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Merged
merged 11 commits into from
Oct 7, 2024
Merged

Add databricks_automl to the conda env for arima and deepar #151

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6bb8b46
hardcode extra pip dependency
sabhya-db Oct 5, 2024
4274b69
fix typo
sabhya-db Oct 5, 2024
3a2cf30
add dep to arima and deepar
sabhya-db Oct 5, 2024
9b4dac0
style fix
sabhya-db Oct 5, 2024
79e2e60
reqs test
sabhya-db Oct 7, 2024
f4f3cad
test fix attemp 1
sabhya-db Oct 7, 2024
f3aebaa
deepar add dep test
sabhya-db Oct 7, 2024
0f82e84
prophet test updated
sabhya-db Oct 7, 2024
0060de6
small rename
sabhya-db Oct 7, 2024
ac27c97
address nits
sabhya-db Oct 7, 2024
0930423
nit
sabhya-db Oct 7, 2024
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13 changes: 9 additions & 4 deletions runtime/databricks/automl_runtime/forecast/deepar/model.py
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Expand Up @@ -23,12 +23,17 @@
from mlflow.utils.environment import _mlflow_conda_env

from databricks.automl_runtime.forecast.model import ForecastModel, mlflow_forecast_log_model
from databricks.automl_runtime import version


DEEPAR_ADDITIONAL_PIP_DEPS = [
f"gluonts[torch]=={gluonts.__version__}",
f"pandas=={pd.__version__}",
f"databricks-automl-runtime=={version.__version__}"
]

DEEPAR_CONDA_ENV = _mlflow_conda_env(
additional_pip_deps=[
f"gluonts[torch]=={gluonts.__version__}",
f"pandas=={pd.__version__}",
]
additional_pip_deps=DEEPAR_ADDITIONAL_PIP_DEPS
)


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10 changes: 7 additions & 3 deletions runtime/databricks/automl_runtime/forecast/model.py
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Expand Up @@ -57,10 +57,14 @@ def mlflow_forecast_log_model(forecast_model: ForecastModel,
:param forecast_model: Forecast model wrapper
:param sample_input: sample input Dataframes for model inference
"""
# log the model without signature if infer_signature is failed.
# TODO: [ML-46185] we should not be logging without a signature since it cannot be registered to UC then
try:
signature = forecast_model.infer_signature(sample_input)
except Exception: # noqa
signature = None
mlflow.pyfunc.log_model("model", conda_env=forecast_model.model_env,
python_model=forecast_model, signature=signature)
mlflow.pyfunc.log_model(
artifact_path="model",
conda_env=forecast_model.model_env,
python_model=forecast_model,
signature=signature
)
12 changes: 8 additions & 4 deletions runtime/databricks/automl_runtime/forecast/pmdarima/model.py
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Expand Up @@ -28,13 +28,17 @@
from databricks.automl_runtime.forecast.model import ForecastModel, mlflow_forecast_log_model
from databricks.automl_runtime.forecast.utils import calculate_period_differences, is_frequency_consistency, \
make_future_dataframe, make_single_future_dataframe
from databricks.automl_runtime import version


ARIMA_ADDITIONAL_PIP_DEPS = [
f"pmdarima=={pmdarima.__version__}",
f"pandas=={pd.__version__}",
f"databricks-automl-runtime=={version.__version__}"
]

ARIMA_CONDA_ENV = _mlflow_conda_env(
additional_pip_deps=[
f"pmdarima=={pmdarima.__version__}",
f"pandas=={pd.__version__}",
]
additional_pip_deps=ARIMA_ADDITIONAL_PIP_DEPS
)


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6 changes: 4 additions & 2 deletions runtime/databricks/automl_runtime/forecast/prophet/model.py
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Expand Up @@ -29,12 +29,14 @@
from databricks.automl_runtime.forecast.utils import is_quaterly_alias, make_future_dataframe


PROPHET_CONDA_ENV = _mlflow_conda_env(
additional_pip_deps=[
PROPHET_ADDITIONAL_PIP_DEPS = [
f"prophet=={prophet.__version__}",
f"cloudpickle=={cloudpickle.__version__}",
f"databricks-automl-runtime=={version.__version__}",
]

PROPHET_CONDA_ENV = _mlflow_conda_env(
additional_pip_deps=PROPHET_ADDITIONAL_PIP_DEPS
)


