This guide is intended to guide students into the basics of using the mlp1/mlp2 GPU clusters. It is not intended to be an exhaustive guide that goes deep into micro-details of the Slurm ecosystem. For an exhaustive guide please visit the Slurm Documentation page.
It's cluster consisting of server rack machines, each equipped with 8 NVIDIA 1060 GTX 6GB. Initially there are 9 servers (72 GPUs) available for use, during February this should grow up to 25 servers (200 GPUs). The system has is managed using the open source cluster management software named Slurm. Slurm has various advantages over the competition, including full support of GPU resource scheduling.
Most Deep Learning experiments require a large amount of compute as you have noticed in term 1. Usage of GPU can accelerate experiments around 30-50x therefore making experiments that require a large amount of time feasible by slashing their runtimes down by a massive factor. For a simple example consider an experiment that required a month to run, that would make it infeasible to actually do research with. Now consider that experiment only requiring 1 day to run, which allows one to iterate over methodologies, tune hyperparameters and overall try far more things. This simple example expresses one of the simplest reasons behind the GPU hype that surrounds machine learning research today.
- If you are not on a DICE machine, then ssh into your dice home using
ssh [email protected] - Then ssh into either mlp1 or mlp2 which are the headnodes of the GPU cluster - it does not matter which you use. To do that
run
ssh mlp1orssh mlp2. - You are now logged into the gpu cluster. If this is your first time logging in you'll need to build your environment. This is because your home directory on the GPU cluster is separate to your usual AFS home directory on DICE.
- Start by downloading the miniconda3 installation file using
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh. - Now run the installation using
bash Miniconda3-latest-Linux-x86_64.sh. At the first prompt reply yes.
Do you accept the license terms? [yes|no]
[no] >>> yes
- At the second prompt simply press enter.
Miniconda3 will now be installed into this location:
/home/sxxxxxxx/miniconda3
- Press ENTER to confirm the location
- Press CTRL-C to abort the installation
- Or specify a different location below
- Now you need to activate your environment by first running:
source .bashrc. This reloads .bashrc which includes the new miniconda path. - Run
source activateto load miniconda root. - Now run
conda create -n mlp python=3this will create the mlp environment. At the prompt choose y. - Now run
source activate mlp. - Install git using
conda install git. Then config git using:
git config --global user.name "[your name]"
git config --global user.email "[matric-number]@sms.ed.ac.uk"
- Now clone the mlpractical repo using
git clone https://github.com/CSTR-Edinburgh/mlpractical.git. - Checkout the semester_2 branch using
git checkout mlp2017-8/semester_2_materials. cd mlpracticaland then install the required packages usingpip install -r requirements_gpu.txt.- Once this is done you will need to setup the MLP_DATA path using the following block of commands:
cd ~/miniconda3/envs/mlp
mkdir -p ./etc/conda/activate.d
mkdir -p ./etc/conda/deactivate.d
echo -e '#!/bin/sh\n' >> ./etc/conda/activate.d/env_vars.sh
echo "export MLP_DATA_DIR=$HOME/mlpractical/data" >> ./etc/conda/activate.d/env_vars.sh
echo -e '#!/bin/sh\n' >> ./etc/conda/deactivate.d/env_vars.sh
echo 'unset MLP_DATA_DIR' >> ./etc/conda/deactivate.d/env_vars.sh
export MLP_DATA_DIR=$HOME/mlpractical/data
- This includes all of the required installations. Proceed to the next section outlining how to use the slurm cluster
management software. Please remember to clean your setup files using
conda clean -t
Slurm provides us with some commands that can be used to submit, delete, view, explore current jobs, nodes and resources among others.
To submit a job one needs to use sbatch script.sh which will automatically find available nodes and pass the job,
resources and restrictions required. The script.sh is the bash script containing the job that we want to run. Since we will be using the NVIDIA CUDA and CUDNN libraries
we have provided a sample script which should be used for your job submissions. The script is explained in detail below:
#!/bin/sh
#SBATCH -N 1 # nodes requested
#SBATCH -n 1 # tasks requested
#SBATCH --gres=gpu:1 # use 1 GPU
#SBATCH --mem=16000 # memory in Mb
#SBATCH -o outfile # send stdout to outfile
#SBATCH -e errfile # send stderr to errfile
#SBATCH -t 1:00:00 # time requested in hour:minute:seconds
# Setup CUDA and CUDNN related paths
export CUDA_HOME=/opt/cuda-8.0.44
export CUDNN_HOME=/opt/cuDNN-6.0_8.0
export STUDENT_ID=$(whoami)
export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
export LIBRARY_PATH=${CUDNN_HOME}/lib64:$LIBRARY_PATH
export CPATH=${CUDNN_HOME}/include:$CPATH
export PATH=${CUDA_HOME}/bin:${PATH}
export PYTHON_PATH=$PATH
# Setup a folder in the very fast scratch disk which can be used for storing experiment objects and any other files
# that may require storage during execution.
mkdir -p /disk/scratch/${STUDENT_ID}
export TMPDIR=/disk/scratch/${STUDENT_ID}/
export TMP=/disk/scratch/${STUDENT_ID}/
# Activate the relevant virtual environment:
source /home/${STUDENT_ID}/miniconda3/bin/activate mlp
# Run the python script that will train our network
python network_trainer.py --batch_size 128 --epochs 200 --experiment_prefix vgg-net-emnist-sample-exp --dropout_rate 0.4 --batch_norm_use True --strided_dim_reduction True --seed 25012018
To actually run this use sbatch gpu_cluster_tutorial_training_script.sh. When you do this, the job will be submitted and you will be given a job id.
[burly]sxxxxxxx: sbatch gpu_cluster_tutorial_training_script.sh
Submitted batch job 147
To view a list of all running jobs use squeue for a minimal presentation and smap for a more involved presentation. Furthermore to view node information use sinfo.
squeue
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
143 interacti bash iainr R 8:00 1 landonia05
147 interacti gpu_clus sxxxxxxx R 1:05 1 landonia02
Also in case you want to stop/delete a job use scancel job_id where job_id is the id of the job.
Furthermore in case you want to test some of your code interactively to prototype your solution before you submit it to
a node you can use srun -p interactive --gres=gpu:2 --pty python my_code_exp.py.
For a nice list of most commonly used Slurm commands please visit here.
At some point you will need to copy your data to DICE so you can analyse them and produce charts, write reports, store for future use etc. To do that there is a couple of ways:
-
If you want to get your files while you are in dice simply run
scp mlp1:/home/<username>/output outputwhere username is the student id and output is the file you want to copy. Use scp -r for folders. Furthermore you might want to just selectively sync only new files. You can achieve that via syncing using rsync.rsync -ua --progress mlp1:/home/<username>/project_dir target_dir. The option -u updates only changed files, -a will pack the files before sending and --progress will give you a progress bar that shows what is being sent and how fast. rsync is useful when you write code remotely and want to push it to the cluster, since it can track files and automatically update changed files it saves both compute time and human time, because you won't have to spent time figuring out what to send. -
If you want to send your files while in mlp1-2 to dice. First run
rencgive your password and enter. Then run:
cp ~/output /afs/inf.ed.ac.uk/u/s/<studentUUN>
This should directly copy the files to AFS. Furthermore one can use rsync as shown before.
If you require additional help as usual please post on piazza or ask in the tech support helpdesk.