It does not require the original model building code to run, which makes it useful for sharing or deploying. extra subfolder of the SavedModel directory. Extra assets may be written into the SavedModel via the assets_extra argument. HDF5 files are still supported, and may be used by specifying save_format="h5" when saving. If you are switching between MXNet or TensorFlow Elastic Inference environments, you must Stop and then Start your instance to reattach the Elastic Inference Accelerator. Tensorflow serving in a nutshell. 0, if no session is passed to this function, MLflow will attempt to load the model using the default TensorFlow session. 11 Jan 2018. tfruns Track, visualize, and manage TensorFlow training runs and experiments. This is the preferred way of saving while you are training. You'll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. I would like to use Google OAuth2 tokens for this rather than roll my own authentication, since Android devices with Google Play installed make this available to app developers. Note that model_to_saved_model is only available for TensorFlow version greater than 1. The easiest way to get started contributing to Open Source c++ projects like tensorflow Pick your favorite repos to receive a different open issue in your inbox every day. Load a SavedModel. cloudml R interface to Google Cloud Machine. TensorFlow large model support (TFLMS) V2 provides an approach to training large models that cannot be fit into GPU memory. SavedModel offers the flexibility to build and load TensorFlow graphs for a variety of use-cases. Cartoonize using TensorFlow. You can disable the feature by setting it to -1 or if you only want to load the model once and never update it, you can set it to 0. tensorflow latest nightly build 1. Data Preprocessing Download Data. This creates a new context to execute a TFGraph. However, in TensorFlow 2. For more information, see TensorFlow SavedModel ; Convert a Keras model to the SavedModel format. To trigger warmup of the model at load time, attach a warmup data file under the assets. This left us with a folder containing various binary files containing the graph structure, learned parameters, optional assets and the signature (input and output. pb with my own Inception_resnet_v2 saved_model. js converter is an open source library to load a pretrained TensorFlow SavedModel, Frozen Model or Session Bundle into the browser and run inference through TensorFlow. This is the convenience method that allows the model to be loaded once and subsequently use it for querying schema and creation of TensorFlowEstimator using ScoreTensorFlowModel(String, String, Boolean). saved_model. These examples are extracted from open source projects. seed(42) # The below is necessary for starting core Python generated random numbers # in a well-defined state. tensorflow simple_save (2) I changed from tf. This is a practical exercise to learn how to make predictions with TensorFlow, but it is a naive approach to the real forecasting problem. 0 functionalities. See Section 3. Estimator exposes an export_savedmodel method, which requires two arguments: the export directory and a receiver function. ) A 2-step process to import your model:. As part of the TensorFlow models repository, you can find the script for a simple Deep Neural Network using the Iris dataset for classification. If no default session is available, then the function raises an exception. With TensorFlow, you can very easily train and save a model on one platform (like TPU) and load and serve it on another platform (like GPU or CPU). TensorFlow is famous for neural networks and deep learning, however it also supports simpler models like logistic regression. It does not require the original model building code to run, which makes it useful for sharing or deploying. save may now save to the TensorFlow SavedModel format. The folder structure of image recognition code implementation is as shown below − The dataset. Thankfully, the TensorFlow project includes a ready to use “model server” that’s free to use: TensorFlow Serving. 7 with TensorRT 4. 