From 08264c5be1a187cc8bb74cda496cf85006ae78aa Mon Sep 17 00:00:00 2001 From: James Fulton Date: Wed, 19 Jul 2023 16:09:09 +0000 Subject: [PATCH 1/3] mvp - still not working with multiprocessing dataloader --- configs/callbacks/default.yaml | 42 ++++ configs/callbacks/none.yaml | 0 configs/config.yaml | 40 ++++ configs/datamodule/default.yaml | 6 + configs/hydra/default.yaml | 12 + configs/logger/wandb.yaml | 15 ++ configs/model/default.yaml | 34 +++ configs/readme.md | 7 + configs/trainer/all_params.yaml | 48 ++++ configs/trainer/default.yaml | 17 ++ environment.yml | 2 +- pvnet_summation/data/datamodule.py | 128 ++++++++++ pvnet_summation/models/base_model.py | 226 ++++++++++++++++++ pvnet_summation/models/model.py | 79 ++++++ pvnet_summation/models/model_card_template.md | 54 +++++ pvnet_summation/training.py | 139 +++++++++++ pvnet_summation/utils.py | 60 +++++ requirements.txt | 30 +++ run.py | 59 +++++ setup.py | 2 +- tests/conftest.py | 129 ++++++++++ .../sample_batches/data_configuration.yaml | 81 +++++++ tests/data/sample_batches/datamodule.yaml | 14 ++ tests/data/sample_batches/train/000000.pt | Bin 0 -> 3057047 bytes tests/data/test_datamodule.py | 61 +++++ tests/models/test_model.py | 36 +++ tests/test_end2end.py | 6 + 27 files changed, 1325 insertions(+), 2 deletions(-) create mode 100644 configs/callbacks/default.yaml create mode 100644 configs/callbacks/none.yaml create mode 100644 configs/config.yaml create mode 100644 configs/datamodule/default.yaml create mode 100644 configs/hydra/default.yaml create mode 100644 configs/logger/wandb.yaml create mode 100644 configs/model/default.yaml create mode 100644 configs/readme.md create mode 100644 configs/trainer/all_params.yaml create mode 100644 configs/trainer/default.yaml create mode 100644 pvnet_summation/data/datamodule.py create mode 100644 pvnet_summation/models/base_model.py create mode 100644 pvnet_summation/models/model.py create mode 100644 pvnet_summation/models/model_card_template.md create mode 100644 pvnet_summation/training.py create mode 100644 pvnet_summation/utils.py create mode 100644 run.py create mode 100644 tests/conftest.py create mode 100644 tests/data/sample_batches/data_configuration.yaml create mode 100644 tests/data/sample_batches/datamodule.yaml create mode 100644 tests/data/sample_batches/train/000000.pt create mode 100644 tests/data/test_datamodule.py create mode 100644 tests/models/test_model.py create mode 100644 tests/test_end2end.py diff --git a/configs/callbacks/default.yaml b/configs/callbacks/default.yaml new file mode 100644 index 0000000..d3e6224 --- /dev/null +++ b/configs/callbacks/default.yaml @@ -0,0 +1,42 @@ +#pretrain_early_stopping: +# _target_: pvnet.callbacks.PretrainEarlyStopping +# monitor: "MAE/val" # name of the logged metric which determines when model is improving +# mode: "min" # can be "max" or "min" +# patience: 10 # how many epochs (or val check periods) of not improving until training stops +# min_delta: 0.001 # minimum change in the monitored metric needed to qualify as an improvement + +#pretrain_encoder_freezing: +# _target_: pvnet.callbacks.PretrainFreeze + +early_stopping: + _target_: pvnet.callbacks.MainEarlyStopping + # name of the logged metric which determines when model is improving + monitor: "${resolve_monitor_loss:${model.output_quantiles}}" + mode: "min" # can be "max" or "min" + patience: 10 # how many epochs (or val check periods) of not improving until training stops + min_delta: 0 # minimum change in the monitored metric needed to qualify as an improvement + +learning_rate_monitor: + _target_: lightning.pytorch.callbacks.LearningRateMonitor + logging_interval: "epoch" + +model_summary: + _target_: lightning.pytorch.callbacks.ModelSummary + max_depth: 3 + +model_checkpoint: + _target_: lightning.pytorch.callbacks.ModelCheckpoint + # name of the logged metric which determines when model is improving + monitor: "${resolve_monitor_loss:${model.output_quantiles}}" + mode: "min" # can be "max" or "min" + save_top_k: 1 # save k best models (determined by above metric) + save_last: True # additionaly always save model from last epoch + every_n_epochs: 1 + verbose: False + filename: "epoch={epoch}-step={step}" + dirpath: "checkpoints/pvnet_summation/${model_name}" #${..model_name} + auto_insert_metric_name: False + save_on_train_epoch_end: False +#device_stats_monitor: +# _target_: lightning.pytorch.callbacks.DeviceStatsMonitor +# cpu_stats: True diff --git a/configs/callbacks/none.yaml b/configs/callbacks/none.yaml new file mode 100644 index 0000000..e69de29 diff --git a/configs/config.yaml b/configs/config.yaml new file mode 100644 index 0000000..fbbe429 --- /dev/null +++ b/configs/config.yaml @@ -0,0 +1,40 @@ +# @package _global_ + +# specify here default training configuration +defaults: + - trainer: default.yaml + - model: default.yaml + - datamodule: default.yaml + - callbacks: default.yaml # set this to null if you don't want to use callbacks + - logger: wandb.yaml # set logger here or use command line (e.g. `python run.py logger=wandb`) + - hydra: default.yaml + + # enable color logging +# - override hydra/hydra_logging: colorlog +# - override hydra/job_logging: colorlog + +# path to original working directory +# hydra hijacks working directory by changing it to the current log directory, +# so it's useful to have this path as a special variable +# learn more here: https://hydra.cc/docs/next/tutorials/basic/running_your_app/working_directory +work_dir: ${hydra:runtime.cwd} + +model_name: "default" + +# use `python run.py debug=true` for easy debugging! +# this will run 1 train, val and test loop with only 1 batch +# equivalent to running `python run.py trainer.fast_dev_run=true` +# (this is placed here just for easier access from command line) +debug: False + +# pretty print config at