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            "Development Status :: 3 - Alpha",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries"
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        "description": "# Gradient ML SDK\n\nThis is an SDK for performing Machine Learning with Gradient\u00ba, it can be installed in addition to [gradient-cli](https://github.com/Paperspace/gradient-cli).\n\n# Requirements\n\nThis SDK requires Python 3.5+.\n\nTo install it, run:\n\n```bash\npip install gradient-sdk\n```\n\n# Usage\n\n## Multinode Helper Functions\n\n### Multinode GRPC Tensorflow\n\n**Set the TF_CONFIG environment variable**\nFor multi-worker training, you need to set the `TF_CONFIG` environment variable for each binary running in your cluster. Set the value of `TF_CONFIG` to a JSON string that specifies each task within the cluster, including each task's address and role within the cluster. We've provided a Kubernetes template in the tensorflow/ecosystem repo which sets `TF_CONFIG` for your training tasks.\n\n**get_tf_config()**\n\nFunction to set value of `TF_CONFIG` when run on machines within Paperspace infrastructure.\n\nIt can raise a `ConfigError` exception with message if there's a problem with its configuration in a particular machine.\n\n**_Usage example:_**\n\n```python\nfrom gradient_sdk import get_tf_config\n\nget_tf_config()\n```\n\n## Hyperparameter Tuning\n\nCurrently, Gradient\u00ba only supports _Hyperopt_ for Hyperparameter Tuning.\n\n**hyper_tune()**\n\nFunction to run hyperparameter tuning.\n\nIt accepts the following arguments:\n\n- `train_model`\n  User model to tune.\n- `hparam_def`\n  User definition (scope) of search space.\n  To set this value, refer to [hyperopt documentation](https://github.com/hyperopt/hyperopt).\n- `algo`\n  Search algorithm.\n  _Default_: `tpe.suggest` (from hyperopt).\n- `max_ecals`\n  Maximum number of function evaluations to allow before returning.\n  _Default_: `25`.\n- `func`\n  Function to be run by hyper tune.\n  _Default_: `fmin` (from hyperopt). _Do not change this value if you do not know what you are doing!_\n\nIt returns a dict with information about the tuning process.\n\nIt can raise a `ConfigError` exception with message if there's no connection to MongoDB.\n\n**Note:** _You do not need to worry about setting your MongoDB version; it will be set within Paperspace infrastructure for hyperparameter tuning._\n\n**Usage example:**\n\n```python\nfrom gradient_sdk import hyper_tune\n\n# Prepare model and search scope\n\n# minimal version\nargmin1 = hyper_tune(model, scope)\n\n# pass more arguments\nargmin2 = hyper_tune(model, scope, algo=tpe.suggest, max_evals=100)\n```\n\n## Utility Functions\n\n**get_mongo_conn_str()**\n\nFunction to check and construct MongoDB connection string.\n\nIt returns a connection string to MongoDB.\n\nIt can raise a `ConfigError` exception with message if there's a problem with any values used to prepare the MongoDB connection string.\n\nUsage example:\n\n```python\nfrom gradient_sdk import get_mongo_conn_str\n\nconn_str = get_mongo_conn_str()\n```\n\n**data_dir()**\n\nFunction to retrieve path to job space.\n\nUsage example:\n\n```python\nfrom gradient_sdk import data_dir\n\njob_space = data_dir()\n```\n\n**model_dir()**\n\nFunction to retrieve path to model space.\n\nUsage example:\n\n```python\nfrom gradient_sdk import model_dir\n\nmodel_path = model_dir(model_name)\n```\n\n**export_dir()**\n\nFunction to retrieve path for model export.\n\nUsage example:\n\n```python\nfrom gradient_sdk import export_dir\n\nmodel_path = export_dir(model_name)\n```\n\n**worker_hosts()**\n\nFunction to retrieve information about worker hosts.\n\nUsage example:\n\n```python\nfrom gradient_sdk import worker_hosts\n\nmodel_path = worker_hosts()\n```\n\n**ps_hosts()**\n\nFunction to retrieve information about Paperspace hosts.\n\nUsage example:\n\n```python\nfrom gradient_sdk import ps_hosts\n\nmodel_path = ps_hosts()\n```\n\n**task_index()**\n\nFunction to retrieve information about task index.\n\nUsage example:\n\n```python\nfrom gradient_sdk import task_index\n\nmodel_path = task_index()\n```\n\n**job_name()**\n\nFunction to retrieve information about job name.\n\nUsage example:\n\n```python\nfrom gradient_sdk import job_name\n\nmodel_path = job_name()\n```\n\n\n",
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