4 (actually) ***** I tried to compile the Tensorflow-serving r1. TensorFlow SavedModel defines a language-neutral format to save machine-learned models that is recoverable and hermetic. It is required to use Google Function to trigger the Deep Learning predictions. js model format. So a clear understanding of the API hierarchy will make it easier to work with TensorFlow, as well as make it easier to learn more from reading other TensorFlow implementations with clarity. In TensorFlow 2. WarmStartSetting only supports ckpt as well. In this implementation of the service, we will use the Tensorflow framework to load the previously trained model and feed the inputs to fetch the outputs. TensorFlow SavedModel defines a language-neutral format to save machine-learned models that is recoverable and hermetic. Checkpoint, including any Layer instances or Optimizer instances assigned to object attributes. The last step in this project is to serve our trained model as a web service. Make it possible for the Java library to load exported TF models in SavedModel format. While TensorFlow models are typically defined and trained using R or Python code, it is possible to deploy TensorFlow models in a wide variety of environments without any runtime dependency on R or Python: TensorFlow Serving is an open-source software library for serving TensorFlow models using a gRPC interface. TensorFlow best practice series. simple-tensorflow-serving. What's Coming in TensorFlow 2. 为了方便Android平台使用,用freeze_graph脚本将SavedModel合并 查看这个脚本的源码,在最开头可以看到 Converts checkpoint variables into Const ops in a standalone GraphDef file. Graph()) as sess: meta_graph_def = t…. 本文总结了 TensorFlow 常见模型格式和载入、保存方法。 部署在线服务(Serving)时官方推荐使用 SavedModel 格式,而部署到手机等移动端的模型一般使用 FrozenGraphDef 格式(最近推出的 TensorFlow Lite 也有专门的轻量级模型格式 *. Better support for training models from data tensors in TensorFlow (e. Session(graph=ops. Add a related example script. pyfunc = mlflow. predict_df = pyfunc. You can choose from different Google Cloud Platform services such Cloud Machine Learning Engine, Kubernetes Engine, or Compute Engine to serve your models. Part 3 in the series about Java / TensorFlow Interoperability, showing how to load a TensorFlow SavedModel in Java. Today we're looking at running inference / forward pass on a neural network model in Golang. If you're a beginner like me, using a framework like Keras, makes writing deep learning algorithms significantly easier. The directory to load the SavedModel containing the input graph to transform. Doing inference using Estimator ’s predict method with living graph would still work, but we will not be able to use predictor any more for the SavedModel. For a detailed guide, see Using SavedModel with Estimators. OK, I Understand. 在《Tensorflow SavedModel模型的保存与加载》中,我们谈到了Tensorflow模型如何保存为SavedModel格式,以及如何加载之。在《如何查看tensorflow SavedModel格式模型的信息》中,我们演示了如何查看模型的signature和计算图结构。在本文中,我们将探讨如何合并两个模型. In the Python program that will use the SavedModel, we need code like this:. They are SavedModel, metagraph/checkpoint, frozen_graph. You can view and change this field later by using GCP Console. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. I have exported a SavedModel and now I with to load it back in and make a prediction. In tensorflow 1. A local path can be exported using export_savedmodel(), a REST URL can be created using serve_savedmodel(), a CloudML model can be deployed usin cloudml::cloudml_deploy() and a graph object loaded using load_savedmodel(). Follow along in this episode of Coding TensorFlow with the Colab provided below. js to load the pretrained models and transform your images. import tensorflow as tf export_dir = ". @timmolter It would be fantastic if you could add links (e. load_savedmodel (sess, model_dir = NULL) Arguments. saved_model. export_savedmodel(初学者) 如何使用Tensorflow模型进行预测? python - 生产中的TensorFlow用于高流量应用中的实时预测 - 如何使用? 如何使用tensorflow中的seq2seq预测简单序列? 如何在tensorflow会话中运行Keras. Save/Load models using HDF5 files. 2, TensorFlow installed from - pip, TensorFlow version - cpu version 1. For the set of most common expected use-cases, SavedModel's APIs provide a set of constants in Python and C++ that are easy to reuse and share across tools consistently. load() should be a directory containing a. Checkpoint, including any Layer instances or Optimizer instances assigned to object attributes. Datasets, TFRecords). The export_savedmodel() function creates a SavedModel from a model trained using the keras, tfestimators, or tensorflow R packages. It enables higher-level systems and tools to produce, consume and transform TensorFlow models. Kerasで学習済のモデルをTensorflowのSavedModelに簡単に変換する from keras. Add a related example script. This left us with a folder containing various binary files containing the graph structure, learned parameters, optional assets and the signature (input and output. The built-in ‘keras. TensorFlow model preparation. You can view and change this field later by using GCP Console. It's constantly being improved and lately introduced to new languages - including Java and JavaScript. Not every frame is passed. import tensorflow as tf export_dir = ". TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. This is the preferred way of saving while you are training. The SavedModel API allows you to save a trained model into a format that can be easily loaded in Python, Java, (soon JavaScript), upload to GCP: ML Engine or use a TensorFlow Serving