Train and serve a TensorFlow model with TensorFlow Serving

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track-metadata-position="4" track-metadata-eventdetail="" > Tutorials </a> </li> </ul> <devsite-thumb-rating position="header"> </devsite-thumb-rating> </div> <h1 class="devsite-page-title" tabindex="-1"> Train and serve a TensorFlow model with TensorFlow Serving </h1> <devsite-feature-tooltip ack-key="AckCollectionsBookmarkTooltipDismiss" analytics-category="Site-Wide Custom Events" analytics-action-show="Callout Profile displayed" analytics-action-close="Callout Profile dismissed" analytics-label="Create Collection Callout" class="devsite-page-bookmark-tooltip nocontent" dismiss-button="true" id="devsite-collections-dropdown" dismiss-button-text="Dismiss" close-button-text="Got it"> <devsite-bookmark></devsite-bookmark> Stay organized with collections Save and categorize content based on your preferences. </devsite-feature-tooltip> <div class="devsite-page-title-meta"><devsite-view-release-notes></devsite-view-release-notes></div> <devsite-toc class="devsite-nav" depth="2" devsite-toc-embedded > </devsite-toc> <div class="devsite-article-body clearfix "> <devsite-mathjax config="TeX-AMS-MML_SVG"></devsite-mathjax>  <aside class="warning">Warning: This notebook is designed to be run in a Google Colab only**. It installs packages on the system and requires root access. If you want to run it in a local Jupyter notebook, please proceed with caution.</aside><aside class="note">Note: You can run this example right now in a Jupyter-style notebook, no setup required! Just click "Run in Google Colab"</aside> <div class="devsite-table-wrapper"><table class="tfo-notebook-buttons" align="left"> <tr><td><a target="_blank" href="https://www.tensorflow.org/tfx/tutorials/serving/rest_simple"> <img src="https://www.tensorflow.org/images/tf_logo_32px.png">View on TensorFlow.org</a></td> <td><a target="_blank" href="https://colab.research.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/serving/rest_simple.ipynb"> <img src="https://www.tensorflow.org/images/colab_logo_32px.png">Run in Google Colab</a></td> <td><a target="_blank" href="https://github.com/tensorflow/tfx/blob/master/docs/tutorials/serving/rest_simple.ipynb"> <img width="32px" src="https://www.tensorflow.org/images/GitHub-Mark-32px.png">View source on GitHub</a></td> <td><a href="https://storage.googleapis.com/tensorflow_docs/tfx/docs/tutorials/serving/rest_simple.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png">Download notebook</a></td> </tr></table></div> This guide trains a neural network model to classify <a href="https://github.com/zalandoresearch/fashion-mnist">images of clothing, like sneakers and shirts</a>, saves the trained model, and then serves it with <a href="https://www.tensorflow.org/tfx/guide/serving">TensorFlow Serving</a>. The focus is on TensorFlow Serving, rather than the modeling and training in TensorFlow, so for a complete example which focuses on the modeling and training see the <a href="https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/keras/basic_classification.ipynb">Basic Classification example</a>. This guide uses <a href="https://github.com/tensorflow/docs/blob/master/site/en/r1/guide/keras.ipynb">tf.keras</a>, a high-level API to build and train models in TensorFlow. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">import sys # Confirm that we're using Python 3 assert sys.version_info.major == 3, 'Oops, not running Python 3. Use Runtime > Change runtime type' </code></pre></devsite-code><div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"># TensorFlow and tf.keras print("Installing dependencies for Colab environment") !pip install -Uq grpcio==1.26.0 import tensorflow as tf from tensorflow import keras # Helper libraries import numpy as np import matplotlib.pyplot as plt import os import subprocess print('TensorFlow version: {}'.format(tf.