the start of the run using Rich library +print_config: True + +# disable python warnings if they annoy you +ignore_warnings: True + +# check performance on test set, using the best model achieved during training +# lightning chooses best model based on metric specified in checkpoint callback +test_after_training: False + +seed: 2727831 diff --git a/configs/datamodule/default.yaml b/configs/datamodule/default.yaml new file mode 100644 index 0000000..3ed7921 --- /dev/null +++ b/configs/datamodule/default.yaml @@ -0,0 +1,6 @@ +_target_: pvnet_summation.data.datamodule.DataModule +batch_dir: "/mnt/disks/bigbatches/concurrent_batches_v3.6_-60mins" +gsp_zarr_path: "/mnt/disks/nwp/pv_gsp.zarr" +batch_size: 8 +num_workers: 20 +prefetch_factor: 2 \ No newline at end of file diff --git a/configs/hydra/default.yaml b/configs/hydra/default.yaml new file mode 100644 index 0000000..a086d12 --- /dev/null +++ b/configs/hydra/default.yaml @@ -0,0 +1,12 @@ +# output paths for hydra logs +run: + dir: logs/runs/${now:%Y-%m-%d}/${now:%H-%M-%S} +sweep: + dir: logs/multiruns/${now:%Y-%m-%d_%H-%M-%S} + subdir: ${hydra.job.num} + +# you can set here environment variables that are universal for all users +# for system specific variables (like data paths) it's better to use .env file! +job: + env_set: + EXAMPLE_VAR: "example_value" diff --git a/configs/logger/wandb.yaml b/configs/logger/wandb.yaml new file mode 100644 index 0000000..7af259e --- /dev/null +++ b/configs/logger/wandb.yaml @@ -0,0 +1,15 @@ +# https://wandb.ai + +wandb: + _target_: lightning.pytorch.loggers.wandb.WandbLogger + project: "pvnet_summation" + name: "${model_name}" + save_dir: "/mnt/disks/batches/" + offline: False # set True to store all logs only locally + id: null # pass correct id to resume experiment! + # entity: "" # set to name of your wandb team or just remove it + log_model: False + prefix: "" + job_type: "train" + group: "" + tags: [] diff --git a/configs/model/default.yaml b/configs/model/default.yaml new file mode 100644 index 0000000..0418c70 --- /dev/null +++ b/configs/model/default.yaml @@ -0,0 +1,34 @@ +_target_: pvnet_summation.models.model.Model + +output_quantiles: null + +model_name: "openclimatefix/pvnet_v2" +model_version: "898630f3f8cd4e8506525d813dd61c6d8de86144" + +#-------------------------------------------- +# Tabular network settings +#-------------------------------------------- + +output_network: + _target_: pvnet.models.multimodal.linear_networks.networks.ResFCNet2 + _partial_: True +output_network_kwargs: + fc_hidden_features: 128 + n_res_blocks: 6 + res_block_layers: 2 + dropout_frac: 0.0 + + +# Foreast and time settings +forecast_minutes: 480 + +# ---------------------------------------------- + +optimizer: + _target_: pvnet.optimizers.AdamWReduceLROnPlateau + lr: 0.0001 + weight_decay: 0.25 + amsgrad: True + patience: 5 + factor: 0.1 + threshold: 0.002 diff --git a/configs/readme.md b/configs/readme.md new file mode 100644 index 0000000..13cbbf2 --- /dev/null +++ b/configs/readme.md @@ -0,0 +1,7 @@ +The following folders how the configuration files + +This idea is copied from +https://github.com/ashleve/lightning-hydra-template/blob/main/configs/experiment/example_simple.yaml + +run experiments by: +`python run.py experiment=example_simple ` diff --git a/configs/trainer/all_params.yaml b/configs/trainer/all_params.yaml new file mode 100644 index 0000000..64f5fdf --- /dev/null +++ b/configs/trainer/all_params.yaml @@ -0,0 +1,48 @@ +_target_: pytorch_lightning.Trainer + +# default values for all trainer parameters +checkpoint_callback: True +default_root_dir: null +gradient_clip_val: 0.0 +process_position: 0 +num_nodes: 1 +num_processes: 1 +gpus: null +auto_select_gpus: False +tpu_cores: null +log_gpu_memory: null +overfit_batches: 0.0 +track_grad_norm: -1 +check_val_every_n_epoch: 1 +fast_dev_run: False +accumulate_grad_batches: 1 +max_epochs: 1 +min_epochs: 1 +max_steps: null +min_steps: null +limit_train_batches: 1.0 +limit_val_batches: 1.0 +limit_test_batches: 1.0 +val_check_interval: 1.0 +flush_logs_every_n_steps: 100 +log_every_n_steps: 50 +accelerator: null +sync_batchnorm: False +precision: 32 +weights_save_path: null +num_sanity_val_steps: 2 +truncated_bptt_steps: null +resume_from_checkpoint: null +profiler: null +benchmark: False +deterministic: False +reload_dataloaders_every_epoch: False +auto_lr_find: False +replace_sampler_ddp: True +terminate_on_nan: False +auto_scale_batch_size: False +prepare_data_per_node: True +plugins: null +amp_backend: "native" +amp_level: "O2" +move_metrics_to_cpu: False diff --git a/configs/trainer/default.yaml b/configs/trainer/default.yaml new file mode 100644 index 0000000..5f05108 --- /dev/null +++ b/configs/trainer/default.yaml @@ -0,0 +1,17 @@ +_target_: lightning.pytorch.trainer.trainer.Trainer + +# set `1` to train on GPU, `0` to train on CPU only +accelerator: gpu +devices: auto + +min_epochs: null +max_epochs: null +reload_dataloaders_every_n_epochs: 0 +num_sanity_val_steps: 8 +fast_dev_run: false +#profiler: 'simple' + +accumulate_grad_batches: 4 +#val_check_interval: 800 +#limit_val_batches: 800 +log_every_n_steps: 50 diff --git a/environment.yml b/environment.yml index 96986b8..c7b29cb 100644 --- a/environment.yml +++ b/environment.yml @@ -1,4 +1,4 @@ -name: ocf_template +name: pvnet_summation channels: - pytorch - conda-forge diff --git a/pvnet_summation/data/datamodule.py b/pvnet_summation/data/datamodule.py new file mode 100644 index 0000000..64d4d14 --- /dev/null +++ b/pvnet_summation/data/datamodule.py @@ -0,0 +1,128 @@ +""" Data module for pytorch lightning """ + +import torch +from lightning.pytorch import LightningDataModule +from torchdata.dataloader2 import DataLoader2, MultiProcessingReadingService +from torchdata.datapipes.iter import FileLister, IterDataPipe +from