a644350589
Tacotron2+Waveglow/PyT * AMP support * Data preprocessing for Tacotron 2 training * Fixed dropouts on LSTMCells SSD/PyT * script and notebook for inference * AMP support * README update * updates to examples/* BERT/PyT * initial release GNMT/PyT * Default container updated to NGC PyTorch 19.05-py3 * Mixed precision training implemented using APEX AMP * Added inference throughput and latency results on NVIDIA Tesla V100 16G * Added option to run inference on user-provided raw input text from command line NCF/PyT * Updated performance tables. * Default container changed to PyTorch 19.06-py3. * Caching validation negatives between runs Transformer/PyT * new README * jit support added UNet Medical/TF * inference example scripts added * inference benchmark measuring latency added * TRT/TF-TRT support added * README updated GNMT/TF * Performance improvements Small updates (mostly README) for other models.
91 lines
2.7 KiB
Bash
Executable file
91 lines
2.7 KiB
Bash
Executable file
# Copyright (c) 2018, deepakn94, robieta. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# -----------------------------------------------------------------------
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#
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#!/bin/bash
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set -e
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set -x
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DATASET_NAME=${1:-'ml-20m'}
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RAW_DATADIR=${2:-'/data'}
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CACHED_DATADIR=${3:-"${RAW_DATADIR}/cache/${DATASET_NAME}"}
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# you can add another option to this case in order to support other datasets
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case ${DATASET_NAME} in
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'ml-20m')
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ZIP_PATH=${RAW_DATADIR}/'ml-20m.zip'
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RATINGS_PATH=${RAW_DATADIR}'/ml-20m/ratings.csv'
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;;
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'ml-1m')
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ZIP_PATH=${RAW_DATADIR}/'ml-1m.zip'
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RATINGS_PATH=${RAW_DATADIR}'/ml-1m/ratings.dat'
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;;
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*)
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echo "Unsupported dataset name: $DATASET_NAME"
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exit 1
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esac
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if [ ! -d ${RAW_DATADIR} ]; then
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mkdir -p ${RAW_DATADIR}
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fi
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if [ ! -d ${CACHED_DATADIR} ]; then
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mkdir -p ${CACHED_DATADIR}
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fi
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if [ -f log ]; then
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rm -f log
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fi
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if [ ! -f ${ZIP_PATH} ]; then
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echo 'Dataset not found, downloading...'
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./download_dataset.sh ${DATASET_NAME} ${RAW_DATADIR}
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fi
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if [ ! -f ${RATINGS_PATH} ]; then
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unzip -u ${ZIP_PATH} -d ${RAW_DATADIR}
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fi
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if [ ! -f ${CACHED_DATADIR}/train_ratings.pt ]; then
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echo "preprocessing ${RATINGS_PATH} and save to disk"
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t0=$(date +%s)
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python convert.py --path ${RATINGS_PATH} --output ${CACHED_DATADIR}
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t1=$(date +%s)
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delta=$(( $t1 - $t0 ))
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echo "Finish preprocessing in $delta seconds"
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else
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echo 'Using cached preprocessed data'
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fi
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echo "Dataset $DATASET_NAME successfully prepared at: $CACHED_DATADIR\n"
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echo "You can now run the training with: python -m torch.distributed.launch --nproc_per_node=<number_of_GPUs> ncf.py --data ${CACHED_DATADIR}"
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