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[0]: # -*- coding: utf-8 -*- [1]: """ [2]: Created on Sat Oct 31st [3]: @author: zhiyuan [4]: """ [5]: import sys [6]: import os [7]: import pandas as pd [8]: import numpy as np [9]: import torch [10]: import argparse [11]: import random [12]: from sklearn.metrics import f1_score [13]: sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../") [14]: from model.AEN import AEN [15]: from utils.config import get_config [16]: from utils.logger import Logger [17]: from utils.dataset import load_data [18]: def get_args(): [19]: parser = argparse.ArgumentParser(description='Anomaly Detection') [20]: parser.add_argument('--config', type=str, [21]: default=os.path.join(os.path.dirname(os.path.abspath(__file__)), '../config/config.yaml'), [22]: help='configuration file') [23]: parser.add_argument('--seed', type=int, [24]: default=2021, [25]: help='random seed') [26]: parser.add_argument('--gpu', type=int, [27]: default=0, [28]: help='gpu id') [29]: args = parser.parse_args() [30]: return args [31]: def main(args): [32]: print(args) [33]: # set seed for reproducibility [34]: torch.manual_seed(args.seed) [35]: # device setting [36]: device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu") [37]: # read config file [38]: cfg = get_config(args.config) [39]: # set logger [40]: log = Logger(cfg['exp_name'], cfg['log_dir']) [41]: # load data [42]: train_df = load_data(cfg['data_dir'], cfg['train_data']) [43]: test_df = load_data(cfg['data_dir'], cfg['test_data']) ***** Tag Data ***** ID: 3 description: Main function handling various aspects such as setting seeds for reproducibility, device setup, configuration reading, logging setup, and loading datasets. start line: 31 end line: 42 dependencies: - type: Function name: get_args start line: 18 end line: 30 - type: Function/Method name: get_config start line: 15 end line: 15 - type: Function/Method name: Logger.__init__ start line: 16 end line: 16 - type: Function/Method name: load_data start line: 17 end line: 17 context description: The main function orchestrates several critical steps required for setting up an anomaly detection task using an autoencoder neural network. algorithmic depth: 4 algorithmic depth external: N obscurity: 3 advanced coding concepts: 4 interesting for students: 5 self contained: N ************ ## Challenging aspects ### Challenging aspects in above code 1. **Configuration Management**: The code involves reading configurations from an external YAML file which introduces complexity in ensuring that configurations are properly parsed, validated, and used throughout the code. 2. **Logging**: Setting up a logger using custom configurations requires understanding logging best practices including log levels, log formats, log rotation policies etc. 3. **Device Management**: Ensuring that computations are performed on GPU if available otherwise falling back to CPU introduces intricacies related to CUDA device management. 4. **Reproducibility**: Setting seeds for reproducibility is critical but also tricky when dealing with parallel computations or distributed training scenarios. 5. **Data Loading**: Efficiently loading data while considering potential large dataset sizes introduces challenges related to memory management and I/O operations. ### Extension 1. **Dynamic Configuration Update**: Extend functionality so that configurations can be dynamically updated during runtime without restarting the application. 2. **Advanced Logging**: Implement structured logging with JSON format output for better integration with modern log management systems. 3. **Distributed Training Support**: Extend device management logic to support distributed training across multiple GPUs or even multiple nodes. 4. **Real-time Data Ingestion**: Modify data loading logic so that it supports real-time ingestion where new data files can be added while training is ongoing. 5. **Enhanced Reproducibility**: Introduce mechanisms to ensure reproducibility across different environments including handling non-deterministic operations in CUDA. ## Exercise ### Problem Statement You are tasked with enhancing an anomaly detection framework using an autoencoder neural network by addressing several advanced requirements: 1. Extend configuration management so that configurations can be dynamically updated during runtime without restarting the application. 2. Implement structured logging using JSON format output. 3. Enhance device management logic to support distributed training across multiple GPUs or even multiple nodes. 4. Modify data loading logic so that it supports real-time ingestion where new data files can be added while training is ongoing. 5. Ensure enhanced reproducibility across different environments including handling non-deterministic operations in CUDA. Use [SNIPPET] as your starting point. ### Requirements: 1. **Dynamic Configuration Update**: - Implement a mechanism (e.g., watching file changes or using an API endpoint) to reload configurations dynamically during runtime. - Ensure existing running processes adapt seamlessly to these configuration changes without needing a restart. 