Meteos/ExampleRecommend

Recommend a Movie using Meteos
In this example, you create a prediction model which recommend a movie by using Recommendation Model.



1. Create a experiment template
Create template of experiment. Experiment is a workspace of Machine Learning.

You have to confirm a glance image id of meteos image, and a neutron network id before creating a template.

You can use a format located in python-meteosclient/sample/json/template.json

$ glance image-list | grep meteos $ neutron net-list | grep public $ vim sample/json/template.json $ cat sample/json/template.json {   "display_name": "example-template", "display_description": "This is a sample template of experiment", "image_id" : "a6b7de0b-10ff-462c-9c86-25c8a5622a57", "master_nodes_num": 1, "master_flavor_id": "4", "worker_nodes_num": 2, "worker_flavor_id": "2", "spark_version": "1.6.0", "floating_ip_pool": "4222b557-6d9f-405c-b1ff-0f454d2f35bf" } $ meteos template-create --json sample/json/template.json +---+-+ +---+-+ +---+-+
 * a6b7de0b-10ff-462c-9c86-25c8a5622a57 | meteos                         |
 * 4222b557-6d9f-405c-b1ff-0f454d2f35bf | public | 1f979ae3-d6b7-4d03-ba0f-9d9112581783             |
 * Property     | Value                                   |
 * cluster_id   | None                                    |
 * created_at   | 2016-12-15T22:55:03.000000              |
 * description  | This is a sample template of experiment |
 * id           | d3180a78-74cc-432d-9e9d-68640b18abae    |
 * master_flavor | 4                                      |
 * master_nodes | 1                                       |
 * name         | example-template                        |
 * project_id   | 4034bb3cd9324776a68c140fdd18baa4        |
 * spark_version | 1.6.0                                  |
 * status       | available                               |
 * user_id      | 64ad04e101df40b4b51e5f370a83412c        |
 * worker_flavor | 2                                      |
 * worker_nodes | 2                                       |

2. Create a experiment from template
Create a experiment by using template created in the above step. You have to confirm a neutron private network id and create keypair before creating a template.

You can use a format located in python-meteosclient/sample/json/experiment.json

$ nova keypair-add key1 > ~/key1.pem && chmod 600 ~/key1.pem $ neutron net-list | grep private $ vim sample/json/experiment.json $ cat sample/json/experiment.json {   "display_name": "example-experiment", "display_description": "This is a sample experiment", "key_name": "key1", "neutron_management_network": "e4fcc49c-48e5-48f8-9599-bb5eba1339c9", "template_id": "d3180a78-74cc-432d-9e9d-68640b18abae" } $ meteos experiment-create --json sample/json/experiment.json ++--+ ++--+ ++--+
 * e4fcc49c-48e5-48f8-9599-bb5eba1339c9 | private | e15c24a5-dfdd-4428-b27d-9827b35600c0 10.0.0.0/26 |
 * Property          | Value                                |
 * created_at        | 2016-12-15T22:56:51.000000           |
 * description       | This is a sample experiment          |
 * id                | fcc8d055-e801-4652-af8c-5aabedbf0286 |
 * key_name          | key1                                 |
 * management_network | e4fcc49c-48e5-48f8-9599-bb5eba1339c9 |
 * name              | example-experiment                   |
 * project_id        | 4034bb3cd9324776a68c140fdd18baa4     |
 * status            | creating                             |
 * user_id           | 64ad04e101df40b4b51e5f370a83412c     |

Meteos creates a experiment using OpenStack Sahara spark plugin.

You can see a sahara cluster and nova VMs created by Meteos as below.

$ openstack dataprocessing cluster list (or sahara cluster-list) +--+--+-++--+ +--+--+-++--+ +--+--+-++--+ $ openstack server list (or nova list) +--++++-+--+ +--++++-+--+ +--++++-+--+
 * Name            | Id                                   | Plugin name | Plugin version | Status   |
 * cluster-fcc8d055 | 5736d157-ac7c-41de-8aca-78f7afa7e99c | spark      | 1.6.0          | Spawning |
 * ID                                  | Name                       | Status | Task State | Power State | Networks         |
 * 48a9f429-7756-4bed-8dd6-6dc6140ef897 | cluster-fcc8d055-master-0 | ACTIVE | -          | Running     | private=10.0.0.5 |
 * 88ff2070-dfe8-45da-aa5c-02ac3e9de3b8 | cluster-fcc8d055-workers-0 | ACTIVE | -         | Running     | private=10.0.0.7 |
 * a57dfa5d-8b55-47c7-aae7-d5b3c8779787 | cluster-fcc8d055-workers-1 | ACTIVE | -         | Running     | private=10.0.0.4 |

