[Help] Alternatives to AWS and Microsoft Azure

@DBAggie My trouble was having cygwin’s g++ in my system path, when I removed all instances of mingw except the on installed by conda, theano worked.

Which part are you stuck on? Have you looked through the documentation? http://deeplearning.net/software/theano/install_windows.html#install-windows

I mainly used these commands:
conda install mingw libpython
pip install theano
pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git bleeding edge installation
pip install keras

Can you show some screenshots of the errors?

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@herk The issue is there seems to be no issue. I am able to download Theano and run the setup with no errors but the module/library isnt being found by python. I have read through the documentation. I am going to attempt the install process again tomorrow and I will update with any new information. My problem may be due to not using the bleeding-edge install. I appreciate all of your help.

A 970m should be just fine for this course. The steps @herk shows above look great. You’ll also want to install visual studio 2013 community edition and the CUDA sdk, and you’ll need to add the appropriate path to your .theanorc .

I am almost positive I missed the PATH step @jeremy. Its been a hectic few days and I havent yet fixed the issue.

Setting up gpu support on windows is as simple as following these steps: https://lukassteindlblog.wordpress.com/2016/03/31/

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I’ve found another technology stack that’s a bit more recent that works for:

Windows 10 / Visual Studio 2015 / Python 2.7 / Anaconda 4.2.0 / Cuda 8.0.44 / MinGW-w64 5.4.0 / Theano 0.8.2 / Keras 1.1.0 / OpenBlas 0.2.14 (optional) / cuDNN 5.1 (conditional for convolution neural networks)

https://github.com/glyphx/dlwin/blob/master/README.md <- I mainly used these instructions, perhaps I will modify them to reflect a few of the idiosyncrasies I found.

http://ankivil.com/installing-keras-theano-and-dependencies-on-windows-10/ <-- this helped me solve a few problems with the first link.

Namely to modify %USERPROFILE%/.keras/keras.json so that keras uses theano instead of tensorflow:
keras.json

{
“floatx”: “float32”,
“epsilon”: 1e-07,
“image_dim_ordering”: “th”,
“backend”: “theano”
}

I also had memory issues, we’ll see if that goes away when I get my own TitanXP on weds, muhahaha! To resolve the memory problems I had to pass new parameters to THEANO_FLAGS. The author has lib.cnmem=0.8, I had to lower mine to .7 or .65 to avoid crashing or slowdown.

My environment: https://gist.github.com/glyphx/0dd774b25f5ad63fbcbe10a205992d1a

https://drive.google.com/file/d/0B7OZ3ORJZNOISE95SnIxeFRBa1U/view?usp=sharing

Somehow I matched the author’s speed results, his titan, vs my 980: https://gist.github.com/glyphx/74dfce1a1736b8d669614a5570db8f86

If anyone implements this and has questions feel free to ask, I think I have a decent working understanding now.

Now that I finally have my environment set and I understand a bit more python it’s time to re-watch the first lessons and implement!

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How about a 960M running on a Windows 10 notebook (32GB RAM, i5, 760GB SSD)?

Thanks - wg

Is there a way to set everything up without Anaconda?

I’m using virtualenv and manual install of python and I think I’m really close to getting everything to work. I am getting an error and I don’t know how to resolve it:

"WARNING (theano.configdefaults): g++ not available, if using conda: conda install m2w64-toolchain"

Like I said, I’d rather avoid Anaconda but I don’t have a clue as to how to resolve this.

Thanks

I tried @herk 's steps, I got a lot of errors. That’s why I sought a different approach, think it might have had something to do with 32/64 bits.

You have to link mingw and put it into your path so it can find g++.

You can find instructions on how to do that in the steps I listed above

I swear …

I checked my PATH right after I posted my issue and boom, there it was. I had fixed the link to mingw BUT I had to restart powershell for it to take effect.

Anyhow, thanks for the reply and the initial instructions. I was able to thus far get Theano operational without Anaconda … will work on Keras next.

Thanks again -wg

Yeah, the good ol’ reset your environment wasted like 1h of my total setup time, lol.

btw, how far have you gotten in the course running things on your Windows machine?

I got my AWS instance all setup but I’d love to reduce my expenses by doing as much development as possible on my notebook and then only deploying to AWS once things look good.

Not sure what other folks are doing but if you’re doing all the class work on your AWS instance, I can see that getting pricey depending on how many times you spin it up.

Well, no I haven’t gotten far, I just got the environment setup yesterday the way I wanted it. But, I don’t expect any problems now.

If you run into any other stuff, just lmk, I should be here all day.

Ok. All is working with keras now installed.

Questions:

1.Any idea what this warning is about and whether I should be concerend?

DEBUG: nvcc STDOUT nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be remove
d in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
mod.cu
Creating library C:/Users/<username>/AppData/Local/Theano/compiledir_Windows-10-10.0.14393-SP0-Intel64_Family_6_Model_9
4_Stepping_3_GenuineIntel-3.5.2-64/tmps2ta3n9j/m91973e5c136ea49268a916ff971b7377.lib and object C:/Users/<username>/AppDat
a/Local/Theano/compiledir_Windows-10-10.0.14393-SP0-Intel64_Family_6_Model_94_Stepping_3_GenuineIntel-3.5.2-64/tmps2ta3n
9j/m91973e5c136ea49268a916ff971b7377.exp

2.What are these warnings and is there a way to suppress them?

> DEBUG: nvcc STDOUT mod.cu
> Creating library C:/Users/<username>/AppData/Local/Theano/compiledir_Windows-10-10.0.14393-SP0-Intel64_Family_6_Model_9
> 4_Stepping_3_GenuineIntel-3.5.2-64/tmp6z22v7rz/m4b03c91158bcf78ba154a9d2acbfd1bc.lib and object C:/Users/<username>/AppDat
> a/Local/Theano/compiledir_Windows-10-10.0.14393-SP0-Intel64_Family_6_Model_94_Stepping_3_GenuineIntel-3.5.2-64/tmp6z22v7
> rz/m4b03c91158bcf78ba154a9d2acbfd1bc.exp

The first time I run python.\minst_cnn.py’ I get a million of these debug statements. I don’t know why and/or whether I should be concerned.


I didn’t install OpenBLAS and running against the 960M I see the follow time/epoch:

Using CPU: Forget it about (i.e., forever)
Using GPU: 25s
Using GPU + cuDNN: 9s

These aren’t as good as you are getting with graphics card but they seem pretty decent for the 960M running on an XPS laptop, no?

The author did mention one of the warnings which was coming from using a cuDNN that is above the recommended for Theano, however, the speed improved and accuracy stayed constant, so, seems all good.

As for the others, I have no idea whats going with the deprecated arch’s but it does say you can suppress the warning with a flag. I only got that I think on the first run. Sorry that it took me awhile to respond, I was newUser limited. :stuck_out_tongue:

As for your results they look pretty damn good for a 960m. better than I would’ve guessed.

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@DBAggie https://education.github.com/pack

I think this might be useful for you to be able to afford AWS for the purposes of this class. It comes with ~$150 of free AWS time.

Hi
Can you please provide the link for setting up without a GPU. Not finding that anywhere.

Cheers… Ange