Any hints on how doing this? I tried with the auto-install from a downloaded zip from this here, extracted here: OPENSHIFT_DATA_DIR/hg and executable location here: OPENSHIFT_DATA_DIR/jenkins/data/tools/Mercurial/mercurial-2.2.1/bin/hg
I'm doing something wrong for sure, I'm not Linux saavy. Jenkins says is unable to find mercurial executable.
Any help is more than welcomed.
Here's the answer from here:
Thanks for the email discussion.
Mercurial includes a README which explains a couple of modes of execution:
Basic install:
$ make # see install targets
$ make install # do a system-wide install
$ hg debuginstall # sanity-check setup
$ hg # see help
Running without installing:
$ make local # build for inplace usage
$ ./hg --version # should show the latest version
"make install" will not work as it attempts to do a system-wide install. The user on the gears will not have access to write to system files.
"make install-home" will not work either.
"make local" works and will install it in cwd such that running the following will should work just fine:
./hg --version
Mercurial Distributed SCM (version 2.2.1)
(see http://mercurial.selenic.com for more information)
Related
I am trying to install mysql workbench on a system without network. I downloaded the mysql-workbench-community, mysql-community-{server, client, common, libs} which were noted in the "Installing RPM Packages" section of MySQL Install Manual. It states that these are the standard rpm packages needed for a basic functional install of mysql community. So with that I downloaded all the rpm packages and attempted to manually install each using:
sudo rpm -ivh mysql-community-package-name.rpm
Unfortunately I keep getting dependency errors. I found this link to obtain all the dependencies for a package. So on my second attempt I ran the following:
Repoquery -R --resolve --recursive mysql-community-server | xargs -r yumdownloader
Which gave me about 100 rpm packages. I transferred them onto my machine and unfortunately more dependencies like mysql-connectors-community and mysql-=tools-community came up which were never documented or mentioned as dependencies with the script.
What am i doing wrong? Is there a way to download all the rpms and bundle them together as a custom RPM in the future? I see ubuntu has a apt-offline command mentioned here. Is there a similar method I can apply for redhat?
Update1:
I have an idea to create a container rhel7 instance, mounting /root/tmpkg and running this example. But is there another way I should consider?
I'd like to install programs with conda in one particular conda environment and to be able to use the associated commands from all conda environments.
My goal is to allow students to install Mercurial (plus few Mercurial extensions and related utilities like Meld and TortoiseHg) on any platforms (especially Windows) with one simple command (or few simple commands), and of course without compilation.
Of course the hg command should be available in the terminal from any conda environments (anaconda prompt on Windows). The Mercurial packages cannot be installed in the base environment because Mercurial still works better in Python 2.7 (anyway, it wouldn't be clean).
Now Mercurial and the extensions we need can be installed on all platforms with something like:
conda create -n py27_mercurial -c conda-forge python=2.7 mercurial dulwich ipaddress
conda activate py27_mercurial
pip install hg-evolve hg-git
Working a bit with conda-forge and a conda meta-package, it won't be difficult to do that with one very simple command. Moreover, it should not be difficult to create conda packages for Meld and TortoiseHg.
From this stage, the hg command is available when the environment is activated (and it is very simple to install other Mercurial extensions). To make it available from other environment (and in the base environment), one need to append the path of the directory containing hg to the environment variable PATH or on Unix to create a symbolic link (I don't know Windows enough to know if something similar would work). Both solutions are not straightforward and the commands are not platform independent.
I didn't find a command to do something like this in conda but sometimes conda experts are able to do impressive things! What would be an elegant solution to this issue?
It would also be nice to create icons somewhere (in the Anaconda launcher?) for the graphical applications (Meld and TortoiseHg). Is it possible?
Edit: Conda applications
I discovered that there is a way to specify in the meta.yaml file that a package is an application: https://docs.conda.io/projects/conda-build/en/latest/resources/define-metadata.html#app-section
It may help to solve the issue.