3. Upload a raw data
Upload a raw data (in this example movie rating data) to OpenStack Swift.

You can use a sample data located in python-meteosclient/sample/data/recommendation_data.txt

Raw data shows "UserID", "MovieID", "Rating" from left.

$ cd sample/data/ /sample/data$ head recommendation_data.txt 1,1,4.5 1,2,1.5 1,3,5.0 1,4,2.0 2,1,5.0 2,2,1.0 2,3,4.0 2,4,1.0 3,1,1.5 3,2,4.0 /sample/data$ swift upload meteos recommendation_data.txt recommendation_data.txt

4. Create a prediction model
In this example, User creates a Recommendation Model from swift directly.

$ vim sample/json/model_recommendation.json $ cat sample/json/model_recommendation.json {   "display_name": "recommendation-model", "display_description": "Sample Recommendation Model", "source_dataset_url": "swift://meteos/recommendation_data.txt", "model_type": "Recommendation", "model_params": "{'numIterations': 10}", "experiment_id": "fcc8d055-e801-4652-af8c-5aabedbf0286", "swift_tenant": "demo", "swift_username": "demo", "swift_password": "nova" } $ meteos model-create --json sample/json/model_recommendation.json +-+--+ +-+--+ +-+--+ $ meteos model-list +--+--+---+++ +--+--+---+++ +--+--+---+++
 * Property   | Value                                |
 * created_at | 2016-12-15T23:56:47.000000           |
 * description | Sample Recommendation Model         |
 * id         | f2c119a6-1a61-4727-9d41-87bb3971d6a3 |
 * name       | recommendation-model                 |
 * params     | eydudW1JdGVyYXRpb25zJzogMTB9         |
 * project_id | 4034bb3cd9324776a68c140fdd18baa4     |
 * status     | creating                             |
 * stderr     | None                                 |
 * stdout     | None                                 |
 * type       | Recommendation                       |
 * user_id    | 64ad04e101df40b4b51e5f370a83412c     |
 * id                                  | name                 | status    | type           | source_dataset_url                     |
 * f2c119a6-1a61-4727-9d41-87bb3971d6a3 | recommendation-model | available | Recommendation | swift://meteos/recommendation_data.txt |

5. Predict
Create a learning job predicting a Rating of specified user to determine weather you should recommend this movie to user or not.

Specify the input value as "args" parameter.

$ vim sample/json/learning.json $ cat sample/json/learning.json {   "display_name": "example-learning-job", "display_description": "This is a sample job", "model_id": "f2c119a6-1a61-4727-9d41-87bb3971d6a3", "method": "predict", "args": "5,3" } $ meteos learning-create --json sample/json/learning.json +-+--+ +-+--+ +-+--+
 * Property   | Value                                |
 * args       | NSwz                                 |
 * created_at | 2016-12-15T23:59:28.000000           |
 * description | This is a sample job                |
 * id         | caaf8a86-6e62-44f3-80b2-de297364f6bc |
 * method     | predict                              |
 * name       | example-learning-job                 |
 * project_id | 4034bb3cd9324776a68c140fdd18baa4     |
 * status     | creating                             |
 * stderr     | None                                 |
 * stdout     | None                                 |
 * user_id    | 64ad04e101df40b4b51e5f370a83412c     |

Retrieve a predicted data as a stdout of job execution.

$ meteos learning-list +--+--+---+--+---+ +--+--+---+--+---+ +--+--+---+--+---+
 * id                                  | name                 | status    | args | stdout        |
 * caaf8a86-6e62-44f3-80b2-de297364f6bc | example-learning-job | available | 5,3 | 1.76754552466 |

6. Online Prediction
You can load a Prediction Model in advance for online prediction by using "meteos-load" command.

In online prediction, user can retrieve a predicted data immediately.

For more details, please see here.