commands used in the tutorial

tutorial_commands.sh

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+# clone our tutorial code
+git clone https://github.com/bumic/BostonHacks2020Workshop.git
+cd BostonHacks2020Workshop
+
+# load modules (only applicable if you're using BU's Shared Computing Cluster)
+# You will need to install java and python on your machine.
+module load openjdk/11.0.2
+module load python3/3.7.7
+module load tensorflow/2.1.0
+module load pytorch/1.6.0
+
+# use python virtual environment for best practice
+virtualenv venv
+source venv/bin/activate
+
+# install torchserve
+pip install torchserve torch-model-archiver
+
+# clone TorchServe repo
+git clone https://github.com/pytorch/serve.git
+
+# create model_store directory
+mkdir model_store
+# download densenet161 pretrained model weights
+wget https://download.pytorch.org/models/densenet161-8d451a50.pth
+# archive the model weights to the model_store directory
+torch-model-archiver --model-name densenet161 --version 1.0 --model-file ./serve/examples/image_classifier/densenet_161/model.py --serialized-file densenet161-8d451a50.pth --export-path model_store --extra-files ./serve/examples/image_classifier/index_to_name.json --handler image_classifier
+
+# start TorchServe
+torchserve --start --ncs --model-store model_store --models densenet161.mar
+
+# In a new window, try to use the API
+# Download an example image
+curl -O https://raw.githubusercontent.com/pytorch/serve/master/docs/images/kitten_small.jpg
+# Classify the image with a POST request
+curl http://127.0.0.1:8080/predictions/densenet161 -T kitten_small.jpg
+
+# Let's do the same thing in python
+module load python3/3.7.7
+# run out example app code
+python example_app.py
+
+# stop TorchServe
+torchserve --stop