{"library":"pytorch-ignite","title":"PyTorch-Ignite","description":"PyTorch-Ignite is a lightweight and user-friendly library designed to simplify training and evaluating neural networks with PyTorch. It provides a high-level API for setting up training loops, handling events, and integrating various experiment tracking tools. Currently at version 0.5.4, it maintains an active release cadence with frequent bug fixes and feature enhancements.","language":"python","status":"active","last_verified":"Sun May 17","install":{"commands":["pip install pytorch-ignite"],"cli":null},"imports":["from ignite.engine import Engine","from ignite.engine import Events","from ignite.engine import create_supervised_trainer","from ignite.metrics import Accuracy","from ignite.handlers import ModelCheckpoint"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom ignite.engine import Engine, Events, create_supervised_trainer, create_supervised_evaluator\nfrom ignite.metrics import Accuracy, Loss\n\n# 1. Define a simple model, optimizer, loss function\nclass SimpleModel(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.fc = nn.Linear(10, 2)\n    def forward(self, x):\n        return self.fc(x)\n\nmodel = SimpleModel()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\ncriterion = nn.CrossEntropyLoss()\n\n# 2. Create dummy data\nX = torch.randn(100, 10)\ny = torch.randint(0, 2, (100,))\ndataset = TensorDataset(X, y)\ndataloader = DataLoader(dataset, batch_size=10)\n\n# 3. Create trainer and evaluator\ntrainer = create_supervised_trainer(model, optimizer, criterion)\nevaluator = create_supervised_evaluator(model, criterion, metrics={'accuracy': Accuracy(), 'nll': Loss(criterion)})\n\n# 4. Define handlers for events\n@trainer.on(Events.EPOCH_COMPLETED)\ndef log_training_results(engine):\n    evaluator.run(dataloader)\n    metrics = evaluator.state.metrics\n    print(f\"Epoch {engine.state.epoch}/{engine.state.max_epochs} - Avg accuracy: {metrics['accuracy']:.2f}, Avg loss: {metrics['nll']:.2f}\")\n\n# 5. Run the training\ntrainer.run(dataloader, max_epochs=2)\n\nprint(\"\\nTraining complete.\")","lang":"python","description":"This quickstart demonstrates setting up a basic training loop with PyTorch-Ignite. It defines a simple PyTorch model, creates a trainer and evaluator using `create_supervised_trainer` and `create_supervised_evaluator`, attaches a handler to log results after each epoch, and runs the training process. The example includes dummy data for immediate execution.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":{"tag":null,"tag_description":null,"last_tested":"2026-05-17","installed_version":"0.5.4","pypi_latest":"0.5.4","is_stale":false,"summary":{"python_range":"3.10–3.9","success_rate":40,"avg_install_s":65.1,"avg_import_s":10.17,"wheel_type":"wheel"},"results":[{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine (musl)","variant":"pytorch-ignite","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.10-slim","python_version":"3.10","os_libc":"slim (glibc)","variant":"pytorch-ignite","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"noisy","install_time_s":75.3,"import_time_s":7.14,"mem_mb":95.9,"disk_size":"4.6G"},{"runtime":"python:3.11-alpine","python_version":"3.11","os_libc":"alpine (musl)","variant":"pytorch-ignite","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.11-slim","python_version":"3.11","os_libc":"slim (glibc)","variant":"pytorch-ignite","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"noisy","install_time_s":69,"import_time_s":11.33,"mem_mb":107.8,"disk_size":"4.7G"},{"runtime":"python:3.12-alpine","python_version":"3.12","os_libc":"alpine (musl)","variant":"pytorch-ignite","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.12-slim","python_version":"3.12","os_libc":"slim (glibc)","variant":"pytorch-ignite","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"noisy","install_time_s":59.5,"import_time_s":13.47,"mem_mb":104.3,"disk_size":"4.7G"},{"runtime":"python:3.13-alpine","python_version":"3.13","os_libc":"alpine (musl)","variant":"pytorch-ignite","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.13-slim","python_version":"3.13","os_libc":"slim (glibc)","variant":"pytorch-ignite","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"noisy","install_time_s":56.7,"import_time_s":8.74,"mem_mb":106.4,"disk_size":"4.7G"},{"runtime":"python:3.9-alpine","python_version":"3.9","os_libc":"alpine (musl)","variant":"pytorch-ignite","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.9-slim","python_version":"3.9","os_libc":"slim (glibc)","variant":"pytorch-ignite","exit_code":1,"wheel_type":null,"failure_reason":"timeout","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null}]}}