tensorflow-serving-api | TensorFlow Serving API of all programming language

by AlexanderJLiu Java Version: Current License: BSD-2-Clause

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | tensorflow-serving-api Summary

tensorflow-serving-api is a Java library typically used in Web Services, Tensorflow, Apollo, Docker, Prometheus applications. tensorflow-serving-api has no vulnerabilities, it has a Permissive License and it has low support. However tensorflow-serving-api has 37 bugs and it build file is not available. You can download it from GitHub.

The goal of this project is to generate tensorflow serving api for various programming language supported by protocol buffer and grpc, like go, java, c++, c# and python etc. This project not only teaches you how to generate tensorflow serving api step by step but also tell you how to use the grpc api for making a serving request.

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Quality

Security

License

Reuse

Support

tensorflow-serving-api has a low active ecosystem.

It has 17 star(s) with 2 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-serving-api is current.

Quality

tensorflow-serving-api has 37 bugs (0 blocker, 0 critical, 13 major, 24 minor) and 1919 code smells.

Security

tensorflow-serving-api has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorflow-serving-api code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

tensorflow-serving-api is licensed under the BSD-2-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

tensorflow-serving-api releases are not available. You will need to build from source code and install.

tensorflow-serving-api has no build file. You will be need to create the build yourself to build the component from source.

It has 83761 lines of code, 9122 functions and 117 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed tensorflow-serving-api and discovered the below as its top functions. This is intended to give you an instant insight into tensorflow-serving-api implemented functionality, and help decide if they suit your requirements.

Main entry point
Returns the service descriptor
Create a Method for GetModelStatus
Get the handleReloadConfigRequest method
Creates a stub for ModelServiceBlocking
Creates a ModelServiceF stub for the given channel
Creates a stub for the service
Get classification
Get the GetModelMetadata method
Get MultiInference method
Get a predictor method
Get Regress method
Creates a future stub for a prediction service
Creates a stub for a prediction service
Returns the Method for the SessionRun method
Creates a stub for a SessionServiceBlockingStub
Creates a future stub for a SessionServiceFuture
Creates a stub for the session service

Get all kandi verified functions for this library.

tensorflow-serving-api Key Features

No Key Features are available at this moment for tensorflow-serving-api.

tensorflow-serving-api Examples and Code Snippets

No Code Snippets are available at this moment for tensorflow-serving-api.

Community Discussions

Trending Discussions on tensorflow-serving-api

Installing version 1.15 of tensorflow-serving-api to Centos 8

No Gradients Provided Keras Tensorflow when nesting Models

QUESTION

Installing version 1.15 of tensorflow-serving-api to Centos 8

Asked 2021-Mar-22 at 09:24

I am trying to install Tensorflow-serving to my Centos 8 machine. Installing with Docker image is not an option for Centos. So I try to install with pip. These are the commands for installing tensorflow-model-server:

...

ANSWER

Answered 2021-Mar-22 at 09:24

I found the links:

Source https://stackoverflow.com/questions/66733616

QUESTION

No Gradients Provided Keras Tensorflow when nesting Models

Asked 2021-Jan-04 at 06:28

Im started to work with Keras a little bit but i run in to this issue where it tells me that no gradients are provided. I know that this question was posted like 100 times before but the solutions are always talking about using GradientTape but i don't see why i should do this (also i don't even understand what it does)

...

ANSWER

Answered 2021-Jan-04 at 06:28

I fixed your code. When you get that error, there is not path in the graph between your loss function and your trainable variables, which was true in your case.

You don't have labels to train your autoencoder. I added train_x as your labels.
I don't think SparseCategoricalCrossentropy would work for the architecture you have defined. So, I changed it to BinaryCrossEntropy
When you assigned a name to a vector, spaces are not allowed, so I changed "AutoEncoder Input" to "AutoEncoder_Input"

Here is the code

Source https://stackoverflow.com/questions/65556042

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tensorflow-serving-api

You can download it from GitHub.
You can use tensorflow-serving-api like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the tensorflow-serving-api component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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