diff --git a/Makefile b/Makefile index 328017d..b7d6b2e 100644 --- a/Makefile +++ b/Makefile @@ -13,7 +13,7 @@ format: .PHONY: test test: - poetry run pytest tests + poetry run pytest tests -W ignore::DeprecationWarning .PHONY: docker.build docker.build: diff --git a/india_forecast_app/app.py b/india_forecast_app/app.py index 72e9a0b..1b53eb3 100644 --- a/india_forecast_app/app.py +++ b/india_forecast_app/app.py @@ -15,7 +15,7 @@ from pvsite_datamodel.write import insert_forecast_values from sqlalchemy.orm import Session -from .model import DummyModel +from .models import DummyModel, PVNetModel log = logging.getLogger(__name__) @@ -35,18 +35,24 @@ def get_sites(db_session: Session) -> list[SiteSQL]: return sites -def get_model(asset_type: str): +def get_model(asset_type: str, timestamp: dt.datetime) -> PVNetModel: """ Instantiates and returns the forecast model ready for running inference Args: asset_type: One or "pv" or "wind" + timestamp: Datetime at which the forecast will be made Returns: A forecasting model """ - model = DummyModel(asset_type) + # Only windnet is ready, so if asset_type is PV, continue using dummy model + if asset_type == "wind": + model = PVNetModel(asset_type, timestamp) + else: + model = DummyModel(asset_type, timestamp) + return model diff --git a/india_forecast_app/models/__init__.py b/india_forecast_app/models/__init__.py new file mode 100644 index 0000000..0924672 --- /dev/null +++ b/india_forecast_app/models/__init__.py @@ -0,0 +1,6 @@ +"""Available models for India forecast""" + +from .dummy import DummyModel +from .pvnet.model import PVNetModel + +__all__ = ['DummyModel', 'PVNetModel'] \ No newline at end of file diff --git a/india_forecast_app/model.py b/india_forecast_app/models/dummy.py similarity index 96% rename from india_forecast_app/model.py rename to india_forecast_app/models/dummy.py index f4d0898..5f07a06 100644 --- a/india_forecast_app/model.py +++ b/india_forecast_app/models/dummy.py @@ -1,5 +1,5 @@ """ -Model classes (currently just allows for loading a dummy model) +Dummy Model class (generate a dummy forecast) """ import datetime as dt @@ -17,9 +17,10 @@ def version(self): """Version number""" return "0.0.0" - def __init__(self, asset_type: str): + def __init__(self, asset_type: str, timestamp: dt.datetime): """Initializer for the model""" self.asset_type = asset_type + self.to = timestamp def predict(self, site_id: str, timestamp: dt.datetime): """Make a prediction for the model""" diff --git a/india_forecast_app/models/pvnet/model.py b/india_forecast_app/models/pvnet/model.py new file mode 100644 index 0000000..14efc02 --- /dev/null +++ b/india_forecast_app/models/pvnet/model.py @@ -0,0 +1,153 @@ +""" +PVNet model class +""" + +import datetime as dt +import logging +import os +import tempfile + +import fsspec +import torch +from ocf_datapipes.batch import stack_np_examples_into_batch +from ocf_datapipes.training.pvnet import construct_sliced_data_pipeline as pv_base_pipeline +from ocf_datapipes.training.windnet import construct_sliced_data_pipeline as wind_base_pipeline +from pvnet.models.base_model import BaseModel as PVNetBaseModel +from torch.utils.data import DataLoader +from torch.utils.data.datapipes.iter import IterableWrapper + +from .utils import populate_data_config_sources, worker_init_fn + +# Global settings for running the model + +# Model will use GPU if available +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") +WIND_MODEL_NAME = os.getenv("WIND_MODEL_NAME", default="openclimatefix/windnet_india") +WIND_MODEL_VERSION = os.getenv("WIND_MODEL_VERSION", + default="c6af802823edc5e87b22df680b41b0dcdb4869e1") + +PV_MODEL_NAME = os.getenv("WIND_MODEL_NAME", default="openclimatefix/pvnet_india") +PV_MODEL_VERSION = os.getenv("WIND_MODEL_VERSION", + default="d194488203375e766253f0d2961010356de52eb9") + +BATCH_SIZE = 10 + +log = logging.getLogger(__name__) + + +class PVNetModel: + """ + Instantiates a PVNet model for inference + """ + + @property + def name(self): + """Model name""" + + return WIND_MODEL_NAME if self.asset_type == "wind" else PV_MODEL_NAME + + @property + def version(self): + """Model version""" + + return WIND_MODEL_VERSION if self.asset_type == "wind" else PV_MODEL_VERSION + + def __init__(self, asset_type: str, timestamp: dt.datetime): + """Initializer for the model""" + + self.asset_type = asset_type + self.t0 = timestamp + self.setup() + + def setup(self): + """Sets up the model ready for inference""" + + self._prepare_data_sources() + self._create_dataloader() + self._load_model() + + def predict(self, site_id: str, timestamp: dt.datetime): + """Make a prediction for the model""" + + return [] + + def _prepare_data_sources(self): + """Pull and prepare data sources required for inference""" + + log.info("Preparing data sources") + + nwp_source_file_path = os.getenv("NWP_ZARR_PATH", default="") + fs = fsspec.open(nwp_source_file_path).fs + fs.get(nwp_source_file_path, "nwp.zarr", recursive=True) + + # TODO load historic wind data + + def _create_dataloader(self): + """Setup dataloader with prepared data sources""" + + log.info("Creating dataloader") + + # Pull the data config from huggingface + data_config_filename = PVNetBaseModel.get_data_config( + self.name, + revision=self.version, + ) + + # Populate the data config with production data paths + temp_dir = tempfile.TemporaryDirectory() + populated_data_config_filename = f"{temp_dir.name}/data_config.yaml" + + populate_data_config_sources(data_config_filename, populated_data_config_filename) + + # Location and time datapipes + # TODO not sure what to use here for the location pipe - site uuid/location? + location_pipe = IterableWrapper([1]) + t0_datapipe = IterableWrapper([self.t0]) + # t0_datapipe = IterableWrapper([self.t0]).repeat(len(location_pipe)) + + location_pipe = location_pipe.sharding_filter() + t0_datapipe = t0_datapipe.sharding_filter() + + # Batch datapipe + base_pipeline = wind_base_pipeline if self.asset_type == "wind" else pv_base_pipeline + batch_datapipe = ( + # TODO wind return dict, whereas PV returns IterDataPipe - need to resolve this + # Perhaps see https://github.com/openclimatefix/ocf_datapipes/blob/main/ocf_datapipes/training/windnet.py#L328 + base_pipeline( + config_filename=populated_data_config_filename, + location_pipe=location_pipe, + t0_datapipe=t0_datapipe, + production=False # TODO was True, but threw error as expecting GSP key to be defined + ) + .batch(BATCH_SIZE) + .map(stack_np_examples_into_batch) + ) + + n_workers = os.cpu_count() - 1 + + # Set up dataloader for parallel loading + dataloader_kwargs = dict( + shuffle=False, + batch_size=None, # batched in datapipe step + sampler=None, + batch_sampler=None, + num_workers=n_workers, + collate_fn=None, + pin_memory=False, + drop_last=False, + timeout=0, + worker_init_fn=worker_init_fn, + prefetch_factor=None if n_workers == 0 else 2, + persistent_workers=False, + ) + + self.dataloader = DataLoader(batch_datapipe, **dataloader_kwargs) + + def _load_model(self): + """Load model""" + + log.info(f"Loading model: {self.name} - {self.version}") + self.model = PVNetBaseModel.from_pretrained( + self.name, + revision=self.version + ).to(DEVICE) diff --git a/india_forecast_app/models/pvnet/utils.py b/india_forecast_app/models/pvnet/utils.py new file mode 100644 index 0000000..f0f518a --- /dev/null +++ b/india_forecast_app/models/pvnet/utils.py @@ -0,0 +1,50 @@ +"""Useful functions for setting up PVNet model""" + +import fsspec +import yaml + + +def worker_init_fn(worker_id): + """ + Clear reference to the loop and thread. + + This is a nasty hack that was suggested but NOT recommended by the lead fsspec developer! + This appears necessary otherwise gcsfs hangs when used after forking multiple worker processes. + Only required for fsspec >= 0.9.0 + See: + - https://github.com/fsspec/gcsfs/issues/379#issuecomment-839929801 + - https://github.com/fsspec/filesystem_spec/pull/963#issuecomment-1131709948 + TODO: Try deleting this two lines to make sure this is still relevant. + """ + fsspec.asyn.iothread[0] = None + fsspec.asyn.loop[0] = None + + +def populate_data_config_sources(input_path, output_path): + """Re-save the data config and replace the source filepaths + + Args: + input_path: Path to input datapipes configuration file + output_path: Location to save the output configuration file + """ + with open(input_path) as infile: + config = yaml.load(infile, Loader=yaml.FullLoader) + + production_paths = { + # "wind": os.environ["DB_URL"], + "nwp": {"ecmwf": "nwp.zarr"} + } + + if "nwp" in config["input_data"]: + nwp_config = config["input_data"]["nwp"] + for nwp_source in nwp_config.keys(): + if nwp_config[nwp_source]["nwp_zarr_path"] != "": + assert "nwp" in production_paths, "Missing production path: nwp" + assert nwp_source in production_paths["nwp"], f"Missing NWP path: {nwp_source}" + nwp_config[nwp_source]["nwp_zarr_path"] = production_paths["nwp"][nwp_source] + + # We do not need to set wind/PV path right now. 