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25 changes: 22 additions & 3 deletions runtime/tests/automl_runtime/forecast/deepar/model_test.py
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Expand Up @@ -25,7 +25,9 @@
from gluonts.torch.model.predictor import PyTorchPredictor

from databricks.automl_runtime.forecast.deepar.model import (
DeepARModel, mlflow_deepar_log_model,
DeepARModel,
mlflow_deepar_log_model,
DEEPAR_ADDITIONAL_PIP_DEPS
)


Expand Down Expand Up @@ -104,8 +106,12 @@ def test_model_save_and_load_single_series(self):
mlflow_deepar_log_model(deepar_model, sample_input)

run_id = run.info.run_id
loaded_model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model")

# check if all additional dependencies are logged
self._check_requirements(run_id)

# load the model and predict
loaded_model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model")
pred_df = loaded_model.predict(sample_input)

assert pred_df.columns.tolist() == [time_col, "yhat"]
Expand Down Expand Up @@ -145,10 +151,23 @@ def test_model_save_and_load_multi_series(self):
mlflow_deepar_log_model(deepar_model, sample_input)

run_id = run.info.run_id
loaded_model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model")

# check if all additional dependencies are logged
self._check_requirements(run_id)

# load the model and predict
loaded_model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model")
pred_df = loaded_model.predict(sample_input)

assert pred_df.columns.tolist() == [time_col, "yhat", id_col]
assert len(pred_df) == self.prediction_length * 2
assert pred_df[time_col].min() > sample_input[time_col].max()

def _check_requirements(self, run_id: str):
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nit: if a method is being tested in a TestClass, make it a public method (i.e. without prefix _)

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It's not a unit test itself, it's a helper method called only within other unit test cases, should I still remove prefix?

# read requirements.txt from the run
requirements_path = mlflow.artifacts.download_artifacts(f"runs:/{run_id}/model/requirements.txt")
with open(requirements_path, "r") as f:
requirements = f.read()
# check if all additional dependencies are logged
for dependency in DEEPAR_ADDITIONAL_PIP_DEPS:
self.assertIn(dependency, requirements, f"requirements.txt should contain {dependency} but got {requirements}")
28 changes: 26 additions & 2 deletions runtime/tests/automl_runtime/forecast/pmdarima/model_test.py
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Expand Up @@ -25,8 +25,13 @@
from mlflow.protos.databricks_pb2 import ErrorCode, INVALID_PARAMETER_VALUE
from pmdarima.arima import ARIMA

from databricks.automl_runtime.forecast.pmdarima.model import ArimaModel, MultiSeriesArimaModel, AbstractArimaModel, \
mlflow_arima_log_model
from databricks.automl_runtime.forecast.pmdarima.model import (
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please break imports into multiple lines:

See https://peps.python.org/pep-0008/#imports

ArimaModel,
MultiSeriesArimaModel,
AbstractArimaModel,
mlflow_arima_log_model,
ARIMA_ADDITIONAL_PIP_DEPS,
)


class TestArimaModel(unittest.TestCase):
Expand Down Expand Up @@ -438,6 +443,11 @@ def test_mlflow_arima_log_model(self):

# Load the saved model from mlflow
run_id = run.info.run_id

# Check additonal requirements logged correctly
self._check_requirements(run_id)

# Load the model
loaded_model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model")

# Make sure can make forecasts with the saved model
Expand All @@ -460,6 +470,11 @@ def test_mlflow_arima_log_model_multiseries(self):

# Load the saved model from mlflow
run_id = run.info.run_id

# Check additonal requirements logged correctly
self._check_requirements(run_id)

# Load the model
loaded_model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model")

# Make sure can make forecasts with the saved model
Expand All @@ -473,3 +488,12 @@ def test_mlflow_arima_log_model_multiseries(self):

