I want to use the C++ kernel with Jupyter Notebook and lab on Windows 10. I have tried following the instructions at https://github.com/QuantStack/xeus-cling?fbclid=IwAR3EOC0yxcpe3hf0Lo82V1ioCZYQzagirnY-y4uVvWy6dOIpUL5TthHcx7M.
I am seeing the following output:
(base) C:\Users\Rogan>conda activate cling
(cling) C:\Users\Rogan>conda install xeus-cling -c QuantStack
Collecting package metadata (current_repodata.json): done
Solving environment: failed with current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed
PackagesNotFoundError: The following packages are not available from current channels:
- xeus-cling
Current channels:
---
To search for alternate channels that may provide the conda package you're
looking for, navigate to
https://anaconda.org
and the search bar at the top of the page is:
(cling) C:\Users\Rogan>
Can anyone tell me what the problem might be?
I would like to add Java, R, and C later on but am unable to even get C++ working.
cling is not available on windows environment, so you should install Linux on your windows. Download WSL , and try above codes on Linux kernel.
Related
I'd like to use caffe2 with GPU support. I succesfully installed caffe2 (Ubuntu 16.04, python2.7) with conda environment (command : conda install pytorch-nightly -c pytorch)
It is successfully installed (I checked it with the command: python2 -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure" and it says "Success")
However, when I check caffe2 GPU build (command : python2 -c 'from caffe2.python import workspace; print(workspace.NumCudaDevices())), it returns 0.
I already have cuda, cuDNN, nccl and I don't understand why caffe2 does not detect available GPU..
I guess you are going to implement Detectron (otherwise nobody wanna use this dumb Caffe2 these days)
I'm pretty sure that it is caused by the mismatch of the CUDA version and CuDNN. I got stacked by this problem for a while (you don't know which version is correct for the Caffe2), finally, I got two solutions at almost the same time. Both of them work for me.
At first, just update the Nvidia driver to the latest version. My version is updated to 410.78, you can simply update the driver by selecting the certain driver in the System settings-> Software and update-> Additional Drivers.
Don't forget to restart your PC.
Then, there are two ways to implement it.
Build the environment with a Docker.
It is simple and fast. You just install Docker (as well as nvidia-docker for GPU usage) and use this pre-implemented environment with this command:
sudo docker pull ylashin/detectron
sudo nvidia-docker run --rm -it ylashin/detectron
Then you can test your Caffe2 with that NumCudaDeivce command.
It works for me!
See the whole introduction here, thanks to the efforts:
Build a Detectron environment with Docker
If you have some problem with the Docker installation (especially for the nvidia-docker), you can just skip it to the next one.
Use Detectron2
The newest Detectron is published recently (actually three days ago!). We can now work on that one now which is supported by Pytorch.
Here is the Detectron2:
Detectron2 link
Just go to the lastest one, you can even distribute everything in the Google Colab, it's much easier.
Maybe someone else asked the same question too. But this question is difficult. I tried everything. The place I am stuck is with installing dependencies. Some of the dependencies are old and not easily available. But I managed to install them.
The problems lies here.. There are dependencies that need to get the build from their source code. I already installed Visual C++ Build and MSMPI. Also installed HDF5 for H5PY but it doesn't let me build old versions of H5PY. So, I tried installing the latest version of H5PY but still, I am stuck at errors like file not found. Some of the files which the build process cannot find are "h5py/h5f.pyx", "mpi_c", "mpi.h". Solving error for one missing file leads to other and so on..
On trying hard to solve such errors and installing one or the other package to do the same task, I am tired up.. Something I found, at last, was that "mpi_c" file was replaced with some other file in newer versions of MPI4PY. But my dependencies depend on older version. I tried installing an older version of MPI4PY but HDF5 won't let me install that giving other errors. At last, I quit the task with my whole day wasted after this.
So can someone here please provide a step by step guideline for installing Rasa Stack on Windows Machine?
Windows 10 with Python 3.7.. Let me know if I need to downgrade python as well.. It was my first time building some project from source with python on windows. Thanks...
Please try the below steps to install Rasa:
Install Conda
Create a virtual environment:
conda create -n myenv python=3.5
Activate the virtual environment
conda activate myenv
pip install rasa_nlu rasa_core
Currently, I am working on the Ns3 simulator and now trying to enable the pyviz visualizer. According to the doc, I have downloaded the three dependencies which are
py27-pygtk
py27-pygoocanvas
py27-pygraphviz
Now in order to use this, I still need to enable the python bindings which I used /usr/bin/python2.7 ./waf configure wanna to check what needs for enabling python bindings. The result shows that
Python Bindings : not enabled (PyBindGen version not correct and newer version could not be retrieved)
So I checked the Doc and downloaded PyBindGen (version 0.18.0). The output shows
Installed /Library/Python/2.7/site-packages/PyBindGen-0.18.0-py2.7.egg
Processing dependencies for PyBindGen==0.18.0
Finished processing dependencies for PyBindGen==0.18.0
After I ran the configuration check the results still showed that PyBindGen version not correct and newer version could not be retrieved
So I presume that is that because I installed the wrong version of PyBindGen? If so how can I get the suitable version for enabling Python Binding?
