   import loom.tasks
   with loom.tasks.query("quickstart") as server:
       related = server.relate(["feature1", "feature2", "feature3"])
       print related