# Make sure can make forecasts for one-row dataframe
loaded_model.predict(test_df[0:1])

def _check_requirements(self, run_id: str):
# read requirements.txt from the run
requirements_path = mlflow.artifacts.download_artifacts(f"runs:/{run_id}/model/requirements.txt")
with open(requirements_path, "r") as f:
requirements = f.read()
# check if all additional dependencies are logged
for dependency in ARIMA_ADDITIONAL_PIP_DEPS:
self.assertIn(dependency, requirements, f"requirements.txt should contain {dependency} but got {requirements}")
36 changes: 30 additions & 6 deletions runtime/tests/automl_runtime/forecast/prophet/model_test.py
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Expand Up @@ -32,12 +32,22 @@
ProphetModel,
OFFSET_ALIAS_MAP,
DATE_OFFSET_KEYWORD_MAP,
PROPHET_ADDITIONAL_PIP_DEPS
)

PROPHET_MODEL_JSON = '{"growth": "linear", "n_changepoints": 6, "specified_changepoints": false, "changepoint_range": 0.8, "yearly_seasonality": "auto", "weekly_seasonality": "auto", "daily_seasonality": "auto", "seasonality_mode": "additive", "seasonality_prior_scale": 10.0, "changepoint_prior_scale": 0.05, "holidays_prior_scale": 10.0, "mcmc_samples": 0, "interval_width": 0.8, "uncertainty_samples": 1000, "y_scale": 8.0, "logistic_floor": false, "country_holidays": null, "component_modes": {"additive": ["weekly", "additive_terms", "extra_regressors_additive", "holidays"], "multiplicative": ["multiplicative_terms", "extra_regressors_multiplicative"]}, "changepoints": "{\\"name\\":\\"ds\\",\\"index\\":[1,2,3,4,5,6],\\"data\\":[\\"2020-10-04T00:00:00.000\\",\\"2020-10-07T00:00:00.000\\",\\"2020-10-10T00:00:00.000\\",\\"2020-10-13T00:00:00.000\\",\\"2020-10-16T00:00:00.000\\",\\"2020-10-19T00:00:00.000\\"]}", "history_dates": "{\\"name\\":\\"ds\\",\\"index\\":[0,1,2,3,4,5,6,7,8],\\"data\\":[\\"2020-10-01T00:00:00.000\\",\\"2020-10-04T00:00:00.000\\",\\"2020-10-07T00:00:00.000\\",\\"2020-10-10T00:00:00.000\\",\\"2020-10-13T00:00:00.000\\",\\"2020-10-16T00:00:00.000\\",\\"2020-10-19T00:00:00.000\\",\\"2020-10-22T00:00:00.000\\",\\"2020-10-25T00:00:00.000\\"]}", "train_holiday_names": null, "start": 1601510400.0, "t_scale": 2073600.0, "holidays": null, "history": "{\\"schema\\":{\\"fields\\":[{\\"name\\":\\"ds\\",\\"type\\":\\"datetime\\"},{\\"name\\":\\"y\\",\\"type\\":\\"integer\\"},{\\"name\\":\\"floor\\",\\"type\\":\\"integer\\"},{\\"name\\":\\"t\\",\\"type\\":\\"number\\"},{\\"name\\":\\"y_scaled\\",\\"type\\":\\"number\\"}],\\"pandas_version\\":\\"1.4.0\\"},\\"data\\":[{\\"ds\\":\\"2020-10-01T00:00:00.000\\",\\"y\\":0,\\"floor\\":0,\\"t\\":0.0,\\"y_scaled\\":0.0},{\\"ds\\":\\"2020-10-04T00:00:00.000\\",\\"y\\":1,\\"floor\\":0,\\"t\\":0.125,\\"y_scaled\\":0.125},{\\"ds\\":\\"2020-10-07T00:00:00.000\\",\\"y\\":2,\\"floor\\":0,\\"t\\":0.25,\\"y_scaled\\":0.25},{\\"ds\\":\\"2020-10-10T00:00:00.000\\",\\"y\\":3,\\"floor\\":0,\\"t\\":0.375,\\"y_scaled\\":0.375},{\\"ds\\":\\"2020-10-13T00:00:00.000\\",\\"y\\":4,\\"floor\\":0,\\"t\\":0.5,\\"y_scaled\\":0.5},{\\"ds\\":\\"2020-10-16T00:00:00.000\\",\\"y\\":5,\\"floor\\":0,\\"t\\":0.625,\\"y_scaled\\":0.625},{\\"ds\\":\\"2020-10-19T00:00:00.000\\",\\"y\\":6,\\"floor\\":0,\\"t\\":0.75,\\"y_scaled\\":0.75},{\\"ds\\":\\"2020-10-22T00:00:00.000\\",\\"y\\":7,\\"floor\\":0,\\"t\\":0.875,\\"y_scaled\\":0.875},{\\"ds\\":\\"2020-10-25T00:00:00.000\\",\\"y\\":8,\\"floor\\":0,\\"t\\":1.0,\\"y_scaled\\":1.0}]}", "train_component_cols": "{\\"schema\\":{\\"fields\\":[{\\"name\\":\\"additive_terms\\",\\"type\\":\\"integer\\"},{\\"name\\":\\"weekly\\",\\"type\\":\\"integer\\"},{\\"name\\":\\"multiplicative_terms\\",\\"type\\":\\"integer\\"}],\\"pandas_version\\":\\"1.4.0\\"},\\"data\\":[{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0},{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0},{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0},{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0},{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0},{\\"additive_terms\\":1,\\"weekly\\":1,\\"multiplicative_terms\\":0}]}", "changepoints_t": [0.125, 0.25, 0.375, 0.5, 0.625, 0.75], "seasonalities": [["weekly"], {"weekly": {"period": 7, "fourier_order": 3, "prior_scale": 10.0, "mode": "additive", "condition_name": null}}], "extra_regressors": [[], {}], "fit_kwargs": {}, "params": {"lp__": [[202.053]], "k": [[1.19777]], "m": [[0.0565623]], "delta": [[-0.86152, 0.409957, -0.103241, 0.528979, 0.535181, -0.509356]], "sigma_obs": [[2.53056e-13]], "beta": [[-0.00630566, 0.016248, 0.0318587, -0.068705, 0.0029986, -0.00410522]], "trend": [[0.0565623, 0.206283, 0.248314, 0.341589, 0.421959, 0.568452, 0.781842, 0.931562, 1.08128]]}, "__prophet_version": "1.1.1"}'


