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WaveML / DAI / Save

Save and load Wave Models built using Driverless AI.

import os
from h2o_wave import main, app, Q, copy_expando, uifrom h2o_wave_ml import build_model, get_model, ModelTypefrom h2o_wave_ml.utils import list_dai_instances
from sklearn.datasets import load_winefrom sklearn.model_selection import train_test_split
STEAM_URL = os.environ.get('STEAM_URL')MLOPS_URL = os.environ.get('MLOPS_URL')
DATASET_TEXT = '''The sample dataset used is the    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html" target="_blank">wine dataset</a>.'''STEAM_TEXT = f'''No Driverless AI instances available. You may create one in     <a href="{STEAM_URL}/#/driverless/instances" target="_blank">AI Engines</a> and refresh the page.'''

def dai_experiment_url(instance_id: str, instance_name: str):    # URL link to Driverless AI experiment    return f'''**Driverless AI Experiment:**        <a href="{STEAM_URL}/oidc-login-start?forward=/proxy/driverless/{instance_id}/openid/callback" target="_blank">{instance_name}</a>'''

def mlops_deployment_url(project_id: str):    # URL link to MLOps deployment    return f'**MLOps Deployment:** <a href="{MLOPS_URL}/projects/{project_id}" target="_blank">{project_id}'

def form_unsupported():    # display when app is not running on cloud    return [        ui.text('''This example requires access to Driverless AI running on            <a href="https://h2oai.github.io/h2o-ai-cloud" target="_blank">H2O AI Hybrid Cloud</a>             and does not support standalone app instances.'''),        ui.text('''Sign up at <a href="https://h2o.ai/free" target="_blank">https://h2o.ai/free</a>            to run apps on cloud.''')    ]

def form_default(q: Q):    # display when app is initialized    return [        ui.text(content=DATASET_TEXT),        ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,                    choices=q.client.choices_dai_instances, required=True),        ui.text(content=STEAM_TEXT, visible=q.client.disable_training),        ui.buttons(items=[            ui.button(name='train', label='Train', primary=True, disabled=q.client.disable_training),            ui.button(name='predict', label='Predict', primary=True, disabled=True),        ])    ]

def form_training_progress(q: Q):    # display when model training is in progress    return [        ui.text(content=DATASET_TEXT),        ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,                    choices=q.client.choices_dai_instances, required=True),        ui.buttons(items=[            ui.button(name='train', label='Train', primary=True, disabled=True),            ui.button(name='predict', label='Predict', primary=True, disabled=True)        ]),        ui.progress(label='Training in progress...', caption='This can take a few minutes...'),        ui.text(content=q.client.model_details)    ]

def form_training_completed(q: Q):    # display when model training is completed    return [        ui.text(content=DATASET_TEXT),        ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,                    choices=q.client.choices_dai_instances, required=True),        ui.buttons(items=[            ui.button(name='train', label='Train', primary=True),            ui.button(name='predict', label='Predict', primary=True)        ]),        ui.message_bar(type='success', text='Training successfully completed!'),        ui.text(content=q.client.model_details)    ]

def form_prediction_completed(q: Q):    # display when model prediction is completed    return [        ui.text(content=DATASET_TEXT),        ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,                    choices=q.client.choices_dai_instances, required=True),        ui.buttons(items=[            ui.button(name='train', label='Train', primary=True),            ui.button(name='predict', label='Predict', primary=True)        ]),        ui.message_bar(type='success', text='Prediction successfully completed!'),        ui.text(content=q.client.model_details),        ui.text(content=f'''**Example predictions:** <br />            {q.client.preds[0]} <br /> {q.client.preds[1]} <br /> {q.client.preds[2]}''')    ]

@app('/demo')async def serve(q: Q):    if 'H2O_CLOUD_ENVIRONMENT' not in os.environ:        # show appropriate message if app is not running on cloud        q.page['example'] = ui.form_card(            box='1 1 -1 -1',            items=form_unsupported()        )    elif q.args.train:        # get DAI instance name        copy_expando(q.args, q.client)
        for dai_instance in q.client.dai_instances:            if dai_instance['id'] == int(q.client.dai_instance_id):                q.client.dai_instance_name = dai_instance['name']
        # set DAI model details        q.client.model_details = dai_experiment_url(q.client.dai_instance_id, q.client.dai_instance_name)
        # show training progress and details        q.page['example'].items = form_training_progress(q)        await q.page.save()
        # train WaveML Model using Driverless AI        wave_model = await q.run(            func=build_model,            train_df=q.client.train_df,            target_column='target',            model_type=ModelType.DAI,            refresh_token=q.auth.refresh_token,            _steam_dai_instance_name=q.client.dai_instance_name,            _dai_accuracy=1,            _dai_time=1,            _dai_interpretability=10        )
        # update and save DAI model details        q.client.project_id = wave_model.project_id        q.client.endpoint_url = wave_model.endpoint_url        q.client.model_details += f'<br />{mlops_deployment_url(q.client.project_id)}'
        # show prediction option        q.page['example'].items = form_training_completed(q)    elif q.args.predict:        # load model from it's endpoint URL        wave_model = get_model(endpoint_url=q.client.endpoint_url, refresh_token=q.auth.refresh_token)
        # predict on test data        q.client.preds = wave_model.predict(test_df=q.client.test_df)
        # show predictions        q.page['example'].items = form_prediction_completed(q)    else:        # prepare sample train and test dataframes        data = load_wine(as_frame=True)['frame']        q.client.train_df, q.client.test_df = train_test_split(data, train_size=0.8)
        # DAI instances        q.client.dai_instances = list_dai_instances(refresh_token=q.auth.refresh_token)        q.client.choices_dai_instances = [            ui.choice(                name=str(x['id']),                label=f'{x["name"]} ({x["status"].capitalize()})',                disabled=x['status'] != 'running'            ) for x in q.client.dai_instances        ]
        running_dai_instances = [x['id'] for x in q.client.dai_instances if x['status'] == 'running']        q.client.disable_training = False if running_dai_instances else True        q.client.dai_instance_id = str(running_dai_instances[0]) if running_dai_instances else ''
        # display ui        q.page['example'] = ui.form_card(            box='1 1 -1 -1',            items=form_default(q)        )
    await q.page.save()