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import os
import random
from datetime import datetime
import numpy as np
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def seed_everything(seed: int):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
os.environ["PYTHONHASHSEED"] = str(seed)
def generate_descriptive_model_name(config):
return (
f"{config['model_name']}_"
f"BATCH{config['batch_size']}_"
f"ITER{config['num_training_iterations']}_"
f"ACCUM_{config['gradient_accumulation_enabled']}_"
f"ACC_STEPS{config['accumulation_steps']}_"
f"Emb{config['TimeSeriesModel']['embed_size']}_"
f"L{config['TimeSeriesModel']['num_encoder_layers']}_"
f"H{config['TimeSeriesModel']['encoder_config']['num_householder']}_"
f"LR_SCHEDULER_{config['lr_scheduler']}_"
f"PEAK_LR{config['peak_lr']}_"
f"{datetime.now().strftime('_%Y%m%d_%H%M%S')}"
)
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