server. Data Preprocessing Download Data. Add clone_model() function, enabling to construct a new model, given an existing model to use as a template. You can disable the feature by setting it to -1 or if you only want to load the model once and never update it, you can set it to 0. pyfunc = mlflow. As mentioned in the previous post however, there are cases where you use the trained state in a different framework or in hand-rolled code. TensorFlow will standardize on SavedModel as an interchange format for TensorFlow Serving, TensorFlow Lite, TensorFlow. x, it will become the default mode of TensorFlow 2. HDF5 files are still supported, and may be used by specifying save_format="h5" when saving. Twitter as an enterprise data source often revolves around marketing, customer services, customer feedback, sentiment analysis. import tensorflow as tf export_dir = ". The Caffe Model Zoo is an extraordinary place where reasearcher share their models. They are SavedModel, metagraph/checkpoint, frozen_graph. The TensorFlow SavedModel format on the other hand includes all information about a model (graph, checkpoint state, other metadata) and to use in Java you'd want to use SavedModelBundle. A type parameter can be specified to explicitly choose the type model performing the prediction. Finally, for Keras models there are 2 other representations: HDF5 files and serialized R objects. 4 (actually) ***** I tried to compile the Tensorflow-serving r1. If you have a SavedModel representation of your TensorFlow model, you can create a TensorRT inference graph directly from your SavedModel, for example:. Read the guide to custom prediction routines. 返回值:The Path to which the SavedModel protocol buffer was written. tensorflow simple_save (2) I changed from tf. tfruns Track, visualize, and manage TensorFlow training runs and experiments. 14, it's obvious that tf. tensorflow or tensorflow-python2 will invoke a Tensorflow-enabled python2 interpretter within the container. The SavedModel format is another way to serialize models. If using TensorFlow version >= 2. Flexible Data Ingestion. Learn how to save trained machine learning models in TensorFlow and load them again for later use. js model format. NOTE: This is much easier using the Keras API in Tutorial #03-C!. After you train a model in Tensorflow: How do you save the trained model? How do you later restore this saved model?. TensorFlow Read And Execute a SavedModel on MNIST Train MNIST classifier Training Tensorflow MLP Edit MNIST SavedModel Translating From Keras to TensorFlow KerasMachine Translation Training Deployment Cats and Dogs Preprocess image data Fine-tune VGG16 Python Train simple CNN Fine-tune VGG16 Generate Fairy Tales Deployment Training Generate Product Names With LSTM Deployment Training Classify. We’ll build a custom model and use Keras to do it. How to save a trained model (Estimator) and Load it back to test it with data in Tensorflow? by Surjith Bhagvath Singh Last Updated April 07, 2017 21:26 PM 0 Votes 2 Views. Loads a SavedModel using the given TensorFlow session and returns the model's graph. with session. Valid values are export, webapi and graph. In this blog post, I'll show you how to convert the Places 365 model to TensorFlow. SavedModel is a standalone serialization format for Tensorflow objects, supported by TensorFlow serving as well as TensorFlow implementations other than Python. Load a TensorFlow SavedModel from disk. @timmolter It would be fantastic if you could add links (e. k_get_session. load_pyfunc(estimator_path) # We can now apply the model on Pandas DataFrames to make predictions. pb file and predict the image of dogs and cats for classification. SavedModel是一种与语言无关,可恢复的密封式序列化格式。SavedModel使更高级别的系统和工具能够生成,消耗和变换TensorFlow模型。TensorFlow提供了多种与SavedModel交互的机制,包括tf. It is also assumed that model weights can be accessed from relative paths described by the paths fields in weights manifest. 2018-01-13 11:34:40 cdxy Keras,TensorFlow,SavedModel from keras import backend as K import tensorflow as tf from keras. The idea behind it is that we can run it, pointing it at the directory containing the models saved in the TensorFlow’s SavedModel format. Loads a SavedModel using the given TensorFlow session and returns the model's graph. Estimator exposes an export_savedmodel method, which requires two arguments: the export directory and a receiver function. Value The path to the exported directory, as a string. This creates a new context to execute a TFGraph. Saver 에서 SavedModel 형식으로 바뀌 었습니다. @timmolter It would be fantastic if you could add links (e. import tensorflow as tf export_dir = ". Here’s what it means By Michael Berthold, KNIME Pick up a magazine, scroll through the tech blogs, or simply chat with your peers at an industry conference. Loads a SavedModel using the given TensorFlow session and returns the model's graph. load完以后,也是从sess对应的graph中获取需要的tensor来inference。 