Edit after a first answer based on a bash function:
Of course, I'm looking for a solution involving very small work (and understanding) for the users and with cross-platform commands.
Note that for Linux and Bash, one can just do:
CONDA_APP_DIR=$HOME/.local/bin/bin-conda-app/
mkdir -p $CONDA_APP_DIR
echo -e "\nexport PATH=\$PATH:$CONDA_APP_DIR\n" >> ~/.bashrc
ln -s $(which hg) $CONDA_APP_DIR/hg
No need to activate/deactivate the environment each time hg is used...
Of course, such solutions dependent of the system and the shell are not satisfactory. It should be possible to do such things with cross-platform conda-like commands (see https://github.com/conda/conda/issues/8556), something like
conda config --add channels conda-forge
conda install conda-app
conda-app install mercurial
Now, I just have to implement conda-app 🙂
One can always create a shell function/alias and shove it in their shell's runtime configuration file. For example, for your use case, I'd add the following in my ~/.bashrc:
hg() {
(conda activate py27_mercurial
command hg "$#"
_hg_exit_code=$?
conda deactivate
exit $_hg_exit_code)
}
Then, regardless of which environment you are in, you always run hg from the environment it was installed in. To make sure that this function is loaded for you shell in a new session, one can always take a look at the output for: type -a hg
I do this one-time-setup for all the tools (some are custom compiled) and have an alias/shell function for each. This way I can happily switch b/w environments without having to worry much.
The solution https://stackoverflow.com/a/55900964/1779806 is buggy for scripts using command hg ... and too inefficient for this case (installation of a command-line application). See https://github.com/conda/conda/issues/8556#issuecomment-488703716
I created a tiny Python package conda-app (https://pypi.org/project/conda-app/) to improve this situation.
This should now works on Unix systems (with Bash and Fish):
conda activate base
conda config --add channels conda-forge
pip install conda-app
conda-app install mercurial
It should not be difficult to improve conda-app to also support Windows.
For the time being, Windows users can install Mercurial and important extensions by installing TortoiseHG.
I copied the commands (from these instructions: http://www.shogun-toolbox.org/install#ubuntu) into the terminal and they seem to have worked, but there is no documentation on how to make Octave find the libraries. I have tried modshogun and init_shogun but Octave cannot find them. I do have the libraries in usr/lib, and I have put that directory on PATH. I have even set usr/lib as my working directory in Octave and that did not help. As far as I have found, there is no Shogun documentation on what to do at this point.
I have also tried compiling Shogun from source, but configure couldn't find GCC. Apparently, this is a known problem with newer versions of GCC. I decided to ask for help with the former method because at least I have the libraries with that.
Edit: I am following the instructions here http://www.shogun-toolbox.org/install#manual-basics
When i do cd build and then "cmake -DINTERFACE_OCTAVE=ON" it tells me there is no cmakelists.txt. There is one in in the above folder, but when I go to that directory and do "cmake -DINTERFACE_OCTAVE=ON" again, it tells me "Shogun can only be built with GPL codes if the source files are in /home/derose/shogun/src/shogun/src/gpl. Please download or disable with LICENSE_GPL_SHOGUN=OFF."
However, when I add -LICENSE_GPL_SHOGUN=OFF as an option, i get the error "CMake Error: The source directory "/home/derose/shogun/src/shogun/-LICENSE_GPL_SHOGUN=OFF" does not exist."
You've linked to the Ubuntu install instructions. From there
These currently do contain the C++ library and Python bindings..
No word that this would include the GNU Octave binding. See below on the same page:
The native C++ interface is always included. The cmake options for building interfaces are -DINTERFACE_PYTHON=ON -DINTERFACE_R .. etc. For example, replace the cmake step above by cmake -DINTERFACE_PYTHON=ON...
