NMAKE : fatal error U1077: '"C:\Program Files (x86)\Microsoft Visual Studio 11.0\VC\bin\nmake.exe"' : return code '0x2'įirst try this in your Windows cmd window: pip3 install pyqt5 Microsoft (R) Program Maintenance Utility Version 7.1Ĭopyright (C) Microsoft Corporation. I added C:\Program Files (x86)\Microsoft Visual Studio 11.0\VC\bin (contains nmake.exe) to PATH and I got this error: Querying qmake about your Qt installation.Ĭ:\Qt\Qt5.0.2\5.0.2\msvc2012_64\bin\qmake.exe -spec win32-msvc2008 -o qtdetail.mk qtdetail.pro Ou externo, um programa operável ou um arquivo em lotes.
'nmake' não é reconhecido como um comando interno The -verbose flag to see more detail about the problem.Īnd when I execute the command python configure.py -verbose: Querying qmake about your Qt installation.Ĭ:\Qt\Qt5.0.2\5.0.2\msvc2012_64\bin\qmake.exe -spec win32-msvc2008 -o qtdetail.m
INSTALL PYQT5 WINDOWS 10 DOWNLOAD
I installed Qt 5.0.2 for Windows 64-bit (VS 2012, 500 MB) from the Qt Download page and now I have this error: Querying qmake about your Qt installation.ĭetermining the details of your Qt installation.Įrror: Failed to determine the detail of your Qt installation. I get this error: Error: Make sure you have a working Qt qmake on your PATH. : Cannot set tracer on a started CamelContextĪt .(AbstractCamelContext.java:4601) ~Īt .TargetAtomConfig.reloadRoutes(TargetAtomConfig.java:54) ~Īt .TargetAtomConfig$$FastClassBySpringCGLIB$$7b88810d.invoke() ~Īt .MethodProxy.invoke(MethodProxy.java:218) ~Īt .CglibAopProxy$CglibMethodInvocation.invokeJoinpoint(CglibAopProxy.java:771) ~Īt .ReflectiveMethodInvocation.proceed(ReflectiveMethodInvocation.java:163) ~Īt .CglibAopProxy$CglibMethodInvocation.proceed(CglibAopProxy.java:749) ~Īt .MethodInvocationProceedingJoinPoint.proceed(MethodInvocationProceedingJoinPoint.When I try installing the PyQt5 on Windows using the command python configure.py 17:04:18.540 ERROR 10264 - o.s.boot.SpringApplication : Application run failed I start to think the above is a misusage of Webflux reactive redis. If only one lettuce-epool thread is used, then performance would be very bad. I think first query redis, and then call a handler on returned data is an ordinary usage for webflux. (I printed for every ai function, and they all have the same lettuce-epoll-thread number) Then I checked log, it shows only one lettuce-epoll-thread is used for the whole redisOps.get() to return part, which is a heavy CPU calculation. Given a 32 CPU server, very small part of CPU is used, 1 shows a lettuce-epoll-thread has a high CPU consumption like 90%. In a performance test, the above works fine given a 4 CPU server, like 380% is used.
Return Mono.just(new RecognitionResponse(r1,r2)) Return redisOps.put("aLib"， "field", r1.getVec()).map(b -> new RecognitionResponse(r1,r2)) R r2 = ai.calculationSimilar(tuple.getT2(), vec) R r1 = ai.calculationSimilar(tuple.getT1(), vec) Mono bLib = redisOps.get("bLib", "field").switchIfEmpty(.a default matrix.) Mono aLib= redisOps.get("aLib", "field").switchIfEmpty(.a default matrix.)
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