{"library":"pytorch","title":"PyTorch","description":"PyTorch is an open-source machine learning framework that accelerates the path from research prototyping to production deployment. It provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration, and a deep neural network library built on a tape-based autograd system. The `pytorch` PyPI meta-package (current version 2.2.2) provides a convenient way to install the core `torch`, `torchvision`, and `torchaudio` libraries. PyTorch has frequent updates, typically releasing major stable versions multiple times a year, with minor patch releases in between.","language":"python","status":"active","last_verified":"Mon May 18","install":{"commands":["pip install pytorch"],"cli":null},"imports":["import torch","import torch.nn as nn","import torch.optim as optim","from torch.utils.data import DataLoader, TensorDataset"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import TensorDataset, DataLoader\n\n# 1. Prepare Data\nx_data = torch.randn(100, 1)\ny_data = 2 * x_data + 1 + torch.randn(100, 1) * 0.1 # y = 2x + 1 + noise\n\n# Create a Dataset and DataLoader\ndataset = TensorDataset(x_data, y_data)\ndataloader = DataLoader(dataset, batch_size=10, shuffle=True)\n\n# 2. Define Model\nclass LinearRegression(nn.Module):\n    def __init__(self):\n        super(LinearRegression, self).__init__()\n        self.linear = nn.Linear(1, 1) # One input feature, one output feature\n\n    def forward(self, x):\n        return self.linear(x)\n\nmodel = LinearRegression()\n\n# 3. Define Loss and Optimizer\ncriterion = nn.MSELoss() # Mean Squared Error Loss\noptimizer = optim.SGD(model.parameters(), lr=0.01) # Stochastic Gradient Descent\n\n# 4. Train the Model\nnum_epochs = 100\nfor epoch in range(num_epochs):\n    for batch_x, batch_y in dataloader:\n        # Forward pass\n        outputs = model(batch_x)\n        loss = criterion(outputs, batch_y)\n\n        # Backward and optimize\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n    if (epoch+1) % 10 == 0:\n        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n\n# 5. Make Predictions\npredicted_value = model(torch.tensor([[5.0]]))\nprint(f\"\\nPredicted value for x=5.0: {predicted_value.item():.4f}\")\nprint(f\"Learned parameters: Weight={model.linear.weight.item():.4f}, Bias={model.linear.bias.item():.4f}\")","lang":"python","description":"This quickstart demonstrates a simple linear regression model in PyTorch. It covers defining a dataset and dataloader, creating a neural network module, setting up a loss function and optimizer, and running a basic training loop. It uses randomly generated data for a simple y = 2x + 1 relationship.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":{"tag":null,"tag_description":null,"last_tested":"2026-05-18","installed_version":null,"pypi_latest":"1.0.2","is_stale":null,"summary":{"python_range":"3.10–3.9","success_rate":0,"avg_install_s":null,"avg_import_s":null,"wheel_type":null},"results":[{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine (musl)","variant":"pytorch","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","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":2.1,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.11-alpine","python_version":"3.11","os_libc":"alpine (musl)","variant":"pytorch","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","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":2.1,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.12-alpine","python_version":"3.12","os_libc":"alpine (musl)","variant":"pytorch","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","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":3.1,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.13-alpine","python_version":"3.13","os_libc":"alpine (musl)","variant":"pytorch","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","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":2.7,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.9-alpine","python_version":"3.9","os_libc":"alpine (musl)","variant":"pytorch","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","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":2.5,"import_time_s":null,"mem_mb":null,"disk_size":null}]}}