- name: <string>  # API name (required)
  kind: RealtimeAPI
  predictor:
    type: python
    path: <string>  # path to a python file with a PythonPredictor class definition, relative to the Cortex root (required)
    multi_model_reloading:  # use this to serve one or more models with live reloading (optional)
      path: <string> # S3 path to an exported model directory (e.g. s3://my-bucket/exported_model/) (either this, 'dir', or 'paths' must be provided if 'multi_model_reloading' is specified)
      paths:  # list of S3 paths to exported model directories (either this, 'dir', or 'path' must be provided if 'multi_model_reloading' is specified)
        - name: <string>  # unique name for the model (e.g. text-generator) (required)
          path: <string>  # S3 path to an exported model directory (e.g. s3://my-bucket/exported_model/) (required)
        ...
      dir: <string>  # S3 path to a directory containing multiple models (e.g. s3://my-bucket/models/) (either this, 'path', or 'paths' must be provided if 'multi_model_reloading' is specified)
      cache_size: <int>  # the number models to keep in memory (optional; all models are kept in memory by default)
      disk_cache_size: <int>  # the number of models to keep on disk (optional; all models are kept on disk by default)
    server_side_batching:  # (optional)
      max_batch_size: <int>  # the maximum number of requests to aggregate before running inference
      batch_interval: <duration>  # the maximum amount of time to spend waiting for additional requests before running inference on the batch of requests
    processes_per_replica: <int>  # the number of parallel serving processes to run on each replica (default: 1)
    threads_per_process: <int>  # the number of threads per process (default: 1)
    config: <string: value>  # arbitrary dictionary passed to the constructor of the Predictor (optional)
    python_path: <string>  # path to the root of your Python folder that will be appended to PYTHONPATH (default: folder containing cortex.yaml)
    image: <string>  # docker image to use for the Predictor (default: quay.io/cortexlabs/python-predictor-cpu:0.25.0 or quay.io/cortexlabs/python-predictor-gpu:0.25.0 based on compute)
    env: <string: string>  # dictionary of environment variables
  networking:
    endpoint: <string>  # the endpoint for the API (default: <api_name>)
    api_gateway: public | none  # whether to create a public API Gateway endpoint for this API (if not, the API will still be accessible via the load balancer) (default: public, unless disabled cluster-wide) (aws only)
  compute:
    cpu: <string | int | float>  # CPU request per replica, e.g. 200m or 1 (200m is equivalent to 0.2) (default: 200m)
    gpu: <int>  # GPU request per replica (default: 0)
    inf: <int>  # Inferentia ASIC request per replica (default: 0) (aws only)
    mem: <string>  # memory request per replica, e.g. 200Mi or 1Gi (default: Null)
  autoscaling:
    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>)
    max_replica_concurrency: <int>  # the maximum number of in-flight requests per replica before requests are rejected with error code 503 (default: 1024)
    target_replica_concurrency: <float>  # the desired number of in-flight requests per replica, which the autoscaler tries to maintain (default: processes_per_replica * threads_per_process) (aws only)
    window: <duration>  # the time over which to average the API's concurrency (default: 60s) (aws only)
    downscale_stabilization_period: <duration>  # the API will not scale below the highest recommendation made during this period (default: 5m) (aws only)
    upscale_stabilization_period: <duration>  # the API will not scale above the lowest recommendation made during this period (default: 1m) (aws only)
    max_downscale_factor: <float>  # the maximum factor by which to scale down the API on a single scaling event (default: 0.75) (aws only)
    max_upscale_factor: <float>  # the maximum factor by which to scale up the API on a single scaling event (default: 1.5) (aws only)
    downscale_tolerance: <float>  # any recommendation falling within this factor below the current number of replicas will not trigger a scale down event (default: 0.05) (aws only)
    upscale_tolerance: <float>  # any recommendation falling within this factor above the current number of replicas will not trigger a scale up event (default: 0.05) (aws only)
  update_strategy:
    max_surge: <string | int>  # maximum number of replicas that can be scheduled above the desired number of replicas during an update; can be an absolute number, e.g. 5, or a percentage of desired replicas, e.g. 10% (default: 25%) (set to 0 to disable rolling updates)
    max_unavailable: <string | int>  # maximum number of replicas that can be unavailable during an update; can be an absolute number, e.g. 5, or a percentage of desired replicas, e.g. 10% (default: 25%)

- name: <string>  # API name (required)
  kind: RealtimeAPI
  predictor:
    type: tensorflow
    path: <string>  # path to a python file with a TensorFlowPredictor class definition, relative to the Cortex root (required)
    models:  # (required)
      path: <string> # S3 path to an exported SavedModel directory (e.g. s3://my-bucket/exported_model/) (either this, 'dir', or 'paths' must be provided)
      paths:  # list of S3 paths to exported SavedModel directories (either this, 'dir', or 'path' must be provided)
        - name: <string>  # unique name for the model (e.g. text-generator) (required)
          path: <string>  # S3 path to an exported SavedModel directory (e.g. s3://my-bucket/exported_model/) (required)
          signature_key: <string>  # name of the signature def to use for prediction (required if your model has more than one signature def)
        ...
