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Deploying your ML models has never been so easy.\n\n* __Test ML API__: Once you have set up your API, test all the API end-points to ensure you get the expected results before pushing your API to deployment.\n\n* __Dockerizing__: A simplified, single-command, easy dockerization for your ML API.  \n\n* __ML Model Test Suite__: The package comes with a built-in test suite that evaluates your PyTorch models over a set of tests to look for any errors that otherwise might not be traceable easily.\n\n### Here are the available list of commands:\n---\n\n* Setting-up the Template Project:\n\n```console\nfoo@bar:~$ torchblaze generate_template --project_name example\n```\n\n* Building Docker Image (Requires Docker Installed):\n\u003e First cd to the root project directory containing app.py file.\n\n```console\nfoo@bar:~$ torchblaze generate_docker --image_name example_image\n```\n\n* Run Docker Image (Requires Docker Installed):\n\n```console\nfoo@bar:~$ torchblaze run_docker --image_name example\n```\n\n* Performing API Tests:\n\n\u003e First cd to the root project directory containing app.py file.\n```console\nfoo@bar:~$ torchblaze api_tests\n```\n\n* Performing Model Testing:\n\n\n\u003e Import the mltests package\n```py\nimport torchblaze.mltests as mls\n```\n\u003e Then use the variety of testing methods available in the mltests package. Run the following command to get the list of available methods.\n```py\ndir(mls)\n```\n\u003e To check the documentation for any of the available tests, use the help method:\n```py\nhelp(mls.\u003cmethod_name\u003e)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmlh-fellowship%2Ftorchblaze","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmlh-fellowship%2Ftorchblaze","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmlh-fellowship%2Ftorchblaze/lists"}