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The flatc compiler is in the flatbuffers conda package.", "dev_url": "https://github.com/google/flatbuffers", "doc_source_url": "https://github.com/google/flatbuffers/tree/master/docs", "doc_url": "https://google.github.io/flatbuffers/", "home": "https://google.github.io/flatbuffers/", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "Apache 2.0", "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/f/flatbuffers/flatbuffers-1.12.tar.gz", "subdirs": [ "noarch" ], "summary": "Python runtime library for use with the Flatbuffers serialization format.", "tags": null, "text_prefix": false, "timestamp": 1611362731, "version": "1.12" }, "pytorch": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": null, "dev_url": null, "doc_source_url": null, "doc_url": null, "home": null, "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": null, "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": null, "source_url": null, "subdirs": [ "linux-64", "linux-ppc64le" ], "summary": "Meta-package to install GPU-enabled PyTorch variant", "tags": null, "text_prefix": false, "timestamp": 1612476117, "version": "1.7.1" }, "pytorch-base": { "activate.d": true, "binary_prefix": false, "deactivate.d": true, "description": "PyTorch is an optimized tensor library for deep learning using GPUs and CPUs", "dev_url": null, "doc_source_url": null, "doc_url": null, "home": "http://pytorch.org/", "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": "https://github.com/pytorch/pytorch.git", "source_url": null, "subdirs": [ "linux-64", "linux-ppc64le" ], "summary": "PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.", "tags": null, "text_prefix": true, "timestamp": 1612529700, "version": "1.7.1" }, "pytorch-cpu": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": null, "dev_url": null, "doc_source_url": null, "doc_url": null, "home": null, "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": null, "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": null, "source_url": null, "subdirs": [ "linux-64", "linux-ppc64le" ], "summary": "Meta-package to install CPU-only PyTorch variant", "tags": null, "text_prefix": false, "timestamp": 1612460817, "version": "1.7.1" }, "pytorch-lightning": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. 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Each dataset definition contains the logic necessary to download and prepare the dataset, as well as to read it into a model using the `tf.data.Dataset` API. 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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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As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.", "dev_url": "https://github.com/tensorflow/probability", "doc_source_url": null, "doc_url": "https://www.tensorflow.org/probability/overview", "home": "https://www.tensorflow.org/probability/", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "Apache-2.0", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": "https://github.com/tensorflow/probability.git", "source_url": null, "subdirs": [ "linux-64", "linux-ppc64le" ], "summary": "TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow", "tags": null, "text_prefix": false, "timestamp": 1612526067, "version": "0.12.1" }, "tensorflow-serving": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data.", "dev_url": "https://github.com/tensorflow/serving/", "doc_source_url": "https://github.com/tensorflow/serving/tree/master/tensorflow_serving/g3doc", "doc_url": "https://www.tensorflow.org/tfx/guide/serving", "home": "https://www.tensorflow.org/tfx/guide/serving", "icon_hash": null, "icon_url": null, "identifiers": null, "keywords": null, "license": "Apache 2.0", "post_link": false, "pre_link": false, "pre_unlink": false, "recipe_origin": null, "run_exports": {}, "source_git_url": "https://github.com/tensorflow/serving.git", "source_url": null, "subdirs": [ "linux-64", "linux-ppc64le" ], "summary": "TensorFlow Serving is an open-source library for serving machine learning models", "tags": null, "text_prefix": false, "timestamp": 1612541417, "version": "2.4.0" }, "tensorflow-serving-api": { "activate.d": false, "binary_prefix": false, "deactivate.d": false, "description": "TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. 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