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setup.py
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setup.py
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# -*- coding: utf-8 -*-
from setuptools import setup
packages = \
['arch',
'arch.api',
'arch.api.utils',
'fate_arch',
'fate_arch.abc',
'fate_arch.common',
'fate_arch.computing',
'fate_arch.computing.eggroll',
'fate_arch.computing.spark',
'fate_arch.computing.standalone',
'fate_arch.federation',
'fate_arch.federation.eggroll',
'fate_arch.federation.rabbitmq',
'fate_arch.federation.pulsar',
'fate_arch.federation.standalone',
'fate_arch.federation.transfer_variable',
'fate_arch.federation.transfer_variable.scripts',
'fate_arch.protobuf.python',
'fate_arch.session',
'fate_arch.storage',
'fate_arch.storage.eggroll',
'fate_arch.storage.file',
'fate_arch.storage.hdfs',
'fate_arch.storage.metastore',
'fate_arch.storage.mysql',
'fate_arch.storage.standalone',
'fate_flow',
'fate_flow.apps',
'fate_flow.components',
'fate_flow.components.param',
'fate_flow.controller',
'fate_flow.db',
'fate_flow.entity',
'fate_flow.examples',
'fate_flow.examples.manage_mq',
'fate_flow.manager',
'fate_flow.operation',
'fate_flow.pipelined_model',
'fate_flow.scheduler',
'fate_flow.scheduling_apps',
'fate_flow.scheduling_apps.client',
'fate_flow.tests',
'fate_flow.tests.api_tests',
'fate_flow.utils',
'federatedml',
'federatedml.conf',
'federatedml.ensemble',
'federatedml.ensemble.basic_algorithms',
'federatedml.ensemble.basic_algorithms.decision_tree',
'federatedml.ensemble.basic_algorithms.decision_tree.hetero',
'federatedml.ensemble.basic_algorithms.decision_tree.homo',
'federatedml.ensemble.basic_algorithms.decision_tree.tree_core',
'federatedml.ensemble.boosting.boosting_core',
'federatedml.ensemble.boosting.hetero',
'federatedml.ensemble.boosting.homo',
'federatedml.ensemble.images',
'federatedml.ensemble.test',
'federatedml.evaluation',
'federatedml.evaluation.metrics',
'federatedml.evaluation.test',
'federatedml.feature',
'federatedml.feature.binning',
'federatedml.feature.binning.optimal_binning',
'federatedml.feature.binning.test',
'federatedml.feature.binning.test.test_optimal_binning',
'federatedml.feature.binning.test.test_quantile_binning_module',
'federatedml.feature.feature_scale',
'federatedml.feature.feature_scale.test',
'federatedml.feature.feature_selection',
'federatedml.feature.feature_selection.model_adapter',
'federatedml.feature.feature_selection.test',
'federatedml.feature.hetero_feature_binning',
'federatedml.feature.hetero_feature_selection',
'federatedml.feature.homo_feature_binning',
'federatedml.feature.homo_onehot',
'federatedml.feature.test',
'federatedml.feature.test.test_new_binning',
'federatedml.feature.test.test_new_feature_selection',
'federatedml.framework',
'federatedml.framework.hetero',
'federatedml.framework.hetero.procedure',
'federatedml.framework.hetero.sync',
'federatedml.framework.hetero.util',
'federatedml.framework.homo',
'federatedml.framework.homo.blocks',
'federatedml.framework.homo.procedure',
'federatedml.framework.homo.test',
'federatedml.framework.homo.test.blocks',
'federatedml.framework.homo.util',
'federatedml.framework.test',
'federatedml.framework.test.homo',
'federatedml.linear_model',
'federatedml.linear_model.linear_regression',
'federatedml.linear_model.linear_regression.hetero_linear_regression',
'federatedml.linear_model.linear_regression.images',
'federatedml.linear_model.logistic_regression',
'federatedml.linear_model.logistic_regression.hetero_logistic_regression',
'federatedml.linear_model.logistic_regression.homo_logsitic_regression',
'federatedml.linear_model.logistic_regression.images',
'federatedml.linear_model.poisson_regression',
'federatedml.linear_model.poisson_regression.hetero_poisson_regression',
'federatedml.linear_model.poisson_regression.images',
'federatedml.local_baseline',
'federatedml.local_baseline.test',
'federatedml.loss',
'federatedml.loss.test',
'federatedml.model_selection',
'federatedml.model_selection.data_split',
'federatedml.model_selection.data_split.test',
'federatedml.model_selection.stepwise',
'federatedml.model_selection.stepwise.test',
'federatedml.model_selection.test',
'federatedml.nn',
'federatedml.nn.backend',
'federatedml.nn.backend.pytorch',
'federatedml.nn.backend.tf',
'federatedml.nn.backend.tf_keras',
'federatedml.nn.backend.tf_keras.layers',
'federatedml.nn.hetero_nn',
'federatedml.nn.hetero_nn.backend',
'federatedml.nn.hetero_nn.backend.tf_keras',
