Torch: Difference between revisions
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You can compile [http://torch.ch/ Torch] on our RHEL7 machines. Please make sure to ask staff@umiacs.umd.edu if have issues as there are some local packages that are required and could cause the install to fail. | You can compile [http://torch.ch/ Torch] on our RHEL7 machines. Please make sure to ask staff@umiacs.umd.edu if have issues as there are some local packages that are required and could cause the install to fail. | ||
Note you have to use Cuda 8 or Cuda 7.5 when compiling Torch and that certain newer GPU cards are not supported under Cuda 7.5. We will use Cuda 8 in this example. | '''Note you have to use Cuda 8 or Cuda 7.5 when compiling Torch and that certain newer GPU cards are not supported under Cuda 7.5. We will use Cuda 8 in this example.''' | ||
First load the following software into your environment with GNU [Modules]. | First load the following software into your environment with GNU [Modules]. |
Revision as of 21:52, 7 March 2018
You can compile Torch on our RHEL7 machines. Please make sure to ask staff@umiacs.umd.edu if have issues as there are some local packages that are required and could cause the install to fail.
Note you have to use Cuda 8 or Cuda 7.5 when compiling Torch and that certain newer GPU cards are not supported under Cuda 7.5. We will use Cuda 8 in this example.
First load the following software into your environment with GNU [Modules].
module add cuda/8.0.61 cudnn zeromq nodejs sox GraphicsMagick
Next we are going to create a python environment and active it (this assumes you are using the bash shell)
virtualenv env source env/bin/activate
Then we are going to upgrade the python pip package manager and install two more dependencies
pip install --upgrade pip pip install pyzmq ipython
Next clone the repository from Torch ensuring you do it with the recursive option to get all its sub dependencies.
git clone https://github.com/torch/distro.git torch --recursive
Finally build Torch, this will take awhile.
cd torch && ./install.sh
At the end it will give you example of how to activate your Torch environment here is an example.
source install/bin/torch-activate
Now you can test you your install with the command th torch_test.th
where the contents of the file torch_test.th
are the following.
a = torch.Tensor(5,3) a = torch.rand(5,3) b = torch.rand(3,4) c = torch.Tensor(5,4) c:mm(a,b) print(c) require 'cutorch'; a = a:cuda() b = b:cuda() c = c:cuda() c:mm(a,b) print(c)