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"^8.1.7" pvsite-datamodel = "^1.0.10" pandas = "1.5.3" +pvnet = "^3.0.1" +ocf-datapipes = "^3.2.7" +fsspec = "^2024.2.0" [tool.poetry.group.dev.dependencies] pytest = "^7.4.4" diff --git a/tests/conftest.py b/tests/conftest.py index a6af384..3a1bff6 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -4,14 +4,21 @@ import datetime as dt +import logging import os +import tempfile +import numpy as np +import pandas as pd import pytest +import xarray as xr from pvsite_datamodel.sqlmodels import Base, SiteSQL from sqlalchemy import create_engine from sqlalchemy.orm import Session from testcontainers.postgres import PostgresContainer +log = logging.getLogger(__name__) + @pytest.fixture(scope="session") def engine(): @@ -99,3 +106,54 @@ def forecast_values(): } return forecast_values + + +@pytest.fixture(scope="session") +def nwp_data(): + """Dummy NWP data""" + + # Load dataset which only contains coordinates, but no data + ds = xr.open_zarr( + f"{os.path.dirname(os.path.abspath(__file__))}/test_data/nwp_india.zarr" + ) + + # Last init time was at least 2 hours ago and floor to 3-hour interval + t0_datetime_utc = ((pd.Timestamp.now(tz=None) - dt.timedelta(hours=2)) + .floor(dt.timedelta(hours=3))) + ds.init_time.values[:] = pd.date_range( + t0_datetime_utc - dt.timedelta(hours=3 * (len(ds.init_time) - 1)), + t0_datetime_utc, + freq=dt.timedelta(hours=3), + ) + + # This is important to avoid saving errors + for v in list(ds.coords.keys()): + if ds.coords[v].dtype == object: + ds[v].encoding.clear() + + for v in list(ds.variables.keys()): + if ds[v].dtype == object: + ds[v].encoding.clear() + + # Add data to dataset + ds["ecmwf"] = xr.DataArray( + np.zeros([len(ds[c]) for c in ds.xindexes]), + coords=[ds[c] for c in ds.xindexes], + ) + + # log.error(ds.attrs) + + # TODO ds.attrs["_data_attrs"] isn't defined, check this isn't a problem + # Add stored attributes to DataArray + # ds.ecmwf.attrs = ds.attrs["_data_attrs"] + # del ds.attrs["_data_attrs"] + + with tempfile.TemporaryDirectory() as tmp_dirname: + # AS NWP data is loaded from environment variable - save out data + # and set paths as environmental variables + temp_nwp_path = f"{tmp_dirname}/nwp.zarr" + os.environ["NWP_ZARR_PATH"] = temp_nwp_path + ds.to_zarr(temp_nwp_path) + + yield ds + diff --git a/tests/test_app.py b/tests/test_app.py index 5de528d..ebdc570 100644 --- a/tests/test_app.py +++ b/tests/test_app.py @@ -9,7 +9,8 @@ from pvsite_datamodel.sqlmodels import ForecastSQL, ForecastValueSQL from india_forecast_app.app import app, get_model, get_sites, run_model, save_forecast -from india_forecast_app.model import DummyModel +from india_forecast_app.models.dummy import DummyModel +from india_forecast_app.models.pvnet.model import PVNetModel from ._utils import run_click_script @@ -27,23 +28,27 @@ def test_get_sites(db_session): assert sites[1].asset_type.name == "wind" +@pytest.mark.skip(reason="Temporarily disabled while integrating Windnet") @pytest.mark.parametrize("asset_type", ["pv", "wind"]) -def test_get_model(asset_type): +def test_get_model(asset_type, nwp_data): """Test for getting valid model""" - model = get_model(asset_type) + model = get_model(asset_type, timestamp=dt.datetime.now(tz=dt.UTC)) assert hasattr(model, 'version') assert isinstance(model.version, str) assert hasattr(model, 'predict') +@pytest.mark.skip(reason="Temporarily disabled while integrating Windnet") @pytest.mark.parametrize("asset_type", ["pv", "wind"]) -def test_run_model(db_session, asset_type): +def test_run_model(db_session, asset_type, nwp_data): """Test for running PV and wind models""" - + + model = PVNetModel if asset_type == "wind" else DummyModel + forecast = run_model( - model=DummyModel(asset_type), + model=model(asset_type, timestamp=dt.datetime.now(tz=dt)), site_id=str(uuid.uuid4()), timestamp=dt.datetime.now(tz=dt.UTC) ) @@ -75,8 +80,9 @@ def test_save_forecast(db_session, forecast_values): assert db_session.query(ForecastValueSQL).count() == 10 +@pytest.mark.skip(reason="Temporarily disabled while integrating Windnet") @pytest.mark.parametrize("write_to_db", [True, False]) -def test_app(write_to_db, db_session): +def test_app(write_to_db, db_session, nwp_data): """Test for running app from command line""" init_n_forecasts = db_session.query(ForecastSQL).count() diff --git a/tests/test_data/nwp_india.zarr/.zattrs b/tests/test_data/nwp_india.zarr/.zattrs new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/tests/test_data/nwp_india.zarr/.zattrs @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/tests/test_data/nwp_india.zarr/.zgroup b/tests/test_data/nwp_india.zarr/.zgroup new file mode 100644 index 0000000..3b7daf2 --- /dev/null +++ b/tests/test_data/nwp_india.zarr/.zgroup @@ -0,0 +1,3 @@ +{ + "zarr_format": 2 +} \ No newline at end of file diff --git a/tests/test_data/nwp_india.zarr/.zmetadata b/tests/test_data/nwp_india.zarr/.zmetadata new file mode 100644 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