2020-03-04 20:05:36 -05:00
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#!/usr/bin/env bash
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2019-07-12 21:19:42 -04:00
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#
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# This script runs through the code in each of the python examples.
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2022-01-20 23:19:54 +08:00
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# The purpose is just as an integration test, not to actually train models in any meaningful way.
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# For that reason, most of these set epochs = 1 and --dry-run.
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2019-07-12 21:19:42 -04:00
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#
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# Optionally specify a comma separated list of examples to run.
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# can be run as:
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# ./run_python_examples.sh "install_deps,run_all,clean"
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2022-01-20 23:19:54 +08:00
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# to pip install dependencies (other than pytorch), run all examples, and remove temporary/changed data files.
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2020-07-02 00:43:39 +03:00
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# Expects pytorch, torchvision to be installed.
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2019-07-12 21:19:42 -04:00
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BASE_DIR=`pwd`"/"`dirname $0`
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EXAMPLES=`echo $1 | sed -e 's/ //g'`
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2020-07-02 00:43:39 +03:00
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USE_CUDA=$(python -c "import torchvision, torch; print(torch.cuda.is_available())")
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case $USE_CUDA in
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"True")
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echo "using cuda"
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CUDA=1
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CUDA_FLAG="--cuda"
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;;
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"False")
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echo "not using cuda"
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CUDA=0
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CUDA_FLAG=""
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;;
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"")
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exit 1;
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;;
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esac
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2019-07-12 21:19:42 -04:00
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ERRORS=""
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function error() {
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ERR=$1
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ERRORS="$ERRORS\n$ERR"
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echo $ERR
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}
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function install_deps() {
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echo "installing requirements"
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cat $BASE_DIR/*/requirements.txt | \
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sort -u | \
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# testing the installed version of torch, so don't pip install it.
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grep -vE '^torch$' | \
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pip install -r /dev/stdin || \
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{ error "failed to install dependencies"; exit 1; }
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}
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function start() {
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EXAMPLE=${FUNCNAME[1]}
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cd $BASE_DIR/$EXAMPLE
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echo "Running example: $EXAMPLE"
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}
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function dcgan() {
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start
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2022-09-20 16:18:33 -07:00
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python main.py --dataset fake $CUDA_FLAG --mps --dry-run || error "dcgan failed"
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2019-07-12 21:19:42 -04:00
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}
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2022-07-01 16:02:03 -07:00
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function distributed() {
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2022-12-02 17:57:37 -06:00
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start
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2023-05-10 18:14:07 +00:00
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python tensor_parallelism/tensor_parallel_example.py || error "tensor parallel example failed"
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python tensor_parallelism/sequence_parallel_example.py || error "sequence parallel example failed"
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python tensor_parallelism/two_d_parallel_example.py || error "2D parallel example failed"
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2022-12-02 17:57:37 -06:00
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python ddp/main.py || error "ddp example failed"
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2022-07-01 16:02:03 -07:00
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}
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2019-07-12 21:19:42 -04:00
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function fast_neural_style() {
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start
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if [ ! -d "saved_models" ]; then
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echo "downloading saved models for fast neural style"
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python download_saved_models.py
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fi
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test -d "saved_models" || { error "saved models not found"; return; }
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echo "running fast neural style model"
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2022-09-20 16:18:33 -07:00
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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 --mps || error "neural_style.py failed"
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2019-07-12 21:19:42 -04:00
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}
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function imagenet() {
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start
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if [[ ! -d "sample/val" || ! -d "sample/train" ]]; then
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mkdir -p sample/val/n
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mkdir -p sample/train/n
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2022-09-20 16:18:33 -07:00
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curl -O "https://upload.wikimedia.org/wikipedia/commons/5/5a/Socks-clinton.jpg" || { error "couldn't download sample image for imagenet"; return; }
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2019-07-12 21:19:42 -04:00
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mv Socks-clinton.jpg sample/train/n
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cp sample/train/n/* sample/val/n/
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fi
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python main.py --epochs 1 sample/ || error "imagenet example failed"
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}
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function mnist() {
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start
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2020-07-02 00:43:39 +03:00
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python main.py --epochs 1 --dry-run || error "mnist example failed"
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2019-07-12 21:19:42 -04:00
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}
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2023-02-27 05:17:16 +05:30
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function mnist_forward_forward() {
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start
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python main.py --epochs 1 --no_mps --no_cuda || error "mnist forward forward failed"
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2019-07-12 21:19:42 -04:00
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2023-02-27 05:17:16 +05:30
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}
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2019-07-12 21:19:42 -04:00
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function mnist_hogwild() {
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start
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2020-07-02 00:43:39 +03:00
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python main.py --epochs 1 --dry-run $CUDA_FLAG || error "mnist hogwild failed"
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2019-07-12 21:19:42 -04:00
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}
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2022-10-27 14:31:20 -07:00
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function mnist_rnn() {
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start
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python main.py --epochs 1 --dry-run || error "mnist rnn example failed"
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}
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2019-07-12 21:19:42 -04:00
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function regression() {
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start
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python main.py --epochs 1 $CUDA_FLAG || error "regression failed"
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}
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2022-05-07 19:10:31 -07:00
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function siamese_network() {
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start
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python main.py --epochs 1 --dry-run || error "siamese network example failed"
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}
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2019-07-12 21:19:42 -04:00
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function reinforcement_learning() {
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start
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2022-03-22 18:25:58 -04:00
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python reinforce.py || error "reinforcement learning reinforce failed"
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python actor_critic.py || error "reinforcement learning actor_critic failed"
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2019-07-12 21:19:42 -04:00
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}
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function snli() {
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start
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echo "installing 'en' model if not installed"
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python -m spacy download en || { error "couldn't download 'en' model needed for snli"; return; }
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echo "training..."
