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
kandi X-RAY | hypothesis Summary
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
Quality
- hypothesis has 24 bugs (6 blocker, 0 critical, 17 major, 1 minor) and 494 code smells.
Security
- 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.
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.
Reuse
- 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.
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
<button .... type="button" (click)="removeHypothesis(rowIndex)">
Community Discussions
Trending Discussions on hypothesis
QUESTION
Recommended way of measuring execution time in Tensorflow Federated
Asked 2021-Jun-15 at 13:49I 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 12:01There are multiple potential places to measure execution time, first might be defining very specifically what is the intended measurement.
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 theclient_updatefunction seems reasonable. The question correctly notes that this happens in parallel, summing all of these times would be akin to CPU time.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.
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.timestampcalls to the outer training loop. This would be wrapping the call totrainer.next()in the snippet on https://www.tensorflow.org/federated. This would be most similar to elapsed real time (wall clock time).
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install hypothesis
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.
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