f1 | writing load test scenarios in Golang | Testing library
kandi X-RAY | f1 Summary
kandi X-RAY | f1 Summary
f1 is a flexible load testing framework using the go language for test scenarios. This allows test scenarios to be developed as code, utilising full development principles such as test driven development. Test scenarios with multiple stages and multiple modes are ideally suited to this environment. At Form3, many of our test scenarios using this framework combine REST API calls with asynchronous notifications from message queues. To achieve this, we need to have a worker pool listening to messages on the queue and distribute them to the appropriate instance of an active test run. We use this with thousands of concurrent test iterations in tests covering millions of iterations and running for multiple days.
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Top functions reviewed by kandi - BETA
- chartCmdExecute returns a function that executes the chart command .
- GuussianRate returns a new api . Builder
- runCmdExecute is a wrapper around runCmdExecute
- newStagesWorker returns a new WorkTriggerer .
- PumpRate creates a new API .
- CalculateRampRate calculates a rate based on the given parameters .
- StagedRate returns an API .
- Instance returns a new Metrics instance .
- Cmd returns the cobra command for the given Scenarios
- NewIterationWorker returns a WorkTriggerer .
f1 Key Features
f1 Examples and Code Snippets
Lines of Code : 42License : Permissive (Apache-2.0)
Copy// ScenarioFn initialises a scenario and returns the iteration function (RunFn) to be invoked for every iteration
// of the tests.
type ScenarioFn func(t *T) RunFn
// RunFn performs a single iteration of the scenario. 't' may be used for asserting
/ Community Discussions
Trending Discussions on f1
QUESTION
I'm trying to install eth-brownie using 'pipx install eth-brownie' but I get an error saying
...ANSWER
Answered 2022-Jan-02 at 09:59I used pip install eth-brownie and it worked fine, I didnt need to downgrade. Im new to this maybe I could be wrong but it worked fine with me.
QUESTION
Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/
This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things?
I fit the sequential model with model.fit, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below:
...ANSWER
Answered 2021-Aug-19 at 03:49This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference
update to
QUESTION
I stumbled over the following piece of code. The "DerivedFoo" case produces different results on MSVC than on clang or gcc. Namely, clang 13 and gcc 11.2 call the copy constructor of Foo while MSVC v19.29 calls the templated constructor. I am using C++17.
Considering the non-derived case ("Foo") where all compilers agree to call the templated constructor, I think that this is a bug in clang and gcc and that MSVC is correct? Or am I interpreting things wrong and clang/gcc are correct? Can anyone shed some light on what might be going on?
Code (https://godbolt.org/z/bbjasrraj):
...ANSWER
Answered 2022-Feb-06 at 21:41It is correct that the constructor template is generally a better match for the constructor call with argument of type DerivedFoo& or Foo& than the copy constructors are, since it doesn't require a const conversion.
However, [over.match.funcs.general]/8 essentially (almost) says, in more general wording, that an inherited constructor that would have the form of a move or copy constructor is excluded from overload resolution, even if it is instantiated from a constructor template. Therefore the template constructor will not be considered.
Therefore the implicit copy constructor of DerivedFoo will be chosen by overload resolution for
QUESTION
I'm running Kafka schema registry version 5.5.2, and trying to register a schema that contains a reference to another schema. I managed to do this when the referenced schema was in the same package with the referencing schema, with this curl command:
ANSWER
Answered 2022-Feb-02 at 10:55First you should registrer your other proto to the schema registry.
Create a json (named other-proto.json) file with following syntax:
QUESTION
The following code compiles without warnings in Visual Studio 2019 msvc x64:
...ANSWER
Answered 2022-Jan-18 at 08:13Does this mean that I should have written:
QUESTION
I borrowed the R code from the link and produced the following graph:
Using the same idea, I tried with my data as follows:
...ANSWER
Answered 2021-Dec-27 at 22:55You can do calculations within a function for the x and y values to construct the ggplot which extends the circle all the way round and gives labels correct heights.
I've adapted a function to work with other datasets. This takes a dataset in a tidy format, with:
- a 'year' column
- one row per 'event'
- a grouping variable (such as country)
I've used Nobel laurate data from here as an example dataset to show the function in practice. Data setup:
QUESTION
I need to create a logger facility that outputs from different places of code to the same or different files depending on what the user provides. It should recreate a file for logging if it is not opened. But it must append to an already opened file.
This naive way such as
...ANSWER
Answered 2021-Dec-13 at 05:54So here is a simple Linux specific code that checks whether a specified target file is open by the current process (using --std=c++17 for dir listing but any way can be used of course).
QUESTION
I'm trying to speed up a piece of code convolving a 1D array (filter) over each column of a 2D array. Somehow, when I run it with numba's njit, I get a 7x slow down. My thoughts:
- Maybe column indexing is slowing it down, but switching to row indexing didn't affect performance
- Maybe slice indexing the results of the convolution is slow, but removing it didn't change anything
- I've checked that numba understands all the types properly
(tested on Windows 10, python 3.9.4 from conda, numpy 1.12.2, numba 0.53.1)
Can anyone tell me why this code is slow?
...ANSWER
Answered 2021-Dec-11 at 04:14The problem comes from the Numba implementation of np.convolve. This is a known issue. It turns out that the current Numba implementation is much slower than the one of Numpy (version <=0.54.1 tested on Windows).
On one hand, the Numpy implementation call correlate which itself performs a dot product that should be implemented by the fast BLAS library available on your system. On the other hand, the Numba implementation calls _get_inner_prod which use np.dot that should also use the same BLAS library (assuming a BLAS is detected which should be the case)...
That being said, there are multiple issues related to the dot product:
First of all, if the internal variable _HAVE_BLAS of numba/np/arraymath.py is manually disabled, Numba use a fallback implementation of the dot product supposed to be significantly slower. However, it turns out that using the fallback dot product implementation used by np.convolve result in a 5 times faster execution than with the BLAS wrapper on my machine! Using additionally the parameter fastmath=True in the njit Numba decorator results in an overall 8.7 times faster execution! Here is the testing code:
QUESTION
#include
int main()
{
auto f1 = [](auto&) mutable {};
static_assert(std::is_invocable_v); // ok
auto const f2 = [](auto&) {};
static_assert(std::is_invocable_v); // ok
auto const f3 = [](auto&) mutable {};
static_assert(std::is_invocable_v); // failed
}
ANSWER
Answered 2021-Dec-10 at 19:09You get an error for this for the very same reason:
QUESTION
I am trying code from this page. I ran up to the part LR (tf-idf) and got the similar results
After that I decided to try GridSearchCV. My questions below:
1)
...ANSWER
Answered 2021-Dec-09 at 23:12You end up with the error with precision because some of your penalization is too strong for this model, if you check the results, you get 0 for f1 score when C = 0.001 and C = 0.01
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