tensorflow-serving

I have the following input_fn. def input_fn(filenames, batch_size): # Create a dataset containing the text lines. dataset = tf.data.TextLineDataset(filenames).skip(1) # Parse each line. dataset = dataset.map(_parse_line) # Shuffle, repeat, and batch the examples. dataset = dataset.shuffle(10000).repeat().batch(batch_size) # Return the dataset. return dataset It works great if filenames=['file1.csv'] or filenames=['file2.csv'] . It gives me an error if filenames=['file1.csv', 'file2.csv'] . In Tensorflow documentation it says filenames is a tf.string tensor containing one or more filenames. How

The goal is to open in Java a model created/trained in python with tensorflow.contrib.learn.learn.DNNClassifier . At the moment the main issue is to know the name of the "tensor" to give in java on the session runner method. I have this test code in python : from __future__ import division, print_function, absolute_import import tensorflow as tf import pandas as pd import tensorflow.contrib.learn as learn import numpy as np from sklearn import metrics from sklearn.cross_validation import train_test_split from tensorflow.contrib import layers from tensorflow.contrib.learn.python.learn.utils

Is there any example code for deploying a Tensorflow Model via a RESTful API? I see examples for a command line program and for a mobile app. Is there a framework for this or people just load the model and expose the predict method via a web framework (like Flask)to take input (say via JSON) and return the response? By framework I mean scaling for large number of predict requests. Of course since the models are immutable we can launch multiple instances of our prediction server and put it behind a load balancer (like HAProxy). My question is, are people using some framework for this or doing

I try to build tensorflow-serving using bazel but I've encountered some errors during the building ERROR:/private/var/tmp/_bazel_Kakadu/3f0c35881c95d2c43f04614911c03a57/external/local_config_cc/BUILD:49:5: in apple_cc_toolchain rule @local_config_cc//:cc-compiler-darwin_x86_64: Xcode version must be specified to use an Apple CROSSTOOL. ERROR: Analysis of target '//tensorflow_serving/sources/storage_path:file_system_storage_path_source_proto' failed; build aborted. I've already tried to use bazel clean and bazel clean --expunge but it didn't help and still Bazel doesn't see my xcode (I suppose)

I am referring ( here ) to freeze models into .pb file. My model is CNN for text classification I am using ( Github ) link to train CNN for text classification and exporting in form of models. I have trained models to 4 epoch and My checkpoints folders look as follows: I want to freeze this model into (.pb file). For that I am using following script: import os, argparse import tensorflow as tf # The original freeze_graph function # from tensorflow.python.tools.freeze_graph import freeze_graph dir = os.path.dirname(os.path.realpath(__file__)) def freeze_graph(model_dir, output_node_names): ""

I trained a resnet with tf.estimator, the model was saved during the training process. The saved files consist of .data , .index and .meta . I'd like to load this model back and get predictions for new images. The data was fed to the model during training using tf.data.Dataset . I have closely followed the resnet implementation given here . I would like to restore the model and feed inputs to the nodes using a feed_dict. First attempt #rebuild input pipeline images, labels = input_fn(data_dir, batch_size=32, num_epochs=1) #rebuild graph prediction= imagenet_model_fn(images,labels,{'batch_size'