So you have to grab the source and fire up cmake with something like -DINTERFACE_OCTAVE=ON
Steps to build the bleeding edge of shogun (the github repo) and the Octave interface:
git clone https://github.com/shogun-toolbox/shogun && cd shogun
git submodule update --init
mkdir build && cd build
cmake .. -DINTERFACE_OCTAVE=ON
make -j4
I am trying to setup a MySQL server using CentOS (No GUI) and I need to switch to OpenSSL instead of YaSSL in order to have access to the encryption tools.
The issues happen when runing the cmake. At first I got the error that cmake was not able to find boost, I fixed this adding the parameter -DWITH_BOOST.
The cmake line is as follows.
cmake . -DWITH_READLINE=ON -DWITH_SSL=system -DWITH_BOOST=/usr/local/src/mysql-5.7.20/boost/
After the adjustment I ran again the CMAKE the I got several errors.
SSL Error, cmake can not find the OpenSSL files. I checked if the library was installed, I also downloaded the tar.gz file and decompress it and pointed the cmake to the folder, none of this worked.
Can not find NUMA libraries, again I checked and it is installed, at this point I ran the system update to check for everything but this did not solve the issue.
Can not find the ncurses, the same thing, is on the system but for some reason cmake is not able to find those.
Can not fin libaio, I didn't have this one installed, I installed, ran cmake again, and again cmake was not able to find it.
I been looking around, trying to figure out all this issues, I've been joining information from different websites but still not able to figure out this.
Thanks ahead to everyone for the help.
You're facing the dependencies hell with MySQL. If you don't really need to compile from the sources, you still can install with the RPM which is much easier. The RPM method is described here : https://dev.mysql.com/doc/mysql-yum-repo-quick-guide/en/
As you're asking a ready-to-go install from the sources, this is what I just did and it worked, on a fresh CentOS 7.4 minimal, 2 vcpus 3Gb :
yum group install -y 'Development Tools'
yum install -y cmake ncurses-devel curl
curl -Ovk https://cdn.mysql.com/Downloads/MySQL-5.7/mysql-5.7.20.tar.gz
tar zxf mysql-5.7.20.tar.gz
cd mysql-5.7.20
cmake . -DDOWNLOAD_BOOST=1 -DWITH_BOOST=$HOME/boost -DENABLE_DOWNLOADS=1
make -j2
make install
After that you need to configure it, add the startup scripts, and of course secure it. Here are some additional docs :
http://howtolamp.com/lamp/mysql/5.6/installing/
https://dev.mysql.com/doc/refman/5.7/en/mysql-secure-installation.html
Perhaps try make clean; cmake clean; ldconfig then run your cmake command. Sometimes the system can't find the shared libraries, and ldconfig refreshes the library search path. This helped once when I was compiling something (emscripten?) which required a lot of libraries which I was installing as compilation errors arose.
The make clean; cmake clean will ensure that the compiler isn't looking at the old library search path when you recompile.
I know that I can install Cuda with the following:
wget http://developer.download.nvidia.com/compute/cuda/7_0/Prod/local_installers/cuda_7.0.28_linux.run
chmod +x cuda_7.0.28_linux.run
./cuda_7.0.28_linux.run -extract=`pwd`/nvidia_installers
cd nvidia_installers
sudo ./NVIDIA-Linux-x86_64-346.46.run
sudo modprobe nvidia
sudo ./cuda-linux64-rel-7.0.28-19326674.run
Just wondering if I can install Cuda without root?
Thanks,
Update The installation UI for 10.1 changed. The following works:
Deselect driver installation (pressing ENTERon it)
Change options -> root install path to a non-sudo directory.
Press A on the line marked with a + to access advanced options. Deselect create symbolic link, and change the toolkit install path.
Now installation should work without root permissions
Thank you very much for the hints in the question! I just want to complete it with an approach that worked for me, also inspired in this gist and that hopefully helps in situations where a valid driver is installed, and installing a more recent CUDA on Linux without root permissions is still needed.
