DeepLearningExamples/PyTorch/Detection/SSD/examples/SSD300_FP16_8GPU.sh

# This script launches SSD300 training in FP16 on 8 GPUs using 512 batch size (64 per GPU)
# Usage ./SSD300_FP16_8GPU.sh <path to this repository> <path to dataset> <additional flags>

python -m torch.distributed.launch --nproc_per_node=8 $1/main.py --backbone resnet50 --warmup 300 --bs 64 --amp --data $2 ${@:3}
Adding 9 new models: ResNet50/TF, SSD/TF, BERT/TF, NCF/TF, UNet/TF, GNMT/TF, SSD/PyT, MaskRCNN/PyT, Tacotron2+Waveglow/PyT 2019-03-18 19:45:05 +01:00			`# This script launches SSD300 training in FP16 on 8 GPUs using 512 batch size (64 per GPU)`
			`# Usage ./SSD300_FP16_8GPU.sh <path to this repository> <path to dataset> <additional flags>`

Updating models and adding BERT/PyT 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. 2019-07-16 21:13:08 +02:00			`python -m torch.distributed.launch --nproc_per_node=8 $1/main.py --backbone resnet50 --warmup 300 --bs 64 --amp --data $2 ${@:3}`