ocf_datapipes.utils.consts import BatchKey +from ocf_datapipes.load import OpenGSP +from ocf_datapipes.training.pvnet import normalize_gsp + +from pvnet.data.datamodule import ( + copy_batch_to_device, + batch_to_tensor, + split_batches, +) + + +class GetNationalPVLive(IterDataPipe): + def __init__(self, gsp_zarr_path, sample_datapipe, return_times=False): + self.gsp_datapipe = OpenGSP( + gsp_pv_power_zarr_path=gsp_zarr_path + ).map(normalize_gsp) + self.sample_datapipe = sample_datapipe + self.return_times = return_times + + def __iter__(self): + gsp_data = next(iter(self.gsp_datapipe)).sel(gsp_id=0).compute() + for sample in self.sample_datapipe: + # Times for each GSP in the sample batch should be the same - take first + id0 = sample[BatchKey.gsp_t0_idx] + times = sample[BatchKey.gsp_time_utc][0, id0+1:] + national_outputs = torch.from_numpy( + gsp_data.sel(time_utc=times.cpu().numpy().astype("datetime64[s]")).values + ) + + if self.return_times: + yield national_outputs, times + else: + yield national_outputs + + +class ReorganiseBatch(IterDataPipe): + """Reoragnise batches for pvnet_summation""" + def __init__(self, source_datapipe): + """Reoragnise batches for pvnet_summation + + Args: + source_datapipe: Zipped datapipe of list[tuple(NumpyBatch, national_outputs)] + """ + self.source_datapipe = source_datapipe + + def __iter__(self): + for batch in self.source_datapipe: + yield dict( + pvnet_inputs = [sample[0] for sample in batch], + national_targets = torch.stack([sample[1] for sample in batch]), + times = torch.stack([sample[2] for sample in batch]), + ) + +class DataModule(LightningDataModule): + """Datamodule for training pvnet_summation.""" + + def __init__( + self, + batch_dir: str, + gsp_zarr_path: str, + batch_size=16, + num_workers=0, + prefetch_factor=2, + ): + """Datamodule for training pvnet_summation. + + Args: + batch_dir: Path to the directory of pre-saved batches. + gsp_zarr_path: Path to zarr file containing GSP ID 0 outputs + batch_size: Batch size. + num_workers: Number of workers to use in multiprocess batch loading. + prefetch_factor: Number of data will be prefetched at the end of each worker process. + """ + super().__init__() + self.gsp_zarr_path = gsp_zarr_path + self.batch_size = batch_size + self.batch_dir = batch_dir + + self.readingservice_config = dict( + num_workers=num_workers, + multiprocessing_context="spawn", + worker_prefetch_cnt=prefetch_factor, + ) + + def _get_premade_batches_datapipe(self, subdir, shuffle=False): + data_pipeline = FileLister(f"{self.batch_dir}/{subdir}", masks="*.pt", recursive=False) + if shuffle: + data_pipeline = data_pipeline.shuffle(buffer_size=10_000) + + data_pipeline = data_pipeline.sharding_filter().map(torch.load) + + # Add the national target + data_pipeline, dp = data_pipeline.fork(2, buffer_size=5) + national_targets_datapipe, times_datapipe = ( + GetNationalPVLive(self.gsp_zarr_path, dp, return_times=True).unzip(sequence_length=2) + ) + data_pipeline = data_pipeline.zip(national_targets_datapipe, times_datapipe) + + data_pipeline = ReorganiseBatch(data_pipeline.batch(self.batch_size)) + + return data_pipeline + + def train_dataloader(self): + """Construct train dataloader""" + datapipe = self._get_premade_batches_datapipe("train", shuffle=True) + + rs = MultiProcessingReadingService(**self.readingservice_config) + return DataLoader2(datapipe, reading_service=rs) + + def val_dataloader(self): + """Construct val dataloader""" + datapipe = self._get_premade_batches_datapipe("val") + + rs = MultiProcessingReadingService(**self.readingservice_config) + return DataLoader2(datapipe, reading_service=rs) + + def test_dataloader(self): + """Construct test dataloader""" + datapipe = self._get_premade_batches_datapipe("test") + + rs = MultiProcessingReadingService(**self.readingservice_config) + return DataLoader2(datapipe, reading_service=rs) diff --git a/pvnet_summation/models/base_model.py b/pvnet_summation/models/base_model.py new file mode 100644 index 0000000..0d10862 --- /dev/null +++ b/pvnet_summation/models/base_model.py @@ -0,0 +1,226 @@ +"""Base model for all PVNet submodels""" +import json +import logging +import os +from pathlib import Path +from typing import Dict, Optional, Union + +import hydra +import torch +import wandb +from nowcasting_utils.models.loss import WeightedLosses +import lightning.pytorch as pl +from torch import nn +from pvnet.models.base_model import PVNetModelHubMixin, BaseModel as PVNetBaseModel + +from pvnet.models.utils import ( + MetricAccumulator, + PredAccumulator, +) + + +from pvnet.optimizers import AbstractOptimizer +from pvnet_summation.utils import plot_forecasts + +#from pvnet.models.base_model import BaseModel as PVNetBaseModel + + +logger = logging.getLogger(__name__) + +activities = [torch.profiler.ProfilerActivity.CPU] +if torch.cuda.is_available(): + activities.append(torch.profiler.ProfilerActivity.CUDA) + + +class BaseModel(PVNetBaseModel): + """Abtstract base class for PVNet summation submodels""" + + def __init__( + self, + forecast_minutes: int, + model_name: str, + model_version: Optional[str], + optimizer: AbstractOptimizer, + output_quantiles: Optional[list[float]] = None, + ): + """Abtstract base class for PVNet summation submodels. + + Args: + forecast_minutes (int): Length of the GSP forecast period in minutes + model_name: Model path either locally or on huggingface. + model_version: Model version if using huggingface. Set to None if using local. + optimizer (AbstractOptimizer): Optimizer + output_quantiles: A list of float (0.0, 1.0) quantiles to predict values for. If set to + None the output is a single value. + """ + pl.LightningModule.__init__(self) + PVNetModelHubMixin.