class TestProphetModel(unittest.TestCase):
class BaseProphetModelTest(unittest.TestCase):
def _check_requirements(self, run_id: str):
# read requirements.txt from the run
requirements_path = mlflow.artifacts.download_artifacts(f"runs:/{run_id}/model/requirements.txt")
with open(requirements_path, "r") as f:
requirements = f.read()
# check if all additional dependencies are logged
for dependency in PROPHET_ADDITIONAL_PIP_DEPS:
self.assertIn(dependency, requirements, f"requirements.txt should contain {dependency} but got {requirements}")

class TestProphetModel(BaseProphetModelTest):
@classmethod
def setUpClass(cls) -> None:
num_rows = 9
Expand Down Expand Up @@ -74,8 +84,13 @@ def test_model_save_and_load(self):

with mlflow.start_run() as run:
mlflow_prophet_log_model(prophet_model)
# Load the saved model from mlflow

run_id = run.info.run_id

# Check additonal requirements logged correctly
self._check_requirements(run_id)

# Load the saved model from mlflow
prophet_model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model")

# Check the prediction with the saved model
Expand Down Expand Up @@ -144,7 +159,7 @@ def test_validate_predict_cols(self):
assert e.value.error_code == ErrorCode.Name(INTERNAL_ERROR)


class TestMultiSeriesProphetModel(unittest.TestCase):
class TestMultiSeriesProphetModel(BaseProphetModelTest):
@classmethod
def setUpClass(cls) -> None:
cls.model_json = PROPHET_MODEL_JSON
Expand Down Expand Up @@ -178,8 +193,13 @@ def test_model_save_and_load(self):
with mlflow.start_run() as run:
mlflow_prophet_log_model(self.prophet_model, sample_input=test_df)

# Load the saved model from mlflow

run_id = run.info.run_id

# Check additonal requirements logged correctly
self._check_requirements(run_id)

# Load the saved model from mlflow
loaded_model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model")

# Check the prediction with the saved model
Expand Down Expand Up @@ -239,9 +259,13 @@ def test_model_save_and_load_multi_ids(self):
)
with mlflow.start_run() as run:
mlflow_prophet_log_model(prophet_model, sample_input=test_df)

run_id = run.info.run_id

# Check additonal requirements logged correctly
self._check_requirements(run_id)

# Load the saved model from mlflow
run_id = run.info.run_id
loaded_model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model")

# Check the prediction with the saved model
Expand Down
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