Added training script to remotely train diffusion model

src/training_scripts/diffusion_training.py

@@ -0,0 +1,60 @@
+from clearml import Task
+from src.data import DataProcessor, DataConfig
+from src.trainers.trainer import Trainer
+from src.utils.clearml import ClearMLHelper
+from src.models import *
+from src.losses import *
+import torch
+import numpy as np
+from torch.nn import MSELoss, L1Loss
+from datetime import datetime
+import torch.nn as nn
+from src.models.time_embedding_layer import TimeEmbedding
+from src.models.diffusion_model import SimpleDiffusionModel
+from src.trainers.diffusion_trainer import DiffusionTrainer
+
+
+clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
+task = clearml_helper.get_task(task_name="Diffusion Training")
+
+# execute remotely
+task.execute_remotely(queue_name="default", exit_process=True)
+
+
+#### Data Processor ####
+data_config = DataConfig()
+data_config.NRV_HISTORY = True
+data_config.LOAD_HISTORY = True
+data_config.LOAD_FORECAST = True
+
+data_config.WIND_FORECAST = True
+data_config.WIND_HISTORY = True
+
+data_config.QUARTER = False
+data_config.DAY_OF_WEEK = False
+
+data_config.NOMINAL_NET_POSITION = True
+
+data_config = Task.connect(data_config, name="data_features")
+
+data_processor = DataProcessor(data_config, path="", lstm=False)
+data_processor.set_batch_size(8192)
+data_processor.set_full_day_skip(True)
+
+inputDim = data_processor.get_input_size()
+
+model_parameters = {
+    "epochs": 5000,
+    "learning_rate": 0.0001,
+    "hidden_sizes": [512, 512, 512],
+    "time_dim": 64,
+}
+
+model_parameters = task.connect(model_parameters, name="model_parameters")
+
+#### Model ####
+model = SimpleDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[1], time_dim=model_parameters["time_dim"])
+
+#### Trainer ####
+trainer = DiffusionTrainer(model, data_processor, "cuda")
+trainer.train(model_parameters["epochs"], model_parameters["learning_rate"], task)