Predictor implementation

Once your model is exported, you can implement one of Cortex's Predictor classes to deploy your model. A Predictor is a Python class that describes how to initialize your model and use it to make predictions.
Which Predictor you use depends on how your model is exported:
​TensorFlow Predictor if your model is exported as a TensorFlow SavedModel
​ONNX Predictor if your model is exported in the ONNX format
​Python Predictor for all other cases
Project files
Cortex makes all files in the project directory (i.e. the directory which contains cortex.yaml) available for use in your Predictor implementation. Python bytecode files (*.pyc, *.pyo, *.pyd), files or folders that start with ., and the api configuration file (e.g. cortex.yaml) are excluded.
The following files can also be added at the root of the project's directory:
.cortexignore file, which follows the same syntax and behavior as a .gitignore file.
.env file, which exports environment variables that can be used in the predictor. Each line of this file must follow the VARIABLE=value format.
For example, if your directory looks like this:
./my-classifier/
├── cortex.yaml
├── values.json
├── predictor.py
├── ...
└── requirements.txt
You can access values.json in your Predictor like this:
import json
​
class PythonPredictor:
    def __init__(self, config):
        with open('values.json', 'r') as values_file:
            values = json.load(values_file)
        self.values = values
Python Predictor
Interface
# initialization code and variables can be declared here in global scope
​
class PythonPredictor:
    def __init__(self, config, job_spec):
        """(Required) Called once during each worker initialization. Performs
        setup such as downloading/initializing the model or downloading a
        vocabulary.
​
        Args:
            config (required): Dictionary passed from API configuration (if
                specified) merged with configuration passed in with Job
                Submission API. If there are conflicting keys, values in
                configuration specified in Job submission takes precedence.
            job_spec (optional): Dictionary containing the following fields:
                "job_id": A unique ID for this job
                "api_name": The name of this batch API
                "config": The config that was provided in the job submission
                "workers": The number of workers for this job
                "total_batch_count": The total number of batches in this job
                "start_time": The time that this job started
        """
        pass
​
    def predict(self, payload, batch_id):
        """(Required) Called once per batch. Preprocesses the batch payload (if
        necessary), runs inference, postprocesses the inference output (if
        necessary), and writes the predictions to storage (i.e. S3 or a
        database, if desired).
​
        Args:
            payload (required): a batch (i.e. a list of one or more samples).
            batch_id (optional): uuid assigned to this batch.
        Returns:
            Nothing
        """
        pass
​
    def on_job_complete(self):
        """(Optional) Called once after all batches in the job have been
        processed. Performs post job completion tasks such as aggregating
        results, executing web hooks, or triggering other jobs.
        """
        pass
For proper separation of concerns, it is recommended to use the constructor's config parameter for information such as from where to download the model and initialization files, or any configurable model parameters. You define config in your API configuration, and it is passed through to your Predictor's constructor. The config parameters in the API configuration can be overridden by providing config in the job submission requests.
Examples
You can find an example of a BatchAPI using a PythonPredictor in examples/batch/image-classifier.
Pre-installed packages
The following Python packages are pre-installed in Python Predictors and can be used in your implementations:
boto3==1.14.53
cloudpickle==1.6.0
Cython==0.29.21
dill==0.3.2
fastapi==0.61.1
joblib==0.16.0
Keras==2.4.3
msgpack==1.0.0
nltk==3.5
np-utils==0.5.12.1
numpy==1.19.1
opencv-python==4.4.0.42
pandas==1.1.1
Pillow==7.2.0
pyyaml==5.3.1
requests==2.24.0
scikit-image==0.17.2
scikit-learn==0.23.2
scipy==1.5.2
six==1.15.0
statsmodels==0.12.0
sympy==1.6.2
tensorflow-hub==0.9.0
tensorflow==2.3.0
torch==1.6.0
torchvision==0.7.0
xgboost==1.2.0
Inferentia-equipped APIs
The list is slightly different for inferentia-equipped APIs:
boto3==1.13.7
cloudpickle==1.6.0
Cython==0.29.21
dill==0.3.1.1
fastapi==0.54.1
joblib==0.16.0
msgpack==1.0.0
neuron-cc==1.0.20600.0+0.b426b885f
nltk==3.5
np-utils==0.5.12.1
numpy==1.18.2
opencv-python==4.4.0.42
pandas==1.1.1
Pillow==7.2.0
pyyaml==5.3.1
requests==2.23.0
scikit-image==0.17.2
scikit-learn==0.23.2
scipy==1.3.2
six==1.15.0
statsmodels==0.12.0
sympy==1.6.2
tensorflow==1.15.4
tensorflow-neuron==1.15.3.1.0.2043.0
torch==1.5.1
torch-neuron==1.5.1.1.0.1721.0
torchvision==0.6.1
The pre-installed system packages are listed in images/python-predictor-cpu/Dockerfile (for CPU), images/python-predictor-gpu/Dockerfile (for GPU), or images/python-predictor-inf/Dockerfile (for Inferentia).
If your application requires additional dependencies, you can install additional Python packages and system packages.
TensorFlow Predictor
Interface
class TensorFlowPredictor:
    def __init__(self, tensorflow_client, config, job_spec):
        """(Required) Called once during each worker initialization. Performs
        setup such as downloading/initializing the model or downloading a
        vocabulary.
