mcw-rcc / tensorflow

Singularity container for TensorFlow.

TensorFlow

TensorFlow Version: 2.0-gpu

Batch Job Script

#!/bin/bash
#PBS -N tensorflow_test
#PBS -S /bin/bash
#PBS -l nodes=1:ppn=1:gpus=1
#PBS -l mem=5gb
#PBS -l walltime=72:00:00
#PBS -j oe

module load tensorflow/2.0

cd $PBS_O_WORKDIR
export SCR_DIR=/scratch/global/$USER/$PBS_JOBID
mkdir -p $SCR_DIR
cp my_tf_script.py $SCR_DIR                   
cd $SCR_DIR
tensorflow-gpu my_tf_script.py >> output.log  
cp -R output.log $PBS_O_WORKDIR              
rm -rf $SCR_DIR                             

1. Copy contents of tf.pbs (example above) to a file in your home directory.

2. Open terminal on cluster login node and submit the job script:

$ qsub tf.pbs

TensorFlow Jupyter Notebook Job Script

#!/bin/bash
#PBS -N tf_jnb
#PBS -l nodes=1:ppn=1:gpus=1
#PBS -l mem=5gb
#PBS -l walltime=1:00:00
#PBS -j oe

# get tunneling info
port=$(shuf -i8000-9999 -n1)
node=$(hostname -s)
user=$(whoami)
submit_host=$(echo ${PBS_O_HOST%%.*}.rcc.mcw.edu)
cd $PBS_O_WORKDIR

# print tunneling instructions jupyter-log
echo -e "
1. SSH tunnel from your workstation using the following command:

   ssh -L ${port}:${node}:${port} ${user}@${submit_host}

   and point your web browser to http://localhost:${port}.

2. Copy/paste the token below when you connect for the first time.
"

# load modules
module load tensorflow/2.0

jupyter-notebook

1. Copy contents of tfjupyternb.pbs (example above) to a file in your home directory.

2. Open terminal on cluster login node and submit the job script:

$ qsub tfjupyternb.pbs

Connect to Notebook

1. Check output file (jobname.oJOBNUM) for details.

Example output file: tf_jnb.o152668

1. SSH tunnel from your workstation using the following command:

   ssh -L 9623:node01:9623 tester@loginnode

   and point your web browser to http://localhost:9623.

2. Copy/paste the token below when you connect for the first time.

[I 18:24:35.251 NotebookApp] Serving notebooks from local directory: /home/tester/jupyter-test
[I 18:24:35.251 NotebookApp] 0 active kernels
[I 18:24:35.251 NotebookApp] The Jupyter Notebook is running at:
[I 18:24:35.251 NotebookApp] http://node01:9623/?token=a2caff9717f5dcefc22c4c387fa0a02390d1ef71f0f3747d
[I 18:24:35.251 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 18:24:35.256 NotebookApp]

    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://node01:9623/?token=a2caff9717f5dcefc22c4c387fa0a02390d1ef71f0f3747d

2. Open second terminal and run tunneling command from the output file:

$ ssh -L 9623:node01:9623 tester@loginnode

3. Open a browser and enter the URL from the output file:

http://localhost:9623

4. Enter the token (long string after ?token=) from the output file.

You should now be connected to your Jupyter Notebook that is running on a cluster compute node. To close the notebook, select Logout. If you need to reconnect, repeat steps. If you're done with your Notebook, remember to stop the job with qdel.

TensorBoard Job Script

#!/bin/bash
#PBS -N tensorboard
#PBS -l nodes=1:ppn=1:gpus=1
#PBS -l mem=5gb
#PBS -l walltime=1:00:00
#PBS -j oe

# get tunneling info
port=$(shuf -i8000-9999 -n1)
node=$(hostname -s)
user=$(whoami)
submit_host=$(echo ${PBS_O_HOST%%.*}.rcc.mcw.edu)

# add your own log directory (required)
logdir="~/path/to/your/logdir"

cd $PBS_O_WORKDIR

# print tunneling instructions 
echo -e "
1. SSH tunnel from your workstation using the following command:

   ssh -L ${port}:${node}:${port} ${user}@${submit_host}

   and point your web browser to http://localhost:${port}.

2. Copy/paste the token below when you connect for the first time.
"

# load modules
module load tensorflow/2.0

tensorboard-gpu

1. Copy contents of tensorboard.pbs (example above) to a file in your home directory. Modify the logdir variable.

2. Open terminal on cluster login node and submit the job script:

$ qsub tensorboard.pbs

Connect to TensorBoard

1. Check output file (jobname.oJOBNUM) for details.

Example output file: tensorboard.o178936

SSH tunnel from your workstation using the following command:

   ssh -L 8754:node01:8754 tester@loginnode

and point your web browser to http://localhost:8754.

2. Open second terminal and run tunneling command from the output file:

$ ssh -L 8754:node01:8754 tester@loginnode

3. Open a browser and enter the URL from the output file:

http://localhost:8754

You should now be connected to TensorBoard. To close TensorBoard, close browser window. If you need to reconnect, repeat steps.