ERR=$1

ERRORS="$ERRORS\n$ERR"

echo $ERR

echo "installing requirements"

cat $BASE_DIR/*/requirements.txt | \

sort -u | \

# testing the installed version of torch, so don't pip install it.

grep -vE '^torch$' | \

pip install -r /dev/stdin || \

{ error "failed to install dependencies"; exit 1; }

EXAMPLE=${FUNCNAME[1]}

cd $BASE_DIR/$EXAMPLE

echo "Running example: $EXAMPLE"

start

if [ ! -d "lsun" ]; then

echo "cloning repo to get lsun dataset"

git clone https://github.com/fyu/lsun || { error "couldn't clone lsun repo needed for dcgan"; return; }

fi

# 'classroom' much smaller than the default 'bedroom' dataset.

DATACLASS="classroom"

if [ ! -d "lsun/${DATACLASS}_train_lmdb" ]; then

pushd lsun

python download.py -c $DATACLASS || { error "couldn't download $DATACLASS for dcgan"; return; }

unzip ${DATACLASS}_train_lmdb.zip || { error "couldn't unzip $DATACLASS"; return; }

popd

fi

python main.py --dataset lsun --dataroot lsun --classes $DATACLASS --niter 1 $CUDA_FLAG --dry-run || error "dcgan failed"

start

if [ ! -d "saved_models" ]; then

echo "downloading saved models for fast neural style"

python download_saved_models.py

fi

test -d "saved_models" || { error "saved models not found"; return; }

echo "running fast neural style model"

python neural_style/neural_style.py eval --content-image images/content-images/amber.jpg --model saved_models/candy.pth --output-image images/output-images/amber-candy.jpg --cuda $CUDA || error "neural_style.py failed"

start

if [[ ! -d "sample/val" || ! -d "sample/train" ]]; then

mkdir -p sample/val/n

mkdir -p sample/train/n

wget "https://upload.wikimedia.org/wikipedia/commons/5/5a/Socks-clinton.jpg" || { error "couldn't download sample image for imagenet"; return; }

mv Socks-clinton.jpg sample/train/n

cp sample/train/n/* sample/val/n/

fi

python main.py --epochs 1 sample/ || error "imagenet example failed"

start

echo "installing 'en' model if not installed"

python -m spacy download en || { error "couldn't download 'en' model needed for snli"; return; }

echo "training..."

python train.py --epochs 1 --dev_every 1 --no-bidirectional --dry-run || error "couldn't train snli"

start

python generate_sine_wave.py || { error "generate sine wave failed"; return; }

python train.py --steps 2 || error "time sequence prediction training failed"

cd $BASE_DIR

echo "running clean to remove cruft"

rm -rf dcgan/_cache_lsun_classroom_train_lmdb \

dcgan/fake_samples_epoch_000.png dcgan/lsun/ \

dcgan/_cache_lsunclassroomtrainlmdb \

dcgan/netD_epoch_0.pth dcgan/netG_epoch_0.pth \

dcgan/real_samples.png \

fast_neural_style/saved_models.zip \

fast_neural_style/saved_models/ \

imagenet/checkpoint.pth.tar \

imagenet/lsun/ \

imagenet/model_best.pth.tar \

imagenet/sample/ \

snli/.data/ \

snli/.vector_cache/ \

snli/results/ \

super_resolution/dataset/ \

super_resolution/model_epoch_1.pth \

time_sequence_prediction/predict*.pdf \

time_sequence_prediction/traindata.pt \

word_language_model/model.pt || error "couldn't clean up some files"

git checkout fast_neural_style/images/output-images/amber-candy.jpg || error "couldn't clean up fast neural style image"

# cpp

dcgan

# distributed

fast_neural_style

imagenet

mnist

mnist_hogwild

regression

reinforcement_learning

snli

super_resolution

time_sequence_prediction

vae

word_language_model