I am trying to set parameter values for solving a pyomo model using SCIP. When I run
opt = SolverFactory('scipampl')
solver_message = opt.solve(model, tee=True)
SCIP solves the model mich means that the setup of scipampl has worked. Yet I'm not sure what's the right way to add options. I found that this was adressed here before and seems to have been integrated in this git commit.
I have tried setting options via
opt.options['limits/time'] = 100
which results in the error
.../scipoptsuite-6.0.2/scip/src/scip/paramset.c:2569] ERROR: cannot open file <limits/time=100> for reading
limits/time=100: No such file or directory
Any suggestions how to set SCIP options properly in pyomo?
Edit:
While I would still be interested in how passing SCIP-options via pyomo is intended, I found a workaround which worked for me.
I set options via the SCIP interactive shell and saved the file (also via shell) as scip.set at the place from which the python script is run.
Related
I am struggling to find out how to install spatialite for geodjango on windows but I don't know what I am doing wrong. I follow the instructions as per the django page https://docs.djangoproject.com/en/4.0/ref/contrib/gis/install/spatialite/ but the tutorial doesn't say what to do after getting the binarys of spatialite
I have tried to put them everywhere but every time I get the same error:
Exception Value:
Unable to load the SpatiaLite library extension as specified in your SPATIALITE_LIBRARY_PATH setting
I tried to put the mod_spatialite.dll file everywhere and try to set to SPATIALITE_LIBRARY_PATH but it seems I can't get the solution
Any suggestions would be appriciated
Thanks
Extracting the binary into Python installation folder eg: C:\Users\huy\AppData\Local\Programs\Python\Python39.
Then add SPATIALITE_LIBRARY_PATH = "mod_spatialite". This works for me.
I have a TensorFlow model which I want to convert into a tflite model, which is going to be deployed on an ARM64 platform.
It happens to be that two operations of my model (RandomStandardNormal, Softplus) seem to require custom implementations. Due to execution time being not that important, I decided to go with a hybrid model that uses the extended runtime. I converted it via:
graph_def_file = './model/frozen_model.pb'
inputs = ['eval_inputs']
outputs = ['model/y']
converter = tf.lite.TFLiteConverter.from_frozen_graph(graph_def_file, inputs, outputs)
converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
tflite_file_name = 'vae_' + str(tf.__version__) + '.tflite'
tflite_model = converter.convert()
open(tflite_file_name, 'wb').write(tflite_model)
This worked and I ended up with a seemingly valid tflite model file. Whenever I try to load this model with an interpreter, I get an error (it does not matter if I use the Python or C++ API):
ERROR: Regular TensorFlow ops are not supported by this interpreter. Make sure you invoke the Flex delegate before inference.
ERROR: Node number 4 (FlexSoftplus) failed to prepare.
I have a hard time to find documentation on the tf website on how to invoke the Flex delegate for both APIs. I have stumbled across a header file ("tensorflow/lite/delegates/flex/delegate_data.h") which seems to be related to this issue, but including it in my C++ project yields another error:
In file included from /tensorflow/tensorflow/core/common_runtime/eager/context.h:28:0,
from /tensorflow/tensorflow/lite/delegates/flex/delegate_data.h:18,
from /tensorflow/tensorflow/lite/delegates/flex/delegate.h:19,
from demo.cpp:7:
/tensorflow/tensorflow/core/lib/core/status.h:23:10: fatal error: tensorflow/core/lib/core/error_codes.pb.h: No such file or directory
#include "tensorflow/core/lib/core/error_codes.pb.h"
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
By any chance, has anybody encountered and resolved this before? If you have an example snippet, please share the link!
When building TensorFlow Lite libraries using the bazel pipeline, the additional TensorFlow ops library can be included and enabled as follows:
Enable monolithic builds if necessary by adding the --config=monolithic build flag.
Add the TensorFlow ops delegate library dependency to the build dependencies: tensorflow/lite/delegates/flex:delegate.
Note that the necessary TfLiteDelegate will be installed automatically when creating the interpreter at runtime as long as the delegate is linked into the client library. It is not necessary to explicitly install the delegate instance as is typically required with other delegate types.
Python pip package
Python support is actively under development.
source: https://www.tensorflow.org/lite/guide/ops_select
According to https://www.tensorflow.org/lite/guide/ops_select#android_aar on 2019/9/25
Python support of 'select operators' is actively under development.
