- kind: api
  name: <string>  # API name (required)
  endpoint: <string>  # the endpoint for the API (default: /<deployment_name>/<api_name>)
  predictor:
    path: <string>  # path to the predictor Python file, relative to the Cortex root (required)
    model: <string>  # S3 path to a file or directory (e.g. s3://my-bucket/exported_model) (optional)
    python_path: <string>  # path to the root of your Python folder that will be appended to PYTHONPATH (default: folder containing cortex.yaml)
    metadata: <string: value>  # dictionary that can be used to configure custom values (optional)
  tracker:
    key: <string>  # the JSON key in the response to track (required if the response payload is a JSON object)
    model_type: <string>  # model type, must be "classification" or "regression" (required)
  compute:
    min_replicas: <int>  # minimum number of replicas (default: 1)
    max_replicas: <int>  # maximum number of replicas (default: 100)
    init_replicas: <int>  # initial number of replicas (default: <min_replicas>)
    target_cpu_utilization: <int>  # CPU utilization threshold (as a percentage) to trigger scaling (default: 80)
    cpu: <string | int | float>  # CPU request per replica (default: 200m)
    gpu: <int>  # GPU request per replica (default: 0)
    mem: <string>  # memory request per replica (default: Null)

- kind: api
  name: my-api
  predictor:
    path: predictor.py
  compute:
    gpu: 1

# initialization code and variables can be declared here in global scope
​
def init(model_path, metadata):
    """Called once before the API is made available. Setup for model serving such
    as downloading/initializing the model or downloading vocabulary can be done here.
    Optional.
​
    Args:
        model_path: Local path to model file or directory if specified by user in API configuration, otherwise None.
        metadata: Custom dictionary specified by the user in API configuration.
    """
    pass
​
def predict(sample, metadata):
    """Called once per request. Model prediction is done here, including any
    preprocessing of the request payload and postprocessing of the model output.
    Required.
​
    Args:
        sample: The JSON request payload (parsed as a Python object).
        metadata: Custom dictionary specified by the user in API configuration.
​
    Returns:
        A prediction
    """

import boto3
from my_model import IrisNet
​
# variables declared in global scope can be used safely in both functions (one replica handles one request at a time)
labels = ["iris-setosa", "iris-versicolor", "iris-virginica"]
model = IrisNet()
​
def init(model_path, metadata):
    # Initialize the model
    model.load_state_dict(torch.load(model_path))
    model.eval()
​
​
def predict(sample, metadata):
    # Convert the request to a tensor and pass it into the model
    input_tensor = torch.FloatTensor(
        [
            [
                sample["sepal_length"],
                sample["sepal_width"],
                sample["petal_length"],
                sample["petal_width"],
            ]
        ]
    )
​
    # Run the prediction
    output = model(input_tensor)
​
    # Translate the model output to the corresponding label string
    return labels[torch.argmax(output[0])]

boto3==1.10.13
cloudpickle==1.2.2
dill==0.3.1.1
joblib==0.14.0
Keras==2.3.1
msgpack==0.6.2
nltk==3.4.5
np-utils==0.5.11.1
numpy==1.17.3
pandas==0.25.3
Pillow==6.2.1
requests==2.22.0
scikit-image==0.16.2
scikit-learn==0.21.3
scipy==1.3.1
six==1.13.0
statsmodels==0.10.1
sympy==1.4
tensor2tensor==1.14.1
tensorflow-hub==0.7.0
tensorflow==2.0.0
torch==1.3.1
torchvision==0.4.2
xgboost==0.90