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        "author": "Doc.ai",
        "author_email": "neeraj@doc.ai",
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        "description": "# tensorio-bundler\nCreate TensorIO model bundles\n\n\n## Running the bundler from the command line\n\nNOTE: Working on making a PyPI package. Once that is done, these instructions will change\nto use whatever binary the corresponding `pip install` produces.\n\n### Requirements\n+ Python 3\n\n### Instructions\nThe `tensorio_bundler` module comes with a `bundler` utility that you can use to create TensorIO\nzipped tiobundle files directly from your command line.\n\nFor more information on how to run the `bundler`, run:\n```\npython -m tensorio_bundler.bundler -h\n```\n\nA sample invocation (using test data, assumed to be run from project root -- same directory as this\nREADME):\n```\npython -m tensorio_bundler.bundler \\\n    --tflite-model ./tensorio_bundler/fixtures/test.tflite \\\n    --model-json ./tensorio_bundler/fixtures/test.tiobundle/model.json \\\n    --assets-dir ./tensorio_bundler/fixtures/test.tiobundle/assets \\\n    --bundle-name sample.tiobundle \\\n    --outfile sample.tiobundle.zip\n```\n\n\n## Calling the bundler locally through the REST API\n\nTo run the REST API locally from project root (same directory as this README):\n```\ngunicorn tensorio_bundler.rest:api\n```\n\nIn a separate terminal window, you can invoke the bundler as follows:\n```\nTFLITE_PATH=\"\\\"$(mktemp -d)/model.tflite\\\"\"\n\nread -r -d '' REQUEST_BODY <<-EOF\n    {\n        \"saved_model_dir\": \"./tensorio_bundler/fixtures/test-model\",\n        \"build\": true,\n        \"tflite_path\": $TFLITE_PATH,\n        \"model_json_path\": \"./tensorio_bundler/fixtures/test.tiobundle/model.json\",\n        \"assets_path\": \"./tensorio_bundler/fixtures/test.tiobundle/assets\",\n        \"bundle_name\": \"curl-test.tiobundle\",\n        \"bundle_output_path\": \"curl-test.tiobundle.zip\"\n    }\nEOF\n\ncurl -v -X POST \\\n    -H \"Content-Type: application/json\" \\\n    -d \"$REQUEST_BODY\" \\\n    http://localhost:8000/bundle\n```\n\n\n## Running the bundler via docker\n\n### Requirements\n+ Docker\n\nIf you don't have it, [get it](https://docs.docker.com/install/linux/docker-ce/ubuntu/)\n\n### Instructions\nYou can either bind mount the paths to the inputs into your docker container when you run the\nbundler or you can bind mount in a service account credentials file and set the\n`GOOGLE_APPLICATION_CREDENTIALS` environment variable to point at the mount path in the container.\n\nNOTE: These instructions are extremely sparse at the moment. They will not be so forever.\n\n\n## TensorIO Models repositories\n\nThe TensorIO bundler is now integrated with [tensorio-models](https://github.com/doc-ai/tensorio-models)\nvia the Repository REST API. Once a bundle has been built, you can use the\n`tensorio_bundler.bundler.register_bundle` method to register it against a TensorIO Models\nrepository. The `tensorio_bundler.bundler` CLI allows you to do this automatically through the\n`--repository-path` argument.\n\nThis requires two environment variables to be set in your environment:\n\n1. `REPOSITORY` -- a URL for a TensorIO models repository API URL (e.g. https://tio-models-test.dev.docai.beer/rest/v1/repository)\n\n1. `REPISITORY_API_KEY` -- a basic auth token used to authenticate requests against the repository\nREST API.\n\n## Running tests if you want to contribute to this project\n\n### Requirements\n+ Docker\n\nIf you don't have it, [get it](https://docs.docker.com/install/linux/docker-ce/ubuntu/)\n\n### Instructions\nSimply run:\n```\n./test.sh\n```\n\n\n",
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