Metadata-Version: 2.1
Name: microcosm-sagemaker
Version: 0.2.1
Summary: Opinionated machine learning organization and configuration
Home-page: https://github.com/globality-corp/microcosm-sagemaker
Author: Globality Engineering
Author-email: engineering@globality.com
License: UNKNOWN
Description: # microcosm-sagemaker
        Opinionated machine learning with SageMaker
        
        ## Usage
        After creating a new model, there are a few steps to integrate with microcosm-sagemaker.
        
        1. Create a training graph
        
           The training graph holds the dependencies that are required at train time.  These typically include the bundles you have defined or any related helper functions.
        
           ```py
           from microcosm_sagemaker.loaders import load_train_conventions
        
           def create_app(debug=False, testing=False):
               config_loader = load_each(
                   load_from_environ,
                   load_train_conventions,
               )
        
               graph = create_object_graph(
                   name="my model",
               )
        
               graph.use(
                   "active_bundle",
                   "my_primary_bundle",
               )
        
               return graph.lock()
           ```
        
        2. Create a service graph.
        
           The service graph holds the dependencies that are required at service time.  These typically include Flask and the web service routes.
        
           ```py
           from microcosm_sagemaker.loaders import load_model_artifact_config
        
           def create_app(artifact_path, debug=False, testing=False, model_only=False):
               loader = load_each(
                   load_model_artifact_config(artifact_path),
               )
        
               graph = create_object_graph(
                   name="my model",
               )
        
               graph.use(
                   "active_bundle",
                   "active_evaluation",
               )
        
               if not model_only:
                   graph.use(
                       "my_primary_bundle",
                       "my_primary_evaluator",
                   )
        
               return graph.lock()
           ```
        
        3. Expose the graphs in `setup.py`.
        
           ```py
           setup(
               name="my_model",
               entry_points={
                   "microcosm_sagemaker.app_hooks": [
                       "train = my_model.train.app:create_app",
                       "serve = my_model.serve.app:create_app",
                   ],
               },
           )
           ```
        
Keywords: microcosm
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
