WaveML / DAI / Save
Save and load Wave Models built using Driverless AI.
import os
from h2o_wave import main, app, Q, copy_expando, ui
from h2o_wave_ml import build_model, get_model, ModelType
from h2o_wave_ml.utils import list_dai_instances
from sklearn.datasets import load_wine
from 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()