Could anyone please tell if Scilab has a Server version so tasks may be executed by running Scilab from 1 central location?
It depends on your usecase.
If you have a high perfomance server that you want to perform the calculations, scilab has a scilab-cli mode which you could start remotely over ssh (as suggested here: https://stackoverflow.com/a/2732991/1149326). See description on --no-gui flag in documentation.
If you want a more interactive session running on a server, (so people don't have to install scilab locally), take a look at the efforts to make a scilab kernel that integrates into jupyter notebooks.
You can see an example Scilab Jupyter notebook here.
Related
I have looked at many resources. I just want a simple way to use the newest OpenCV with Matlab 2017a. MinGW doesn't work and a compiler but I can't seem to get Visual C++ working. I can use OpenCV in Visual Studio but what compiler does Matlab want from me and how do I obtain it?
There is a way to use opencv from matlab without compiling any mex files, and without using the computer vision toolbox.
You can call opencv through matlab via python commands.
1) Download and install the CPython distribution, not Anaconda
2) from the command prompt, install the opencv and other packages you want using the pip command > python -m pip install opencv-python
3) Follow the procedure in the matlab help for ensuring matlab knows where to look for the python installation
4) use the syntax py.cv2.function to call the opencv function
If you keep the data types as python data types in matlab while using the py.cv2 functions there isn't too much of a speed hit.
As #Miki mentions in the their comment, have you checked out this page on the MathWorks site? It has many resources, the main one is the Computer Vision System Toolbox OpenCV Interface. It comes with some examples which would be a good starting point. And if those aren't close to what you want, try another one of the community examples on the File Exchange and trying to make small changes to it.
For each version of MATLAB you can find the supported compilers in the documentation. You can also use -v flag as shown blow to see all the paths where MATLAB looks for a C++ compiler.
mex -setup -v
I'm developing a Jupyter Notebook for my team to use to catalogue and analyse some proprietary data. I'm ready to share it with the team for on-going execution and development. The team generally have Windows 10 workstations and are skilled engineers, though not data scientists. No one currently uses Jupyter.
I now realise I might have thoroughly misjudged Jupyter's ability to support this sort of working environment.
Option 1: Individual installations
This is the worst case scenario. Anyone that wants to run or modify the notebook needs to install Jupyter. Anaconda is probably the best way to go, but its a big, ugly, scary install. Worse, every user will have to install and manage additional libraries. Any notebook change that requires a kernel change will have to be manually applied to each installation.
Surely, being client-server, this is not the intention of Jupyter.
Option 2: One server, many clients
The obvious alternative is to host the Jupyter server on a network accessible computer and have all users connect to it with a browser. That way there's only one shared installation to manage and each user just needs a URL to access it.
But there's a gotcha - the server expects the notebook to be on its own file system! So every user will access the same notebook file. This makes version control very problematic - no one can check out their own copy of the notebook for independent edit and commit sessions. Instead, changes will overwrite the only copy, and commits/reverts/diffs will have to be done on the server (or by mounting the server's file system).
Option 3: Server in Docker image, each user runs a container
Docker to the rescue? That way we can build/maintain one server image (and even version control it) and each user only needs to have a Docker engine installed to instantiate the image (which is a friendly 8GB download!!). They connect to their own container which, with a bit of scripting trickery, will be pointing at their own copy of the notebook.
This option only took 20 hours to investigate before discovering that it fundamentally sucks. Working with the kernel is tricky with lots of new skills necessary. But more showstopping - nothing that shows a Qt window will work. The qtconsole we can do without, but part of our notebook shows a File Open dialog and the best way to do that is with a Qt Widget. With the server in a Docker Container expecting an X Windows environment, and the client in a Windows browser, the Widget cannot be shown.
The Qt issue was the last of many, many issues trying to get the Docker option running. Everything from matplotlib to path mapping, from os library calls to ipywidgets needed to be investigated, tweaked, Googled, chopped and changed to work. I'm fairly convinced that these dramas would be on-going.
Conclusion
There are lots of discussions around Jupyter version control. There's lots of options for read-only sharing. And there's even a project for runtime-building a Docker container to provide executable access to a notebook. But there is scant advice on using Jupyter in a team environment.
Given the endless complications when the server is not natively running on the same machine as the client, I'm starting to believe Option 1 is the only sane way to go. Before I go to my colleagues with the crappy news, are there any other suggestions?
Ended up having a fruitful discussion on the Jupyter Google Group and have confirmed that out-of-the-box, Jupyter does not support this sort of working environment. Indeed, crucially, Jupyter expects the server to have a single user.
The most promising DIY solution was firstly to deploy JupyterHub, for two reasons:
It launches a new server instance for each user, preventing any multi-user per server issues.
It prompts for users to identify themselves, allowing different actions to be taken depending on the user.
And secondly, have the server mount each user's file system (or equivalent network architecture), so it can point the user back to their own local files.
I have not implemented this strategy (making do with Option 1 for now!) but it certainly makes sense.
I installed Tesseract and its basic functionality is fine. But when I try following this instruction on language file generation, tesseract-dependent commands like wordlist2dawg are "not found" by the shell.
Q: How do I install Tesseract with all these commands available? It's my understanding that they should work once I installed Tesseract, but it isn't the case. I installed Tesseract via port install tesseract, might be that I missed something.
Q2: How do I actually train Tesseract? I know it's an opaque topic; most results I get online are 3 years old at best, and it's difficult to figure out the exact training mechanism.
You'll need to build the training tools and then follow the instructions in the page.
https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract#building-the-training-tools
I'm using this amazing IPython notebook. I'm very interested into parallel computing right now and would like to use MPI with IPython (and MPI4py). But I can't start a cluster with
ipcluster start -n 4
on Windows7. I just get back "failed to create process". If I use the notebook and start a cluster in the "Clusters" register it's all working fine. But with cmd (even with admin rights) I just get this message. Same with all attempts of using MPI (MPICH2). All path vars are set. Maybe this problem has no connection to Python at all...
I can't say anything about IPython's parallel features, but if you're having problems with MPI in Windows in general, I would offer these suggestions. I've had quite a few issues in the past in trying to get MPI working in Windows. The most convenient method for me in the past has been to use an OpenMPI Windows binary http://www.open-mpi.org/software/ompi/v1.6/. These are now only available in previous releases. And even then, you might have to try more than one before you find one that works. I don't know why, but the latest didn't work on my machine. The release before that one did, however. After this, you have to call mpicc and mpiexec from the Microsoft Visual Studio Command Prompt or it won't work (without a lot of other stuff).
After you have verified that MPI is working, you can try installing mpi4py separately and see if that works. In my experience, sometimes this has worked fine and sometimes I've had to wrestle with configurations. You might just try your luck with an unofficial, prepackaged binary (for example, http://www.lfd.uci.edu/~gohlke/pythonlibs/).
Hope this helps!
I there a way (plugin) in eclipse that can run (console) application over ssh (of course do a synchronize before that with something like rsync), and display results in standard console?
Try following:
best-sftp-plugin-eclipse
eclipse-sftp
eclipse-cvsssh2
eclipsesshconsole
Also take a look at: are-there-any-good-ssh-consoles-for-eclipse
Hope this helps.
You could also run Eclipse remotely.
It should be fairly easy: use ssh -Y server_ip to connect to it, execute eclipse & and it will have direct access to the filesystem.
Of course, this solution is only practical if you have a decent network connection to the remote PC since GUI processing will be handled by the other machine (not yours).
Anyway, someone asked a similar question here. Take a look at the answer.