{"library":"onnx-weekly","title":"Open Neural Network Exchange (ONNX) - Weekly Builds","description":"ONNX (Open Neural Network Exchange) is an open ecosystem for AI developers, providing an open standard format for machine learning models, including deep learning and traditional ML. It defines an extensible computation graph model, built-in operators, and standard data types to enable model interoperability across various frameworks. The `onnx-weekly` package offers continuous integration builds, providing early access to experimental features and allowing users to test upcoming changes ahead of official stable releases. The current version is 1.22.0.dev20260330, reflecting a rapid release cadence for development purposes.","language":"python","status":"active","last_verified":"Sun May 17","install":{"commands":["pip install onnx-weekly"],"cli":null},"imports":["import onnx","from onnx import helper","from onnx import checker","from onnx import TensorProto","import onnx.parser","import onnx.shape_inference"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import onnx\nfrom onnx import helper, checker, TensorProto\n\n# Create a simple ONNX graph for Y = X * A + B\n# Define inputs and outputs\nX = helper.make_tensor_value_info('X', TensorProto.FLOAT, [None, 2])\nA = helper.make_tensor_value_info('A', TensorProto.FLOAT, [2, 3])\nB = helper.make_tensor_value_info('B', TensorProto.FLOAT, [3])\nY = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [None, 3])\n\n# Create nodes (operators)\n# MatMul operation: C = X * A\nnode_matmul = helper.make_node(\n    'MatMul',\n    inputs=['X', 'A'],\n    outputs=['C'],\n)\n\n# Add operation: Y = C + B\nnode_add = helper.make_node(\n    'Add',\n    inputs=['C', 'B'],\n    outputs=['Y'],\n)\n\n# Create the graph\ngraph_def = helper.make_graph(\n    [node_matmul, node_add], # Nodes in the graph\n    'simple-linear-regression', # Graph name\n    [X, A, B], # Graph inputs\n    [Y], # Graph outputs\n)\n\n# Create the model\nmodel_def = helper.make_model(graph_def, producer_name='onnx-example')\n\n# Check the model for validity\ntry:\n    checker.check_model(model_def)\n    print(\"Model is valid!\")\nexcept checker.ValidationError as e:\n    print(f\"Model is invalid: {e}\")\n\n# Optionally, save the model\n# onnx.save(model_def, \"simple_model.onnx\")","lang":"python","description":"This quickstart demonstrates how to programmatically construct a simple ONNX model (a linear regression: Y = X * A + B) using the `onnx.helper` module, and then validate it using `onnx.checker`. This illustrates the core functionality of defining and manipulating ONNX graphs.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":{"tag":null,"tag_description":null,"last_tested":"2026-05-17","installed_version":"1.19.0.dev20250726","pypi_latest":"1.22.0.dev20260515","is_stale":true,"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":7.1,"avg_import_s":0.54,"wheel_type":"wheel"},"results":[{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine (musl)","variant":"onnx-weekly","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},{"runtime":"python:3.10-slim","python_version":"3.10","os_libc":"slim (glibc)","variant":"onnx-weekly","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":6.7,"import_time_s":0.36,"mem_mb":15.1,"disk_size":"194M"},{"runtime":"python:3.11-alpine","python_version":"3.11","os_libc":"alpine (musl)","variant":"onnx-weekly","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},{"runtime":"python:3.11-slim","python_version":"3.11","os_libc":"slim (glibc)","variant":"onnx-weekly","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":6.3,"import_time_s":0.62,"mem_mb":17,"disk_size":"203M"},{"runtime":"python:3.12-alpine","python_version":"3.12","os_libc":"alpine (musl)","variant":"onnx-weekly","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},{"runtime":"python:3.12-slim","python_version":"3.12","os_libc":"slim (glibc)","variant":"onnx-weekly","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":6.2,"import_time_s":0.82,"mem_mb":18.9,"disk_size":"191M"},{"runtime":"python:3.13-alpine","python_version":"3.13","os_libc":"alpine (musl)","variant":"onnx-weekly","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},{"runtime":"python:3.13-slim","python_version":"3.13","os_libc":"slim (glibc)","variant":"onnx-weekly","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":6.3,"import_time_s":0.5,"mem_mb":14.8,"disk_size":"191M"},{"runtime":"python:3.9-alpine","python_version":"3.9","os_libc":"alpine (musl)","variant":"onnx-weekly","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":"onnx-weekly","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":10.1,"import_time_s":0.38,"mem_mb":14.5,"disk_size":"200M"}]}}