- 1 year ago
Simplest way to create a predict deployment for keras model on local Windows computers using the GPU
To create a predict deployment for a Keras model on a local Windows computer using the GPU, you can use TensorFlow Serving, which is a high-performance serving system for machine learning models. Here are the steps:
Install TensorFlow Serving: You can install TensorFlow Serving using pip. Open a command prompt and run the following command:
pip install tensorflow-serving-api
Export your Keras model: Before you can deploy your Keras model, you need to export it in the TensorFlow SavedModel format. You can use the
tf.keras.models.save_model()
function to do this. Here's an example:pythonimport tensorflow as tf
# Define and train your Keras model
model = tf.keras.Sequential([...])
model.compile([...])
model.fit([...])
# Export the model in the SavedModel format
tf.saved_model.save(model, 'path/to/saved_model')
Start the TensorFlow Serving server: Once you have exported your Keras model, you can start the TensorFlow Serving server using the following command:
csstensorflow_model_server --port=9000 --model_name=my_model --model_base_path=path/to/saved_model
This command starts the server on port 9000 and specifies the name of your model (
my_model
) and the path to the SavedModel directory.Send requests to the server: You can use the TensorFlow Serving API to send requests to the server and get predictions for your model. Here's an example:
pythonimport tensorflow as tf
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
# Create a gRPC channel to connect to the server
channel = tf.compat.v1.grpc.insecure_channel('localhost:9000')
# Create a stub for the prediction service
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
# Create a request message for the prediction
request = predict_pb2.PredictRequest()
request.model_spec.name = 'my_model'
request.model_spec.signature_name = 'serving_default'
request.inputs['input'].CopyFrom(tf.make_tensor_proto([...]))
# Send the request and get the response
response = stub.Predict(request)
outputs = response.outputs['output'].float_val
In this example, we create a gRPC channel to connect to the server, create a stub for the prediction service, create a request message for the prediction, and send the request using the
stub.Predict()
function. We then get the output tensor from the response and convert it to a NumPy array.
By following these steps, you should be able to create a predict deployment for a Keras model on a local Windows computer using the GPU. Note that you may need to install additional dependencies such as the GPU version of TensorFlow and the CUDA Toolkit in order to use the GPU.