I had previously brought down 0.8 and want to use new version.
Is it ok to update local repo and 'build_all' or must I remove all the older docker images first?
This may be brute force, but this is what I ended up doing.
Caution, the docker command will take out all images so if you want to
preserve some of them you may want a more selective approach.
Sawtooth platform
Remove all docker images using this command docker rmi -f $(docker images -a -q)
Bring down the latest sawtooth compose file sawtooth-default.yaml
Execute compose docker-compose -f sawtooth-default.yaml up
Sawtooth repo development
Clone the latest repository
Go to the root directory of the repo cd ~\sawtooth-core
At a minimum do bin\build_all -l python
I am using java so I do a bin\build_all -l java as well
Access to individual CLI and dev languages tested out 100% as per the Hyperledger Sawtooth documentation
I'm getting an error while trying to connect raspberry running ubuntu mate to my Google Cloud SQL instance.
These are the step I did to install:
git clone https://github.com/GoogleCloudPlatform/cloudsql-proxy
cd cloudsql-proxy/
sudo sh download_proxy.sh
My instance is configured this way (I deleted some characters in the image and in the code):
I didn't set the network because I'll be using proxy
Then I download into the same folder my JSON key.
wget https://drive.google.com/file/d/my_key.json
And the start the proxy
sudo ./cloud_sql_proxy -instances=be - 21:us-central1:be =tcp:3306 \
-credential_file=./my_key.json &
But I'm getting the error:
pi#pi:~/cloudsql-proxy$ ./cloud_sql_proxy: 1: ./cloud_sql_proxy:
Syntax error: ")" unexpected
I've tried removing the .json and I was getting the same error before without credential, I think that the problem is in the setup.
My dir ls is:
Any help is appreciated :)
download_proxy.sh downloads the proxy compiled for the amd64 architecture of CPU (aka x86_64). Your raspberry Pi has a ARM CPU, so this binary cannot run on your machine.
Google does not provide pre-build ARM versions of the proxy. I don't even know if it is able to build on ARM CPU. If it is possible, this is how you must do it:
Install go, e.g. with apt-get install golang
Setup a GOPATH, as per https://github.com/golang/go/wiki/GOPATH
Run go get github.com/GoogleCloudPlatform/cloudsql-proxy/cmd/cloud_sql_proxy
Run the proxy with $GOPATH/cloud_sql_proxy -instances=...
Ok.
I'm sharing what I did to make it work, as David I don't know what version was I downloading.
I tried to avoid installing Go but it was the only way to get it installed.
sudo apt-get install golang-go
export GOPATH=$HOME/go
go get github.com/GoogleCloudPlatform/cloudsql-proxy/cmd/cloud_sql_proxy
cd $GOPATH/bin
wget your_key.json
sudo ./cloud_sql_proxy -instances=the_full_name_of_the_instance=tcp:3306 -credential_file=./your_key.json &
But I was getting a error because I already have mysql running localy in the same port
So now I'm using a unix soquet
sudo ./cloud_sql_proxy -instances=the_full_name_of_the_instance -credential_file=./your_key.json &
And then it's ready for connections :)
Thanks guys
I found issues with this when compiling SQL-proxy. I did, however, find the instructions here worked great on my raspberry pi 3. Have to make sure to remove all prior installations then reinstall it
wget https://storage.googleapis.com/golang/go1.9.linux-armv6l.tar.gz
sudo tar -C /usr/local -xzf go1.9.linux-armv6l.tar.gz
export PATH=$PATH:/usr/local/go/bin # put into ~/.profile`
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.
Similarly to this question ("How can I automatically rebuild a package with a different compiler?" on askubuntu.com), I would like to know how can I automate fetching source and compilation of a C program using Fedora build scripts using a specific, non-default compiler - in my case afl-gcc. I would definitely welcome an example of the pv program, but I would like the solution to work for other packages, like libreoffice as well, with minimal modifications. I would like to achieve something similar to aflize (which is for Debian only right now). I have heard of mock and it would be best if I could use it for that.
I do not know to do it automatically, but manually:
Prepare build environnement
$ rpmdev-setuptree
Download corresonding srpms
$ yumdownloader --source foo
Extract files from SRPMS
$ rpm -i foo*.src.rpm
Replace the compiler used
$ sed -i 's/make all/make CC=afl-gcc all/g' ~/rpmbuild/SPECS/foo.spec
setting CC var your corresponding compiler will do the jobs.
If you use cmake take a look to CMAKE_C_COMPILER
Rebuild
$ cd ~/rpmbuild/SPECS/
$ rpmbuild -ba foo.spec
Generated rpm files are located into ~/rpmbuild/RPMS
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)