So gonna give machine learning a try on AWS. Ultimately, I think this is not economically viable, but since I’m just screwing around, whatever.
I searched for a prebuilt AMI ami-180ad670 called gpu_theano or something. Picked the first one for no reason except it seemed reasonable.
I picked a spot instance with .30$ per hour as max rate. g2.2xlarge
ran some script inside called check_theano.py. Theano appears to be working on the gpu. (theano )
Let’s see if we can get this puppy running
Error: Command ‘[‘/home/ubuntu/neural-doodle/pyvenv/bin/python3’, ‘-Im’, ‘ensurepip’, ‘–upgrade’, ‘–default-pip’]’ returned non-zero exit status 1
Okay. Forget doing the virtual env stuff. Not worth it on a burner computer.
sudo apt-get install python3-dev
sudo apt-get install python3-pip
sudo python3 -m pip install numpy
sudo python3 -m pip install scipy
Why aren’t these in requirements.txt
I wonder if I could use python2. scipy and numpy are probably already installed.
sudo python3 -m pip install –upgrade setuptools
sudo python3 -m pip install –upgrade cython
sudo apt-get update
<span class="pln">sudo apt</span><span class="pun">-</span><span class="pln">get build</span><span class="pun">-</span><span class="pln">dep matplotlib</span>
sudo apt-get install libfreetype6-dev
sudo apt-get build-dep pillow
sudo python3 -m pip install –upgrade scikit-image
sudo python3 -m pip install theano
sudo python3 -m pip install lasagne
Bad move DOn’t install lasagne and theano on their own
python3 -m pip install --ignore-installed -r requirements.txt
This is running slow as hell. What is up. Github page
Using screen so ssh failing won’t quit job
run your job
detach with ctrl-a ctrl-d
then you can reattach with screen -r
Interesting Link. Should try this next time.
Okay. I got a free nvidia graphics card (GTX 560 ti) from a bro. I set up my router to forward port 22 to my desktop so I can ssh in from anywhere. Installed cuda and cudnn. Tensorflow by default does support this old of a graphics card. Saw some rumblings
FInally got scipy to install once I download the blas and lapack libaryr prerequisites using apt-get.
I was having a lot of trouble installing scikit-image with some kind of error about pgen. Eventually I renamed /usr/local/bin/pgen which is not the porgram it is expecting to /usr/local/bin/pgentmp and then it seems txo get past it fine.
sudo apt-get install libatlas-base-dev
Needed to install some matplotlib dependancies
Needed to set a .theanorc file. Change cude5.5 to the verison you have
I’d say the speed is on par with the AWS. It will take about an hour or two to finish the job at 40 iterations.
Phase 3 is the beast.
Failed at phase 3. My card has only 1GB of ram. Not enough I guess.
I’ll post this for now, but clearly a work in progress.