tensorflow-serving

问题 I'm ready to try out my TensorFlow Serving REST API based on a saved model, and was wondering if there was an easy way to generate the JSON instances (row-based) or inputs (columnar) I need to send with my request. I have several thousand features in my model and I would hate to manually type in a JSON. Is there a way I can use existing data to come up with serialized data I can throw at the predict API? I'm using TFX for the entire pipeline (incl. tf.Transform), so I'm not sure if there is a

问题 How do you configure TensorFlow Serving to use files stored in DigitalOcean Spaces? It's important that the solution: provides access to both the configuration and model files provides non-public access to the data I have configured a bucket named your_bucket_name in DigitalOcean Spaces with the following structure: - your_bucket_name - config - batching_parameters.txt - monitoring_config.txt - models.config - models - model_1 - version_1.1 - variables - variables.data-00000-of-00001 -

问题 How do you configure TensorFlow Serving to use files stored in DigitalOcean Spaces? It's important that the solution: provides access to both the configuration and model files provides non-public access to the data I have configured a bucket named your_bucket_name in DigitalOcean Spaces with the following structure: - your_bucket_name - config - batching_parameters.txt - monitoring_config.txt - models.config - models - model_1 - version_1.1 - variables - variables.data-00000-of-00001 -

问题 I'm stuck on one line of code and have been stalled on a project all weekend as a result. I am working on a project that uses BERT for sentence classification. I have successfully trained the model, and I can test the results using the example code from run_classifier.py. I can export the model using this example code (which has been reposted repeatedly, so I believe that it's right for this model): def export(self): def serving_input_fn(): label_ids = tf.placeholder(tf.int32, [None], name=

问题 I'm stuck on one line of code and have been stalled on a project all weekend as a result. I am working on a project that uses BERT for sentence classification. I have successfully trained the model, and I can test the results using the example code from run_classifier.py. I can export the model using this example code (which has been reposted repeatedly, so I believe that it's right for this model): def export(self): def serving_input_fn(): label_ids = tf.placeholder(tf.int32, [None], name=

问题 I have a data for a regression task. The independent features( X_train ) are scaled with a standard scaler. Built a Keras sequential model adding hidden layers. Compiled the model. Then fitting the model with model.fit(X_train_scaled, y_train ) Then I saved the model in a .hdf5 file. Now how to include the scaling part inside the saved model, so that the same scaling parameters can be applied to unseen test data. #imported all the libraries for training and evaluating the model X_train, X

问题 I have created a binary classifier with Tensorflow BERT language model. Here is the link. After the model is trained, it saves the model and produces the following files. Prediction code. from tensorflow.contrib import predictor #MODEL_FILE = 'graph.pbtxt' with tf.Session() as sess: predict_fn = predictor.from_saved_model(f'/content/drive/My Drive/binary_class/bert/graph.pbtxt') predictions = predict_fn(pred_sentences) print(predictions) Error OSError: SavedModel file does not exist at:

问题 I have created a binary classifier with Tensorflow BERT language model. Here is the link. After the model is trained, it saves the model and produces the following files. Prediction code. from tensorflow.contrib import predictor #MODEL_FILE = 'graph.pbtxt' with tf.Session() as sess: predict_fn = predictor.from_saved_model(f'/content/drive/My Drive/binary_class/bert/graph.pbtxt') predictions = predict_fn(pred_sentences) print(predictions) Error OSError: SavedModel file does not exist at:

问题 I'm unable to find the proper documentation to successfully serve the inception or mobilenet models and write a grpc client to connect to the server and perform image classification. Till now, I've successfully configured the tfserving image on CPU only. Unable to run it on my GPU. But, when I make a grpc client request, the request fails with the error. grpc._channel._Rendezvous: <_Rendezvous of RPC that terminated with: status = StatusCode.INVALID_ARGUMENT details = "Expects arg[0] to be

问题 I am trying to index into a tensor to get a slice or single element from 1d tensors. I find that there is significant performance difference when using the numpy way of indexing [:] and slice vs tf.gather (almost 30-40% ). Also I observe that tf.gather has significant overhead when used on scalars (looping over unstacked tensor) as opposed to tensor . Is this a known issue ? example code (inefficient) : for node_idxs in graph.nodes(): node_indice_list = tf.unstack(node_idxs) result = [] for