I want to use caffe in Google colab for experiment purpose, and my understanding is that i need to setup and install caffe every time i open python notebook in every runtime. This is very hard and time consuming because caffe takes lot of time to install and sometimes fails to install.
So i wanted to know if i am doing something wrong or is that the way how colab runtime works?
I would like to know of any other alternative platform for free which i can use if i need to setup caffe every time i use colab. I want to use Alex net pre tained caffe model
And moreover i wasted last 2 complete days setting up caffe locally on windows 10 machine, which by the way still not working, i am really getting sick of this caffe.
On Colab you can install the pre-compiled Caffe using
!apt install caffe-cpu
For GPU runtime, you can use caffe-cuda as well.
See instruction here.
is that the way how colab runtime works?
Yes it is!
To have persistent data you have to save your notebook to your drive:
File/Save a copy in Drive
Related
I'm new in caffe and I have successfully installed caffe using make and trained several example caffe models. But I still have one question remained about its installation. Is it true that it can be installed by apt-get in ubuntu >= 17.04 mentioned in official website or conda install? I've tried these methods but get many errors when trying to build or train a model.
Did I miss some important operations after apt-get install caffe, or it just doesn't have a one-step way to install caffe? If it doesn't, I want to know the reason.
I'll appreciate it if someone can give your opinions about my questions.
I am trying to run a sql query with a 50 GB CSV file but my GPU Memory is of only 40GB. How can I do the processing?
Also I am only able to run blazingsql with the jupyter notebook available with their docker image, can anyone please help me how to install it locally?
As it is not being possible with the conda command available on their github.
One way to do this today is to use Dask-SQL. Because it's built on Dask, Dask-SQL inherits Dask's ability to handle larger-than-memory workloads.
The easiest way to install Dask-SQL and use GPUs is to create a conda environment or pull a Docker container using the RAPIDS release selector.
Mxnet is supposed to build and run, on CPU as well as on GPU, for multiple OSs including Windows.
I'm trying to build mxnet from source on Windows Server 2016 that has NVIDIA K80 GPU on it.
I followed all the instructions in https://mxnet.incubator.apache.org/get_started/windows_setup.html but not able to move past the point of building mxnet in Visual Studio 2013.
The error I'm seeing is
'mshadow::cuda::AddTakeGrad' : ambiguous call to overloaded function indexing_op.h
If I fix this generic call to AddTakeGrad to make it a specific call to mshadow::cuda::, then some other polymorphic function ends up with the same error and so on ...
I tried searching a lot to find if anyone was successful in building mxnet for windows (on both cpu mode and gpu mode) but couldn't find any.
Question: Has anyone been able to successfully build mxnet on Windows? If so, could you help with this error as well as any specific instructions to get it to build for both cpu mode as well as gpu mode?
These days it should be possible to just pip install.
I am beginner. I want install freebsd on VM and test open source world! I want to write a small function and to put it into kernel of OS, and then I want to use it in another program as a system function.
I just installed freebsd11 on VM. I know a command line environment! I should use a GUI? Where is kernel?
FreeBSD is a wonderful beast once you know all its capabilities, if you want to play with jails, ZFS and build your own kernels, probably this already build image can be a good starting point:
https://fabrik.red/post/test/ more info can be found here including scripts about how to create your own images/jails (custom kernel): https://github.com/fabrik-red/images
There is no GUI on the images, and maybe you will never need one, unless you want to use FreeBSD as a desktop, but FreeBSD shines more on headless systems (no GUI).
Update:
For GUI probably you can give a try to trueos.org or you can install your own Xorg, desk environments
Sounds like you should learn a bit more about what the operating system does before trying to modify the kernel. There are lots of resources about the unix kernels. See the developers handbook, https://www.freebsd.org/doc/en/books/developers-handbook/ or just google and you will find many resources.
I have recently updated to Fedora 21 Beta, and updated all packages. As the final release is scheduled early next month, I don't expect anything big to change. So I think the problem I am facing may well persist into the production release.
The problem is that when using some terminal based applications, the terminal responses very slowly. For example, say I edit a file with Vim in terminal, after a few minutes, it becomes increasingly difficult to use. Every time I hit a keystroke, the cursor will wait like a second to respond. Edit the same file (which is of only a dozen of lines) in GVim, everything works as smoothly as expected. Other terminal based applications shows the same slow response. However, using the terminal itself as an interactive shell has no problem at all.
I understand it is very hard to come by an answer to why it is experiencing this kind of slow response based only on my vague description. However, if anyone can point to where I should look for diagnostics of the problem, some log files etc., it will highly appreciated.
Below are some more details of the system.
The computer is a Dell T3500 workstation with Xeon W3550 CPU, NVIDIA Quadro 2000 GPU. I am using the latest NVIDIA binary driver (both the long live version 340.58 as well as the beta series 346.16 are tried). The open-source driver was uninstalled and its kernel module were blacklisted (lsmod | grep nouveau shows nothing, as expected). The desktop environment is GNOME Classic.
I believe the issue is with the Nvidia driver configuration. I know this question is old, but I was looking for a solution to the same problem in Fedora 22. I experienced this issue when I was using Fedora 21, but I mostly put up with it.
The solution for me was to uncheck Sync to VBlank in the Nvidia X Server settings.
Are you running in a VM whose storage grows as you need it? Perhaps the problem is that the VM is taking time to allocate space as your storage needs grow. If that's the case, after a while you won't have the problem anymore, as your virtual disk will have grown as big as it needs to be.
As for diagnostics, try running "top", look for paging activity and resident set size.
Maybe something has a memory leak. It might not be in the terminal, but in one of its dependencies.