You can test the model in Android by using FlexDelegate.
I ran my model successfully in the same way.
e.g. https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/java/src/test/java/org/tensorflow/lite/InterpreterFlexTest.java
So, I'm working on a robotframework test project, and the goal is to run several test suites in parallel. For this purpose, pabot was chosen as the solution. I am trying to implement it, but with little success.
My issue is: after installing Pabot (which, I might say, I did by cloning the project and running "setup.py install", instead of using pip, since the corporate proxy I'm behind has proven an obstacle I can't overcome), I created a new directory in the project tree, moved some suites there, and ran:
pabot --processes 2 --outputdir pabot_results Login*.robot
Doing so results in the following error message:
2018-10-10 10:27:30.449000 [PID:9676] [0] EXECUTING Suites.LoginAdmin
2018-10-10 10:27:30.449000 PID:400 EXECUTING Suites.LoginUser
2018-10-10 10:27:30.777000 PID:400 FAILED Suites.LoginUser
2018-10-10 10:27:30.777000 [PID:9676] [0] FAILED Suites.LoginAdmin
WARN: No output files in "pabot_results\pabot_results"
Output:
[ ERROR ] Reading XML source '' failed: invalid mode ('rb') or filename
Try --help for usage information.
Elapsed time: 0 minutes 0.578 seconds
Upon inspecting the stderr file that was generated, I have this message:
Traceback (most recent call last):
File "C:\Python27\Lib\site-packages\robotframework-3.1a2.dev1-py2.7.egg\robot\running\runner.py", line 22, in
from .context import EXECUTION_CONTEXTS
ValueError: Attempted relative import in non-package
Apparently, this has to do with something from the runner.py script, which, if I'm not mistaken, came with the installation of robotframework. Since manually modifying that script does not seem to me the optimal solution, my question is, what am I missing here? Did I forget to do anything while setting this up? Or is this an issue of compatibility between versions?
This project is using Maven as the tool to manage dependencies. The version I am running is 3.5.4. I am using a Windows 10, 64bit system; I have Python 2.7.14, and Robot Framework 3.1a2.dev1. The Pabot version is 0.44. Obviously, I added C:\Python27 and C:\Python27\Scripts to the PATH environment variable.
Edit: I am also using robotframework-maven-plugin version 1.4.0.8, if that happens to be relevant.
Edit 2: added the error messages in text format.
I believe I've come across an issue similar when setting up parallel execution on my machine. Firstly I would confirm that pabot is installed using pip show robotframework-pabot.
Then you should define the directory your results are going to using -d.
I then modified the name of the -o to Output.xml to make it easy to identify.
This is a copy of the code I use. Runs optimally with 8 processes
pabot --processes 8 -d results -o Output.xml Tests
Seems that you stumbled on a bug in the prerelease version of robot framework (3.1a2.dev1).
Please install a release version of robot framework. For example 3.0.4.
Just in case anyone happens to stumble upon this issue in the future:
Since I can't use pip, and I tried a good deal of workarounds that eventually made things more unstable, I ended up saving my project and removing everything Python-related from my system, so as to allow me to install everything from scratch. In a Windows 10, 64bit system, I used:
Python 2.7.14
wxPython 2.8.12.1, win64, unicode, for py27
setuptools 40.2.0 (to allow me to use the easy_install command)
Robot Framework 3.0.4
robotremoteserver 1.1
Selenium2Library 3.0.0
and Pabot version 0.45.
I might add that, when installing the Selenium2Library the way I described above, it eventually tries to download some things from the pip repositories - which, if you have a proxy, will cause you trouble. I solved this problem by browsing https://pypi.org/simple/selenium/, manually downloading the 2.53.6 .tar.gz file, then extracting it and running setup.py install on the command line.
PS: Ideally, though, anyone should be able to use proxy settings from the command line (--proxy http://user:password#server:port) to get pip and then use it; however, for some reason, probably related to network security configurations that I didn't want to lose time with, this didn't work in my case.
I am new to pycharm and I am having trouble to run a simple file which is just to import pandas. I am having following issues:
Whenever, I open the project, "Scanning files to index" takes
forever.
I can see that in indexing, it is using packages/files from
Anaconda2 and that is why it takes forever for my program to run.
