# Starting DJL Serving Use the following command to start model server locally: ```sh djl-serving ``` The model server will be listening on port 8080. You can also load a model for serving on start up: ```sh djl-serving -m "https://resources.djl.ai/demo/mxnet/resnet18_v1.zip" ``` Open another terminal, and type the following command to test the inference REST API: ```sh curl -O https://resources.djl.ai/images/kitten.jpg curl -X POST http://localhost:8080/predictions/resnet18_v1 -T kitten.jpg or: curl -X POST http://localhost:8080/predictions/resnet18_v1 -F "data=@kitten.jpg" [ { "className": "n02123045 tabby, tabby cat", "probability": 0.4838452935218811 }, { "className": "n02123159 tiger cat", "probability": 0.20599420368671417 }, { "className": "n02124075 Egyptian cat", "probability": 0.18810515105724335 }, { "className": "n02123394 Persian cat", "probability": 0.06411745399236679 }, { "className": "n02127052 lynx, catamount", "probability": 0.010215568356215954 } ] ``` ### Examples for loading models ```shell # Load models from the DJL model zoo on startup djl-serving -m "djl://ai.djl.pytorch/resnet" # Load version v1 of a PyTorch model on GPU(0) from the local file system djl-serving -m "resnet:v1:PyTorch:0=file:$HOME/models/pytorch/resnet18/" # Load a TensorFlow model from TFHub djl-serving -m "resnet=https://tfhub.dev/tensorflow/resnet_50/classification/1" ``` ### Examples for customizing data processing ```shell # Use the default data processing for a well-known application djl-serving -m "file:/resnet?application=CV/image_classification" # Specify a custom data processing with a Translator djl-serving -m "file:/resnet?translatorFactory=MyFactory" ## Pass parameters for data processing djl-serving -m "djl://ai.djl.pytorch/resnet?applySoftmax=false" ``` ### Using DJL Extensions ```shell # Load a model from an AWS S3 Bucket djl-serving -m "s3://djl-ai/demo/resnet/resnet18.zip" # Load a model from HDFS djl-serving -m "hdfs://localhost:50070/models/pytorch/resnet18/" # Use a HuggingFace tokenizer djl-serving -m "file:/bertqa?transaltorFactory=ai.djl.huggingface.BertQATranslator" ```