如何查看Tensorflow SavedModel格式模型的信息 10-15 阅读数 1725. I like especially the ability to run predictions in browsers. Why you might ask? Well the examples (at least to me) didn't show how to achieve the end-2-end flow and that piqued an interest to get a working example of the flows diagrammed in the announcement note. This is for image processing within videos. If you want to deploy your TensorFlow model as part of a custom prediction routine, you can export it as a SavedModel or as a different set of artifacts. sess: The active TensorFlow session. blessing, serving_model_dir=serving_model_dir). saved_model API, Estimator API 以及 CLI方式。 操作SavedModel API. SavedModel is the universal serialization format for TensorFlow models. In this blog post, we explored how to use estimators for text classification, in particular for the IMDB Reviews. 1 release of Watson Machine Learning Community Edition (WML-CE) added packages for both TensorRT and TensorFlow Serving. Deploy your model using TensorFlow Serving, CloudML, or RStudio Connect. Converting a SavedModel into model. The corresponding value gives the full path of the source file to be copied. 返回值:The Path to which the SavedModel protocol buffer was written. tflite file already, so naturally I landed on a simple neural network trained on MNIST data (currently there are 3 TensorFlow Lite models supported: MobileNet, Inception v3, and On Device Smart Reply). Have I written custom code : No OS Platform and Distribution: CentOS 7 TensorFlow installed from: source TensorFlow version: tensorflow-serving branch r1. load_model and are compatible with TensorFlow Serving. all variables, operations, collections etc. 0 functionalities. TensorFlow will standardize on SavedModel as an interchange format for TensorFlow Serving, TensorFlow Lite, TensorFlow. To trigger warmup of the model at load time, attach a warmup data file under the assets. Valid values are export, webapi and graph. 何の話かというと TF1. NOTE: This is much easier using the Keras API in Tutorial #03-C!. github博客传送门 csdn博客传送门. A local path can be exported using export_savedmodel(), a REST URL can be created using serve_savedmodel(), a CloudML model can be deployed usin cloudml::cloudml_deploy() and a graph object loaded using load_savedmodel(). If you are interested in exporting the models to disk in a fully recoverable way, you might want to look into the SavedModel class, which is especially useful for serving your model through an API using TensorFlow Serving. Developer Advocate Paige Bailey (@DynamicWebPaige) and TF Software Engineer Alex Passos answer your #AskTensorFlow questions. 在TensorFlow中保存和恢复模型的一种新方法是使用SavedModel,Builder和loader功能。 这个方法实际上是Saver提供的更高级别的序列化,它更适合于商业目的。 虽然这种SavedModel方法似乎不被开发人员完全接受,但它的创作者指出:它显然是未来。. If you don't want to spend time building and training your model, it's. The KNIME Deep Learning - TensorFlow Integration gives easy access to the powerful machine learning library TensorFlow within KNIME (since version 3. This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensor s, and then calling this Estimator 's model_fn to generate the model graph based on those features. ) A 2-step process to import your model:. contrib and TensorFlow session will not be exposed to users. The SavedModel guide goes into detail about how to serve/inspect the SavedModel. You Should Know. You can call deploy on a TensorFlow estimator to create a SageMaker Endpoint, or you can call transformer to create a Transformer that you can use to run a batch transform job. If not specified and endpoint_type is 'tensorflow. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. Hence, Tensorflow model has two main files: a) Meta graph: This is a protocol buffer which saves the complete Tensorflow graph; i. I can see 30% improvement with savedmodel, but no improvment with frozen graph. pkl file, and our plotter interprets the contents of progress. placeholder Start serving to load the. ) A 2-step process to import your model:. js, we need to convert it into a compatible version and then load … - Selection from Hands-On Neural Networks with TensorFlow 2. Model images should be standard TensorFlow SavedModel as well. Additional feature flags are:--file_system_poll_wait_seconds=1. As an example, we will build a simple TensorFlow model that classifies flowers and is built on top of MobileNet v2 thanks to transfer learning. SavedModel Warmup is supported for Regress, Classify, MultiInference and Predict. filter_center_focus Set input_model_from to be tensorflow. Instead of having many ways of saving models, TF 2. js Node directory. load_savedmodel (sess, model_dir = NULL) Arguments. We do not use [batch_size, r, g, b] or [batch_size, r, b, g] as signature input because it is not compatible with arbitrary image files. These two lines should most likely apply to your problem as well. There are a few command-line arguments listed in the following with TensorFlow 1. with session. 