Quick Start (Model Developers)¶

As a Model Developer, you can manage models, datasets, train jobs & inference jobs on SINGA-Auto. This guide only highlights the key methods available to manage models.

To learn about how to manage datasets, train jobs & inference jobs, go to Quick Start (Application Developers).

This guide assumes that you have access to a running instance of SINGA-Auto Admin at <singa_auto_host>:<admin_port> and SINGA-Auto Web Admin at <singa_auto_host>:<web_admin_port>.

To learn more about what else you can do on SINGA-Auto, explore the methods of singa_auto.client.Client

Installing the client¶

Install Python 3.6 such that the python and pip point to the correct installation of Python (see Installing Python)
Clone the project at https://github.com/nusdbsystem/singa-auto (e.g. with Git)
Within the project’s root folder, install SINGA-Auto’s client-side Python dependencies by running:
pip install -r ./singa_auto/requirements.txt

Initializing the client¶

Example:

from singa_auto.client import Client
client = Client(admin_host='localhost', admin_port=3000)
client.login(email='superadmin@singa_auto', password='singa_auto')

See also

singa_auto.client.Client.login()

Creating models¶

To create a model, you’ll need to submit a model class that conforms to the specification by singa_auto.model.BaseModel, written in a single Python file. The model’s implementation should conform to a specific task (see tasks).

Refer to the parameters of singa_auto.client.Client.create_model() for configuring how your model runs on SINGA-Auto, and refer to Model Development Guide to understand more about how to write & test models for SINGA-Auto.

Example:

client.create_model(
    name='TfFeedForward',
    task='IMAGE_CLASSIFICATION',
    model_file_path='examples/models/image_classification/TfFeedForward.py',
    model_class='TfFeedForward',
    dependencies={ 'tensorflow': '1.12.0' }
)

client.create_model(
    name='SkDt',
    task='IMAGE_CLASSIFICATION',
    model_file_path='examples/models/image_classification/SkDt.py',
    model_class='SkDt',
    dependencies={ 'scikit-learn': '0.20.0' }
)

See also

singa_auto.client.Client.create_model()

Listing available models by task¶

Example:

client.get_available_models(task='IMAGE_CLASSIFICATION')
# While leave the "task" unspecified, the method will retrieve information of all uploaded models
client.get_available_models()

Output:

[{'access_right': 'PRIVATE',
 'datetime_created': 'Mon, 17 Dec 2018 07:06:03 GMT',
 'dependencies': {'tensorflow': '1.12.0'},
 'id': '45df3f34-53d7-4fb8-a7c2-55391ea10030',
 'name': 'TfFeedForward',
 'task': 'IMAGE_CLASSIFICATION',
 'user_id': 'fb5671f1-c673-40e7-b53a-9208eb1ccc50'},
 {'access_right': 'PRIVATE',
 'datetime_created': 'Mon, 17 Dec 2018 07:06:03 GMT',
 'dependencies': {'scikit-learn': '0.20.0'},
 'id': 'd0ea96ce-478b-4167-8a84-eb36ae631235',
 'name': 'SkDt',
 'task': 'IMAGE_CLASSIFICATION',
 'user_id': 'fb5671f1-c673-40e7-b53a-9208eb1ccc50'}]

See also

singa_auto.client.Client.get_available_models()

Deleting a model¶

Example:

client.delete_model('fb5671f1-c673-40e7-b53a-9208eb1ccc50')

See also

singa_auto.client.Client.delete_model()