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By default, the Cortex CLI is installed at `/usr/local/bin/cortex`. To install the executable elsewhere, export the `CORTEX_INSTALL_PATH` environment variable to your desired location before running the command above.
Copy file name to clipboardExpand all lines: docs/workloads/batch/configuration.md
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path: <string> # path to a python file with a PythonPredictor class definition, relative to the Cortex root (required)
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config: <string: value> # arbitrary dictionary passed to the constructor of the Predictor (can be overridden by config passed in job submission) (optional)
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python_path: <string> # path to the root of your Python folder that will be appended to PYTHONPATH (default: folder containing cortex.yaml)
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image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/python-predictor-cpu:master or quay.io/cortexlabs/python-predictor-gpu:master based on compute)
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image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/python-predictor-cpu:0.26.0 or quay.io/cortexlabs/python-predictor-gpu:0.26.0 based on compute)
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env: <string: string> # dictionary of environment variables
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log_level: <string> # log level that can be "debug", "info", "warning" or "error" (default: "info")
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shm_size: <string> # size of shared memory (/dev/shm) for sharing data between multiple processes, e.g. 64Mi or 1Gi (default: Null)
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batch_interval: <duration> # the maximum amount of time to spend waiting for additional requests before running inference on the batch of requests
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config: <string: value> # arbitrary dictionary passed to the constructor of the Predictor (can be overridden by config passed in job submission) (optional)
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python_path: <string> # path to the root of your Python folder that will be appended to PYTHONPATH (default: folder containing cortex.yaml)
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image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/tensorflow-predictor:master)
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tensorflow_serving_image: <string> # docker image to use for the TensorFlow Serving container (default: quay.io/cortexlabs/tensorflow-serving-gpu:master or quay.io/cortexlabs/tensorflow-serving-cpu:master based on compute)
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image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/tensorflow-predictor:0.26.0)
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tensorflow_serving_image: <string> # docker image to use for the TensorFlow Serving container (default: quay.io/cortexlabs/tensorflow-serving-gpu:0.26.0 or quay.io/cortexlabs/tensorflow-serving-cpu:0.26.0 based on compute)
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env: <string: string> # dictionary of environment variables
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log_level: <string> # log level that can be "debug", "info", "warning" or "error" (default: "info")
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shm_size: <string> # size of shared memory (/dev/shm) for sharing data between multiple processes, e.g. 64Mi or 1Gi (default: Null)
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...
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config: <string: value> # arbitrary dictionary passed to the constructor of the Predictor (can be overridden by config passed in job submission) (optional)
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python_path: <string> # path to the root of your Python folder that will be appended to PYTHONPATH (default: folder containing cortex.yaml)
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image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/onnx-predictor-gpu:master or quay.io/cortexlabs/onnx-predictor-cpu:master based on compute)
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image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/onnx-predictor-gpu:0.26.0 or quay.io/cortexlabs/onnx-predictor-cpu:0.26.0 based on compute)
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env: <string: string> # dictionary of environment variables
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log_level: <string> # log level that can be "debug", "info", "warning" or "error" (default: "info")
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shm_size: <string> # size of shared memory (/dev/shm) for sharing data between multiple processes, e.g. 64Mi or 1Gi (default: Null)
Copy file name to clipboardExpand all lines: docs/workloads/batch/predictors.md
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```
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<!-- CORTEX_VERSION_MINOR -->
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Cortex provides a `tensorflow_client` to your Predictor's constructor. `tensorflow_client` is an instance of [TensorFlowClient](https://github.com/cortexlabs/cortex/tree/master/pkg/cortex/serve/cortex_internal/lib/client/tensorflow.py) 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.
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Cortex provides a `tensorflow_client` to your Predictor's constructor. `tensorflow_client` is an instance of [TensorFlowClient](https://github.com/cortexlabs/cortex/tree/0.26/pkg/cortex/serve/cortex_internal/lib/client/tensorflow.py) 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.
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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")`).
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```
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<!-- CORTEX_VERSION_MINOR -->
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Cortex provides an `onnx_client` to your Predictor's constructor. `onnx_client` is an instance of [ONNXClient](https://github.com/cortexlabs/cortex/tree/master/pkg/cortex/serve/cortex_internal/lib/client/onnx.py) 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.
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Cortex provides an `onnx_client` to your Predictor's constructor. `onnx_client` is an instance of [ONNXClient](https://github.com/cortexlabs/cortex/tree/0.26/pkg/cortex/serve/cortex_internal/lib/client/onnx.py) 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.
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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")`).
Cortex's base Docker images are listed below. Depending on the Cortex Predictor and compute type specified in your API configuration, choose one of these images to use as the base for your Docker image:
Note: the images listed above use the `-slim` suffix; Cortex's default API images are not `-slim`, since they have additional dependencies installed to cover common use cases. If you are building your own Docker image, starting with a `-slim` Predictor image will result in a smaller image size.
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```dockerfile
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# Dockerfile
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FROM quay.io/cortexlabs/python-predictor-cpu-slim:master
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FROM quay.io/cortexlabs/python-predictor-cpu-slim:0.26.0
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