simple_tensorflow_serving | Generic and easy-to-use serving service for machine learning models | Machine Learning library

by tobegit3hub JavaScript Version: 0.8.2 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | simple_tensorflow_serving Summary

simple_tensorflow_serving is a JavaScript library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. simple_tensorflow_serving has a Permissive License and it has low support. However simple_tensorflow_serving has 20 bugs and it has 3 vulnerabilities. You can install using 'pip install simple_tensorflow_serving' or download it from GitHub, PyPI.

Simple TensorFlow Serving is the generic and easy-to-use serving service for machine learning models. Read more in

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Quality

Security

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Support

simple_tensorflow_serving has a low active ecosystem.

It has 760 star(s) with 196 fork(s). There are 31 watchers for this library.

It had no major release in the last 6 months.

There are 28 open issues and 48 have been closed. On average issues are closed in 198 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of simple_tensorflow_serving is 0.8.2

Quality

simple_tensorflow_serving has 20 bugs (1 blocker, 0 critical, 17 major, 2 minor) and 78 code smells.

Security

simple_tensorflow_serving has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

simple_tensorflow_serving code analysis shows 3 unresolved vulnerabilities (3 blocker, 0 critical, 0 major, 0 minor).

There are 15 security hotspots that need review.

License

simple_tensorflow_serving is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

simple_tensorflow_serving releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Installation instructions, examples and code snippets are available.

simple_tensorflow_serving saves you 6569 person hours of effort in developing the same functionality from scratch.

It has 13648 lines of code, 135 functions and 184 files.

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

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simple_tensorflow_serving Key Features

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simple_tensorflow_serving Examples and Code Snippets

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Community Discussions

Trending Discussions on simple_tensorflow_serving

AttributeError: module 'tensorflow' has no attribute 'gfile'

QUESTION

AttributeError: module 'tensorflow' has no attribute 'gfile'

Asked 2020-May-12 at 09:18

I trained a simple mnist model with tensorflow 2.0 on Google Colab and saved it in the .json format. Click here to check out the Colab Notebook where I've written the code. Then on running the command

!simple_tensorflow_serving --model_base_path="/" --model_platform="tensorflow"

It is showing the error AttributeError: module 'tensorflow' has no attribute 'gfile'

simple_tensorflow_serving helps in easily deploying trained tensorflow model into production.

Versions I'm using:

(1) TensorFlow - 2.0

(2) simple_tensorflow_serving - 0.6.4

Thank you in advance :)

...

ANSWER

Answered 2019-Apr-09 at 14:32

Simple Tensorflow Serving is not ready for Tensorflow 2.0, since it is using the old API. In Tensorflow 2.0 the gfile package has been moved into tf.io.

Then, you have to downgrade your Tensorflow instance to TF 1.13 use Simple Tensorflow Serving

Source https://stackoverflow.com/questions/55591437

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

Vulnerabilities

No vulnerabilities reported

Install simple_tensorflow_serving

Install the server with pip. Or install from source code. Or use the docker image. Or deploy in Kubernetes.
Start the server with the TensorFlow SavedModel. Check out the dashboard in http://127.0.0.1:8500 in web browser. Generate Python client and access the model with test data without coding.

Support

For MXNet models, you can load with commands and configuration like these. For ONNX models, you can load with commands and configuration like these. For H2o models, you can load with commands and configuration like these. For Scikit-learn models, you can load with commands and configuration like these. For XGBoost models, you can load with commands and configuration like these. For PMML models, you can load with commands and configuration like these. This relies on Openscoring and Openscoring-Python to load the models.

Find more information at: