# TorchServe
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TorchServe is a flexible tool for serving deep learning models that have been exported from PyTorch. TorchServe comes preinstalled with the Deep Learning AMI with Conda. 

For more information on using TorchServe, see [Model Server for PyTorch Documentation](https://github.com/pytorch/serve/blob/master/docs/README.md). 

 **Topics** 

## Serve an Image Classification Model on TorchServe
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This tutorial shows how to serve an image classification model with TorchServe. It uses a DenseNet-161 model provided by PyTorch. Once the server is running, it listens for prediction requests. When you upload an image, in this case, an image of a kitten, the server returns a prediction of the top 5 matching classes out of the classes that the model was trained on. 

**To serve an example image classification model on TorchServe**

1. Connect to an Amazon Elastic Compute Cloud (Amazon EC2) instance with Deep Learning AMI with Conda v34 or later. 

1. Activate the `pytorch_p310` environment. 

   ```
   source activate pytorch_p310
   ```

1. Clone the TorchServe repository, then create a directory to store your models.  

   ```
   git clone https://github.com/pytorch/serve.git
   mkdir model_store
   ```

1. Archive the model using the model archiver. The `extra-files` param uses a file from the `TorchServe` repo, so update the path if necessary. For more information about the model archiver, see [Torch Model archiver for TorchServe.](https://github.com/pytorch/serve/blob/master/model-archiver/README.md) 

   ```
   wget https://download.pytorch.org/models/densenet161-8d451a50.pth
   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
   ```

1. Run TorchServe to start an endpoint. Adding `> /dev/null` quiets the log output. 

   ```
   torchserve --start --ncs --model-store model_store --models densenet161.mar > /dev/null
   ```

1. Download an image of a kitten and send it to the TorchServe predict endpoint: 

   ```
   curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg
   curl http://127.0.0.1:8080/predictions/densenet161 -T kitten.jpg
   ```

   The predict endpoint returns a prediction in JSON similar to the following top five predictions, where the image has a 47% probability of containing an Egyptian cat, followed by a 46% chance it has a tabby cat. 

   ```
   {
    "tiger_cat": 0.46933576464653015,
    "tabby": 0.463387668132782,
    "Egyptian_cat": 0.0645613968372345,
    "lynx": 0.0012828196631744504,
    "plastic_bag": 0.00023323058849200606
   }
   ```

1. When you finish testing, stop the server: 

   ```
   torchserve --stop
   ```

 **Other Examples** 

TorchServe has a variety of examples that you can run on your DLAMI instance. You can view them on [the TorchServe project repository examples page](https://github.com/pytorch/serve/tree/master/examples). 

 **More Info** 

 For more TorchServe documentation, including how to set up TorchServe with Docker and the latest TorchServe features, see [the TorchServe project page](https://github.com/pytorch/serve)on GitHub.