I have already tried to make certain folder "Excluded" as described here PyCharm 4.0.5 hangs on 'scanning files to index' background task
I have also deleted the "Caches" folder as described here Intelli J IDEA takes forever to update indices
Pycharm Layout
Pycharm indexes your python environment as well as your project folders. This is normal when first changing either of these. If it occurs more frequently:
View the end of the log that #Pavel mentioned in Help | Show Log in Explorer.
Replicate the command that is stalling the scan and fix any errors.
In my case the package scanning stalled with a vague [y/N] as shown
here. Searching the log I found the following conda.exe command:
Command line: C:\Users\<User>\anaconda3\Scripts\conda.exe list -p C:/Users/<User>/anaconda3/envs/py38 -e
Running this on the command line gave me a more detailed description of my environment error and how to fix it.
Does anyone have any experience with trying to install cairo for django using buildout?
It will install pycairo or py2cairo (the first is for python >3.0, the latest is for python 2.6, which I Am using)
I found 2 recipes to use for installation, both of them gives me errors, in buildout.cfg I have them as 2 parts (of course tried them seperately, not thogether).
The errors are totally different, but the result is the same: I can't get cairo installed using buildout.......
based on: http://pypi.python.org/pypi/tl.buildout_gtk/
[cairo_tl]
recipe = tl.buildout_gtk
#pycairo-url = http://cairographics.org/releases/py2cairo-1.10.0.tar.bz2
pycairo-url = http://cairographics.org/releases/py2cairo-1.8.10.tar.gz
pycairo-md5sum = http://cairographics.org/releases/py2cairo-1.8.10.tar.gz.md5
based on: https://bitbucket.org/lgs/yaco.recipe.pycairo
[cairo_yaco]
recipe = yaco.recipe.pycairo
find-links = http://pypi.python.org/pypi/yaco.recipe.pycairo/0.1.1
#find-links = http://pypi.python.org/pypi/yaco.recipe.pycairo/0.1
# pkg-config-path ?
Both need a local install of cairo (pycairo and py2cairo depend on this):
so use on the server:
sudo apt-get install libcairo2-dev
The part jusing tl.buildout_gtk:
Either using the py2cairo-1.10.0 or pycairo-1.8.2 gives me a MD5 checksum mismatch
(see for available releases: http://cairographics.org/releases/)
e.g:
Error: MD5 checksum mismatch downloading 'http://cairographics.org/releases/py2cairo-1.10.0.tar.bz2'
or:
Error: MD5 checksum mismatch downloading 'http://cairographics.org/releases/py2cairo-1.8.10.tar.gz'
The part jusing yaco:
I Am pretty sure this part is not configured correctly (pkg-config-path refering to the local cairo package), but I do not even get to that point.
It gives an error:
DistributionNotFound: zc.recipe.cmmi
However, this is installed in the eggs
By the way, I had the same kind of headache trying to install PIL by buildout, which I solved combining a lot of options and posts on the internet, together with a lot of time and trials and errors.
If anyone want to know how I finally got it working, just ask, and I will publish it.
(I consider myselve still a django starter, so I do not know for sure if anyone is interested in the solution)
PIL does not have all the options I found in cairo, so I started using cairo. On my local PC everything works fine (ubuntu desktop), on my server (Ubuntu server) I can't getting it to work......
Any other options for making drawings on the fly..... (like matplotlib...) let me know.
Pretty sure you have to give the real md5 hash to the pycairo-md5 option instead of a string with some url in it.
I would avoid specialized recipes like those two whenever possible. There are some cases when a specialized recipe really is needed, but those are cases where the dependency is so egregiously eccentric that no common build/install pattern is usable. I don't know pycairo so that may be the case.
But before assuming it is, try the following. Always try using an egg first by adding it to the eggs option of the relevant buildout part. If that doesn't work, update your question with those details, and then try adding a separate zc.recipe.egg part with build options that may help the distribution build successfully. If that doesn't work or the distribution uses a ./configure && make && make install (AKA "CMMI") build system, update your question with those details, and then try using zc.recipe.cmmi to build the package after which you can use the extra-paths option of your zc.recipe.egg part to include the relevant bits of the CMMI part (extra-paths = ${cmmi-part:location}/weird/path/to/python/modules).
This is the general dance for getting poorly/oddly behaved distributions. If this doesn't work, then a specialized recipe may be in order but in general there's a way to get it to work with the above and that will be much more maintainable.