I would appreciate if there is someone who can help me figure it out. Many thanks.
S.
According to the Google Group
Here is the resolution(tested it worked):
follow the instruction
hg clone http://code.nsnam.org/ns-3-allinone
cd ns-3-allinone && ./download.py
This will solve the Python Binding problem
Updated: after downloading this version of ns3. Solving the python binding problem. Then there will be another problem after running
./waf configure
it will show the result like this:
PyViz visualizer: not enabled (Missing python modules: gtk, goocanvas, pygraphviz)
Even though I have installed all of the three dependencies. So after some researches I found that there has another questions post So there is a guy gave the guessing that
" Waf found the standard Python here (/usr/bin/python is the Apple path), and you installed the python libraries using MacPorts.
Most probably you'll need to configure Python to point to the MacPort-based Python, or it will not see what you installed."
So according to How to: Macports select python
here is the solution:
port select --list python
sudo port select --set python python27
Hope it will help anyone come afterwards to use this.
S.
I'm trying to use the GLPK solver with Pyomo. I have a working model that's been tested, but keep getting an error saying GLPK can't be found.
WARNING: Could not locate the 'glpsol' executable, which is required for solver 'glpk'
I've installed glpk sucessfully. I also added the directory to my path variable so the executed can be called globally. I tested this with glpsol --help from my command line, and see the help info printed.
The below thread says it should be working, but alas, it is not.
How do you install glpk-solver along with pyomo in Winpython
Any ideas?
This answer is late but I want to share the solution that worked for me.
solvername='glpk'
solverpath_folder='C:\\glpk\\w64' #does not need to be directly on c drive
solverpath_exe='C:\\glpk\\w64\\glpsol' #does not need to be directly on c drive
I used to do this:
sys.path.append(solverpath_folder)
solver=SolverFactory(solvername)
This works for the cbc solver in coin-or but it does not work for glpk. Then I tried something different:
solver=SolverFactory(solvername,executable=solverpath_exe)
This worked for both cbc and glpk. No idea why this works (I really didn't do anything else).
Version: Python 2.7 or Python 3.7 (tested both), glpk 4.65
You can install glpk solver using this command -
brew install glpk
Installing the glpk package worked for me. As I use Anaconda:
conda install -c conda-forge glpk
This was after already including the 'glpsol' exectuable's folder path in my PATH variables.
So it looks like the set path variable is not handled by your Python installation.
A normal Python installation is set up for a seperated "PYTHONPATH" environment variable to look up additional modules.
There is also the option to make an entry in the windows registry or (like you already mentioned) move the files to the Python home directory, which is recognized relative to your installation directory if "PYTHONHOME" is not set.
More information in the Python Documentary under 3.3.3.
https://docs.python.org/2/using/windows.html#finding-modules
I was having the same issue. I don't know if this is a solution but it definitely got the solver working.
After downloading the Windows installation. I copied all the files in the w64 folder and pasted them directly into my Python working directory.
After that my python code could locate the solver.
NOTE: this was for Python 3.4.3.4, Windows 8.1 64 bit
Reading the source code here suggests you try:
from pyutilib.services import register_executable, registered_executable
register_executable(name='glpsol')
maybe will it give a clue
I had the same issue on Windows 10 and it was down to glpk being installed in a different conda environment. Full steps for installing pyomo and glpk below.
Test the installation by running 'Repeated Solves' example from https://pyomo.readthedocs.io/en/latest/working_models.html
Instructions (run at an anaconda prompt)
conda create --name myenv
conda activate myenv
conda install -c conda-forge pyomo
conda install -c conda-forge pyomo.extras
conda install -c conda-forge glpk
Run spyder from myenv so that if finds everything
spyder activate myenv
Here is the relevant part where pyomo 6.2 searches for the glpsol executable
https://github.com/Pyomo/pyomo/blob/568c6595a56570c6ea69c3ae3198b73b9f473abd/pyomo/common/fileutils.py#L288
def _path():
return (os.environ.get('PATH','') or os.defpath).split(os.pathsep)
There are two options to solve the PATH problem:
Putting the executable in an available folder in PATH (recommended practice). The glpsol executable must be in one of the folders present in the PATH system environment variable. Use in your code print(os.environ['PATH']) to see the available folders and put it there.
Adding the folder to PATH at runtime. You can add it to the system PATH statically or use code to add it dynamically (only while your script is running):
GLPK_FOLDER_PATH = "path/to/glpk"
os.environ["PATH"] += os.pathsep + str(GLPK_FOLDER_PATH)
In my case, my Python project has a virtual environment .venv, and I have an installation process that pastes the files essential to the glpsol executable when I install the project inside the .venv/Scripts folder. Because that folder is added automatically to the system PATH when Python is called from the virtual environment, libraries like Pyomo can find it. And I don't have to remember to add the folder to PATH at runtime whenever I want to use Pyomo.