2. **Structured Logging**: - Update the `Logger` class or create a new logger class supporting JSON format logs. - Ensure all logs are structured appropriately including metadata like timestamp, log level, message etc. 3. **Distributed Training Support**: - Extend device management logic using PyTorch's `torch.distributed` package. - Ensure training works seamlessly across multiple GPUs/nodes. 4. **Real-time Data Ingestion**: - Modify `load_data` function or create a new data loader class that supports real-time ingestion. - Ensure newly added files during training are detected and included in subsequent epochs without restarting training. 5. **Enhanced Reproducibility**: - Implement measures such as setting random seeds not just in PyTorch but also in other libraries (e.g., NumPy). - Handle non-deterministic operations by configuring CUDA settings appropriately (e.g., disabling certain CUDA operations). ### Solution python import os import yaml import torch import json import random import numpy as np from watchdog.observers import Observer from watchdog.events import FileSystemEventHandler from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.data import DataLoader from utils.config import get_config from utils.logger import Logger from utils.dataset import load_data class ConfigReloader(FileSystemEventHandler): def __init__(self, config_path): self.config_path = config_path self.load_config() def load_config(self): with open(self.config_path) as f: self.cfg = yaml.safe_load(f) def on_modified(self, event): if event.src_path == self.config_path: self.load_config() class JSONLogger(Logger): def __init__(self, exp_name, log_dir): super().__init__(exp_name, log_dir) def log(self, level, message): log_entry = { 'timestamp': datetime.now().isoformat(), 'level': level, 'message': message, } print(json.dumps(log_entry)) def main(args): print(args) # Set seed for reproducibility across libraries torch.manual_seed(args.seed) np.random.seed(args.seed) random.seed(args.seed) # Configure CUDA settings for reproducibility torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False # Device setting with distributed support initialization if 'WORLD_SIZE' in os.environ: torch.distributed.init_process_group(backend='nccl') local_rank = int(os.environ['LOCAL_RANK']) device = torch.device(f'cuda:{local_rank}') torch.cuda.set_device(local_rank) dist_backend = 'nccl' dist_url = f'tcp://localhost:{args.dist_port}' dist.init_process_group(backend=dist_backend, init_method=dist_url) print(f"Initialized distributed training on {local_rank} GPUs.") else: device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu") # Dynamic configuration reloading setup config_reloader = ConfigReloader(cfg['config_file']) observer = Observer() observer.schedule(config_reloader, path=os.path.dirname(cfg['config_file']), recursive=False) observer.start() ### Follow-up exercise 1. Modify your solution so that it can handle interruptions (e.g., power failure) gracefully by periodically saving checkpoints and being able to resume from these checkpoints upon restart. 2. Introduce validation steps within each epoch of training which utilize real-time incoming validation datasets (similarly loaded as training datasets). 3. Implement automated hyperparameter tuning using libraries such as Optuna or Ray Tune integrated into your existing framework. ### Solution python import os import yaml import torch import json import random import numpy as np from watchdog.observers import Observer from watchdog.events import FileSystemEventHandler from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.data import DataLoader from utils.config import get_config from utils.logger import Logger from utils.dataset import load_data class ConfigReloader(FileSystemEventHandler): def __init__(self, config_path): self.config_path = config_path self.load_config() def load_config(self): with open(self.config_path) as f: self.cfg = yaml.safe_load(f) def on_modified(self, event): if event.src_path == self.config_path: self.load_config() class JSONLogger(Logger): def __init__(self, exp_name, log_dir): super().__init__(exp_name, log_dir) def log(self, level, message): log_entry = { 'timestamp': datetime.now().isoformat(), 'level': level, 'message': message, } print(json.dumps(log_entry)) def save_checkpoint(model_state_dict, optimizer_state_dict): torch.save({ 'model_state_dict': model_state_dict, 'optimizer_state_dict': optimizer_state_dict, }, 'checkpoint.pth') def load_checkpoint(model, optimizer): checkpoint = torch.load('checkpoint.pth') model.load_state_dict(checkpoint['model_state_dict']) optimizer.load_state_dict(checkpoint['optimizer_state_dict']) def main(args): print(args) # Set seed for reproducibility across libraries torch.manual_seed(args.seed) np.random.seed(args.seed) random.seed(args.seed) # Configure CUDA settings for reproducibility torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False # Device setting with distributed support initialization if 'WORLD_SIZE' in os.environ: torch.distributed.init_process_group(backend='nccl') local_rank = int(os.environ['LOCAL_RANK']) device = torch.device(f'cuda:{local_rank}') torch.cuda.set_device(local_rank) dist_backend = 'nccl' dist_url = f'tcp://localhost:{args.dist_port}' dist.init_process_group(backend=dist_backend, init_method=dist_url) print(f"Initialized distributed training on {local_rank} GPUs.") else: device