​
        Args:
            tensorflow_client (required): TensorFlow client which is used to
                make predictions. This should be saved for use in predict().
            config (required): Dictionary passed from API configuration (if
                specified) merged with configuration passed in with Job
                Submission API. If there are conflicting keys, values in
                configuration specified in Job submission takes precedence.
            job_spec (optional): Dictionary containing the following fields:
                "job_id": A unique ID for this job
                "api_name": The name of this batch API
                "config": The config that was provided in the job submission
                "workers": The number of workers for this job
                "total_batch_count": The total number of batches in this job
                "start_time": The time that this job started
        """
        self.client = tensorflow_client
        # Additional initialization may be done here
​
    def predict(self, payload, batch_id):
        """(Required) Called once per batch. Preprocesses the batch payload (if
        necessary), runs inference (e.g. by calling
        self.client.predict(model_input)), postprocesses the inference output
        (if necessary), and writes the predictions to storage (i.e. S3 or a
        database, if desired).
​
        Args:
            payload (required): a batch (i.e. a list of one or more samples).
            batch_id (optional): uuid assigned to this batch.
        Returns:
            Nothing
        """
        pass
​
    def on_job_complete(self):
        """(Optional) Called once after all batches in the job have been
        processed. Performs post job completion tasks such as aggregating
        results, executing web hooks, or triggering other jobs.
        """
        pass
Cortex provides a tensorflow_client to your Predictor's constructor. tensorflow_client is an instance of TensorFlowClient that manages a connection to a TensorFlow Serving container to make predictions using your model. It should be saved as an instance variable in your Predictor, and your predict() function should call tensorflow_client.predict() to make an inference with your exported TensorFlow model. Preprocessing of the JSON payload and postprocessing of predictions can be implemented in your predict() function as well.
When multiple models are defined using the Predictor's models field, the tensorflow_client.predict() method expects a second argument model_name which must hold the name of the model that you want to use for inference (for example: self.client.predict(payload, "text-generator")). See the multi model guide for more information.
For proper separation of concerns, it is recommended to use the constructor's config parameter for information such as from where to download the model and initialization files, or any configurable model parameters. You define config in your API configuration, and it is passed through to your Predictor's constructor. The config parameters in the API configuration can be overridden by providing config in the job submission requests.
Examples
You can find an example of a BatchAPI using a TensorFlowPredictor in examples/batch/tensorflow.
Pre-installed packages
The following Python packages are pre-installed in TensorFlow Predictors and can be used in your implementations:
boto3==1.14.53
dill==0.3.2
fastapi==0.61.1
msgpack==1.0.0
numpy==1.19.1
opencv-python==4.4.0.42
pyyaml==5.3.1
requests==2.24.0
tensorflow-hub==0.9.0
tensorflow-serving-api==2.3.0
tensorflow==2.3.0
The pre-installed system packages are listed in images/tensorflow-predictor/Dockerfile.
If your application requires additional dependencies, you can install additional Python packages and system packages.
ONNX Predictor
Interface
class ONNXPredictor:
    def __init__(self, onnx_client, config, job_spec):
        """(Required) Called once during each worker initialization. Performs
        setup such as downloading/initializing the model or downloading a
        vocabulary.
​
        Args:
            onnx_client (required): ONNX client which is used to make
                predictions. This should be saved for use in predict().
            config (required): Dictionary passed from API configuration (if
                specified) merged with configuration passed in with Job
                Submission API. If there are conflicting keys, values in
                configuration specified in Job submission takes precedence.
            job_spec (optional): Dictionary containing the following fields:
                "job_id": A unique ID for this job
                "api_name": The name of this batch API
                "config": The config that was provided in the job submission
                "workers": The number of workers for this job
                "total_batch_count": The total number of batches in this job
                "start_time": The time that this job started
        """
        self.client = onnx_client
        # Additional initialization may be done here
​
    def predict(self, payload, batch_id):
        """(Required) Called once per batch. Preprocesses the batch payload (if
        necessary), runs inference (e.g. by calling
        self.client.predict(model_input)), postprocesses the inference output
        (if necessary), and writes the predictions to storage (i.e. S3 or a
        database, if desired).
​
        Args:
            payload (required): a batch (i.e. a list of one or more samples).
            batch_id (optional): uuid assigned to this batch.
        Returns:
            Nothing
        """
        pass
​
    def on_job_complete(self):
        """(Optional) Called once after all batches in the job have been
        processed. Performs post job completion tasks such as aggregating
        results, executing web hooks, or triggering other jobs.
        """
        pass
Cortex provides an onnx_client to your Predictor's constructor. onnx_client is an instance of ONNXClient that manages an ONNX Runtime session to make predictions using your model. It should be saved as an instance variable in your Predictor, and your predict() function should call onnx_client.predict() to make an inference with your exported ONNX model. Preprocessing of the JSON payload and postprocessing of predictions can be implemented in your predict() function as well.
When multiple models are defined using the Predictor's models field, the onnx_client.predict() method expects a second argument model_name which must hold the name of the model that you want to use for inference (for example: self.client.predict(model_input, "text-generator")). See the multi model guide for more information.
For proper separation of concerns, it is recommended to use the constructor's config parameter for information such as from where to download the model and initialization files, or any configurable model parameters. You define config in your API configuration, and it is passed through to your Predictor's constructor. The config parameters in the API configuration can be overridden by providing config in the job submission requests.
Examples
You can find an example of a BatchAPI using an ONNXPredictor in examples/batch/onnx.
Pre-installed packages
The following Python packages are pre-installed in ONNX Predictors and can be used in your implementations:
boto3==1.14.53
dill==0.3.2
fastapi==0.61.1
msgpack==1.0.0
numpy==1.19.1
onnxruntime==1.4.0
pyyaml==5.3.1
requests==2.24.0
The pre-installed system packages are listed in images/onnx-predictor-cpu/Dockerfile (for CPU) or images/onnx-predictor-gpu/Dockerfile (for GPU).
If your application requires additional dependencies, you can install additional Python packages and system packages.
Deployments - Previous
Batch API
API configuration
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