For anyone that has the same issue, I found a workaround (not a solution!). I copied all the glpk files into my C:/Python27 directory, and (Surprise!) Python can now find them.
I'll hold out for a real solution before accepting this one.
I am tearing my hair out trying to install Spatialite for GeoDjango!
I am already using Homebrew, it's generally easy and convenient so I initially tried to follow the Homebrew instructions for GeoDjango.
But this stops short of installing any database, i.e. Spatialite. The next step is to try and install Spatialite itself, but there are no Homebrew-specific instructions provided by Django docs.
I found this tutorial which looks perfect - a Homebrew and virtualenv-friendly install of Spatialite for GeoDjango.
But it doesn't work... it appears that my pysqlite is linked against the non-spatial-enabled version of SQLite that comes with OS X, rather than the Spatial-ised one I installed from Homebrew, I get this error when Django tried to connect to the db:
"The pysqlite library does not support C extension loading. Both SQLite and pysqlite must be configured to allow the loading of extensions to use SpatiaLite."
The author of pysqlite hasn't responded to my pleas for help on Github and I haven't found anything via Google.
So I went back to the drawing board and decided to follow the "Mac OS X-specific instructions" in the GeoDjango docs... by installing the various geo libs from the KyngChaos binary packages.
The docs say "Install the packages in the order they are listed above" but I found I couldn't install UnixImageIO without installing PROJ first. The link in the docs to download Spatialite binaries (http://www.gaia-gis.it/spatialite-2.3.1/binaries.html) is broken so I used the "Spatialite Tools v4.1" from KyngChaos instead.
Proceeding to the next step I get this error:
$ spatialite geodjango.db "SELECT InitSpatialMetaData();"
SQLite header and source version mismatch
2013-10-17 12:57:35 c78be6d786c19073b3a6730dfe3fb1be54f5657a
2013-09-03 17:11:13 7dd4968f235d6e1ca9547cda9cf3bd570e1609ef
Not really sure what's wrong at this point.
There is someone else here on SO who has gone the KyngChaos route and just ends up with the same "Both SQLite and pysqlite must be configured to allow the loading of extensions" error I got from the Homebrew route anyway.
I found this ticket #17756 for adding pyspatialite support to Django - pyspatialite is supposed to be an easier way to pip install everything but unfortunately it doesn't work either (see comments towards bottom of ticket).
I'm a bit reluctant to start trying to build everything from source by hand as it seems likely I'll just run into the same problems again, but spending hours Googling for info about cryptic compiler errors, magic flags and paths etc along the way.
I'm about ready to give up and just use Postgres/PostGIS.
I was able to get this working now, using the tip here:
https://github.com/ghaering/pysqlite/issues/60#issuecomment-50345210
I'm not sure if it was using the real paths that fixed it, or just the Homebrew kegs or underlying packages have been updated and now install cleanly. Still, it works now.
I reproduce below the steps I took:
brew update
brew install sqlite # 3.8.5
brew install libspatialite # 4.2.0
brew install spatialite-tools # 4.1.1
git clone https://github.com/ghaering/pysqlite.git
cd pysqlite
(where brew reported I had existing versions I unlinked them and installed the latest as commented above)
then edited setup.cfg to comment out #define=SQLITE_OMIT_LOAD_EXTENSION and specify the paths:
include_dirs=/usr/local/opt/sqlite/include
library_dirs=/usr/local/opt/sqlite/lib
activated the virtualenv where I want it installed, then
python setup.py build
python setup.py install
(build_static still fails with clang: error: no such file or directory: 'sqlite3.c')
(maybe I should have done pip install . as suggested in the github issue)
now the spatialite geodjango.db "SELECT InitSpatialMetaData();" succeeds, albeit with an ignorable error:
InitSpatiaMetaData() error:"table spatial_ref_sys already exists"
i.e. it's probably not even necessary to run that command
When I was istalling this i follow this instructions https://docs.djangoproject.com/en/dev/ref/contrib/gis/install/spatialite/#pysqlite2
pysqlite2
If you’ve decided to use a newer version of pysqlite2 instead of the sqlite3 Python stdlib module, then you need to make sure it can load external extensions (i.e. the required enable_load_extension method is available so SpatiaLite can be loaded).
This might involve building it yourself. For this, download pysqlite2 2.6, and untar:
$ wget https://pypi.python.org/packages/source/p/pysqlite/pysqlite-2.6.3.tar.gz
$ tar xzf pysqlite-2.6.3.tar.gz
$ cd pysqlite-2.6.3
Next, use a text editor (e.g., emacs or vi) to edit the setup.cfg file to look like the following:
[build_ext]
#define=
include_dirs=/usr/local/include
library_dirs=/usr/local/lib
libraries=sqlite3
#define=SQLITE_OMIT_LOAD_EXTENSION
I had the same error: SQLite header and source version mismatch.
For me it was enough to update libsqlite3-dev.
After that invoking $ spatialite geo.db "SELECT InitSpatialMetaData();" creates proper database.