View source for Eosc/easybuild/benchmark

= Background =

In the context of the EOSC-Nordic EasyBuild pilot, the following
page provides instructions for how to benchmark different software
configurations on typical problems that are likely to affect NLPL
users.
Relevant variation will typically contrast pre-compiled binary
installations (e.g. `pip` wheels) vs. locally compiled modules,
where architecture-specific optimizations are enabled and
optimized libraries (e.g. Intel MKL) are used.

= NumPy =
We use [https://source.coderefinery.org/nlpl/easybuild/-/blob/ak-dev/tests/numpy/numpy_test.py this Python script] which runs multiple random matrix multiplications and singular value decompositions (SVD).
Only CPU is employed (Numpy 1.18.1). 

To reproduce: after installing the NLPL software stack, load the '''NLPL-numpy''' module and run the commands below: 

== OpenBLAS on Saga ==
 <nowiki>$ module load NLPL-numpy/1.18.1-foss-2019b-Python-3.7.4
$ python3 tests/numpy/numpy_test.py
Multiplication took 74 seconds.
SVD took 50 seconds.</nowiki>

[https://source.coderefinery.org/nlpl/easybuild/-/blob/ak-dev/tests/numpy/test_numpy_openblas.slurm SLURM job script]

[https://source.coderefinery.org/nlpl/easybuild/-/blob/ak-dev/tests/results/numpy/numpy-1-18-1-openblas.out Detailed SLURM log file]

== IMKL on Saga ==
 <nowiki>$ module load NLPL-numpy/1.18.1-gomkl-2019b-Python-3.7.4
$ python3 tests/numpy/numpy_test.py
Multiplication took 52 seconds.
SVD took 40 seconds.</nowiki>

[https://source.coderefinery.org/nlpl/easybuild/-/blob/ak-dev/tests/numpy/test_numpy_mkl.slurm SLURM job script]

[https://source.coderefinery.org/nlpl/easybuild/-/blob/ak-dev/tests/results/numpy/numpy-1.18.1-mkl.out Detailed SLURM log file]

= TensorFlow on CPU =
We use training a sentiment classifier with convolutional neural networks on CPU as a benchmark (TensorFlow 1.15.2, 20 epochs, [https://source.coderefinery.org/nlpl/easybuild/-/blob/ak-dev/tests/text_data/tensorflow_cnn.slurm SLURM job script]). 

To reproduce: after installing the NLPL software stack, load the '''NLPL-TensorFlow''' module (see below) and run the following command:

 <nowiki>$ python3 tensorflow_text_cnn.py -t TRAIN -d DEV </nowiki>

where TRAIN is a path to a training dataset, and DEV is a path to a development dataset.

Ready-to-use toy data adapted from the [https://nlp.stanford.edu/sentiment/treebank.html SST dataset] can be downloaded [https://source.coderefinery.org/nlpl/easybuild/-/tree/ak-dev/tests/text_data/SST here].

== TF with OpenBLAS on Saga ==
 <nowiki>module load module load NLPL-TensorFlow/1.15.2-foss-2019b-Python-3.7.4
$ python3 tensorflow_text_cnn.py -t SST/stanford_sentiment_binary_train.tsv.gz -d SST/stanford_sentiment_binary_dev.tsv.gz 
</nowiki>

Training time: '''55 seconds'''

[https://source.coderefinery.org/nlpl/easybuild/-/blob/ak-dev/tests/results/CNN/tensorflow_cnn_openBLAS.out Detailed SLURM log file]

== TF with OpenMKL on Saga ==

 <nowiki>module load module load NLPL-TensorFlow/1.15.2-gomkl-2019b-Python-3.7.4
$ python3 tensorflow_text_cnn.py -t SST/stanford_sentiment_binary_train.tsv.gz -d SST/stanford_sentiment_binary_dev.tsv.gz
</nowiki>

Training time: '''55 seconds'''

[https://source.coderefinery.org/nlpl/easybuild/-/blob/ak-dev/tests/results/CNN/tensorflow_cnn_mkl.out Detailed SLURM log file]

= TensorFlow on GPU =

We use training a toy BERT model on 4 GPUs as a benchmark (TensorFlow 1.15.2, [https://source.coderefinery.org/nlpl/easybuild/-/blob/ak-dev/tests/text_data/bert_all_in_one.slurm SLURM job script]). 

To reproduce: after installing the NLPL software stack, load the '''NLPL-nvidia_BERT''' module (see below) and run the following command:

 <nowiki>$ train_bert.sh CORPUS VOCAB CONFIG </nowiki>

where CORPUS is a path to a directory with text files, VOCAB is a path to a WordPiece vocabulary, CONFIG is a path to a BERT configuration JSON (defining the model hyperparameters).

Ready-to-use toy data for Norwegian can be downloaded [https://source.coderefinery.org/nlpl/easybuild/-/tree/ak-dev/tests/text_data/no_wiki here], but in principle any plain text corpus can be fed to this code.

== TF with OpenBLAS on Saga ==
 <nowiki>module load NLPL-nvidia_BERT/20.06.8-foss-2019b-TensorFlow-1.15.2-Python-3.7.4
$ train_bert.sh no_wiki/ norwegian_wordpiece_vocab_20k.txt norbert_config.json</nowiki>

Training time: '''00:46:27'''

[https://source.coderefinery.org/nlpl/easybuild/-/blob/ak-dev/tests/results/bert/tensorflow_1.15.2_openBLAS_bert.out Detailed SLURM log file]

== TF with OpenMKL on Saga ==

 <nowiki>module load NLPL-nvidia_BERT/20.06.8-gomkl-2019b-TensorFlow-1.15.2-Python-3.7.4
$ train_bert.sh no_wiki/ norwegian_wordpiece_vocab_20k.txt norbert_config.json</nowiki>

Training time: '''00:46:19'''

[https://source.coderefinery.org/nlpl/easybuild/-/blob/ak-dev/tests/results/bert/tensorflow_1.15.2_mkl_bert.out Detailed SLURM log file]