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Argonne Leadership Computing Facility

Argonne Leadership
Computing Facility

Example Programs

Use a local copy of the model zoo

Make a local copy of the Cerebras modelzoo and anl_shared repository, if not previously done, as follows.

mkdir ~/R1.1.0/
cp -r /software/cerebras/model_zoo/modelzoo-R1.1.0 ~/R1.1.0/modelzoo
cp -r /software/cerebras/model_zoo/anl_shared-R1.1.0/ ~/R1.1.0/anl_shared


An implementation of this: U-Net: Convolutional Networks for Biomedical Image Segmentation, Ronneberger et. al 2015
To run Unet with the Severstal: Steel Defect Detection kaggle dataset, using a pre-downloaded copy of the dataset,

cd ~/R1.1.0/modelzoo/unet/tf
#rm -r model_dir_unet_base_severstal
csrun_cpu python --mode=train --compile_only --params configs/params_severstal_sharedds.yaml --model_dir model_dir_unet_base_severstal --cs_ip
csrun_wse python --mode=train --params configs/params_severstal_sharedds.yaml --model_dir model_dir_unet_base_severstal --cs_ip


An implementation of this: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
This BERT-large msl128 example uses a single sample dataset for both training and evaluation. See the in the source directory for details on how to build a dataset from text input.

cd ~/R1.1.0/modelzoo/transformers/tf/bert
#rm -r model_dir_bert_large_msl128
csrun_cpu python --mode=train --compile_only --params configs/params_bert_large_msl128_sampleds.yaml --model_dir model_dir_bert_large_msl128 --cs_ip
csrun_wse python --mode=train --params configs/params_bert_large_msl128_sampleds.yaml --model_dir model_dir_bert_large_msl128 --cs_ip


An implementation of this: BraggNN: fast X-ray Bragg peak analysis using deep learning
The BraggNN model has two versions:
1) Convolution only - this version does not include the non-local attention block
2) Nonlocal - This version includes the nonlocal attention block as described in

cd ~/R1.1.0/anl_shared/braggnn/tf
#rm -r model_dir_braggnn
csrun_cpu python -p configs/params_bragg_nonlocal.yaml --model_dir model_dir_braggnn --mode train --compile_only --cs_ip
csrun_wse python -p configs/params_bragg_nonlocal.yaml --model_dir model_dir_braggnn --mode train --cs_ip