__version__)) </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> 2024-04-30 10&colon;41&colon;08.716822&colon; E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc&colon;9261] Unable to register cuDNN factory&colon; Attempting to register factory for plugin cuDNN when one has already been registered 2024-04-30 10&colon;41&colon;08.716875&colon; E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc&colon;607] Unable to register cuFFT factory&colon; Attempting to register factory for plugin cuFFT when one has already been registered 2024-04-30 10&colon;41&colon;08.718684&colon; E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc&colon;1515] Unable to register cuBLAS factory&colon; Attempting to register factory for plugin cuBLAS when one has already been registered </pre></devsite-code> <h2 id="create_your_model" data-text="Create your model" tabindex="-1">Create your model</h2> <h3 id="import_the_fashion_mnist_dataset" data-text="Import the Fashion MNIST dataset" tabindex="-1">Import the Fashion MNIST dataset</h3> This guide uses the <a href="https://github.com/zalandoresearch/fashion-mnist">Fashion MNIST</a> dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here: <table> <tr><td> <img src="https://tensorflow.org/images/fashion-mnist-sprite.png" alt="Fashion MNIST sprite" width="600"> </td></tr> <tr><td align="center"> Figure 1. <a href="https://github.com/zalandoresearch/fashion-mnist">Fashion-MNIST samples</a> (by Zalando, MIT License).   </td></tr> </table> Fashion MNIST is intended as a drop-in replacement for the classic <a href="http://yann.lecun.com/exdb/mnist/">MNIST</a> dataset—often used as the "Hello, World" of machine learning programs for computer vision. You can access the Fashion MNIST directly from TensorFlow, just import and load the data. <aside class="note">Note: Although these are really images, they are loaded as NumPy arrays and not binary image objects.</aside><div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() # scale the values to 0.0 to 1.0 train_images = train_images / 255.0 test_images = test_images / 255.0 # reshape for feeding into the model train_images = train_images.reshape(train_images.shape[0], 28, 28, 1) test_images = test_images.reshape(test_images.shape[0], 28, 28, 1) class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] print('\ntrain_images.shape: {}, of {}'.format(train_images.shape, train_images.dtype)) print('test_images.shape: {}, of {}'.format(test_images.shape, test_images.dtype)) </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> Downloading data from https&colon;//storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz 29515/29515 [==============================] - 0s 0us/step Downloading data from https&colon;//storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz 26421880/26421880 [==============================] - 0s 0us/step Downloading data from https&colon;//storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz 5148/5148 [==============================] - 0s 0us/step Downloading data from https&colon;//storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz 4422102/4422102 [==============================] - 0s 0us/step train_images.shape&colon; (60000, 28, 28, 1), of float64 test_images.shape&colon; (10000, 28, 28, 1), of float64 </pre></devsite-code> <h3 id="train_and_evaluate_your_model" data-text="Train and evaluate your model" tabindex="-1">Train and evaluate your model</h3> Let's use the simplest possible CNN, since we're not focused on the modeling part. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">model = keras.Sequential([ keras.layers.Conv2D(input_shape=(28,28,1), filters=8, kernel_size=3, strides=2, activation='relu', name='Conv1'), keras.layers.Flatten(), keras.layers.Dense(10, name='Dense') ]) model.summary() testing = False epochs = 5 model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=[keras.metrics.SparseCategoricalAccuracy()]) model.fit(train_images, train_labels, epochs=epochs) test_loss, test_acc = model.evaluate(test_images, test_labels) print('\nTest accuracy: {}'.format(test_acc)) </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> Model&colon; "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= Conv1 (Conv2D) (None, 13, 13, 8) 80 flatten (Flatten) (None, 1352) 0 Dense (Dense) (None, 10) 13530 ================================================================= Total params&colon; 13610 (53.16 KB) Trainable params&colon; 13610 (53.16 KB) Non-trainable params&colon; 0 (0.00 Byte) _________________________________________________________________ Epoch 1/5 WARNING&colon; All log messages before absl&colon;&colon;InitializeLog() is called are written to STDERR I0000 00&colon;00&colon;1714473676.987867 178558 device_compiler.h&colon;186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process. 