      dir: <string>  # S3 path to a directory containing multiple SavedModel directories (e.g. s3://my-bucket/models/) (either this, 'path', or 'paths' must be provided)
      signature_key:  # name of the signature def to use for prediction (required if your model has more than one signature def)
      cache_size: <int>  # the number models to keep in memory (optional; all models are kept in memory by default)
      disk_cache_size: <int>  # the number of models to keep on disk (optional; all models are kept on disk by default)
    server_side_batching:  # (optional)
      max_batch_size: <int>  # the maximum number of requests to aggregate before running inference
      batch_interval: <duration>  # the maximum amount of time to spend waiting for additional requests before running inference on the batch of requests
    processes_per_replica: <int>  # the number of parallel serving processes to run on each replica (default: 1)
    threads_per_process: <int>  # the number of threads per process (default: 1)
    config: <string: value>  # arbitrary dictionary passed to the constructor of the Predictor (optional)
    python_path: <string>  # path to the root of your Python folder that will be appended to PYTHONPATH (default: folder containing cortex.yaml)
    image: <string>  # docker image to use for the Predictor (default: quay.io/cortexlabs/tensorflow-predictor:0.25.0)
    tensorflow_serving_image: <string>  # docker image to use for the TensorFlow Serving container (default: quay.io/cortexlabs/tensorflow-serving-gpu:0.25.0 or quay.io/cortexlabs/tensorflow-serving-cpu:0.25.0 based on compute)
    env: <string: string>  # dictionary of environment variables
  networking:
    endpoint: <string>  # the endpoint for the API (default: <api_name>)
    api_gateway: public | none  # whether to create a public API Gateway endpoint for this API (if not, the API will still be accessible via the load balancer) (default: public, unless disabled cluster-wide) (aws only)
  compute:
    cpu: <string | int | float>  # CPU request per replica, e.g. 200m or 1 (200m is equivalent to 0.2) (default: 200m)
    gpu: <int>  # GPU request per replica (default: 0)
    inf: <int>  # Inferentia ASIC request per replica (default: 0) (aws only)
    mem: <string>  # memory request per replica, e.g. 200Mi or 1Gi (default: Null)
  autoscaling:
    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>)
    max_replica_concurrency: <int>  # the maximum number of in-flight requests per replica before requests are rejected with error code 503 (default: 1024)
    target_replica_concurrency: <float>  # the desired number of in-flight requests per replica, which the autoscaler tries to maintain (default: processes_per_replica * threads_per_process) (aws only)
    window: <duration>  # the time over which to average the API's concurrency (default: 60s) (aws only)
    downscale_stabilization_period: <duration>  # the API will not scale below the highest recommendation made during this period (default: 5m) (aws only)
    upscale_stabilization_period: <duration>  # the API will not scale above the lowest recommendation made during this period (default: 1m) (aws only)
    max_downscale_factor: <float>  # the maximum factor by which to scale down the API on a single scaling event (default: 0.75) (aws only)
    max_upscale_factor: <float>  # the maximum factor by which to scale up the API on a single scaling event (default: 1.5) (aws only)
    downscale_tolerance: <float>  # any recommendation falling within this factor below the current number of replicas will not trigger a scale down event (default: 0.05) (aws only)
    upscale_tolerance: <float>  # any recommendation falling within this factor above the current number of replicas will not trigger a scale up event (default: 0.05) (aws only)
  update_strategy:
    max_surge: <string | int>  # maximum number of replicas that can be scheduled above the desired number of replicas during an update; can be an absolute number, e.g. 5, or a percentage of desired replicas, e.g. 10% (default: 25%) (set to 0 to disable rolling updates)
    max_unavailable: <string | int>  # maximum number of replicas that can be unavailable during an update; can be an absolute number, e.g. 5, or a percentage of desired replicas, e.g. 10% (default: 25%)

- name: <string>  # API name (required)
  kind: RealtimeAPI
  predictor:
    type: onnx
    path: <string>  # path to a python file with an ONNXPredictor class definition, relative to the Cortex root (required)
    models:  # (required)
      path: <string> # S3 path to an exported model directory (e.g. s3://my-bucket/exported_model/) (either this, 'dir', or 'paths' must be provided)
      paths:  # list of S3 paths to exported model directories (either this, 'dir', or 'path' must be provided)
        - name: <string>  # unique name for the model (e.g. text-generator) (required)
          path: <string>  # S3 path to an exported model directory (e.g. s3://my-bucket/exported_model/) (required)
        ...
