Can any body explain the step by step guide to install the Google syntaxtnet framework and the relative dependencies on windows 7 on 64 bit ?
Regards
samir
The best way to use SyntaxNet in Windows is probably using Docker.
Install Docker
Pull the SyntaxNet container:
docker pull tensorflow/syntaxnet
Run the container:
docker run --rm -ti -p 8888:8888 tensorflow/syntaxnet
It will immediately fire up a Jupyter Notebook. Copy the link and paste it in the browser.
Open a Jupyter Notebook and navigate to this directory:
cd ../../syntaxnet/
Now run demo.sh
'Bob bought a pizza.' | syntaxnet/demo.sh
Related
I'm trying to run the Pix2Pix tutorial for Tensorflow. I'm using the official docker container for this. This is how I start my container:
docker run --gpus all -it -p 8888:8888 --rm -v $PWD:/tf -w /tmp tensorflow/tensorflow:latest-gpu-py3-jupyter
I'm not able to get pass by this cell
generator = Generator()
tf.keras.utils.plot_model(generator, show_shapes=True, dpi=64)
# output -> Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.
I have tried also installing the pydot and graphviz using pip and also apt-get. Even if this libraries are installed I get the same error.
I had same problem and follow this link
In short:
run these command on command prompt.
pip install pydot
pip install graphviz
From website, download and install graphviz software
Note: in install time, check "add to system path" option to add bin
folder to path variable otherwise you should do it manually. restart
your windows
I have installed the Docker Tool on my Windows 10 Pro machine.
Have pulled below images from Docker Hub:
docker pull elgalu/selenium
docker pull dosel/zalenium
When I am trying to start zalenium with the below command:
"docker run --rm -ti --name zalenium -p 4444:4444 -v /var/run/docker.sock:/var/run/docker.sock -v /c/Users/<username>:/home/seluser/videos --privileged dosel/zalenium start"
I am getting error:
C:\Program Files\Docker\Docker\Resources\bin\docker.exe: Error
response from daemon: Windows does not support privileged mode.
I want to start the zalenium with the above command but I could not able to do it, because of this error.
Can anyone assist me on this, please?
Unfortunately you did not provide enough information. Your Windows version, Docker for Windows version, how did you install it, etc.
Officially --priviledged flag is not supported as on the Github thread is already mentioned:
there is no support for privileged containers on Windows in the platform.
It's also confirmed in this thread.
In addition, you might check this thread. It's the same case.
I had the same problem. Update on the newest docker version, fixed the issue.
I have some questions about cudnn and tensorflow-gpu docker image. here is what I did on my computer:
I installed docker-CE
I installed nvidia-docker
I pulled tensorflow:latest-gpu-py3-jupyter image from docker hub.
I create one container with the following command:
sudo docker create -ti --rumtime=nvidia -p 21001:22 -v
/home/project:/project tensorflow:latest-gpu-py3-jupyter /bin/bash
I start and get into the container, then I train my model which is built using Keras. but I get one error message---'Segmentation fault (core dumped)'
It seems that the cudnn is not installed in my container. Does anybody know why it happened? Does tensorflow image does not include the cudnn library?
I need to install a library that is only compatible with Python 3.5. Is there a way to change the Python version in Colaboratory from 3.6 to 3.5?
The only way to vary the Python 3 version is to connect to a local runtime.
You cannot directly change the environment for the notebook.
After hours of exploration, I found a solution:
Initialize a Ngork server in the Colaboratory notebook.
connect to the Ngork server from a local terminal using SSH (or use any editor which supports SSH connections)
Install the required Python version using the terminal.
Install virtualenv.
Create a virtual environment by specifying the Python version installed.
Activate the environment.
Work in that environment from the terminal directly.
Check out Free!! GPUs on your local machine which provides to get detailed description on how to follow the steps.
There is a way to use any version of python you want 3.5 or 3.8 in this example, without having to run a kernel locally or going through an ngrok proxy.
Download the colab notebook. Open a text editor to change the kernel specification to:
"kernelspec": {
"name": "py38",
"display_name": "Python 3.8"
}
This is the same trick as the one used with Javascript, Java, and Golang.
Then upload the edited notebook to Google Drive. Open the notebook in Google Colab. It cannot find the py38 kernel, so it use normal python3 kernel.
You need to install a python 3.8, the google-colab package and the ipykernel under the name you defined above: "py38":
!wget -O mini.sh https://repo.anaconda.com/miniconda/Miniconda3-py38_4.8.2-Linux-x86_64.sh
!chmod +x mini.sh
!bash ./mini.sh -b -f -p /usr/local
!conda install -q -y jupyter
!conda install -q -y google-colab -c conda-forge
!python -m ipykernel install --name "py38" --user
Reload the page, and voilà, you can test the version is correct:
import sys
print("User Current Version:-", sys.version)
A working example can be found there.
I installed Moodle (eLearning PHP based app, but it could be any app) locally on Ubuntu and would like to package it as Docker image/container. There were whole bunch of installations and configurations done. I'd like to package all that so that I can deploy to some Docker enabled hosting service, such as Digital Ocean or AWS.
How do I create Docker image?
Do I need to handle networking, ports and Apache configuration for production deployment?
There ara a lot of Moodle images in dockerhub. just use one of them
The process to create docker images is well documented on Docker's documentation site. See: Build your own images
The idea is simple: You inherit/extend an existing image and make additions to it. This is done in a provisioning file called Dockerfile
Dockerfile Example:
FROM debian:8.4
MAINTAINER John Doe (j.doe#example.com)
# update aptitude
RUN apt-get clean && apt-get update
# utilities
RUN apt-get -y install vim git php5.6 apache2
In the example above I extend a Debian image, update aptitude and install a series of packages.
A full list of commands available in Dockerfiles is available at https://docs.docker.com/engine/reference/builder/
Once your Dockerfile is ready you can build the image using the following command:
docker build -t debian/enhanced:8.4 /path/to/Dockerfile