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hypothesis | use library for propertybased testing | Testing library

 by   HypothesisWorks Python Version: hypothesis-python-6.44.0 License: Non-SPDX

 by   HypothesisWorks Python Version: hypothesis-python-6.44.0 License: Non-SPDX

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kandi X-RAY | hypothesis Summary

hypothesis is a Python library typically used in Testing applications. hypothesis has no vulnerabilities and it has medium support. However hypothesis has 24 bugs, it build file is not available and it has a Non-SPDX License. You can install using 'pip install hypothesis' or download it from GitHub, PyPI.
Hypothesis is a powerful, flexible, and easy to use library for property-based testing.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • hypothesis has a medium active ecosystem.
  • It has 5815 star(s) with 488 fork(s). There are 65 watchers for this library.
  • There were 10 major release(s) in the last 6 months.
  • There are 50 open issues and 1190 have been closed. On average issues are closed in 91 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of hypothesis is hypothesis-python-6.44.0
hypothesis Support
Best in #Testing
Average in #Testing
hypothesis Support
Best in #Testing
Average in #Testing

quality kandi Quality

  • hypothesis has 24 bugs (6 blocker, 0 critical, 17 major, 1 minor) and 494 code smells.
hypothesis Quality
Best in #Testing
Average in #Testing
hypothesis Quality
Best in #Testing
Average in #Testing

securitySecurity

  • hypothesis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • hypothesis code analysis shows 0 unresolved vulnerabilities.
  • There are 42 security hotspots that need review.
hypothesis Security
Best in #Testing
Average in #Testing
hypothesis Security
Best in #Testing
Average in #Testing

license License

  • hypothesis has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
hypothesis License
Best in #Testing
Average in #Testing
hypothesis License
Best in #Testing
Average in #Testing

buildReuse

  • hypothesis releases are available to install and integrate.
  • Deployable package is available in PyPI.
  • hypothesis has no build file. You will be need to create the build yourself to build the component from source.
hypothesis Reuse
Best in #Testing
Average in #Testing
hypothesis Reuse
Best in #Testing
Average in #Testing
Top functions reviewed by kandi - BETA

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

  • Return a pandas . DataFrame containing all rows .
  • Creates a function that passes the given arguments .
  • Construct a search strategy .
  • Generates a strategy .
  • Construct a SearchStrategy from a given thing .
  • Learn a new FFA .
  • Generate new examples .
  • Generate mock code .
  • Recursive calculation of a computed property .
  • Learns the given string .

hypothesis Key Features

Hypothesis is a powerful, flexible, and easy to use library for property-based testing.

hypothesis Examples and Code Snippets

  • Strange behaviour of mat-form-field when pressing Return key
  • Produce a function in Coq which outputs every witness to an existence-uniqueness axiom
  • Why did Hypothesis give a falsifying example, when manually reproducing with numpy arrays does not fail?
  • What makes the different performances between numpy.sum and numpy.cumsum?
  • How to reorder an output in t.test in R?
  • Manipulating C function
  • How do Functions work in a Mongoose Schema?
  • AntDesign and React issue
  • AttributeError: 'generator' object has no attribute 'append'
  • How to create a boxplot from t -test

Strange behaviour of mat-form-field when pressing Return key

<button .... type="button" (click)="removeHypothesis(rowIndex)">

Community Discussions

Trending Discussions on hypothesis
  • Recommended way of measuring execution time in Tensorflow Federated
  • Extract p-value from an Object QuadTypeIndependenceTest and ScalarIndependenceTest from Coin Packages
  • Strange behaviour of mat-form-field when pressing Return key
  • Produce a function in Coq which outputs every witness to an existence-uniqueness axiom
  • Kafka consumer/producer with Python in WSL2 Ubuntu
  • Why did Hypothesis give a falsifying example, when manually reproducing with numpy arrays does not fail?
  • What makes the different performances between numpy.sum and numpy.cumsum?
  • How to reorder an output in t.test in R?
  • Manipulating C function
  • How do Functions work in a Mongoose Schema?
Trending Discussions on hypothesis

QUESTION

Recommended way of measuring execution time in Tensorflow Federated

Asked 2021-Jun-15 at 13:49

I would like to know whether there is a recommended way of measuring execution time in Tensorflow Federated. To be more specific, if one would like to extract the execution time for each client in a certain round, e.g., for each client involved in a FedAvg round, saving the time stamp before the local training starts and the time stamp just before sending back the updates, what is the best (or just correct) strategy to do this? Furthermore, since the clients' code run in parallel, are such a time stamps untruthful (especially considering the hypothesis that different clients may be using differently sized models for local training)?

To be very practical, using tf.timestamp() at the beginning and at the end of @tf.function client_update(model, dataset, server_message, client_optimizer) -- this is probably a simplified signature -- and then subtracting such time stamps is appropriate?

I have the feeling that this is not the right way to do this given that clients run in parallel on the same machine.

Thanks to anyone can help me on that.

ANSWER

Answered 2021-Jun-15 at 12:01

There are multiple potential places to measure execution time, first might be defining very specifically what is the intended measurement.

  1. Measuring the training time of each client as proposed is a great way to get a sense of the variability among clients. This could help identify whether rounds frequently have stragglers. Using tf.timestamp() at the beginning and end of the client_update function seems reasonable. The question correctly notes that this happens in parallel, summing all of these times would be akin to CPU time.

  2. Measuring the time it takes to complete all client training in a round would generally be the maximum of the values above. This might not be true when simulating FL in TFF, as TFF maybe decided to run some number of clients sequentially due to system resources constraints. In practice all of these clients would run in parallel.

  3. Measuring the time it takes to complete a full round (the maximum time it takes to run a client, plus the time it takes for the server to update) could be done by moving the tf.timestamp calls to the outer training loop. This would be wrapping the call to trainer.next() in the snippet on https://www.tensorflow.org/federated. This would be most similar to elapsed real time (wall clock time).

Source https://stackoverflow.com/questions/67982276

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

Vulnerabilities

No vulnerabilities reported

Install hypothesis

You can install using 'pip install hypothesis' or download it from GitHub, PyPI.
You can use hypothesis 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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