1875/1875 [==============================] - 6s 2ms/step - loss&colon; 0.5614 - sparse_categorical_accuracy&colon; 0.8059 Epoch 2/5 1875/1875 [==============================] - 4s 2ms/step - loss&colon; 0.4274 - sparse_categorical_accuracy&colon; 0.8499 Epoch 3/5 1875/1875 [==============================] - 4s 2ms/step - loss&colon; 0.3859 - sparse_categorical_accuracy&colon; 0.8654 Epoch 4/5 1875/1875 [==============================] - 4s 2ms/step - loss&colon; 0.3561 - sparse_categorical_accuracy&colon; 0.8740 Epoch 5/5 1875/1875 [==============================] - 4s 2ms/step - loss&colon; 0.3328 - sparse_categorical_accuracy&colon; 0.8818 313/313 [==============================] - 1s 2ms/step - loss&colon; 0.3635 - sparse_categorical_accuracy&colon; 0.8706 Test accuracy&colon; 0.8705999851226807 </pre></devsite-code> <h2 id="save_your_model" data-text="Save your model" tabindex="-1">Save your model</h2> To load our trained model into TensorFlow Serving we first need to save it in <a href="https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/saved_model">SavedModel</a> format. This will create a protobuf file in a well-defined directory hierarchy, and will include a version number. <a href="https://www.tensorflow.org/tfx/guide/serving">TensorFlow Serving</a> allows us to select which version of a model, or "servable" we want to use when we make inference requests. Each version will be exported to a different sub-directory under the given path. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"># Fetch the Keras session and save the model # The signature definition is defined by the input and output tensors, # and stored with the default serving key import tempfile MODEL_DIR = tempfile.gettempdir() version = 1 export_path = os.path.join(MODEL_DIR, str(version)) print('export_path = {}\n'.format(export_path)) tf.keras.models.save_model( model, export_path, overwrite=True, include_optimizer=True, save_format=None, signatures=None, options=None ) print('\nSaved model:') !ls -l {export_path} </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> export_path = /tmpfs/tmp/1 INFO&colon;tensorflow&colon;Assets written to&colon; /tmpfs/tmp/1/assets INFO&colon;tensorflow&colon;Assets written to&colon; /tmpfs/tmp/1/assets Saved model&colon; total 104 drwxr-xr-x 2 kbuilder kbuilder 4096 Apr 30 10&colon;41 assets -rw-rw-r-- 1 kbuilder kbuilder 54 Apr 30 10&colon;41 fingerprint.pb -rw-rw-r-- 1 kbuilder kbuilder 8798 Apr 30 10&colon;41 keras_metadata.pb -rw-rw-r-- 1 kbuilder kbuilder 78816 Apr 30 10&colon;41 saved_model.pb drwxr-xr-x 2 kbuilder kbuilder 4096 Apr 30 10&colon;41 variables </pre></devsite-code> <h2 id="examine_your_saved_model" data-text="Examine your saved model" tabindex="-1">Examine your saved model</h2> We'll use the command line utility <code translate="no" dir="ltr">saved_model_cli</code> to look at the <a href="https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/MetaGraphDef">MetaGraphDefs</a> (the models) and <a href="https://www.tensorflow.org/tfx/serving/signature_defs">SignatureDefs</a> (the methods you can call) in our SavedModel. See <a href="https://github.com/tensorflow/docs/blob/master/site/en/r1/guide/saved_model.md#cli-to-inspect-and-execute-savedmodel">this discussion of the SavedModel CLI</a> in the TensorFlow Guide. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Bash"><code class='devsite-terminal' translate="no" dir="ltr">saved_model_cli show --dir {export_path} --all</code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> 2024-04-30 10&colon;41&colon;41.354485&colon; E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc&colon;9261] Unable to register cuDNN factory&colon; Attempting to register factory for plugin cuDNN when one has already been registered 2024-04-30 10&colon;41&colon;41.354542&colon; E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc&colon;607] Unable to register cuFFT factory&colon; Attempting to register factory for plugin cuFFT when one has already been registered 2024-04-30 10&colon;41&colon;41.355947&colon; E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc&colon;1515] Unable to register cuBLAS factory&colon; Attempting to register factory for plugin cuBLAS when one has already been registered MetaGraphDef with tag-set&colon; 'serve' contains the following SignatureDefs&colon; signature_def['__saved_model_init_op']&colon; The given SavedModel SignatureDef contains the following input(s)&colon; The given SavedModel SignatureDef contains the following output(s)&colon; outputs['__saved_model_init_op'] tensor_info&colon; dtype&colon; DT_INVALID shape&colon; unknown_rank name&colon; NoOp Method name is&colon; signature_def['serving_default']&colon; The given SavedModel SignatureDef contains the following input(s)&colon; inputs['Conv1_input'] tensor_info&colon; dtype&colon; DT_FLOAT shape&colon; (-1, 28, 28, 1) name&colon; serving_default_Conv1_input&colon;0 The given SavedModel SignatureDef contains the following output(s)&colon; outputs['Dense'] tensor_info&colon; dtype&colon; DT_FLOAT shape&colon; (-1, 10) name&colon; StatefulPartitionedCall&colon;0 Method name is&colon; tensorflow/serving/predict The MetaGraph with tag set ['serve'] contains the following ops&colon; {'SaveV2', 'Pack', 'ShardedFilename', 'NoOp', 'ReadVariableOp', 'Placeholder', 'Reshape', 'RestoreV2', 'StaticRegexFullMatch', 'Const', 'Identity', 'Select', 'Conv2D', 'AssignVariableOp', 'Relu', 'BiasAdd', 'MergeV2Checkpoints', 'DisableCopyOnRead', 'StringJoin', 'StatefulPartitionedCall', 'VarHandleOp', 'MatMul'} Concrete Functions&colon; Function Name&colon; '__call__' Option #1 Callable with&colon; Argument #1 Conv1_input&colon; TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input') Argument #2 DType&colon; bool Value&colon; False Argument #3 DType&colon; NoneType Value&colon; None Option #2 Callable with&colon; Argument #1 Conv1_input&colon; TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input') Argument #2 DType&colon; bool Value&colon; True Argument #3 DType&colon; NoneType Value&colon; None Function Name&colon; '_default_save_signature' Option #1 Callable with&colon; Argument #1 Conv1_input&colon; TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input') Function Name&colon; 'call_and_return_all_conditional_losses' Option #1 Callable with&colon; Argument #1 Conv1_input&colon; TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input') Argument #2 DType&colon; bool Value&colon; False Argument #3 DType&colon; NoneType Value&colon; None Option #2 Callable with&colon; Argument #1 Conv1_input&colon; TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input') Argument #2 DType&colon; bool Value&colon; True Argument #3 DType&colon; NoneType Value&colon; None </pre></devsite-code> That tells us a lot about our model! In this case we just trained our model, so we already know the inputs and outputs, but if we didn't this would be important information. It doesn't tell us everything, like the fact that this is grayscale image data for example, but it's a great start. <h2 id="serve_your_model_with_tensorflow_serving" data-text="Serve your model with TensorFlow Serving" tabindex="-1">Serve your model with TensorFlow Serving</h2> <aside class="warning">Warning: If you are running this NOT on a Google Colab,** following cells will install packages on the system with root access. If you want to run it in a local Jupyter notebook, please proceed with caution.</aside> <h3 id="add_tensorflow_serving_distribution_uri_as_a_package_source" data-text="Add TensorFlow Serving distribution URI as a package source:" tabindex="-1">Add TensorFlow Serving distribution URI as a package source:</h3> We're preparing to install TensorFlow Serving using <a href="https://wiki.debian.org/Aptitude">Aptitude</a> since this Colab runs in a Debian environment. We'll add the <code translate="no" dir="ltr">tensorflow-model-server</code> package to the list of packages that Aptitude knows about. Note that we're running as root. <aside class="note">Note: This example is running TensorFlow Serving natively, but <a href="https://www.tensorflow.org/tfx/serving/docker">you can also run it in a Docker container</a>, which is one of the easiest ways to get started using TensorFlow Serving.</aside><div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">import sys # We need sudo prefix if not on a Google Colab. if 'google.colab' not in sys.modules: SUDO_IF_NEEDED = 'sudo' else: SUDO_IF_NEEDED = '' </code></pre></devsite-code><div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"># This is the same as you would do from your command line, but without the [arch=amd64], and no sudo # You would instead do: # echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list && \ # curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add - !echo "deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | {SUDO_IF_NEEDED} tee /etc/apt/sources.list.d/tensorflow-serving.list && \ curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | {SUDO_IF_NEEDED} apt-key add - !