'federatedml.nn.hetero_nn.backend.tf_keras.interactive',
'federatedml.nn.hetero_nn.images',
'federatedml.nn.hetero_nn.model',
'federatedml.nn.hetero_nn.test',
'federatedml.nn.hetero_nn.util',
'federatedml.nn.homo_nn',
'federatedml.nn.homo_nn.demo',
'federatedml.nn.homo_nn.demo.mnist',
'federatedml.nn.test',
'federatedml.nn.zoo',
'federatedml.one_vs_rest',
'federatedml.optim',
'federatedml.optim.gradient',
'federatedml.optim.gradient.test',
'federatedml.optim.images',
'federatedml.optim.test',
'federatedml.param',
'federatedml.param.test',
'federatedml.protobuf',
'federatedml.protobuf.generated',
'federatedml.protobuf.model_migrate',
'federatedml.protobuf.model_migrate.converter',
'federatedml.protobuf.test',
'federatedml.secure_information_retrieval',
'federatedml.secureprotol',
'federatedml.secureprotol.number_theory',
'federatedml.secureprotol.number_theory.field',
'federatedml.secureprotol.number_theory.group',
'federatedml.secureprotol.oblivious_transfer',
'federatedml.secureprotol.oblivious_transfer.hauck_oblivious_transfer',
'federatedml.secureprotol.random_oracle',
'federatedml.secureprotol.random_oracle.hash_function',
'federatedml.secureprotol.random_oracle.message_authentication_code',
'federatedml.secureprotol.spdz',
'federatedml.secureprotol.spdz.beaver_triples',
'federatedml.secureprotol.spdz.communicator',
'federatedml.secureprotol.spdz.tensor',
'federatedml.secureprotol.spdz.test',
'federatedml.secureprotol.spdz.utils',
'federatedml.secureprotol.symmetric_encryption',
'federatedml.secureprotol.test',
'federatedml.statistic',
'federatedml.statistic.correlation',
'federatedml.statistic.intersect',
'federatedml.statistic.intersect.rsa_cache',
'federatedml.statistic.intersect.test',
'federatedml.statistic.psi',
'federatedml.statistic.psi.test',
'federatedml.statistic.scorecard',
'federatedml.statistic.test',
'federatedml.statistic.union',
'federatedml.test',
'federatedml.toy_example',
'federatedml.transfer_learning',
'federatedml.transfer_learning.hetero_ftl',
'federatedml.transfer_learning.hetero_ftl.test',
'federatedml.transfer_variable',
'federatedml.transfer_variable.transfer_class',
'federatedml.unsupervised_learning.kmeans',
'federatedml.unsupervised_learning.kmeans.hetero_kmeans',
'federatedml.util',
'federatedml.util.test']
package_data = \
{'': ['*'],
'fate_arch': ['protobuf/*', 'protobuf/proto/*'],
'fate_flow': ['doc/*',
'images/*',
'upgrade/1_4_0-1_4_1/*',
'upgrade/1_4_1-1_4_2/*',
'upgrade/1_4_x/*'],
'federatedml': ['unsupervised_learning/image/*'],
'federatedml.conf': ['setting_conf/*'],
'federatedml.feature': ['images/*'],
'federatedml.framework.homo': ['images/*'],
'federatedml.nn.homo_nn': ['images/*'],
'federatedml.protobuf': ['proto/*'],
'federatedml.statistic.correlation': ['img/*'],
'federatedml.statistic.intersect': ['images/*'],
'federatedml.transfer_learning.hetero_ftl': ['images/*'],
'federatedml.transfer_variable': ['auth_conf/*']}
install_requires = \
['apsw==3.9.2.post1',
'beautifultable==1.0.0',
'cachetools==3.0.0',
'cloudpickle==0.6.1',
'cos-python-sdk-v5==1.8.0',
'flask==1.0.2',
'gmpy2==2.0.8',
'kazoo==2.6.1',
'lmdb==0.94',
'numpy==1.18.4',
'pandas==0.23.4',
'peewee==3.9.3',
'psutil==5.6.6',
'pycryptodomex==3.6.6',
'python-dotenv==0.13.0',
'redis==3.0.1',
'requests-toolbelt==0.9.1',
'requests==2.23.0',
'ruamel-yaml==0.16.10',
'scikit-learn==0.19.2',
'scipy==1.1.0',
'tensorflow==1.15.4',
'torch==1.4.0',
'torchvision==0.5.0',
'werkzeug==0.15.3']
setup_kwargs = {
'name': 'fate',
'version': '1.5.0',
'description': "FATE (Federated AI Technology Enabler) is an open-source project initiated by Webank's AI Department to provide a secure computing framework to support the federated AI ecosystem. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). It supports federated learning architectures and secure computation of various machine learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning.",