7 Bazel version: 0. Deploy your model using TensorFlow Serving, CloudML, or RStudio Connect. Latter defines what kind of input data the exported model accepts. TensorFlow Serving 使用SavedModel这种格式来保存其模型,SavedModel是一种独立于语言的,可恢复,密集的序列化格式,支持使用更高级别的系统和工具来生成,使用和转换TensorFlow模型。这里我们直接下载一个预训练好的模型:. Brad Miro explains what deep learning is, why one may want to use it over traditional ML methods, as well as how to get started building deep learning models using TensorFlow 2. However, this script doesn't include a step to export the model using the SavedModel format to be used with the TensorFlow. Estimator to a SavedModel using the serving_input_fn as input function Load the PREDICT. load_pyfunc(estimator_path) # We can now apply the model on Pandas DataFrames to make predictions. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Luckily, the idiom for this is just the same as in low-level TensorFlow. js Converter, tools to import a TensorFlow SavedModel to TensorFlow. Now it is time to get results out of the model. TensorFlow の tf. In TensorFlow 2. Finally, for Keras models there are 2 other representations: HDF5 files and serialized R objects. h5') Weights-only saving in SavedModel format. 0, this argument is ignored. filter_center_focus Set input_model_format to be tf_saved. load_pyfunc(estimator_path) # We can now apply the model on Pandas DataFrames to make predictions. Typical usage for the SavedModelBuilder: … builder = tf. py (trains the model and generates Saved Model succesfully) x. SavedModel offers the flexibility to build and load TensorFlow graphs for a variety of use-cases. APIs to build and load a SavedModel. Before setting up the actual server, I'd like to highlight TensorFlow's SavedModel command line tool, which is useful to quickly inspect the input and output specifications of our model:. For the set of most common expected use-cases, SavedModel's APIs provide a set of constants in Python and C++ that are easy to reuse and share across tools consistently. 14, it's obvious that tf. A type parameter can be specified to explicitly choose the type model performing the prediction. Tensorflow also provides a host of official and research models as starting point for experiments, research or production use. If you don't want to spend time building and training your model, it's. If using TensorFlow <2. 15) and TensorFlow Hub 0. We need to pass the data through command-line arguments. load()为SavedModel提供加载和恢复功能。load指令需要以下信息: 要在其中恢复图定义和变量的会话. View your imported SavedModel model as a graph. all variables, operations, collections etc. save_model’ and ‘keras. The SavedModel format is another way to serialize models. tags : Set of string tags to identify the required MetaGraphDef. Part 3 in the series about Java / TensorFlow Interoperability, showing how to load a TensorFlow SavedModel in Java. SavedModel is a standalone serialization format for TensorFlow objects, supported by TensorFlow serving as well as TensorFlow implementations other than Python. 0 into TensorFlow 2. Once you have exported a TensorFlow model using export_savedmodel() it’s. In order to serve these models with Tensorflow Serving, the frozen graph has to be exported into SavedModel format. jar in project and above dependencies in build. 0 where TRT is not enabled. The code is simple: with tf. placeholder Start serving to load the. Not every frame is passed. Value The path to the exported directory, as a string. A type parameter can be specified to explicitly choose the type model performing the prediction. 0 functionalities. #Maybe this is because of my own understanding and is not due to their naming. signature_name. pbtxt) file storing the actual TensorFlow program, or model, and a set of named signatures. TensorRT-compatible subgraphs consist of TF-TRT supported ops (see Supported Ops for more details) and are direct acyclic graphs (DAG). 