Error Installing Microsoft CNTK - cntk

During installation I received the following error:
This script will setup the CNTK prequisites and the CNTK Python
environment onto the machine. More help is given by calling 'get-help
.\install.ps1' in your powershell environment.
The script will analyse your machine and will determine which
components are required. The required components will be downloaded in
[C:\local\Scripts\windows\InstallCache] Repeated operation of this
script will reuse already downloaded components.
If required VS2012 Runtime and VS2013 Runtime will be installed
If required MSMPI will be installed
If required the standard Git tool will be installed
CNTK source will be cloned from Git into [c:\repos\CNTK]
Anaconda3 will be installed into [C:\local\Anaconda3-4.1.1-Windows-x86_64]
A CNTK-PY34 environment will be created in [C:\local\Anaconda3-4.1.1-Windows-x86_64\envs]
CNTK will be installed into the CNTK-PY34 environment
1 - I agree and want to continue Q - Quit the installation process
1 Determining Operations to perform. This will take a moment...
The following operations will be performed: * Setup/Update CNTK Wheel
* Clone CNTK from Github repository
Do you want to continue? (y/n) y Performing download operations
Download operations finished
Performing install operations Setup/Update of CNTK Wheel environment.
Please be patient.... You are using pip version 8.1.2, however version
9.0.0 is available. You should consider upgrading via the 'python -m pip install --upgrade pip' command. Cloning CNTK (branch v2.0.beta2.0)
repository....
Fatal error during script execution!
System.InvalidOperationException: This command cannot be run due to
the error: The system cannot find the file specifie d. at
System.Management.Automation.MshCommandRuntime.ThrowTerminatingError(ErrorRecord
errorRecord)
PS C:\local\Scripts\windows>
I'm not familiar with powershell, so I am not sure where to go from here.

Couple of questions. Are you using beta1 or beta2?
If you are using beta1, just a reminder that there is a beta2 now available with bug fixes and also some improvements to the install script, but I don't think this particular problem has been addressed!
It looks like the clone of the cntk git repository is failing.
Was Git installed on your machine? Is there a GIT.EXE in c:\Program Files\Git\bin\?
I think the install found an existing git.exe (in a different location), and is now trying to call it at the 'wrong' location.
The easiest workaround for you (if you have git installed), from a command prompt:
cd c:/
md \repos
cd repos
clone --branch v2.0.beta1.0 --recursive https://github.com/Microsoft/CNTK
If you have moved to beta2, replace the v2.0.beta1.0 with v2.0.beta2.0

Related

Installing / Building the newest version of Inkscape (v 1.2) on Linux Ubuntu 20.04

I am trying to install Inkscape 1.2beta on Linux Ubuntu 20.04. The website currently only offers an AppImage and a source tarball. Since I would like to access the newest features of Inkscape via the command line, I need to build and install the source tarball.
INSTALL.md states that I need all submodules and dependencies before install.
How do I find these dependencies to successfully build and install Inkscape?
This list should satisfy all required dependencies on Linux Ubuntu:
apt install
cmake
imagemagick
libdouble-conversion-dev
libgdl-3-dev
libagg-dev
libpotrace-dev
libboost-all-dev
libsoup2.4-dev
libgc-dev
libwpg-dev
poppler-utils
libpoppler-dev
libpoppler-glib-dev
libpoppler-private-dev
libvisio-dev libvisio-tools
libcdr-dev
libgtkmm-3.0-dev
libgspell-1-dev
libxslt-dev libxslt1-dev
libreadline6-dev
lib2geom-dev
lib2geom-dev is needed to solve error "<ieeefp.h> not found".
For building Inkscape:
Download source tarball for Inkscape v1.2 from inkscape.org and extract
cd <extracted inkscape directory>
mkdir build && cd build
cmake ..
make
make install
If you still get an error during cmake .., please comment below with the names of the missing modules in the error message.
The details on how to build Inkscape (and the dependencies) could be found in the repository itself, or Inkscape website (For completness, the steps are copied from the website here):
To obtain the latest source code, use the following command (downloads into a subdirectory of your current working directory called "inkscape" by default):
git clone --recurse-submodules https://gitlab.com/inkscape/inkscape.git
To update this code later, change into the download folder and use:
git pull --recurse-submodules && git submodule update
By default, git will download every branch and every commit. If you are on a slow machine, have limited disk space, or limited internet bandwidth, you can use shallow clone and single branch clone options to limit the amount of data it will download:
git clone --depth=1 --single-branch --recurse-submodules --shallow-submodule https://gitlab.com/inkscape/inkscape.git
Building Inkscape on Linux
Open a terminal at the root of the folder into which you downloaded the source code in the previous step.
Install build dependencies
Download and run the script to install everything required for compiling Inkscape (check script to see if your distribution is supported):
wget -v https://gitlab.com/inkscape/inkscape-ci-docker/-/raw/master/install_dependencies.sh -O install_dependencies.sh
bash install_dependencies.sh --recommended
Compile
To compile with CMake, do the following:
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=${PWD}/install_dir -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache
make -j8
make install
Notes:
Using ccache is optional but speeds up compilation.
The optional -j8 argument to make tells it to run 8 jobs in parallel. Feel free to adjust this to the number of hardware threads (physical cores) available on your computer.
The recommended -DCMAKE_INSTALL_PREFIX argument allows to specify a custom isolated installation location (in the example above install_dir/ inside the build folder). It avoids installation into system locations (where it could conflict with other versions of Inkscape) and allows running multiple versions of Inkscape in parallel. It will still use all the files (including the preferences.xml) that reside in the ~/.config/inkscape directory.
Run
Run it from the build directory:
install_dir/bin/inkscape

E tensorflow/stream_executor/cuda/cuda_driver.cc:351] failed call to cuInit: UNKNOWN ERROR (303)

I am using rasa 1.9.6 on ubuntu in Vmware I have been getting this error in both training as well as running the model. It allows training the model but I am unable to run it I need to run my Bot can someone please help
According to rasa forum, the origin of this issue is due to tensorflow and graphics card configuration. GPU’s do not typically provide an advantage for the Rasa models. This can be safely ignored
Installing nvidia-modprobe can solve this issue.
sudo apt install nvidia-modprobe
Other solutions you can try are :
Uninstall and install CUDA and cuDNN.
Install tensorflow-gpu.
Uninstall and install different Nvidia driver versions.
The problem also could be that only some /dev/nvidia* files are present before running Python with sudo, check using $ ls /dev/nvidia*, after running the Device Node verification script the /dev/nvidia-uvm file gets added.

