Update gpu-cluster-quick-start.md
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@ -54,7 +54,7 @@ git config --global user.name "[your name]"
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git config --global user.email "[matric-number]@sms.ed.ac.uk"
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git config --global user.email "[matric-number]@sms.ed.ac.uk"
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```
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```
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9. Now clone the mlpractical repo using ```git clone https://github.com/CSTR-Edinburgh/mlpractical.git```.
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9. Now clone the mlpractical repo using ```git clone https://github.com/CSTR-Edinburgh/mlpractical.git```.
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10. Checkout the mlp_tf_tutorial branch using ```git checkout mlp2017-8/mlp_tf_tutorial```.
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10. Checkout the mlp_tf_tutorial branch using ```git checkout mlp2017-8/semester_2_materials```.
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11. ```cd mlpractical``` and then install the required packages using ```pip install -r requirements_gpu.txt```.
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11. ```cd mlpractical``` and then install the required packages using ```pip install -r requirements_gpu.txt```.
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12. Once this is done you will need to setup the MLP_DATA path using the following block of commands:
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12. Once this is done you will need to setup the MLP_DATA path using the following block of commands:
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```bash
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```bash
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@ -86,7 +86,7 @@ To submit a job one needs to use ```sbatch script.sh``` which will automatically
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#SBATCH --mem=16000 # memory in Mb
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#SBATCH --mem=16000 # memory in Mb
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#SBATCH -o outfile # send stdout to outfile
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#SBATCH -o outfile # send stdout to outfile
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#SBATCH -e errfile # send stderr to errfile
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#SBATCH -e errfile # send stderr to errfile
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#SBATCH -t 0:01:00 # time requested in hour:minute:seconds
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#SBATCH -t 1:00:00 # time requested in hour:minute:seconds
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# Setup CUDA and CUDNN related paths
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# Setup CUDA and CUDNN related paths
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export CUDA_HOME=/opt/cuda-8.0.44
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export CUDA_HOME=/opt/cuda-8.0.44
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@ -163,4 +163,4 @@ This should directly copy the files to AFS. Furthermore one can use rsync as sho
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##Additional Help
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##Additional Help
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If you require additional help as usual please post on piazza or ask in the tech support helpdesk.
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If you require additional help as usual please post on piazza or ask in the tech support helpdesk.
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