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
Related
I am a beginner in bash programming. I recently learn how to install some tools using wget and make but I have been advised to use brew to install futur tools as it is installing all the dependencies required to use tool (if I have well understood).
For some tools, I could use it without any problems. But when I am trying to use it to instal bwa it is not working and I get this following message.
(base) ML21-0036:~ akurtis$ brew install bwa
Running `brew update --preinstall`...
==> Auto-updated Homebrew!
Updated 1 tap (homebrew/core).
==> Updated Formulae
Updated 10 formulae.
Error: bwa: no bottle available!
You can try to install from source with:
brew install --build-from-source bwa
Please note building from source is unsupported. You will encounter build
failures with some formulae. If you experience any issues please create pull
requests instead of asking for help on Homebrew's GitHub, Twitter or any other
official channels.
I tried also to install it from source but it was not working too.
How should I use brew to install bwa on my computer.
I am running under BigSur
I would like to upgrade my PCL to a newer version to fix a problem I have with QHull related to this issue.
I'm on ubuntu so I installed PCL with sudo apt install libpcl-dev but I can only get the version 1.10.1. How can I install a newer version ?
The problem is apparently fixed by this commit. It's in the tag 1.12.1.
I know that in theory I can use cmake and build my own PCL but from what I saw with the apt install, there is a huge amount of dependencies + I will also need this particular Qhull version that they mention in the issue and commit... I'm not sure I can make this work without the package manager. Any leads on this problem ?
Thanks !
In its simplest form, building and installing PCL goes likes this:
Clone the GitHub repo and cd inside it
Optionally checkout a git tag or stay on the master branch (default)
mkdir build && cd build
Run cmake with cmake ..
Build and install with make -j2 && sudo make install
For more information, see here: https://pcl.readthedocs.io/projects/tutorials/en/master/compiling_pcl_posix.html
When you previously had libpcl-dev installed, you can remove that package without (auto-)removing the dependencies, then you should have most if not all dependencies required for building from source already installed.
You didn't say which Ubuntu version you use, but judging from the version of libpcl-dev, I assume it is focal (20.04). The Qhull version installed there is fine, it already has a reentrant interface.
We need to install R-base version 3.5+ on an offline machine running SLES12.3
We have downloaded all the packages from the the SUSE r repo
http://download.opensuse.org/repositories/devel:/languages:/R:/released/openSUSE_12.3/x86_64/
while running zypper install on the packages there are additional dependencies that we are not able to find the relevant packages to download.
These include:
libtcl8.5.so()(64bit)
libgomp.so.l()(64bit)
But we are not able to find the dependency package that include these libraries.
What should be the correct approach for installing these libraries offline? where can we find these libraries?
Is there a better way for offline installing R-base ? we tried to follow the instructions on the cran rstudio page
The files you downloaded don't match the distribution you're running. SUSE Linux Enterprise (SLE) and openSUSE are similar in some ways, but these are really two separate distributions and you can not always mix binaries between the two. To install R on SLE Server 12.3, you should use the repository https://download.opensuse.org/repositories/devel:/languages:/R:/released/SLE_12/.
You can find out these URLs by looking at the right hand-side column at https://build.opensuse.org/project/show/devel:languages:R:released. Look for things called "SLE" there.
Install the Development Tools, according to this answer
zypper install --type pattern Basis-Devel
Download R source and install it
wget http://cran.univ-paris1.fr/src/base/R-3/R-3.5.0.tar.gz
tar zxf R-3.5.0.tar.gz
cd R-3.5.0
./configure --enable-R-shlib
make
make check
make install
Maybe there are still dependencies missing, which need to be installed with zypper (I don't have any Suse to try myself). With this method you have an "empty" R and you will install R packages one by one (with R CMD INSTALL). Maybe not the best answer for your need, but an answer.
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
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()