      dir: <string>  # S3 path to a directory containing multiple model directories (e.g. s3://my-bucket/models/) (either this, 'path', or 'paths' must be provided)
      cache_size: <int>  # the number models to keep in memory (optional; all models are kept in memory by default)
      disk_cache_size: <int>  # the number of models to keep on disk (optional; all models are kept on disk by default)
    processes_per_replica: <int>  # the number of parallel serving processes to run on each replica (default: 1)
    threads_per_process: <int>  # the number of threads per process (default: 1)
    config: <string: value>  # arbitrary dictionary passed to the constructor of the Predictor (optional)
    python_path: <string>  # path to the root of your Python folder that will be appended to PYTHONPATH (default: folder containing cortex.yaml)
    image: <string>  # docker image to use for the Predictor (default: quay.io/cortexlabs/onnx-predictor-gpu:0.25.0 or quay.io/cortexlabs/onnx-predictor-cpu:0.25.0 based on compute)
    env: <string: string>  # dictionary of environment variables
  networking:
    endpoint: <string>  # the endpoint for the API (default: <api_name>)
    api_gateway: public | none  # whether to create a public API Gateway endpoint for this API (if not, the API will still be accessible via the load balancer) (default: public, unless disabled cluster-wide) (aws only)
  compute:
    cpu: <string | int | float>  # CPU request per replica, e.g. 200m or 1 (200m is equivalent to 0.2) (default: 200m)
    gpu: <int>  # GPU request per replica (default: 0)
    mem: <string>  # memory request per replica, e.g. 200Mi or 1Gi (default: Null)
  autoscaling:
    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>)
    max_replica_concurrency: <int>  # the maximum number of in-flight requests per replica before requests are rejected with error code 503 (default: 1024)
    target_replica_concurrency: <float>  # the desired number of in-flight requests per replica, which the autoscaler tries to maintain (default: processes_per_replica * threads_per_process) (aws only)
    window: <duration>  # the time over which to average the API's concurrency (default: 60s) (aws only)
    downscale_stabilization_period: <duration>  # the API will not scale below the highest recommendation made during this period (default: 5m) (aws only)
    upscale_stabilization_period: <duration>  # the API will not scale above the lowest recommendation made during this period (default: 1m) (aws only)
    max_downscale_factor: <float>  # the maximum factor by which to scale down the API on a single scaling event (default: 0.75) (aws only)
    max_upscale_factor: <float>  # the maximum factor by which to scale up the API on a single scaling event (default: 1.5) (aws only)
    downscale_tolerance: <float>  # any recommendation falling within this factor below the current number of replicas will not trigger a scale down event (default: 0.05) (aws only)
    upscale_tolerance: <float>  # any recommendation falling within this factor above the current number of replicas will not trigger a scale up event (default: 0.05) (aws only)
  update_strategy:
    max_surge: <string | int>  # maximum number of replicas that can be scheduled above the desired number of replicas during an update; can be an absolute number, e.g. 5, or a percentage of desired replicas, e.g. 10% (default: 25%) (set to 0 to disable rolling updates)
    max_unavailable: <string | int>  # maximum number of replicas that can be unavailable during an update; can be an absolute number, e.g. 5, or a percentage of desired replicas, e.g. 10% (default: 25%)