TL;DR: Here are the steps to install CUDA9+CUDNN7 on Debian, and installing a pre-compiled version of TensorFlow1.4 on Python2.7 to test that everything works. Everything without root privileges and via terminal. Should also work for other CUDA, CUDNN, TensorFlow and Python versions on other Linux systems too.
INSTALLATION
Go to NVIDIA's official release web for CUDA (as for Nov. 2017, CUDA9 is out): https://developer.nvidia.com/cuda-downloads.
Under your Linux distro, select the runfile (local)option. Note that the sudo indication present in the installation instructions is deceiving, since it is possible to run this installer without root permissions. On a server, one easy way is to copy the <LINK> of the Download button and, in any location of your home directory, run wget <LINK>. It will download the <INSTALLER> file.
Run chmod +x <INSTALLER> to make it executable, and execute it ./<INSTALLER>.
accept the EULA, say no to driver installation, and enter a <CUDA> location under your home directory to install the toolkit and a <CUDASAMPLES> for the samples.
Not asked here but recommended: Download a compatible CUDNN file from the official web (you need to sign in). In my case, I downloaded the cudnn-9.0-linux-x64-v7.tgz, compatible with CUDA9 into the <CUDNN> folder. Uncompress it: tar -xzvf ....
Optional: compile the samples. cd <CUDASAMPLES> && make. There are some very nice examples there and a very good starting point to write some CUDA scripts of yourself.
(If you did 5.): Copy the required files from <CUDNN> into <CUDA>, and grant reading permission to user (not sure if needed):
cp -P <CUDNN>/cuda/include/cudnn.h <CUDA>/include/
cp -P <CUDNN>/cuda/lib64/libcudnn* <CUDA>/lib64
chmod a+r <CUDA>/include/cudnn.h <CUDA>/lib64/libcudnn*
Add the library to your environment. This is typically done adding this following two lines to your ~/.bashrc file (in this example, the <CUDA> directory was ~/cuda9/:
export PATH=<CUDA>/bin:$PATH
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<CUDA>/lib64/
FOR QUICK TESTING OR TENSORFLOW USERS
The quickest way to get a TensorFlow compatible with CUDA9 and CUDNN7 (and a very quick way to test this) is to download a precompiled wheel file and install it with pip install <WHEEL>. Most of the versions you need, can be found in mind's repo (thanks a lot guys). A minimal test that confirms that CUDNN is also working involves the use of tf.nn.conv2d:
import tensorflow as tf
x = tf.nn.conv2d(tf.ones([1,1,10,1]), tf.ones([1,5,1,1]), strides=[1, 1, 1, 1], padding='SAME')
with tf.Session() as sess:
sess.run(x) # this should output a tensor of shape (1,1,10,1) with [3,4,5,5,5,5,5,5,4,3]
In my case, the wheel I installed required Intel's MKL library, as explained here. Again, from terminal and without root users, this are the steps I followed to install the library and make TensorFlow find it (reference):
git clone https://github.com/01org/mkl-dnn.git
cd mkl-dnn/scripts && ./prepare_mkl.sh && cd ..
mkdir -p build && cd build
cmake -D CMAKE_INSTALL_PREFIX:PATH=<TARGET_DIR_IN_HOME> ..
make # this takes a while
make doc # do this optionally if you have doxygen
make test # also takes a while
make install # installs into <TARGET_DIR_IN_HOME>
add the following to your ~/.bashrc: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<TARGET_DIR_IN_HOME>/lib
Hope this helps!
Andres
You can install using conda with the following command.
conda install -c anaconda cudatoolkit
But you need to have prior accesss to the device(GPU).
EDIT : If you are finding error in anaconda repository then change the repository to conda-forge which is frequently updated.
conda install -c conda-forge cudatoolkit
You can install CUDA and compile programs, but you won't be able to run them for a lack of device access.