__init__(self) + + self._optimizer = optimizer + + # Model must have lr to allow tuning + # This setting is only used when lr is tuned with callback + self.lr = None + + self.forecast_minutes = forecast_minutes + self.output_quantiles = output_quantiles + + # Number of timestemps for 30 minutely data + self.forecast_len_30 = forecast_minutes // 30 + + self.weighted_losses = WeightedLosses(forecast_length=self.forecast_len_30) + + self._accumulated_metrics = MetricAccumulator() + self._accumulated_y = PredAccumulator() + self._accumulated_y_hat = PredAccumulator() + self._accumulated_times = PredAccumulator() + + self.pvnet_model = PVNetBaseModel.from_pretrained( + model_name, + revision=model_version, + ) + self.pvnet_model.requires_grad_(False) + + def predict_pvnet_batch(self, batch): + gsp_batches = [] + for sample in batch: + preds = self.pvnet_model(sample) + gsp_batches += [preds] + return torch.stack(gsp_batches) + + @property + def pvnet_output_shape(self): + if self.pvnet_model.use_quantile_regression: + return (317, self.pvnet_model.forecast_len_30, len(self.pvnet_model.output_quantiles)) + else: + return (317, self.pvnet_model.forecast_len_30) + + + def _training_accumulate_log(self, batch_idx, losses, y_hat, y, times): + """Internal function to accumulate training batches and log results. + + This is used when accummulating grad batches. Should make the variability in logged training + step metrics indpendent on whether we accumulate N batches of size B or just use a larger + batch size of N*B with no accumulaion. + """ + + losses = {k: v.detach().cpu() for k, v in losses.items()} + y_hat = y_hat.detach().cpu() + + self._accumulated_metrics.append(losses) + self._accumulated_y_hat.append(y_hat) + self._accumulated_y.append(y) + self._accumulated_times.append(times) + + if not self.trainer.fit_loop._should_accumulate(): + losses = self._accumulated_metrics.flush() + y_hat = self._accumulated_y_hat.flush() + y = self._accumulated_y.flush() + times = self._accumulated_times.flush() + + self.log_dict( + losses, + on_step=True, + on_epoch=True, + ) + + # Number of accumulated grad batches + grad_batch_num = (batch_idx + 1) / self.trainer.accumulate_grad_batches + + # We only create the figure every 8 log steps + # This was reduced as it was creating figures too often + if grad_batch_num % (8 * self.trainer.log_every_n_steps) == 0: + fig = plot_forecasts(y, y_hat, times, batch_idx, quantiles=self.output_quantiles) + fig.savefig("latest_logged_train_batch.png") + + def training_step(self, batch, batch_idx): + """Run training step""" + y_hat = self.forward(batch['pvnet_inputs']) + y = batch["national_targets"] + times = batch["times"] + + losses = self._calculate_common_losses(y, y_hat) + losses = {f"{k}/train": v for k, v in losses.items()} + + self._training_accumulate_log(batch_idx, losses, y_hat, y, times) + + if self.use_quantile_regression: + opt_target = losses["quantile_loss/train"] + else: + opt_target = losses["MAE/train"] + return opt_target + + def validation_step(self, batch: dict, batch_idx): + """Run validation step""" + y_hat = self.forward(batch['pvnet_inputs']) + y = batch["national_targets"] + times = batch["times"] + + losses = self._calculate_common_losses(y, y_hat) + losses.update(self._calculate_val_losses(y, y_hat)) + + logged_losses = {f"{k}/val": v for k, v in losses.items()} + + self.log_dict( + logged_losses, + on_step=False, + on_epoch=True, + ) + + accum_batch_num = batch_idx // self.trainer.accumulate_grad_batches + + if accum_batch_num in [0, 1]: + # Store these temporarily under self + if not hasattr(self, "_val_y_hats"): + self._val_y_hats = PredAccumulator() + self._val_y = PredAccumulator() + self._val_times = PredAccumulator() + + self._val_y_hats.append(y_hat) + self._val_y.append(y) + self._val_times.append(times) + + # if batch had accumulated + if (batch_idx + 1) % self.trainer.accumulate_grad_batches == 0: + y_hat = self._val_y_hats.flush() + y = self._val_y.flush() + times = self._val_times.flush() + + fig = plot_forecasts(y, y_hat, times, batch_idx, quantiles=self.output_quantiles) + + self.logger.experiment.log( + { + f"val_forecast_samples/batch_idx_{accum_batch_num}": wandb.Image(fig), + } + ) + del self._val_y_hats + del self._val_y + del self._val_times + + return logged_losses + + def test_step(self, batch, batch_idx): + """Run test step""" + y_hat = self.forward(batch['pvnet_inputs']) + y = batch["national_targets"] + + losses = self._calculate_common_losses(y, y_hat) + losses.update(self._calculate_val_losses(y, y_hat)) + losses.update(self._calculate_test_losses(y, y_hat)) + logged_losses = {f"{k}/test": v for k, v in losses.items()} + + self.log_dict( + logged_losses, + on_step=False, + on_epoch=True, + ) + + return logged_losses + + + def configure_optimizers(self): + """Configure the optimizers using learning rate found with LR finder if used""" + if self.lr is not None: + # Use learning rate found by learning rate finder callback + self._optimizer.lr = self.lr + return self._optimizer(self.parameters()) \ No newline at end of file diff --git a/pvnet_summation/models/model.py b/pvnet_summation/models/model.py new file mode 100644 index 0000000..b978ef4 --- /dev/null +++ b/pvnet_summation/models/model.py @@ -0,0 +1,79 @@ +"""Simple model which only uses outputs of PVNet for all GSPs""" + +from typing import Optional + +import numpy as np +import torch + +import pvnet +from pvnet_summation.models.base_model import BaseModel +from pvnet.optimizers import AbstractOptimizer +from pvnet.models.multimodal.linear_networks.networks import DefaultFCNet +from pvnet.models.multimodal.linear_networks.basic_blocks import AbstractLinearNetwork + + + + +class Model(BaseModel): + """Neural network which combines GSP predictions from PVNet + + """ + + name = "pvnet_summation_model" + + def __init__( + self, + model_name: str, + forecast_minutes: int, + model_version: Optional[str], + output_quantiles: Optional[list[float]] = None, + output_network: AbstractLinearNetwork = DefaultFCNet, + output_network_kwargs: dict = dict(), + optimizer: AbstractOptimizer = pvnet.optimizers.Adam(), + + ): + """Neural network which combines GSP predictions from PVNet + + Args: + output_quantiles: A list of float (0.0, 1.0) quantiles to predict values for. If set to + None the output is a single value. + output_network: Pytorch Module class used to combine the 1D features to produce the + forecast. + output_network_kwargs: Dictionary of optional kwargs for the `output_network` module. + model_name: Model path either locally or on huggingface. + model_version: Model version if using huggingface. Set to None if using local. + forecast_minutes (int): Length of the GSP forecast period in minutes + optimizer (AbstractOptimizer): Optimizer + """ + + super().