ResNet-N with TensorFlow and DALI¶
This demo implements residual networks model and use DALI for the data augmentation pipeline from the original paper.
Common utilities for defining the network and performing basic training are located in the nvutils directory. Use of nvutils is demonstrated in the model scripts available in docs/examples/use_cases/tensorflow/resnet-n/resnet.py.
For parallelization, we use the Horovod distribution framework, which
works in concert with MPI. To train ResNet-50 (--layers=50
) using 8
V100 GPUs, for example on DGX-1, use the following command
(--dali_cpu
indicates to the script to use CPU backend for DALI):
$ mpiexec --allow-run-as-root --bind-to socket -np 8 python resnet.py \
--layers=50 \
--data_dir=/data/imagenet \
--data_idx_dir=/data/imagenet-idx \
--precision=fp16 \
--log_dir=/output/resnet50 \
--dali_cpu
Here we have assumed that imagenet is stored in tfrecord format in the directory ‘/data/imagenet’. After training completes, evaluation is performed using the validation dataset.
Some common training parameters can tweaked from the command line. Others must be configured within the network scripts themselves.
Original scripts modified from nvidia-examples
scripts in NGC
TensorFlow Container
Requirements¶
TensorFlow¶
pip install tensorflow-gpu==1.10.0
OpenMPI¶
wget -q -O - https://www.open-mpi.org/software/ompi/v3.0/downloads/openmpi-3.0.0.tar.gz | tar -xz
cd openmpi-3.0.0
./configure --enable-orterun-prefix-by-default --with-cuda --prefix=/usr/local/mpi --disable-getpwuid
make -j"$(nproc)" install
cd .. && rm -rf openmpi-3.0.0
echo "/usr/local/mpi/lib" >> /etc/ld.so.conf.d/openmpi.conf && ldconfig
export PATH=/usr/local/mpi/bin:$PATH
The following works around a segfault in OpenMPI 3.0 when run within a single node without ssh being installed.
/bin/echo -e '#!/bin/bash'\
'\ncat <<EOF'\
'\n======================================================================'\
'\nTo run a multi-node job, install an ssh client and clear plm_rsh_agent'\
'\nin '/usr/local/mpi/etc/openmpi-mca-params.conf'.'\
'\n======================================================================'\
'\nEOF'\
'\nexit 1' >> /usr/local/mpi/bin/rsh_warn.sh && \
chmod +x /usr/local/mpi/bin/rsh_warn.sh && \
echo "plm_rsh_agent = /usr/local/mpi/bin/rsh_warn.sh" >> /usr/local/mpi/etc/openmpi-mca-params.conf
Horovod¶
export HOROVOD_GPU_ALLREDUCE=NCCL
export HOROVOD_NCCL_INCLUDE=/usr/include
export HOROVOD_NCCL_LIB=/usr/lib/x86_64-linux-gnu
export HOROVOD_NCCL_LINK=SHARED
export HOROVOD_WITHOUT_PYTORCH=1
pip install horovod==0.15.1