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2020-07-02 00:43:39 +03:00
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python train.py --epochs 1 --dev_every 1 --no-bidirectional --dry-run || error "couldn't train snli"
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2019-07-12 21:19:42 -04:00
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}
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2022-04-15 10:55:35 -07:00
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function fx() {
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start
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2022-04-15 12:45:08 -07:00
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# python custom_tracer.py || error "fx custom tracer has failed" UnboundLocalError: local variable 'tabulate' referenced before assignment
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2022-04-15 10:55:35 -07:00
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python invert.py || error "fx invert has failed"
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python module_tracer.py || error "fx module tracer has failed"
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python primitive_library.py || error "fx primitive library has failed"
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python profiling_tracer.py || error "fx profiling tracer has failed"
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python replace_op.py || error "fx replace op has failed"
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python subgraph_rewriter_basic_use.py || error "fx subgraph has failed"
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2022-04-15 12:45:08 -07:00
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python wrap_output_dynamically.py || error "vmap output dynamically has failed"
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2022-04-15 10:55:35 -07:00
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}
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2019-07-12 21:19:42 -04:00
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function super_resolution() {
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start
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2022-09-20 16:18:33 -07:00
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python main.py --upscale_factor 3 --batchSize 4 --testBatchSize 100 --nEpochs 1 --lr 0.001 --mps || error "super resolution failed"
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2019-07-12 21:19:42 -04:00
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}
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2020-06-09 22:08:19 -07:00
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function time_sequence_prediction() {
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2019-07-12 21:19:42 -04:00
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start
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python generate_sine_wave.py || { error "generate sine wave failed"; return; }
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2020-07-02 00:43:39 +03:00
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python train.py --steps 2 || error "time sequence prediction training failed"
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2019-07-12 21:19:42 -04:00
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}
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function vae() {
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start
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python main.py --epochs 1 || error "vae failed"
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}
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2023-04-26 21:24:13 +05:30
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function vision_transformer() {
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start
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python main.py --epochs 1 --dry-run || error "vision transformer example failed"
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}
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2019-07-12 21:19:42 -04:00
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function word_language_model() {
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start
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2022-09-20 16:18:33 -07:00
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python main.py --epochs 1 --dry-run $CUDA_FLAG --mps || error "word_language_model failed"
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2019-07-12 21:19:42 -04:00
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}
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function clean() {
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cd $BASE_DIR
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2020-07-05 18:20:41 +03:00
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echo "running clean to remove cruft"
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2022-08-23 17:06:15 -07:00
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rm -rf dcgan/fake_samples_epoch_000.png \
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dcgan/netD_epoch_0.pth \
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dcgan/netG_epoch_0.pth \
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2020-07-02 00:43:39 +03:00
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dcgan/real_samples.png \
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fast_neural_style/saved_models.zip \
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fast_neural_style/saved_models/ \
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imagenet/checkpoint.pth.tar \
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imagenet/lsun/ \
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imagenet/model_best.pth.tar \
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imagenet/sample/ \
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snli/.data/ \
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snli/.vector_cache/ \
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snli/results/ \
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super_resolution/dataset/ \
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super_resolution/model_epoch_1.pth \
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time_sequence_prediction/predict*.pdf \
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time_sequence_prediction/traindata.pt \
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word_language_model/model.pt || error "couldn't clean up some files"
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2019-07-12 21:19:42 -04:00
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git checkout fast_neural_style/images/output-images/amber-candy.jpg || error "couldn't clean up fast neural style image"
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}
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function run_all() {
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2020-07-02 00:43:39 +03:00
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# cpp
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2019-07-12 21:19:42 -04:00
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dcgan
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2020-07-02 00:43:39 +03:00
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# distributed
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2019-07-12 21:19:42 -04:00
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fast_neural_style
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2022-07-01 16:02:03 -07:00
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distributed
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2019-07-12 21:19:42 -04:00
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imagenet
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mnist
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2023-02-27 05:17:16 +05:30
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mnist_forward_forward
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2019-07-12 21:19:42 -04:00
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mnist_hogwild
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2022-10-27 14:31:20 -07:00
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mnist_rnn
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2019-07-12 21:19:42 -04:00
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regression
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reinforcement_learning
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2022-05-07 19:10:31 -07:00
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siamese_network
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2019-07-12 21:19:42 -04:00
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super_resolution
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time_sequence_prediction
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vae
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2023-04-26 21:24:13 +05:30
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vision_transformer
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2019-07-12 21:19:42 -04:00
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word_language_model
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2022-04-15 10:55:35 -07:00
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fx
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2019-07-12 21:19:42 -04:00
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}
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# by default, run all examples
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if [ "" == "$EXAMPLES" ]; then
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run_all
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else
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for i in $(echo $EXAMPLES | sed "s/,/ /g")
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do
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2020-07-05 18:20:41 +03:00
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echo "Starting $i"
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2019-07-12 21:19:42 -04:00
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$i
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2020-07-05 18:20:41 +03:00
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echo "Finished $i, status $?"
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2019-07-12 21:19:42 -04:00
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done
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fi
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if [ "" == "$ERRORS" ]; then
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2020-07-05 18:20:41 +03:00
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echo "Completed successfully with status $?"
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2020-03-04 20:05:36 -05:00
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else
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2019-07-12 21:19:42 -04:00
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echo "Some examples failed:"
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printf "$ERRORS"
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2022-01-16 02:15:13 +02:00
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#Exit with error (0-255) in case of failure in one of the tests.
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2022-01-14 05:19:58 +02:00
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exit 1
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2022-01-16 02:15:13 +02:00
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2019-07-12 21:19:42 -04:00
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fi
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