__init__( + forecast_minutes, + model_name, + model_version, + optimizer, + output_quantiles + ) + + in_features = np.product(self.pvnet_output_shape) + + self.model = output_network( + in_features=in_features, + out_features=self.num_output_features, + **output_network_kwargs, + ) + + self.save_hyperparameters() + + + def forward(self, x): + """Run central model forward""" + pvnet_out = self.predict_pvnet_batch(x) + pvnet_out = torch.flatten(pvnet_out, start_dim=1) + out = self.model(pvnet_out) + + if self.use_quantile_regression: + # Shape: batch_size, seq_length * num_quantiles + out = out.reshape(out.shape[0], self.forecast_len_30, len(self.output_quantiles)) + + return out + diff --git a/pvnet_summation/models/model_card_template.md b/pvnet_summation/models/model_card_template.md new file mode 100644 index 0000000..07c3fd0 --- /dev/null +++ b/pvnet_summation/models/model_card_template.md @@ -0,0 +1,54 @@ +--- +{{ card_data }} +--- + + + + + + +# PVNet_summation + +## Model Description + + +This model class sums the output of the PVNet model's GSP level predictions to make a national forecast of UK PV output. More information can be found in the model repo [1], the PVNet model repo [2], and experimental notes in [this google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing). + +- **Developed by:** openclimatefix +- **Model type:** Fusion model +- **Language(s) (NLP):** en +- **License:** mit + + +# Training Details + +## Data + + + +The model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing) for more details. + + +### Preprocessing + +Data is prepared with the `ocf_datapipes.training.pvnet` datapipe [3]. + + +## Results + +The training logs for the current model can be found [here on wandb](https://wandb.ai/openclimatefix/pvnet_summation/runs/{{ wandb_model_code }}). + +The training logs for all model runs of PVNet_summation can be found [here](https://wandb.ai/openclimatefix/pvnet_summation). + +Some experimental notes can be found at in [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing) + + +### Hardware + +Trained on a single NVIDIA Tesla T4 + +### Software + +- [1] https://github.com/openclimatefix/PVNet_summation +- [2] https://github.com/openclimatefix/PVNet +- [3] https://github.com/openclimatefix/ocf_datapipes diff --git a/pvnet_summation/training.py b/pvnet_summation/training.py new file mode 100644 index 0000000..f7f5324 --- /dev/null +++ b/pvnet_summation/training.py @@ -0,0 +1,139 @@ +"""Training""" +import os +from typing import Optional + +import hydra +import torch +from lightning.pytorch import ( + Callback, + LightningDataModule, + LightningModule, + Trainer, + seed_everything, +) +from lightning.pytorch.callbacks import ModelCheckpoint +from lightning.pytorch.loggers import Logger +from lightning.pytorch.loggers.wandb import WandbLogger +from omegaconf import DictConfig, OmegaConf + +from pvnet import utils + +log = utils.get_logger(__name__) + +torch.set_default_dtype(torch.float32) + + +def resolve_monitor_loss(output_quantiles): + """Return the desired metric to monitor based on whether quantile regression is being used. + + The adds the option to use something like: + monitor: "${resolve_monitor_loss:${model.output_quantiles}}" + + in early stopping and model checkpoint callbacks so the callbacks config does not need to be + modified depending on whether quantile regression is being used or not. + """ + if output_quantiles is None: + return "MAE/val" + else: + return "quantile_loss/val" + + +OmegaConf.register_new_resolver("resolve_monitor_loss", resolve_monitor_loss) + + +def train(config: DictConfig) -> Optional[float]: + """Contains training pipeline. + + Instantiates all PyTorch Lightning objects from config. + + Args: + config (DictConfig): Configuration composed by Hydra. + + Returns: + Optional[float]: Metric score for hyperparameter optimization. + """ + + # Set seed for random number generators in pytorch, numpy and python.random + if "seed" in config: + seed_everything(config.seed, workers=True) + + # Init lightning datamodule + log.info(f"Instantiating datamodule <{config.datamodule._target_}>") + datamodule: LightningDataModule = hydra.utils.instantiate(config.datamodule) + + # Init lightning model + log.info(f"Instantiating model <{config.model._target_}>") + model: LightningModule = hydra.utils.instantiate(config.model) + + # Init lightning loggers + loggers: list[Logger] = [] + if "logger" in config: + for _, lg_conf in config.logger.items(): + if "_target_" in lg_conf: + log.info(f"Instantiating logger <{lg_conf._target_}>") + loggers.append(hydra.utils.instantiate(lg_conf)) + + # Init lightning callbacks + callbacks: list[Callback] = [] + if "callbacks" in config: + for _, cb_conf in config.callbacks.items(): + if "_target_" in cb_conf: + log.info(f"Instantiating callback <{cb_conf._target_}>") + callbacks.append(hydra.utils.instantiate(cb_conf)) + + # Align the wandb id with the checkpoint path + # - only works if wandb logger and