Using Multicore CPUs or GPUs for KERAS Computation

Overview

Teaching: 0 min
Exercises: 0 min

Questions

• How do we use multicore CPUs for KERAS computations?

• How do we use GPUs for KERAS computations?

Objectives

• First learning objective. (FIXME)

The neural network training process takes a long time. We can speed this up by using multiple CPU cores, or better yet, GPUs.

Running on Multicore CPUs

As an example, let’s run the previous training on 8 CPU cores. The salloc statement needs to become:

$ salloc -c 8 -C AVX2

then modify the part in url_analyzer.py so that the intra_op_parallelism_threads and inter_op_parallelism_threads parameters are given value of 8. Then run the training process again.

Measuring parallel speed up

How many times faster did the calculation go compared to the single-core case?

Running on GPU

To run the training on a GPU, please issue the following resource request instead:

$ salloc -p timed-gpu --gres=gpu:1

IMPORTANT: We have only a few GPUs on Turing, not enough for everyone to do this experiment. It is best if you submit a batch job to maximize the utilization of GPU.

Running batch job

A sample job script is provided in your hands-on directory, url_job.sh. To run this job, you need to create a Python script named url_analyzer_script.py that includes the commands for training & cross-validating the model. Start by creating a copy of url_analyzer.py.

Key Points

• First key point. Brief Answer to questions. (FIXME)