{SUDO_IF_NEEDED} apt update </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> deb http&colon;//storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 2943 100 2943 0 0 52553 0 --&colon;--&colon;-- --&colon;--&colon;-- --&colon;--&colon;-- 53509 OK Hit&colon;1 http&colon;//us-central1.gce.archive.ubuntu.com/ubuntu focal InRelease Hit&colon;2 http&colon;//us-central1.gce.archive.ubuntu.com/ubuntu focal-updates InRelease Hit&colon;3 http&colon;//us-central1.gce.archive.ubuntu.com/ubuntu focal-backports InRelease Hit&colon;4 https&colon;//download.docker.com/linux/ubuntu focal InRelease Get&colon;5 https&colon;//nvidia.github.io/libnvidia-container/stable/ubuntu18.04/amd64 InRelease [1484 B] Hit&colon;6 https&colon;//nvidia.github.io/nvidia-container-runtime/stable/ubuntu18.04/amd64 InRelease Hit&colon;7 https&colon;//nvidia.github.io/nvidia-docker/ubuntu18.04/amd64 InRelease Hit&colon;8 http&colon;//security.ubuntu.com/ubuntu focal-security InRelease Get&colon;9 http&colon;//storage.googleapis.com/tensorflow-serving-apt stable InRelease [3026 B] Hit&colon;10 https&colon;//developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64 InRelease Hit&colon;11 http&colon;//ppa.launchpad.net/deadsnakes/ppa/ubuntu focal InRelease Hit&colon;13 http&colon;//ppa.launchpad.net/longsleep/golang-backports/ubuntu focal InRelease Hit&colon;14 http&colon;//ppa.launchpad.net/openjdk-r/ppa/ubuntu focal InRelease Hit&colon;12 https&colon;//apt.llvm.org/focal llvm-toolchain-focal-17 InRelease Get&colon;15 http&colon;//storage.googleapis.com/tensorflow-serving-apt stable/tensorflow-model-server amd64 Packages [341 B] Get&colon;16 http&colon;//storage.googleapis.com/tensorflow-serving-apt stable/tensorflow-model-server-universal amd64 Packages [349 B] Fetched 5200 B in 2s (3446 B/s) 235 packages can be upgraded. Run 'apt list --upgradable' to see them. </pre></devsite-code> <h3 id="install_tensorflow_serving" data-text="Install TensorFlow Serving" tabindex="-1">Install TensorFlow Serving</h3> This is all you need - one command line! <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Bash"><code class='devsite-terminal' translate="no" dir="ltr"># TODO: Use the latest model server version when colab supports it.</code> <code class='devsite-terminal' translate="no" dir="ltr">#!{SUDO_IF_NEEDED} apt-get install tensorflow-model-server</code> <code class='devsite-terminal' translate="no" dir="ltr"># We need to install Tensorflow Model server 2.8 instead of latest version</code> <code class='devsite-terminal' translate="no" dir="ltr"># Tensorflow Serving >2.9.0 required `GLIBC_2.29` and `GLIBCXX_3.4.26`. Currently colab environment doesn't support latest version of`GLIBC`,so workaround is to use specific version of Tensorflow Serving `2.8.0` to mitigate issue.</code> <code class='devsite-terminal' translate="no" dir="ltr">wget 'http://storage.googleapis.com/tensorflow-serving-apt/pool/tensorflow-model-server-2.8.0/t/tensorflow-model-server/tensorflow-model-server_2.8.0_all.deb'</code> <code class='devsite-terminal' translate="no" dir="ltr">dpkg -i tensorflow-model-server_2.8.0_all.deb</code> <code class='devsite-terminal' translate="no" dir="ltr">pip3 install tensorflow-serving-api==2.8.0</code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> --2024-04-30 10&colon;41&colon;50-- http&colon;//storage.googleapis.com/tensorflow-serving-apt/pool/tensorflow-model-server-2.8.0/t/tensorflow-model-server/tensorflow-model-server_2.8.0_all.deb Resolving storage.googleapis.com (storage.googleapis.com)... 142.251.172.207, 142.251.180.207, 142.251.183.207, ... Connecting to storage.googleapis.com (storage.googleapis.com)|142.251.172.207|&colon;80... connected. HTTP request sent, awaiting response... 