IOT Edge V2 - Deploying and Debugging Python on the Edge Device

I've been following the tutorial at https://learn.microsoft.com/en-us/azure/iot-edge/tutorial-python-module
On my development machine, the python lint in Visual Studio Code is reporting the error: E0401:Unable to import 'iothub_client' on main.py.
I wonder if I'm missing a pre-requisite step, or is this just an advisory that can be ignored on the development side of things and it should be ok once deployed - or do I need to fix this error on the development machine first?
I've successfully run the previous tutorial and have tempSensor running. filterSensor seems to terminate and go into a "backoff" state once deployed, with an error of 1. I don't know how I can find out on the Edge-side why it is failing - how do I go about debugging this? Can I see print statement output anywhere, for example?
I'm developing on Mac, and deploying to a Beaglebone running Ubuntu 16.04 LTS.
For the Python lint issue, yes you are right that this information is just advisory. The cause for it showing up is that you did not install Azure IoT Python SDK which is a pip package on your development machine. You can safely ignore it because the SDK will be installed when building the module image (there is a RUN pip install -r requirements.txt step in the Dockerfile).
If you want to resolve this lint information, you can install the SDK on your development machine manually by running pip install -r requirements.txt in the root folder of the module.

Where is the workspace for tensorflow installed with anaconda?

I'm following the instruction here to build user written op library. But my tensorflow is installed with anaconda. On a step I should run the following command in terminal:
$ bazel build -c opt //tensorflow/core/user_ops:zero_out.so
But I got:
The 'build' command is only supported from within a workspace.
Tensorflow was installed by cloning it from the repository.
So where is the workspace??
A simple solution is to run touch WORKSPACE in the directory you are running the build command.
Bazel looks for an empty file named WORKSPACE in your workspace. You can also set the environment variable $WORKSPACE to another path if you don't want to use your current working directory.
There is a little more info here: https://bazel.build/versions/master/docs/tutorial/workspace.html

Tensorflow with gpu support installation error - the specified --crosstool_top is not a valid cc_toolchain_suite rule

I've been trying to install tensorflow with GPU support using these steps:
http://www.nvidia.com/object/gpu-accelerated-applications-tensorflow-installation.html
and also using:
http://thelazylog.com/install-tensorflow-with-gpu-support-on-sandbox-redhat/
This is the error message that I'm getting when I try to run the bazel build command for building the tensorflow pip package (with the --config-cuda flag set):
The specified --crosstool_top '//third_party/gpus/crosstool:crosstool' is not a valid cc_toolchain_suite rule.
What's strange is that if i remove the --config=cuda flag, I don't get the error message while building and I'm able to install tensorflow successfully - but without GPU support.
I experienced the same issue, using the nvidia instructions. What I did was to drop the git reset line in the instructions, and it works.
Details (from the error message):
Close, reopen terminal
Run git clone (again), and cd tensorflow
Run ./configure
Bazel build, etc
This may be unrelated, but I experienced an issue with the .whl line, the error message was that the wheel cannot be found or something along those lines. This is the "And finally install the TensorFlow pip package" section. To resolve it in my case, I typed in the terminal all the way to "..._pkg/tensorflow", and then pressed tab for auto-completion. The file name that popped up was significantly longer than that in the guide, but it worked. Also, if anyone face a numpy not installed message based on the nvidia instructions, replace the python-pip and dev with python-numpy and run that line again to install.
Configuration: Fresh Ubuntu 16.04, GTX970M, running driver 367.48 (from CUDA installation), CUDA 8.0, CuDNN 5.1
Full setup path:
Fresh Ubuntu, with downloads and 3rd party apps selected during installation.
Control panel => Software and updates => Other Software => Canonical ticked
Install CUDA using nvidia instructions in CUDA documentation, .deb format
CuDNN 5.1 installed, the rest from the nvidia link.
I hope everything works out for you!
(I'm sorry for the poor formatting)
I was going through same problem and recently found the solution. The problem is with the installation of Bazel which leads to this kind of error.
After installation of bazel from installer, make sure that you would give the correct path to ~./bashrc and also activate the path using
source "path-to-your-bin-directory-for-bazel"
Please change the git source version slightly as shown below
$ git clone https://github.com/tensorflow/tensorflow
$ cd tensorflow
// $ git reset --hard 70de76e
$ git reset --hard 287db3a
And please refer the below l
https://github.com/tensorflow/tensorflow/issues/4944
Also, zlib has been updated since this TF build. You need to check http://www.zlib.net/ to get the latest version and SHA-256, then update tensorflow/workspace.bzl with that information (lines 254-266 in this build). At this time, the correct version info would include the following:
url = "http://zlib.net/zlib-1.2.11.tar.gz",
sha256 = "c3e5e9fdd5004dcb542feda5ee4f0ff0744628baf8ed2dd5d66f8ca1197cb1a1",
strip_prefix = "zlib-1.2.11",