DeepLearningExamples/PyTorch/Forecasting/TFT/TemporalFusionTransformers/tft_pyt/scripts/benchmark.sh
2021-11-08 14:08:58 -08:00

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#! /bin/bash
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
NUM_GPUS=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
[ $NUM_GPUS -eq 16 ] && WORKER_NUMS=(1 8 16) || WORKER_NUMS=(1 8)
DATASETS=(electricity traffic)
rm -r /tmp/benchmark_results
for DATASET in ${DATASETS[@]}
do
for NGPU in ${WORKER_NUMS[@]}
do
for BATCH_SIZE in 512 1024 1536 2048 2560
do
for USE_AMP in --use_amp ""
do
for AFFINITY in "--affinity disabled" "--affinity single" "--affinity socket_unique_interleaved"
do
EXP_NAME="TFT_benchmark_${DATASET}_BS_${BATCH_SIZE}_${NGPU}GPU${USE_AMP}_${AFFINITY}"
python -m torch.distributed.launch --nproc_per_node=${NGPU} train.py \
--dataset ${DATASET} \
--data_path /data/processed/${DATASET}_bin \
--batch_size=${BATCH_SIZE} \
--lr 5e-4 \
--epochs 1 \
--sample 100000 5000 \
--seed 1 \
${USE_AMP} \
${AFFINITY} \
--clip_grad 0.1 \
--results /tmp/benchmark_results/${EXP_NAME}
done
done
done
done
done
for P in `ls /tmp/benchmark_results/`;
do
echo ${P}
tail -n 1 /tmp/benchmark_results/${P}/dllogger.json
done