200 OK Length&colon; 340152790 (324M) [application/x-debian-package] Saving to&colon; ‘tensorflow-model-server_2.8.0_all.deb’ tensorflow-model-se 100%[===================>] 324.39M 223MB/s in 1.5s 2024-04-30 10&colon;41&colon;51 (223 MB/s) - ‘tensorflow-model-server_2.8.0_all.deb’ saved [340152790/340152790] dpkg&colon; error&colon; requested operation requires superuser privilege Collecting tensorflow-serving-api==2.8.0 Downloading tensorflow_serving_api-2.8.0-py2.py3-none-any.whl.metadata (1.8 kB) Requirement already satisfied&colon; grpcio<2,>=1.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-serving-api==2.8.0) (1.26.0) Requirement already satisfied&colon; protobuf>=3.6.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-serving-api==2.8.0) (3.20.3) Requirement already satisfied&colon; tensorflow<3,>=2.8.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-serving-api==2.8.0) (2.15.1) Requirement already satisfied&colon; six>=1.5.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from grpcio<2,>=1.0->tensorflow-serving-api==2.8.0) (1.16.0) Requirement already satisfied&colon; absl-py>=1.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (1.4.0) Requirement already satisfied&colon; astunparse>=1.6.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (1.6.3) Requirement already satisfied&colon; flatbuffers>=23.5.26 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (24.3.25) Requirement already satisfied&colon; gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (0.5.4) Requirement already satisfied&colon; google-pasta>=0.1.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (0.2.0) Requirement already satisfied&colon; h5py>=2.9.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (3.11.0) Requirement already satisfied&colon; libclang>=13.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (18.1.1) Requirement already satisfied&colon; ml-dtypes~=0.3.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (0.3.2) Requirement already satisfied&colon; numpy<2.0.0,>=1.23.5 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (1.26.4) Requirement already satisfied&colon; opt-einsum>=2.3.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (3.3.0) Requirement already satisfied&colon; packaging in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (24.0) Requirement already satisfied&colon; 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pyasn1<0.7.0,>=0.4.6 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (0.6.0) Requirement already satisfied&colon; oauthlib>=3.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow<3,>=2.8.0->tensorflow-serving-api==2.8.0) (3.2.2) Downloading tensorflow_serving_api-2.8.0-py2.py3-none-any.whl (37 kB) Downloading grpcio-1.62.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB) Installing collected packages&colon; grpcio, tensorflow-serving-api Attempting uninstall&colon; grpcio Found existing installation&colon; grpcio 1.26.0 Uninstalling grpcio-1.26.0&colon; Successfully uninstalled grpcio-1.26.0 Attempting uninstall&colon; tensorflow-serving-api Found existing installation&colon; tensorflow-serving-api 2.15.1 Uninstalling tensorflow-serving-api-2.15.1&colon; Successfully uninstalled tensorflow-serving-api-2.15.1 ERROR&colon; pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. tf-keras 2.16.0 requires tensorflow<2.17,>=2.16, but you have tensorflow 2.15.1 which is incompatible. tfx 1.15.0 requires tensorflow-serving-api<2.16,>=2.15, but you have tensorflow-serving-api 2.8.0 which is incompatible. tfx-bsl 1.15.1 requires tensorflow-serving-api<3,>=2.13.0, but you have tensorflow-serving-api 2.8.0 which is incompatible. Successfully installed grpcio-1.62.2 tensorflow-serving-api-2.8.0 </pre></devsite-code> <h3 id="start_running_tensorflow_serving" data-text="Start running TensorFlow Serving" tabindex="-1">Start running TensorFlow Serving</h3> This is where we start running TensorFlow Serving and load our model. After it loads we can start making inference requests using REST. There are some important parameters: <ul> <li><code translate="no" dir="ltr">rest_api_port</code>: The port that you'll use for REST requests.</li> <li><code translate="no" dir="ltr">model_name</code>: You'll use this in the URL of REST requests. It can be anything.</li> <li><code translate="no" dir="ltr">model_base_path</code>: This is the path to the directory where you've saved your model.