microcosm-sagemaker 0.2.0

Opinionated machine learning organization and configuration

# 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",
],
},
)
```