Meteos/ExampleRecommend

Recommend a Movie using Meteos

In this example, user creates 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
| 45de4bbd-8419-40ff-8ed7-fc065c05e34f | meteos                          |

$ neutron net-list | grep public
| 84c13e76-ced9-4142-a885-280784f1f7a3 | public  | a14de1c5-b8d4-434b-a056-9b0049b93402             |

$ 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" : "45de4bbd-8419-40ff-8ed7-fc065c05e34f",
    "master_nodes_num": 1,
    "master_flavor_id": "4",
    "worker_nodes_num": 2,
    "worker_flavor_id": "2",
    "spark_version": "1.6.0",
    "floating_ip_pool": "84c13e76-ced9-4142-a885-280784f1f7a3"
}

$ meteos template-create --json sample/json/template.json
+---------------+-----------------------------------------+
| Property      | Value                                   |
+---------------+-----------------------------------------+
| cluster_id    | None                                    |
| created_at    | 2016-12-04T07:16:29.000000              |
| description   | This is a sample template of experiment |
| id            | 8b7b9b89-f119-4b9b-b9b0-31598f819f1a    |
| master_flavor | 4                                       |
| master_nodes  | 1                                       |
| name          | example-template                        |
| project_id    | 67401cca74c2409b939e944bc6c8fcbe        |
| spark_version | 1.6.0                                   |
| status        | available                               |
| user_id       | 181b1caa9d5b470393ca66b9e511d5b0        |
| 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
| 8abc626e-2b06-4c67-9b2c-0231f0cef5b8 | private | cb58940f-859b-48c6-b92a-3861470f1fc1 20.0.0.0/26 |

$ 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": "8abc626e-2b06-4c67-9b2c-0231f0cef5b8",
    "template_id": "8b7b9b89-f119-4b9b-b9b0-31598f819f1a"
}

$ meteos experiment-create --json sample/json/experiment.json
+--------------------+--------------------------------------+
| Property           | Value                                |
+--------------------+--------------------------------------+
| created_at         | 2016-12-04T07:20:11.000000           |
| description        | This is a sample experiment          |
| id                 | 91504a65-01cf-428f-81aa-596be7ca8619 |
| key_name           | key1                                 |
| management_network | 8abc626e-2b06-4c67-9b2c-0231f0cef5b8 |
| name               | example-experiment                   |
| project_id         | 67401cca74c2409b939e944bc6c8fcbe     |
| status             | creating                             |
| user_id            | 181b1caa9d5b470393ca66b9e511d5b0     |
+--------------------+--------------------------------------+

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)
+------------------+--------------------------------------+-------------+----------------+----------+
| Name             | Id                                   | Plugin name | Plugin version | Status   |
+------------------+--------------------------------------+-------------+----------------+----------+
| cluster-91504a65 | 13418fd9-5d2a-4ee6-b384-cb250b7e7714 | spark       | 1.6.0          | Spawning |
+------------------+--------------------------------------+-------------+----------------+----------+

$ openstack server list (or nova list)
+--------------------------------------+----------------------------+--------+----------+------------+
| ID                                   | Name                       | Status | Networks | Image Name |
+--------------------------------------+----------------------------+--------+----------+------------+
| 58818eb5-ade7-407c-8c76-9fd9809632b4 | cluster-91504a65-workers-1 | BUILD  |          | meteos     |
| a151dbd9-de51-43ca-afb8-1fdeecce2891 | cluster-91504a65-workers-0 | BUILD  |          | meteos     |
| d02d85c5-0960-4b7e-880c-26b73c5dd8ad | cluster-91504a65-master-0  | BUILD  |          | meteos     |
+--------------------------------------+----------------------------+--------+----------+------------+

3. Upload a raw data

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

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

Raw data shows "UserID", "MovieID", "Rank" 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": "recommend-movie",
    "display_description": "Sample Model",
    "source_dataset_url": "swift://meteos/recommendation_data.txt",
    "model_type": "Recommendation",
    "model_params": "{'numIterations': 10}",
    "experiment_id": "91504a65-01cf-428f-81aa-596be7ca8619",
    "swift_tenant": "demo",
    "swift_username": "demo",
    "swift_password": "nova"
}

$ meteos model-create --json sample/json/model_recommendation.json
+-------------+--------------------------------------+
| Property    | Value                                |
+-------------+--------------------------------------+
| created_at  | 2016-12-04T07:50:10.000000           |
| description | Sample Model                         |
| id          | 6e0f8633-fac4-46d8-a0ac-e9de00ef8b4b |
| name        | recommend-movie                      |
| params      | eydudW1JdGVyYXRpb25zJzogMTB9         |
| project_id  | 67401cca74c2409b939e944bc6c8fcbe     |
| status      | creating                             |
| stderr      | None                                 |
| stdout      | None                                 |
| type        | Recommendation                       |
| user_id     | 181b1caa9d5b470393ca66b9e511d5b0     |
+-------------+--------------------------------------+

$ meteos model-list
+--------------------------------------+-----------------+--------------+-----------+----------------+----------------------------------------+----------------------------+
| id                                   | name            | description  | status    | type           | source_dataset_url                     | created_at                 |
+--------------------------------------+-----------------+--------------+-----------+----------------+----------------------------------------+----------------------------+
| 6e0f8633-fac4-46d8-a0ac-e9de00ef8b4b | recommend-movie | Sample Model | available | Recommendation | swift://meteos/recommendation_data.txt | 2016-12-04T07:50:10.000000 |
+--------------------------------------+-----------------+--------------+-----------+----------------+----------------------------------------+----------------------------+