</li> </ul> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">os.environ["MODEL_DIR"] = MODEL_DIR </code></pre></devsite-code> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Bash"><code class='devsite-terminal' translate="no" dir="ltr">nohup tensorflow_model_server \</code> <code class='devsite-terminal' translate="no" dir="ltr"> --rest_api_port=8501 \</code> <code class='devsite-terminal' translate="no" dir="ltr"> --model_name=fashion_model \</code> <code class='devsite-terminal' translate="no" dir="ltr"> --model_base_path="${MODEL_DIR}" >server.log 2>&1</code></pre></devsite-code> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Bash"><code class='devsite-terminal' translate="no" dir="ltr">tail server.log</code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> nohup&colon; failed to run command 'tensorflow_model_server'&colon; No such file or directory </pre></devsite-code> <h2 id="make_a_request_to_your_model_in_tensorflow_serving" data-text="Make a request to your model in TensorFlow Serving" tabindex="-1">Make a request to your model in TensorFlow Serving</h2> First, let's take a look at a random example from our test data. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">def show(idx, title): plt.figure() plt.imshow(test_images[idx].reshape(28,28)) plt.axis('off') plt.title('\n\n{}'.format(title), fontdict={'size': 16}) import random rando = random.randint(0,len(test_images)-1) show(rando, 'An Example Image: {}'.format(class_names[test_labels[rando]])) </code></pre></devsite-code> <img src="/static/tfx/tutorials/serving/rest_simple_files/output_Luqm_Jyff9iR_0.png" alt="png"> Ok, that looks interesting. How hard is that for you to recognize? Now let's create the JSON object for a batch of three inference requests, and see how well our model recognizes things: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">import json data = json.dumps({"signature_name": "serving_default", "instances": test_images[0:3].tolist()}) print('Data: {} ... {}'.format(data[:50], data[len(data)-52:])) </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> Data&colon; {"signature_name"&colon; "serving_default", "instances"&colon; ... [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]]]} </pre></devsite-code> <h3 id="make_rest_requests" data-text="Make REST requests" tabindex="-1">Make REST requests</h3> <h4 id="newest_version_of_the_servable" data-text="Newest version of the servable" tabindex="-1">Newest version of the servable</h4> We'll send a predict request as a POST to our server's REST endpoint, and pass it three examples. We'll ask our server to give us the latest version of our servable by not specifying a particular version. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"># docs_infra: no_execute !pip install -q requests import requests headers = {"content-type": "application/json"} json_response = requests.post('http://localhost:8501/v1/models/fashion_model:predict', data=data, headers=headers) predictions = json.loads(json_response.text)['predictions'] show(0, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format( class_names[np.argmax(predictions[0])], np.argmax(predictions[0]), class_names[test_labels[0]], test_labels[0])) </code></pre></devsite-code> <h4 id="a_particular_version_of_the_servable" data-text="A particular version of the servable" tabindex="-1">A particular version of the servable</h4> Now let's specify a particular version of our servable. Since we only have one, let's select version 1. We'll also look at all three results. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"># docs_infra: no_execute headers = {"content-type": "application/json"} json_response = requests.post('http://localhost:8501/v1/models/fashion_model/versions/1:predict', data=data, headers=headers) predictions = json.loads(json_response.text)['predictions'] for i in range(0,3): show(i, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format( class_names[np.argmax(predictions[i])], np.argmax(predictions[i]), class_names[test_labels[i]], test_labels[i])) </code></pre></devsite-code> </div> <devsite-thumb-rating position="footer"> </devsite-thumb-rating> <div class="devsite-floating-action-buttons"> </div> </article> <devsite-content-footer class="nocontent"> Except as otherwise noted, the content of this page is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 License</a>, and code samples are licensed under the <a href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0 License</a>. 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