Deep Learning Example

$ ssh -l <username> hpc7.ncg.ingrid.pt
[username@hpc7 ~]$ _

Prepare a python virtual environment

The default python version for CentOS 7.x is 2.7.5 which is not suitable for our example that rely on version 3.6 and up. So, we will create a python virtual environment and include needed components:

[username@hpc7 ~]$ scl enable rh-python36 bash
[username@hpc7 ~]$ python -m venv ~/pvenv
[username@hpc7 ~]$ . ~/pvenv/bin/activate
[username@hpc7 ~]$ pip install --upgrade pip
[username@hpc7 ~]$ pip install --upgrade setuptools
[username@hpc7 ~]$ pip install tensorflow-gpu
[username@hpc7 ~]$ pip install keras

This opperation is performed only once, the python virtual environment will be reused all over your jobs.

Submit a Job to install TensorFlow and Keras on the python virtual envionment

Since we do not have direct access to the GPU on the submit node then we have to submit one job, and only one, to install TensorFlow and Keras on our python virtual environment.

Create a submit script like as showed bellow and submit it:

[username@hpc7 ~]$ vi pip_install.sh
#!/bin/bash
#$ -q tesla
scl enable rh-python36 bash
. ~/pvenv/bin/activate
pip install tensorflow-gpu
pip install keras

[username@hpc7 ~]$ qsub pip_install.sh

Check the job output files after finished for correct completion, if something is wrong try to solve the problem or request support from helpdesk@incd.pt. You can also include in the job the full python virtual environment preparation as showed on the previous section if you like.

Check the python virtual environment

You may check if the python virtual environment is working as expected, for example:

[username@hpc7 ~]$ python --version
Python 2.7.5
[username@hpc7 ~]$ scl enable rh-python36 bash
[username@hpc7 ~]$ python --version
Python 3.6.9
[username@hpc7 ~]$ source ~/pvenv/bin/activate
[username@hpc7 ~]$ pip list
Package              Version   
-------------------- ----------
...
Keras                2.3.1
Keras-Applications   1.0.8
Keras-Preprocessing  1.1.0
...
setuptools           44.0.0
...
tensorboard          2.0.2     
tensorflow-estimator 2.0.1     
tensorflow-gpu       2.0.0

Prepare your code

Choose a working directory for your code, for the purpose of this example we will run a deep learning python script named run.py, create also a submit script:

[username@hpc7 ~]$ mkdir dl
[username@hpc7 ~]$ cd dl
[username@hpc7 dl]$ wget https://wiki.incd.pt/attachments/70

[username@hpc7 dl]$ vi dl.sh
#!/bin/bash
#$ -q gpu
scl enable rh-python36 bash
. ~/pvenv/bin/activate
module load cuda-10.2
python run.py

[username@hpc7 dl]$ ls -l
-rwxr-----+ 1 username hpc  514 Jan  5 13:42 dl.sh
-rw-r-----+ 1 username hpc 1378 Jan  5 15:42 run.py

Submit the Job

[username@hpc7 dl]$ qsub dl.sh
Your job 2027497 ("dl.sh") has been submitted

[username@hpc7 dl]$ qstat
job-ID  prior   name  user     state submit/start at     queue      slots ja-task-ID 
----------------------------------------------------------------------------------
2027497 0.10134 dl.sh username r     01/06/2020 13:28:36 gpu@hpc046 1

Check Job results

On completion check results on standard output and error files:

[username@hpc7 dl]$ ls -l
-rwxr-----+ 1 username hpc   514 Jan  5 13:42 dl.sh
-rw-r-----+ 1 username hpc  1378 Jan  5 15:42 run.py
-rw-r-----+ 1 username hpc  4956 Jan  6 13:44 dl.sh.e2027497
-rw-r-----+ 1 username hpc 14009 Jan  6 13:44 dl.sh.o2027497