最全Tensorflow 2. The only file in here that you should ever have to use “by hand” is the config. TensorFlow is a powerful, robust and widely-used framework. In addition, SavedModel provides a util to help build a signature-def. Although the Java API doesn't yet have nearly as much functionality as TensorFlow for Python, it can still serve as a good intro to TensorFlow for Java developers. Load a TensorFlow SavedModel from disk. Since the goal is to use Go to deploy the SavedModel of the previously defined pow function, we are going to use the tfgo LoadModel function, which was created to load a SavedModel given the path and the desired tag. For example, you can not update or re-train a graph from a saved model. Once a model is optimized with TensorRT, the traditional Tensorflow workflow is still used for inferencing, including TensorFlow Serving. This SignatureDef says that the model accepts a string of arbitrary length as input, and responds with classes and their probabilities. Whether to load weights by name or by topological order. Load TensorFlow model from SavedModel Now, when we have TensorFlow model saved in SavedModel format, let’s load it. pb file; How to load the. It does not require the original model building code to run, which makes it useful for sharing or deploying (with TFLite, TensorFlow. __version__. sbt file in my existing project ? can i publish this as local sbt ?. Tensorflow ops that are not compatible with TF-TRT, including custom ops. You'll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. The interpreter uses a static graph ordering and a custom (less-dynamic) memory allocator to ensure minimal load, initialization, and execution latency. To use it, you need to provide your model, checkpoint, and define what are the input & output tensors to infer with. If you want to deploy your TensorFlow model as part of a custom prediction routine, you can export it as a SavedModel or as a different set of artifacts. You can use the constructor to create an empty session, or you can load an existing model using the FromSavedModel(TFSessionOptions, TFBuffer, String, String[], TFGraph, TFBuffer, TFStatus) static method in this class. Tensorflow ops that are not compatible with TF-TRT, including custom ops. load()为SavedModel提供加载和恢复功能。load指令需要以下信息: 要在其中恢复图定义和变量的会话. 9 Model Pusher (Deployer) Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Push Good Model to Deployment Target Uses Trained SavedModel Writes Version Data to Metadata Store Write to FileSystem or TensorFlow Hub from tfx import components pusher = components. In this blog post, I'll show you how to convert the Places 365 model to TensorFlow. 2018-01-13 11:34:40 cdxy Keras,TensorFlow,SavedModel from keras import backend as K import tensorflow as tf from keras. load_model and are compatible with TensorFlow Serving. 2, TensorFlow installed from - pip, TensorFlow version - cpu version 1. extra subfolder of the SavedModel directory. It does not require the original model building code to run, which makes it useful for sharing or deploying (with TFLite, TensorFlow. , "Add", "MatMul"). If you're a beginner like me, using a framework like Keras, makes writing deep learning algorithms significantly easier. This enables users to execute, build, and train state of the art deep learning models. Datasets, TFRecords). saved_model. None of the answers in the linked question touch on the SavedModel API (which, once again, is not the same as tf. The last step in this project is to serve our trained model as a web service. Exports inference graph as a SavedModel into given dir. placeholder Start serving to load the. Two decades into the AI revolution, deep learning is becoming a standard part of the analytics toolkit. @timmolter It would be fantastic if you could add links (e. pb (or saved_model. APIs to build and load a SavedModel. Before setting up the actual server, I’d like to highlight TensorFlow’s SavedModel command line tool, which is useful to quickly inspect the input and output specifications of our model:. Kashgari could export model with SavedModel format for tensorflow serving, you could directly deploy it on the cloud. json format Since it is not possible to use a SavedModel directly from TensorFlow. js; If you care about bundle size, you can import those packages individually. Add clone_model() function, enabling to construct a new model, given an existing model to use as a template. The Amazon SageMaker TFS container uses the model’s SignatureDef named serving_default, which is declared when the TensorFlow SavedModel is exported. Using readNetFromTensorflow() and running Frozen Graph, but Fails to predict correctly. Here we use the SavedModelBundle. import tensorflow as tf export_dir = ". import numpy as np import tensorflow as tf import random as rn # The below is necessary for starting Numpy generated random numbers # in a well-defined initial state. tensorflow run pb file (2) tf. Graphpipeをこれまで使ってきましたが、モデルサーバーとして最も使われている(らしい)Tensorflow Servingを試してみようと思います。. Works even in a TensorFlow graph different from that of the original model. In this blog, I'll discuss a library of pluggable ML applications, all runnable via MLflow. After you train a model in Tensorflow: How do you save the trained model? How do you later restore this saved model?. Read the guide to custom prediction routines. Datasets, TFRecords).