tf-serve | Serve TensforFlow Estimator with SavedModel | Machine Learning library

by jizhang Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | tf-serve Summary

tf-serve is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow, Numpy, Docker, Spark applications. tf-serve has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Serve TensforFlow Estimator with SavedModel

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Support

tf-serve has a low active ecosystem.

It has 27 star(s) with 16 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of tf-serve is current.

Quality

tf-serve has 0 bugs and 0 code smells.

Security

tf-serve has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tf-serve code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tf-serve does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

tf-serve releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

tf-serve saves you 58 person hours of effort in developing the same functionality from scratch.

It has 153 lines of code, 6 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Create examples from inputs .
Assemble the result .
Return the path to the export directory .
Return a dataframe of test inputs .
Create dataset .
Creates a tf . dataset .

Get all kandi verified functions for this library.

tf-serve Key Features

No Key Features are available at this moment for tf-serve.

tf-serve Examples and Code Snippets

No Code Snippets are available at this moment for tf-serve.

Community Discussions

Trending Discussions on tf-serve

How to serve a tensorflow-module, specifically Universal Sentence Encoder?

Team foundation Build errors on RestSharp and NewtonSoft

How can I query to REST API runs on tensorflow_model_server?

Exported Keras classification model served by TF Server gives: Expects arg[0] to be float but string is provided

QUESTION

How to serve a tensorflow-module, specifically Universal Sentence Encoder?

Asked 2019-Nov-18 at 15:26

I have spent several hours trying to set up Tensorflow serving of the Tensorflow-hub module, "Universal Sentence Encoder." There is a similar question here:

How to make the tensorflow hub embeddings servable using tensorflow serving?

I have been doing this on a Windows machine.

This is the code I used to build the model:

...

ANSWER

Answered 2019-Nov-18 at 15:26

I was finally able to figure things out. I'll post what I did here in case someone else is trying to do the same thing.

My issue with the saved_model_cli run command was with the quotes (using Windows command prompt). Change 'text=["what this is"]' to "text=['what this is']"

The issue with the POST request was two-fold. One, I noticed that the model's name is model, so should have been http://localhost:8501/v1/models/model:predict

Secondly, the input format was not correct. I used Postman, and the body of the request looks like this: {"inputs": {"text": ["Hello"]}}

Source https://stackoverflow.com/questions/52715499

QUESTION

Team foundation Build errors on RestSharp and NewtonSoft

Asked 2019-Oct-04 at 13:03

I can build my branch locally without any problem but when I try to build in via team foundation i get 2 errors. The errors are generated on a project i recently added to the solution.

The errors are:

EnvoyClient.cs(3,7): error CS0246: The type or namespace name 'Newtonsoft' could not be found (are you missing a using directive or an assembly reference?) [c:\TF-Agents\Agent2017-002\_work\2\s\System\Envoy.Connector\Envoy.Connector.csproj]

EnvoyClient.cs(4,7): error CS0246: The type or namespace name 'RestSharp' could not be found (are you missing a using directive or an assembly reference?) [c:\TF-Agents\Agent2017-002\_work\2\s\System\Envoy.Connector\Envoy.Connector.csproj]

I have tried to remove the nuget packages and re-adding them in my local branch, and then pull requesting them again to the branch i want to build on tf-server, but to no avail.

...

ANSWER

Answered 2019-Oct-01 at 08:57

Update from OP:

So the problem was the nuget packages were not being loaded in, because this project was not part of the solution.

I had to add my new project (with these 2 references) to that solution and then it builded perfectly.

EnvoyClient.cs(3,7): error CS0246: The type or namespace name 'Newtonsoft' could not be found (are you missing a using directive or an assembly reference?)

For this kind of issue, if your local build is successful and just the TFS build is failing then it is usually due to dll reference path issue. Make sure that the Dll is referenced as a relative path in the project file (.csproj).

To add a relative reference in a separate directory, do the following:

Add the reference in Visual Studio by right clicking the project in Solution Explorer and selecting Add Reference.

Find the *.csproj where this reference exist and open it in a text editor. Lets say your .csproj location is c:\tfs_get\sources\myfolder\myproject\myproj.csproj

Edit the < HintPath > to be equal to

Source https://stackoverflow.com/questions/58132124

QUESTION

How can I query to REST API runs on tensorflow_model_server?

Asked 2019-Apr-11 at 12:00

I'd tried to run simple TensorFlow estimators example: official Iris classification problem and saved model using this code implemented by this tutorial.

TensorFlow provides a command line tool to inspect the exported model like the following:

...

ANSWER

Answered 2019-Apr-11 at 12:00

I have tried to reproduce your error and I got the similar error for Curl Predict.

But when I have used Classify, I got the output.

Code is shown below:

Source https://stackoverflow.com/questions/54820451

QUESTION

Exported Keras classification model served by TF Server gives: Expects arg[0] to be float but string is provided

Asked 2018-Aug-03 at 09:32

I have trained a classification model in Keras (latest version of Keras and TF as per this writing) which is similar in input and output as CIFAR10. To serve this model I export it to a classification model (see the type) using the following code:

...

ANSWER

Answered 2018-Aug-03 at 09:32

Try using prediction_service_pb2_grpc.PredictionServiceStub(channel) instead of prediction_service_pb2.beta_create_PredictionService_stub(channel). Apparently this was recently moved from beta. You can refer to this example.

Source https://stackoverflow.com/questions/51615972

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

Vulnerabilities

No vulnerabilities reported

Install tf-serve

You can download it from GitHub.
You can use tf-serve like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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