The following guide has been developed in collaboration with my colleague at Microsoft Christine Matheney and our work at Oxford and Stanford University.
Region |
SKU |
East US |
NV |
North Central US |
NV |
South Central US |
NV |
South East Asia |
NV |
West Europe |
NV |
South Central US |
NC |
East US |
NC |
Regarding the question of running GPU compute for deep learning on NV-Series, the GPU team has indicated that is not recommended. Bottom line is: Big GPU Computes (like deep learning) should only be done on NC-Series. NV is for visualization and graphics. See this blog for more details on NV vs NC series
Using the VM Finding your VM
Login to http://portal.azure.com Click all resources and select your VM. Our subscription has many, but yours will only have one if you just followed the setup instructions.
Spinning up your VM
If you just completed the previous part and the VM has finished deploying, then your VM should be running already.
Connecting (SSH) to your VM
Once your VM is started (it may take a few minutes). Click connect and follow the instructions.
Stopping your VM
Completing CUDA/Tensorflow setup
##Installing CUDA and Tensorflow dependencies.
There are two scripts that you will need to run and your VM will need to reboot in the between running them.
##[Step 1]
First, in your VM do:
git clone https://github.com/leestott/Azure-GPU-Setup .git
cd azure-gpu-setup
You should see the following if you use
ls -all
Run gpu-setup-part1.sh using the following command:
./gpu-setup-part1.sh
This will install some libraries, fetch and install NVIDIA drivers, and trigger a reboot. (The command will take some time to run.)
Once your VM has finished restarting.
[Step 2]SSH into the VM again. Navigate to the azure-gpu-setup directory again. Run the command:
./gpu-setup-part2.sh
This script installs the CUDA toolkit, CUDNN, and Tensorflow. It also sets the required environment variables. Once the script finishes, we must do:
source ~/.bashrc
This ensures that the shell will use the updated environment variables. Now, to test that Tensorflow and the GPU is properly configured, run the gpu test script by executing:
python gpu-test.py
Filing a support ticket
We highly suggest the following for using the GPU instances:
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