'long_description': '[](https://opensource.org/licenses/Apache-2.0) [](https://checkstyle.sourceforge.io/google_style.html) [](https://checkstyle.sourceforge.io/google_style.html) [](https://travis-ci.org/FederatedAI/FATE)\n[](https://codecov.io/gh/FederatedAI/FATE)\n[](https://fate.readthedocs.io/en/latest/?badge=latest)\n\n<div align="center">\n <img src="./doc/images/FATE_logo.png">\n</div>\n\n[DOC](./doc) | [Quick Start](./examples/dsl/v2) | [中文](./README_zh.md)\n\nFATE (Federated AI Technology Enabler) is an open-source project initiated by Webank\'s AI Department to provide a secure computing framework to support the federated AI ecosystem. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). It supports federated learning architectures and secure computation of various machine learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning.\n\n<https://fate.fedai.org>\n\n\n## Federated Learning Algorithms In FATE\nFATE already supports a number of federated learning algorithms, including vertical federated learning, horizontal federated learning, and federated transfer learning. More details are available in [federatedml](./python/federatedml).\n\n\n## Install\n\nFATE can be installed on Linux or Mac. Now, FATE can support：\n\n* Native installation: standalone and cluster deployments;\n\n* KubeFATE installation:\n\n\t- Multipal parties deployment by docker-compose, which for devolopment and test purpose;\n\n\t- Cluster (multi-node) deployment by Kubernetes\n\n### Native installation:\nSoftware environment :jdk1.8+、Python3.6、python virtualenv、mysql5.6+\n\n##### Standalone\nFATE provides Standalone runtime architecture for developers. It can help developers quickly test FATE. Standalone support two types of deployment: Docker version and Manual version. Please refer to Standalone deployment guide: [standalone-deploy](./standalone-deploy/)\n\n##### Cluster\nFATE also provides a distributed runtime architecture for Big Data scenario. Migration from standalone to cluster requires configuration change only. No algorithm change is needed.\n\nTo deploy FATE on a cluster, please refer to cluster deployment guide: [cluster-deploy](./cluster-deploy).\n\n### KubeFATE installation:\nUsing KubeFATE, FATE can be deployed by either docker-compose or Kubernetes:\n\n* For development or testing purposes, docker-compose is recommended. It only requires Docker enviroment. For more detail, please refer to [Deployment by Docker Compose](https://github.com/FederatedAI/KubeFATE/tree/master/docker-deploy).\n\n* For a production or a large scale deployment, Kubernetes is recommended as an underlying infrastructure to manage FATE system. For more detail, please refer to [Deployment on Kubernetes](https://github.com/FederatedAI/KubeFATE/blob/master/k8s-deploy).\n\nMore instructions can be found in [KubeFATE](https://github.com/FederatedAI/KubeFATE).\n\n### FATE-Client Installation\nFATE-client is a easy tool for interacting with FATE. We strongly recommend you install FATE-client and take advantage to use FATE conveniently. Please refer to this [document](./python/fate_client/README.rst) for more details of FATE-client.\n\n\n## Running Tests\n\nA script to run all the unittests has been provided in ./federatedml/test folder.\n\nOnce FATE is installed, tests can be run using:\n\n> sh ./federatedml/test/run_test.sh\n\nAll the unittests shall pass if FATE is installed properly.\n\n## Example Programs\n\n### Quick Start\n\nWe have provided a tutorial for quick starting modeling task. Please refer ["here"](./examples/pipeline/README.rst)\n\n### Obtain Model and Check Out Results\nWe provided functions such as tracking component output models or logs etc. through a tool called fate-flow. The deployment and usage of fate-flow can be found [here](./python/fate_flow/README.md)\n\n\n## Doc\n### API doc\nFATE provides some API documents in [doc-api](https://fate.readthedocs.io/en/latest/?badge=latest)\n### Develop Guide doc\nHow to develop your federated learning algorithm using FATE? you can see FATE develop guide document in [develop-guide](./doc/develop_guide.rst)\n### Other doc\nFATE also provides many other documents in [doc](./doc/). These documents can help you understand FATE better.\n\n## Getting Involved\n\n* Join our maillist [Fate-FedAI Group IO](https://groups.io/g/Fate-FedAI). You can ask questions and participate in the development discussion.\n\n* For any frequently asked questions, you can check in [FAQ](https://github.com/FederatedAI/FATE/wiki).\n\n* Please report bugs by submitting [issues](https://github.com/FederatedAI/FATE/issues).\n\n* Submit contributions using [pull requests](https://github.com/FederatedAI/FATE/pulls)\n\n\n### License\n[Apache License 2.0](LICENSE)\n\n',
'author': 'FederatedAI',
'author_email': '[email protected]',
'maintainer': None,
'maintainer_email': None,
'url': 'https://fate.fedai.org/',
'packages': packages,
'package_data': package_data,
'install_requires': install_requires,
'python_requires': '>=3.6,<4.0',
}
setup(**setup_kwargs)