model checkpoint used + # - this makes it easy to push the model to huggingface + use_wandb_logger = False + for logger in loggers: + log.info(f"{logger}") + if isinstance(logger, WandbLogger): + use_wandb_logger = True + wandb_logger = logger + break + + if use_wandb_logger: + for callback in callbacks: + log.info(f"{callback}") + if isinstance(callback, ModelCheckpoint): + callback.dirpath = "/".join( + callback.dirpath.split("/")[:-1] + [wandb_logger.version] + ) + # Also save model config here - this makes for easy model push to huggingface + os.makedirs(callback.dirpath, exist_ok=True) + OmegaConf.save(config.model, f"{callback.dirpath}/model_config.yaml") + break + + trainer: Trainer = hydra.utils.instantiate( + config.trainer, + logger=loggers, + _convert_="partial", + callbacks=callbacks, + ) + + # Train the model completely + trainer.fit(model=model, datamodule=datamodule) + + if config.test_after_training: + # Evaluate model on test set, using the best model achieved during training + log.info("Starting testing!") + trainer.test(model=model, datamodule=datamodule, ckpt_path="best") + + # Make sure everything closed properly + log.info("Finalizing!") + utils.finish( + config=config, + model=model, + datamodule=datamodule, + trainer=trainer, + callbacks=callbacks, + logger=logger, + ) + + # Print path to best checkpoint + log.info(f"Best checkpoint path:\n{trainer.checkpoint_callback.best_model_path}") + + # Return metric score for hyperparameter optimization + optimized_metric = config.get("optimized_metric") + if optimized_metric: + return trainer.callback_metrics[optimized_metric] diff --git a/pvnet_summation/utils.py b/pvnet_summation/utils.py new file mode 100644 index 0000000..711dc3e --- /dev/null +++ b/pvnet_summation/utils.py @@ -0,0 +1,60 @@ +"""Utils""" + +import matplotlib.pyplot as plt +import pandas as pd +import pylab + + +def plot_forecasts(y, y_hat, times, batch_idx=None, quantiles=None): + """Plot a batch of data and the forecast from that batch""" + + + times_utc = times.cpu().numpy().squeeze().astype("datetime64[s]") + times_utc = [pd.to_datetime(t) for t in times_utc] + y = y.cpu().numpy() + y_hat = y_hat.cpu().numpy() + + batch_size = y.shape[0] + + fig, axes = plt.subplots(4, 4, figsize=(8, 8)) + + for i, ax in enumerate(axes.ravel()): + if i >= batch_size: + ax.axis("off") + continue + ax.plot(times_utc[i], y[i], marker=".", color="k", label=r"$y$") + + if quantiles is None: + ax.plot( + times_utc[i], y_hat[i], marker=".", color="r", label=r"$\hat{y}$" + ) + else: + cm = pylab.get_cmap("twilight") + for nq, q in enumerate(quantiles): + ax.plot( + times_utc[i], + y_hat[i, :, nq], + color=cm(q), + label=r"$\hat{y}$" + f"({q})", + alpha=0.7, + ) + + ax.set_title(f"{times_utc[i][0].date()}", fontsize="small") + + xticks = [t for t in times_utc[i] if t.minute == 0][::2] + ax.set_xticks(ticks=xticks, labels=[f"{t.hour:02}" for t in xticks], rotation=90) + ax.grid() + + axes[0, 0].legend(loc="best") + + for ax in axes[-1, :]: + ax.set_xlabel("Time (hour of day)") + + if batch_idx is not None: + title = f"Normed National output : batch_idx={batch_idx}" + else: + title = "Normed National output" + plt.suptitle(title) + plt.tight_layout() + + return fig \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index e69de29..7c01acb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -0,0 +1,30 @@ +nowcasting_utils +nowcasting_datamodel==1.4.14 +ocf_datapipes==1.2.44 +pvnet==2.1.15 +ocf_ml_metrics +numpy +pandas +matplotlib +xarray +ipykernel +h5netcdf +torch>=2.0 +lightning>=2.0.1 +torchdata +pytest +pytest-cov +typer +sqlalchemy +jedi +fsspec[s3] +tables +tilemapbase +testcontainers +wandb +tensorboard +tqdm +rich +omegaconf +hydra-core +python-dotenv \ No newline at end of file diff --git a/run.py b/run.py new file mode 100644 index 0000000..c0490bc --- /dev/null +++ b/run.py @@ -0,0 +1,59 @@ +"""Run training +""" + + + +import torch + +try: + torch.multiprocessing.set_start_method("spawn") + import torch.multiprocessing as mp + + mp.set_start_method("spawn") +except RuntimeError: + pass + +import logging +import sys +import os + +# Tired of seeing these warnings +import warnings + +import hydra +from omegaconf import DictConfig +from sqlalchemy import exc as sa_exc + +warnings.filterwarnings("ignore", category=sa_exc.SAWarning) + +logging.basicConfig(stream=sys.stdout, level=logging.ERROR) + +os.environ["HYDRA_FULL_ERROR"] = "1" + + +@hydra.main(config_path="configs/", config_name="config.yaml", version_base="1.2") +def main(config: DictConfig): + """Runs training""" + # Imports should be nested inside @hydra.main to optimize tab completion + # Read more here: https://github.com/facebookresearch/hydra/issues/934 + from pvnet_summation.training import train + from pvnet.utils import extras, print_config + + # A couple of optional utilities: + # - disabling python warnings + # - easier access to debug mode + # - forcing debug friendly configuration + # - forcing multi-gpu friendly configuration + # You can safely get rid of this line if you don't want those + extras(config) + + # Pretty print config using Rich library + if config.get("print_config"): + print_config(config, resolve=True) + + # Train model + return train(config) + + +if __name__ == "__main__": + main() diff --git a/setup.py b/setup.py index 2c91e6e..899ded5 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ name="PVNet_summation", version="0.0.1", license="MIT", - description="Package for training summation model for PVNet" + description="Package for training summation model for PVNet", author="James Fulton", author_email="info@openclimatefix.org", company="Open Climate Fix Ltd", diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..1ffb578 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,129 @@ +import os + +import pytest +import pandas as pd +import numpy as np +import xarray as xr +import torch +import math +import glob +import tempfile +from pvnet_summation.models.model import Model + + +from ocf_datapipes.utils.consts import BatchKey +from datetime import timedelta + +from pvnet_summation.data.datamodule import DataModule + + + +@pytest.fixture() +def sample_data(): + + # Copy small batches to fake 317 GSPs in each + with tempfile.TemporaryDirectory() as tmpdirname: + os.makedirs(f"{tmpdirname}/train") + os.makedirs(f"{tmpdirname}/val") + + # Grab times from batch to make national output zarr + times = [] + + file_n = 0 + for file in glob.glob("tests/data/sample_batches/train/*.pt"): + + batch = torch.load(file) + + this_batch = {} + for i in range(batch[BatchKey.gsp_time_utc].shape[0]): + # Duplicate sample to fake 317 GSPs + for key in batch.keys(): + if isinstance(batch[key], torch.Tensor): + n_dims = len(batch[key].shape) + repeats = (317,) + tuple(1 for dim in range(n_dims-1)) + this_batch[key] = batch[key][i:i+1].repeat(repeats)[:317] + else: + this_batch[key] = batch[key] + + # Save fopr both train and val + torch.save(this_batch, f"{tmpdirname}/train/{file_n:06}.pt") + torch.save(this_batch, f"{tmpdirname}/val/{file_n:06}.pt") + + file_n += 1 + + times += [batch[BatchKey.gsp_time_utc][i].numpy().astype("datetime64[s]")] + + times = np.unique(np.sort(np.concatenate(times))) + + da_output = xr.DataArray( + data=np.random.uniform(size=(len(times), 1)), + dims=["datetime_gmt", "gsp_id"], + coords=dict( + datetime_gmt=times, + gsp_id=[0], + ), + ) + + da_cap = xr.DataArray( + data=np.ones((len(times), 1)), + dims=["datetime_gmt", "gsp_id"], + coords=dict( + datetime_gmt=times, + gsp_id=[0], + ), + ) + + ds = xr.Dataset( + data_vars=dict( + generation_mw=da_output, + installedcapacity_mwp=da_cap, + capacity_mwp=da_cap, + ), + ) + + ds.to_zarr(f"{tmpdirname}/gsp.zarr") + + yield tmpdirname, f"{tmpdirname}/gsp.zarr" + + +@pytest.fixture() +def sample_datamodule(sample_data): + batch_dir, gsp_zarr_dir = sample_data + + dm = DataModule( + batch_dir=batch_dir, + gsp_zarr_path=gsp_zarr_dir, + batch_size=2, + num_workers=0, + prefetch_factor=2, + ) + + return dm + + +@pytest.fixture() +def sample_batch(sample_datamodule): + batch = next(iter(sample_datamodule.train_dataloader())) + return batch + + +@pytest.fixture() +def model_kwargs(): + kwargs = dict( + forecast_minutes=480, + model_name= "openclimatefix/pvnet_v2", + model_version= "898630f3f8cd4e8506525d813dd61c6d8de86144", + ) + return kwargs + + +@pytest.fixture() +def model(model_kwargs): + model = Model(**model_kwargs) + return model + + +@pytest.fixture() +def quantile_model(model_kwargs): + model = Model(output_quantiles=[0.1, 0.5, 0.9], **model_kwargs) + return model \ No newline at end of file diff --git a/tests/data/sample_batches/data_configuration.yaml b/tests/data/sample_batches/data_configuration.yaml new file mode 100644 index 0000000..697651b --- /dev/null +++ b/tests/data/sample_batches/data_configuration.yaml @@ -0,0 +1,81 @@ +general: + description: Config for producing batches on GCP + name: gcp_pvnet + +input_data: + default_history_minutes: 120 + default_forecast_minutes: 480 + + gsp: + gsp_zarr_path: gs://solar-pv-nowcasting-data/PV/GSP/v5/pv_gsp.zarr + history_minutes: 120 + forecast_minutes: 480 + time_resolution_minutes: 30 + start_datetime: "2020-01-01T00:00:00" + end_datetime: "2021-09-01T00:00:00" + gsp_image_size_pixels_height: 64 + gsp_image_size_pixels_width: 64 + gsp_meters_per_pixel: 2000 + n_gsp_per_example: 32 + is_live: false + log_level: DEBUG + metadata_only: false + + nwp: + nwp_zarr_path: /mnt/disks/nwp/UKV_intermediate_version_7.zarr + history_minutes: 120 + forecast_minutes: 480 + time_resolution_minutes: 60 + nwp_channels: + - t # live = t2m + - dswrf + nwp_image_size_pixels_height: 24 + nwp_image_size_pixels_width: 24 + nwp_meters_per_pixel: 2000 + log_level: DEBUG + + satellite: + satellite_zarr_path: + - /mnt/disks/data_ssd/2017_nonhrv.zarr + - /mnt/disks/data_ssd/2018_nonhrv.zarr + - /mnt/disks/data_ssd/2019_nonhrv.zarr + - /mnt/disks/data_ssd/2020_nonhrv.zarr + - /mnt/disks/data_ssd/2021_nonhrv.zarr + history_minutes: 90 + forecast_minutes: 0 + time_resolution_minutes: 5 + satellite_channels: + - IR_016 + - IR_039 + - IR_087 + - IR_097 + - IR_108 + - IR_120 + - IR_134 + - VIS006 + - VIS008 + - WV_062 + - WV_073 + satellite_image_size_pixels_height: 24 + satellite_image_size_pixels_width: 24 + satellite_meters_per_pixel: 6000 + keep_dawn_dusk_hours: 4 + live_delay_minutes: 30 + is_live: false + log_level: DEBUG + + hrvsatellite: + hrvsatellite_zarr_path: + - gs://solar-pv-nowcasting-data/satellite/EUMETSAT/SEVIRI_RSS/v4/2019_hrv.zarr + - gs://solar-pv-nowcasting-data/satellite/EUMETSAT/SEVIRI_RSS/v4/2020_hrv.zarr + - gs://solar-pv-nowcasting-data/satellite/EUMETSAT/SEVIRI_RSS/v4/2021_hrv.zarr + history_minutes: 60 + forecast_minutes: 0 + time_resolution_minutes: 5 + hrvsatellite_channels: + - HRV + hrvsatellite_image_size_pixels_height: 24 + hrvsatellite_image_size_pixels_width: 24 + +output_data: + filepath: "not-needed" diff --git a/tests/data/sample_batches/datamodule.yaml b/tests/data/sample_batches/datamodule.yaml new file mode 100644 index 0000000..2c30ed1 --- /dev/null +++ b/tests/data/sample_batches/datamodule.yaml @@ -0,0 +1,14 @@ +_target_: pvnet.data.datamodule.DataModule 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prefetch_factor=2, + ) + +def test_iter(sample_data): + batch_dir, gsp_zarr_dir = sample_data + + dm = DataModule( + batch_dir=batch_dir, + gsp_zarr_path=gsp_zarr_dir, + batch_size=2, + num_workers=0, + prefetch_factor=2, + ) + + batch = next(iter(dm.train_dataloader())) + + # batch size is 2 + assert len(batch['pvnet_inputs'])==2 + + # 317 GSPs in each sample + # 21 timestamps for each GSP from -120 mins to +480 