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        "author": "Jim Fleming",
        "author_email": "jim@fomoro.com",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Topic :: Software Development :: Code Generators"
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        "description": "# TF Stage\n\nA fast and canonical project setup for TensorFlow models. The most difficult part of getting started with TensorFlow isn't deep learning, it's putting together hundreds of API calls into a cohesive model.\n\n```\n$ tfstage --help\nusage: tfstage [-h] name\n\nTensorFlow project scaffolding\n\npositional arguments:\n  name                  Project name\n  install_dependencies  Install pip dependencies\n\noptional arguments:\n  -h, --help            show this help message and exit\n```\n\n## Usage\n\n1. Install `tfstage`:\n\n    ```\n    pip install tfstage\n    ```\n\n2. Create a new empty project directory\n\n    ```\n    $ mkdir my_project/\n    $ cd my_project/\n    ```\n\n3. Run `tfstage my_project`:\n\n    ```\n    $ tfstage my_project\n    Project created: ./my_project\n    ```\n\n4. This stubs out an entire TensorFlow project, completely runnable using a simple XOR dataset and model. For example:\n\n    ```\n    $ python -m my_project.main --job-dir logs/\n\n    ...\n\n    INFO:tensorflow:Saving checkpoints for 1 into logs/model.ckpt.\n    INFO:tensorflow:loss = 1.20236, step = 1\n    INFO:tensorflow:Starting evaluation at 2017-07-13-18:22:20\n    INFO:tensorflow:Restoring parameters from logs/model.ckpt-1\n\n    ...\n    ```\n\n## Workflow\n\nWhen starting a new project we run `tfstage`, run the code to verify everything works, then search and replace the `TODO` comments in the code which mark important changes.\n\n## Environment\n\nHigh-level description of a new project:\n\n- main.py: defines command-line arguments and sets up [`learn_runner`](https://goo.gl/I6TwxA)\n- experiment.py: defines a [`tf.contrib.learn.Experiment`](https://goo.gl/nMvwLx) for training\n- inputs.py: defines the input pipeline for training and evaluation\n- model.py: defines the model, loss, and training optimization\n- augment.py: defines any data augmentation or feature engineering\n- serve.py: defines placeholders for [TensorFlow Serving](https://goo.gl/bM3jpA) and [Google Cloud ML Engine predictions](https://goo.gl/yTBv2e).\n\nIn addition, several common files are created including:\n\n- README.md\n- requirements.txt for local _development_\n- setup.py for local and GCE _deployment_\n- .gitignore\n\n### Local Deployment\n\n```\nPROJECT_NAME=my_project\nMODULE_NAME=\"${PROJECT_NAME}.main\"\nPACKAGE_PATH=\"${PROJECT_NAME}/\"\nJOB_DIR=logs/\n\ngcloud ml-engine local train \\\n  --module-name $MODULE_NAME \\\n  --package-path $PACKAGE_PATH \\\n  --job-dir $JOB_DIR \\\n  -- \\\n  [args]\n```\n\n### Cloud Deployment\n\n```\nMODULE_NAME=\"${PROJECT_NAME}.main\"\nPACKAGE_PATH=\"${PROJECT_NAME}/\"\nJOB_NAME=\"${PROJECT_NAME}_1\"\nJOB_DIR=\"gs://${PROJECT_NAME}/${JOB_NAME}\"\nREGION=us-east1\n\ngcloud ml-engine jobs submit training $JOB_NAME \\\n  --job-dir $JOB_DIR \\\n  --module-name $MODULE_NAME \\\n  --package-path $PACKAGE_PATH \\\n  --region $REGION \\\n  -- \\\n  [args]\n```\n\n\n",
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