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ONNX provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Initially we focus on the capabilities needed for inferencing (evaluation).", "dev_url": null, "doc_source_url": null, "doc_url": null, "home": "https://github.com/onnx/onnx/", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "MIT", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": "https://github.com/onnx/onnx.git", "source_url": null, "subdirs": [ "linux-ppc64le" ], "summary": "Open Neural Network Exchange library", "tags": null, "text_prefix": true, "timestamp": 1664900513, "version": "1.12.0" }, "onnxconverter-common": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": null, "dev_url": null, "doc_source_url": null, "doc_url": null, "home": "https://github.com/microsoft/onnxconverter-common", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "MIT", "post_link": false, "pre_link": false, "pre_unlink": false, 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"dev_url": null, "doc_source_url": null, "doc_url": "https://pybind11.readthedocs.io/", "home": "https://github.com/pybind/pybind11/", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "BSD-3-Clause", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": null, "source_url": "https://github.com/pybind/pybind11/archive/v2.9.2.tar.gz", "subdirs": [ "linux-ppc64le" ], "summary": "Seamless operability between C++11 and Python", "tags": null, "text_prefix": true, "timestamp": 1664483752, "version": "2.9.2" }, "pybind11-abi": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": null, "dev_url": null, "doc_source_url": null, "doc_url": "https://pybind11.readthedocs.io/", "home": "https://github.com/pybind/pybind11/", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "BSD-3-Clause", "post_link": false, "pre_link": false, 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Python", "tags": null, "text_prefix": false, "timestamp": 1664483732, "version": "2.9.2" }, "pydeprecate": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "The common use-case is moving your functions across codebase or outsourcing some functionalities to new packages. For most of these cases, you want to hold some compatibility, so you cannot simply remove past function, and also for some time you want to warn users that functionality they have been using is moved and not it is deprecated in favor of another function (which shall be used instead) and soon it will be removed completely. Another good aspect is to do not overwhelm a user with too many warnings, so per function/class, this warning is raised only N times in the preferable stream (warning, logger, etc.).", "dev_url": "https://github.com/Borda/pyDeprecate", "doc_source_url": null, "doc_url": null, "home": "https://pypi.org/project/pyDeprecate/", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "MIT", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": null, "source_url": "https://pypi.io/packages/source/p/pyDeprecate/pyDeprecate-0.3.2.tar.gz", "subdirs": [ "noarch" ], "summary": "Simple tooling for marking deprecated functions or classes and re-routing to the new successors' instance.", "tags": null, "text_prefix": false, "timestamp": 1663997699, "version": "0.3.2" }, "python-multipart": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "python-multipart is an Apache2 licensed streaming multipart parser for Python. 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The metadata serialization formats include- - A schema describing tabular data (e.g., tf.Examples). - A collection of summary statistics over such datasets. - A problem statement quantifying the objectives of a model. 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