mins + assert batch['pvnet_inputs'][0][BatchKey.gsp_time_utc].shape==(317,21) + + assert batch['times'].shape==(2, 16) + + assert batch['national_targets'].shape==(2, 16) + +def test_iter_multiprocessing(sample_data): + batch_dir, gsp_zarr_dir = sample_data + + dm = DataModule( + batch_dir=batch_dir, + gsp_zarr_path=gsp_zarr_dir, + batch_size=2, + num_workers=2, + prefetch_factor=2, + ) + + for i, batch in zip(range(2), dm.train_dataloader()): + # batch size is 2 + assert len(batch['pvnet_inputs'])==2 + + # 317 GSPs in each sample + # 21 timestamps for each GSP from -120 mins to +480 mins + assert batch['pvnet_inputs'][0][BatchKey.gsp_time_utc].shape==(317,21) + + assert batch['times'].shape==(2, 16) + + assert batch['national_targets'].shape==(2, 16) \ No newline at end of file diff --git a/tests/models/test_model.py b/tests/models/test_model.py new file mode 100644 index 0000000..f4bc30d --- /dev/null +++ b/tests/models/test_model.py @@ -0,0 +1,36 @@ +from torch.optim import SGD +import pytest + + +def test_model_forward(model, sample_batch): + y = model.forward(sample_batch["pvnet_inputs"]) + + # check output is the correct shape + # batch size=2, forecast_len=16 + assert tuple(y.shape) == (2, 16), y.shape + + +def test_model_backward(model, sample_batch): + opt = SGD(model.parameters(), lr=0.001) + + y = model(sample_batch["pvnet_inputs"]) + + # Backwards on sum drives sum to zero + y.sum().backward() + + +def test_quantile_model_forward(quantile_model, sample_batch): + y_quantiles = quantile_model(sample_batch["pvnet_inputs"]) + + # check output is the correct shape + # batch size=2, forecast_len=15, num_quantiles=3 + assert tuple(y_quantiles.shape) == (2, 16, 3), y_quantiles.shape + + +def test_quantile_model_backward(quantile_model, sample_batch): + opt = SGD(quantile_model.parameters(), lr=0.001) + + y_quantiles = quantile_model(sample_batch["pvnet_inputs"]) + + # Backwards on sum drives sum to zero + y_quantiles.sum().backward() diff --git a/tests/test_end2end.py b/tests/test_end2end.py new file mode 100644 index 0000000..856f883 --- /dev/null +++ b/tests/test_end2end.py @@ -0,0 +1,6 @@ +import lightning + + +def test_model_trainer_fit(model, sample_datamodule): + trainer = lightning.pytorch.trainer.trainer.Trainer(fast_dev_run=True) + trainer.fit(model=model, datamodule=sample_datamodule) \ No newline at end of file From 48b82e515d57292403bc266fafa04e84279f995d Mon Sep 17 00:00:00 2001 From: James Fulton Date: Thu, 20 Jul 2023 10:35:03 +0000 Subject: [PATCH 2/3] fix multiprocessign issues --- pvnet_summation/data/datamodule.py | 20 ++++++++++++-------- run.py | 2 -- tests/data/test_datamodule.py | 2 +- 3 files changed, 13 insertions(+), 11 deletions(-) diff --git a/pvnet_summation/data/datamodule.py b/pvnet_summation/data/datamodule.py index 64d4d14..d4f3a8b 100644 --- a/pvnet_summation/data/datamodule.py +++ b/pvnet_summation/data/datamodule.py @@ -13,23 +13,23 @@ batch_to_tensor, split_batches, ) +# https://github.com/pytorch/pytorch/issues/973 +torch.multiprocessing.set_sharing_strategy('file_system') class GetNationalPVLive(IterDataPipe): - def __init__(self, gsp_zarr_path, sample_datapipe, return_times=False): - self.gsp_datapipe = OpenGSP( - gsp_pv_power_zarr_path=gsp_zarr_path - ).map(normalize_gsp) + def __init__(self, gsp_data, sample_datapipe, return_times=False): + self.gsp_data = gsp_data self.sample_datapipe = sample_datapipe self.return_times = return_times def __iter__(self): - gsp_data = next(iter(self.gsp_datapipe)).sel(gsp_id=0).compute() + gsp_data = self.gsp_data for sample in self.sample_datapipe: # Times for each GSP in the sample batch should be the same - take first id0 = sample[BatchKey.gsp_t0_idx] times = sample[BatchKey.gsp_time_utc][0, id0+1:] - national_outputs = torch.from_numpy( + national_outputs = torch.as_tensor( gsp_data.sel(time_utc=times.cpu().numpy().astype("datetime64[s]")).values ) @@ -91,14 +91,18 @@ def __init__( def _get_premade_batches_datapipe(self, subdir, shuffle=False): data_pipeline = FileLister(f"{self.batch_dir}/{subdir}", masks="*.pt", recursive=False) if shuffle: - data_pipeline = data_pipeline.shuffle(buffer_size=10_000) + data_pipeline = data_pipeline.shuffle(buffer_size=1000) data_pipeline = data_pipeline.sharding_filter().map(torch.load) # Add the national target data_pipeline, dp = data_pipeline.fork(2, buffer_size=5) + + gsp_datapipe = OpenGSP(gsp_pv_power_zarr_path=self.gsp_zarr_path).map(normalize_gsp) + gsp_data = next(iter(gsp_datapipe)).sel(gsp_id=0).compute() + national_targets_datapipe, times_datapipe = ( - GetNationalPVLive(self.gsp_zarr_path, dp, return_times=True).unzip(sequence_length=2) + GetNationalPVLive(gsp_data, dp, return_times=True).unzip(sequence_length=2) ) data_pipeline = data_pipeline.zip(national_targets_datapipe, times_datapipe) diff --git a/run.py b/run.py index c0490bc..0c2b3c4 100644 --- a/run.py +++ b/run.py @@ -1,8 +1,6 @@ """Run training """ - - import torch try: diff --git a/tests/data/test_datamodule.py b/tests/data/test_datamodule.py index 596c750..b23c3bc 100644 --- a/tests/data/test_datamodule.py +++ b/tests/data/test_datamodule.py @@ -48,7 +48,7 @@ def test_iter_multiprocessing(sample_data): prefetch_factor=2, ) - for i, batch in zip(range(2), dm.train_dataloader()): + for batch in dm.train_dataloader(): # batch size is 2 assert len(batch['pvnet_inputs'])==2 From b44fecbef7b7c78b8d61c52af5c5531909be77b7 Mon Sep 17 00:00:00 2001 From: James Fulton Date: Thu, 20 Jul 2023 12:16:15 +0000 Subject: [PATCH 3/3] update reqs --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 7c01acb..d80d384 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ nowcasting_utils nowcasting_datamodel==1.4.14 ocf_datapipes==1.2.44 -pvnet==2.1.15 +pvnet==2.1.16 ocf_ml_metrics numpy pandas

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