I am trying to create a deb package for my qt project to install on my sama5d3. I am using Ubuntu 14.04 64bit. have managed to create it for armhf. but when I try to install it on the board it fail with "incompatible architecture".
so I search for the architecture and find it is armv7ahf-vfp. how can I build a package for that architecture?
ok I found how to build for armv7ahf-vfp.. just run the poky environment setup script :
source /environment-setup-cortexa5t2hf-vfp-neon-poky-linux-gnueabi
Related
I'm trying to run Gnat Studio on Ubuntu 22.04 but I get the following error:
/opt/gnatstudio/bin/gnatstudio_exe: error while loading shared libraries: libtinfo.so.5: cannot open shared object file: No such file or directory
I have installed it via the following steps:
Downloaded the "x86 GNU Linux (64 bits)" community edition and ran this
Ran /opt/GNAT/2021/doinstall
Ran /opt/gnatstudio/bin/gnatstudio and got the above error (sudo-running this yields the same error)
I'm wondering if this is down to 22.04 being a very recent release and some shared libraries are missing from the installation bundle?
Any pointers would be much appreciated.
Thanks
It seems you're missing libtinfo5. Just try to install it via
$ sudo apt install libtinfo5
I am trying to install fable running the command install.packages("fable"), as it says in https://cran.r-project.org/web/packages/fable/readme/README.html
however i am getting the following:
It says clearly that it is because i don't have llapack, lblas and lgfortran in my computer. But i am not sure how to procede here. I'm using Ubuntu 20.04.3 LTS
I'll appreciate any guidence.
To install from sources, you will need a GNU Fortran compiler installed. On Ubuntu this can be done with sudo apt install gfortran.
Alternatively, you can install a precompiled binary from the RStudio Package Manager - some guidance on how this can be done can be found here: https://packagemanager.rstudio.com/client/#/repos/1/overview
AWS deep learning AMIs come with mxnet 0.12.0 RC. Apparently this version has a bug that sets random initialization weights to 0. How to I remove the preinstalled mxnet and upgrade?
Logged in via SHH as ec2-user, I tried
cd src
sudo rm -rf mxnet
git clone --recursive github.com/apache/incubator-mxnet.git mxnet
but the R-package build fails. Do I have to make/compile the program prior to R-package? Because that also fails. This package installation only works with a nightmare of inconsistent LD library configurations.
Yes, you need to compile the MXNet program before building R package. The detailed installation guide is here: http://mxnet.incubator.apache.org/install/index.html
I build a rpm package for the cloudify-cli source code with tag 4.0.1 by running command "rpmbuild -bb build.spec". The rpm package was builded and installed successfully even though the rpm package is smaller than the rpm package downloaded from Cloudify.co.
I bootstrap a simple manager with the installed cloudify-cli.
During the bootstrapping process, the log requires me to be in a virtual environment to install plugin, so i go into a virtual environment and bootstrap a simple manager again. Then the log shows me an error was raised, like this:
error log
Can you help me to solve this problem please?
It looks like your RPM lacks some packages that are included in Cloudify RPMs
You should add to the RPM or install manually the following packages:
cloudify-rest-client
cloudify-dsl-parser
cloudify-plugins-common
cloudify-script-plugin
I had Tensorflow installed with Anaconda. Now I want use it in R and I need to reinstall Tensorflow, because the note here
NOTE: You should NOT install TensorFlow with Anaconda as there are
issues with the way Anaconda builds the python shared library that
prevent dynamic linking from R.
I already tried to uninstall from Anaconda and install with pip but its came to the same place in anaconda directory. Tesorflow is working from terminal but in R shows Error: Command failed (1)
Anybody can help me to how I can solve the problem? Should I uninstall anaconda and install Tensorflow using pip?
You have several options on what to do. Probably the cleanest one is to install a system-wide python (if not installed yet) and then create a virtual environment. This basically takes your system python binaries and moves them to its own compartment where everythign is isolated from the rest, incl. anaconda. Once you are inside an activated virtual environment you can install all the necessary Python appendages for TensorFlow. Once that is done, make sure you set up a correct environmental PATH for TensorFlow from where R can reach it:
Sys.setenv(TENSORFLOW_PYTHON="/path/to/virtualenv/python/binary")
devtools::install_github("rstudio/tensorflow")
Example of the path to where you installed the virtual environment project would be, I think, something like ~/minion/medvedi/venv_medvedi/bin/python.
This is no longer an issue, the documentation was updated too.
See here:
https://github.com/rstudio/tensorflow/commit/4e1e11d6ba2fe7efe1a03356f96172dbf8db365e
With the help of Keras, we can install the TensorFlow package in R.
install_keras()
library(keras)
devtools::install_github("rstudio/keras")
install_tensorflow(package_url = "https://pypi.python.org/packages/b8/d6/af3d52dd52150ec4a6ceb7788bfeb2f62ecb6aa2d1172211c4db39b349a2/tensorflow-1.3.0rc0-cp27-cp27mu-manylinux1_x86_64.whl#md5=1cf77a2360ae2e38dd3578618eacc03b")
library(tensorflow)
Keras is a high-level neural network API for deep learning from TensorFlow Google.
my suggestion is to install anaconda and create an environment called "r-reticulate".
you can do it using anaconda navigator or
reticulate::conda_create(envname = "r-reticulate")
then to check that env detected by reticulate, use reticulate::conda_python().it must return directory of python.exe for your env.
after that you can install tensorflow by install_tensorflow(). [not working in my case]
so I install the tesnorflow from CMD.
follow these steps:
open the cmd :]
activate the r-reticulate env using conda activate r-reticulate (you may need your directory to conda directory if you did not add conda to your PATH)
use : conda install -c anaconda tensorflow
now in R, you can use TensorFlow.
for installing Keras, you can use pip install Keras. [i tried install_keras() function after the installation of tensorflow, but it ruined my TensorFlow installation also]
Eventually I found the best and fast method to do it in R:
devtools::install_github("rstudio/keras")
library(keras)
install_keras(method = "conda")
install_keras(tensorflow = "gpu")
tensorflow::install_tensorflow()