Machine Learning · Web Development

Series on Deep Learning — Startup.ML

Setting up GPU Learning on EC2 We will be putting together a series of practical tutorials on deep learning techniques and tools.  This first post will focus on getting the GPU environment set up on EC2.  Step 1: Launching a GPU instance Launch an Amazon EC2 g2.2xlarge GPU instance which has a Nvidia GRID K520  with 3,072 cores.  If you are just testing and playing around, you can start with a EC2 spot instance and save yourself some money.   Recommended Settings * Amazon Linux 64bit (based on Red Hat) * 300GB+ of EBS backed storage After a few minutes of waiting (assuming you bid high enough) you’ll see your spot instance show up.  Connect to the instance via ssh. Step 2: Configuring the Instance Update System sudo yum update -y sudo reboot #yes it is necessary sudo yum groupinstall -y “Development tools” Load Nvidia Drivers wget wget sudo /bin/bash ./ sudo /bin/bash ./ # ignore the message about an unsupported environment.  It’s because of the Amazon Linux flavor.  Verify Nvidia Drivers/Hardware # you should see GRID K520 in the output nvidia-smi -q | head Step 3: Loading Deep Learning Software Install Python Libraries sudo yum install python-devel python-nose python-setuptools gcc gcc-gfortran gcc-c++ blas-devel lapack-devel atlas-devel sudo easy_install pip sudo easy_install ipython sudo pip install numpy==1.6.1 sudo pip install scipy==0.10.1 sudo pip install Theano Configure Environment # add to .bash_profile export PATH=$PATH:$HOME/bin:/usr/local/cuda-7.0/bin export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-7.0/lib64 export THEANO_FLAGS=’cuda.root=/path/to/cuda/root,device=gpu,floatX=float32′ Select and Install Deep Learning Library (choose one) # Keras git clone sudo python install # Lasagne git clone pip install -r requirements.txt python install # Pylearn2 git clone git:// python develop Step 4: Verifying Training on GPU cd keras/examples python  # output should show: Using gpu device 0: GRID K520 Resources * AWS documentation * Theano documentation * Preloaded AMI ( ami-658d7b21) in us-west-1 

Source: Series on Deep Learning — Startup.ML


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