API configuration

Once your model is exported and you've implemented a Predictor, you can configure your API via a YAML file (typically named cortex.yaml).

Reference the section below which corresponds to your Predictor type: Python, TensorFlow, or ONNX.

Python Predictor

- 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)
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: cortexlabs/python-predictor-cpu or cortexlabs/python-predictor-gpu based on compute)
env: <string: string> # dictionary of environment variables
networking:
endpoint: <string> # the endpoint for the API (aws only) (default: <api_name>)
local_port: <int> # specify the port for API (local only) (default: 8888)
api_gateway: public | none # whether to create a public API Gateway endpoint for this API (if not, the load balancer will be accessed directly) (default: public)
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)
mem: <string> # memory request per replica, e.g. 200Mi or 1Gi (default: Null)
monitoring: # (aws only)
model_type: <string> # must be "classification" or "regression", so responses can be interpreted correctly (i.e. categorical vs continuous) (required)
key: <string> # the JSON key in the response payload of the value to monitor (required if the response payload is a JSON object)
autoscaling: # (aws only)
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_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)
max_replica_concurrency: <int> # the maximum number of in-flight requests per replica before requests are rejected with error code 503 (default: 1024)
window: <duration> # the time over which to average the API's concurrency (default: 60s)
downscale_stabilization_period: <duration> # the API will not scale below the highest recommendation made during this period (default: 5m)
upscale_stabilization_period: <duration> # the API will not scale above the lowest recommendation made during this period (default: 1m)
max_downscale_factor: <float> # the maximum factor by which to scale down the API on a single scaling event (default: 0.75)
max_upscale_factor: <float> # the maximum factor by which to scale up the API on a single scaling event (default: 1.5)
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)
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)
update_strategy: # (aws only)
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%)

See additional documentation for parallelism, autoscaling, compute, networking, prediction monitoring, and overriding API images.

TensorFlow Predictor

- 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)
model_path: <string> # S3 path to an exported model (e.g. s3://my-bucket/exported_model) (either this or 'models' must be provided)
signature_key: <string> # name of the signature def to use for prediction (required if your model has more than one signature def)
models: # use this when multiple models per API are desired (either this or 'model_path' must be provided)
- name: <string> # unique name for the model (e.g. text-generator) (required)
model_path: <string> # S3 path to an exported model (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)
...
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: cortexlabs/tensorflow-predictor)
tensorflow_serving_image: <string> # docker image to use for the TensorFlow Serving container (default: cortexlabs/tensorflow-serving-gpu or cortexlabs/tensorflow-serving-cpu based on compute)
env: <string: string> # dictionary of environment variables
networking:
endpoint: <string> # the endpoint for the API (aws only) (default: <api_name>)
local_port: <int> # specify the port for API (local only) (default: 8888)
api_gateway: public | none # whether to create a public API Gateway endpoint for this API (if not, the load balancer will be accessed directly) (default: public)
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)
mem: <string> # memory request per replica, e.g. 200Mi or 1Gi (default: Null)
monitoring: # (aws only)
model_type: <string> # must be "classification" or "regression", so responses can be interpreted correctly (i.e. categorical vs continuous) (required)
key: <string> # the JSON key in the response payload of the value to monitor (required if the response payload is a JSON object)
autoscaling: # (aws only)
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_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)
max_replica_concurrency: <int> # the maximum number of in-flight requests per replica before requests are rejected with error code 503 (default: 1024)
window: <duration> # the time over which to average the API's concurrency (default: 60s)
downscale_stabilization_period: <duration> # the API will not scale below the highest recommendation made during this period (default: 5m)
upscale_stabilization_period: <duration> # the API will not scale above the lowest recommendation made during this period (default: 1m)
max_downscale_factor: <float> # the maximum factor by which to scale down the API on a single scaling event (default: 0.75)
max_upscale_factor: <float> # the maximum factor by which to scale up the API on a single scaling event (default: 1.5)
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)
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)
update_strategy: # (aws only)
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%)

See additional documentation for parallelism, autoscaling, compute, networking, prediction monitoring, and overriding API images.

ONNX Predictor

- 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)
model_path: <string> # S3 path to an exported model (e.g. s3://my-bucket/exported_model.onnx) (either this or 'models' must be provided)
models: # use this when multiple models per API are desired (either this or 'model_path' must be provided)
- name: <string> # unique name for the model (e.g. text-generator) (required)
model_path: <string> # S3 path to an exported model (e.g. s3://my-bucket/exported_model.onnx) (required)
signature_key: <string> # name of the signature def to use for prediction (required if your model has more than one signature def)
...
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: cortexlabs/onnx-predictor-gpu or cortexlabs/onnx-predictor-cpu based on compute)
env: <string: string> # dictionary of environment variables
networking:
endpoint: <string> # the endpoint for the API (aws only) (default: <api_name>)
local_port: <int> # specify the port for API (local only) (default: 8888)
api_gateway: public | none # whether to create a public API Gateway endpoint for this API (if not, the load balancer will be accessed directly) (default: public)
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)
monitoring: # (aws only)
model_type: <string> # must be "classification" or "regression", so responses can be interpreted correctly (i.e. categorical vs continuous) (required)
key: <string> # the JSON key in the response payload of the value to monitor (required if the response payload is a JSON object)
autoscaling: # (aws only)
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_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)
max_replica_concurrency: <int> # the maximum number of in-flight requests per replica before requests are rejected with error code 503 (default: 1024)
window: <duration> # the time over which to average the API's concurrency (default: 60s)
downscale_stabilization_period: <duration> # the API will not scale below the highest recommendation made during this period (default: 5m)
upscale_stabilization_period: <duration> # the API will not scale above the lowest recommendation made during this period (default: 1m)
max_downscale_factor: <float> # the maximum factor by which to scale down the API on a single scaling event (default: 0.75)
max_upscale_factor: <float> # the maximum factor by which to scale up the API on a single scaling event (default: 1.5)
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)
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)
update_strategy: # (aws only)
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%)

See additional documentation for parallelism, autoscaling, compute, networking, prediction monitoring, and overriding API images.