[doc] Update serving docs (deepjavalibrary#312)

* [doc] Update serving docs

* Update serving/docs/modes.md

Co-authored-by: Frank Liu <[email protected]>

Co-authored-by: Frank Liu <[email protected]>

serving/docs/console.md

@@ -16,8 +16,8 @@ DJL Serving console is a DJL model server management platform that can achieve m
     * Dependency List
     * Add Dependency
     * Delete Dependency
-* Configuration management
-* restart the service
+* Configuration Management
+* Restart the Service
 
 ## Model Management
 Users can register models through a friendly interface, view the status of all models in the system and the details of each model, and perform advanced configuration operations such as `bacth_size` adjustment and equipment expansion on the model on the details page. The model inference interface provides functions such as file upload, text input, and custom headers to edit input data. For model output, the inference interface can also directly display general output format such as images and json, as well as download other streaming data files.

serving/docs/inference.md

@@ -0,0 +1,149 @@
+# DJL Serving Inference
+
+## Overview
+
+DJL Serving Inference refers to the process of loading a model into memory with DJL Serving in order to make predictions based on input data.
+
+Predictions API:
+
+`POST /predictions/{model_name}`
+
+`POST /predictions/{model_name}/{version}`
+
+Note: Including `{version}` is optional. If omitted, the latest version of the model is used.
+
+You can run inference using:
+
+1. [UI](#ui)
+2. [Curl](#curl)
+3. [Postman](#postman)
+4. [Python](#python)
+5. [Java](#java)
+
+## UI
+
+See the [Model Inference](console.md#model-inference) section of the DJL Serving Console to see how to make inference requests using UI.
+
+## Curl
+
+We can use the curl tool to send a predict request as a POST to DJL Serving's REST endpoint.
+
+In the first example, let's load an Image Classification model and make predictions. An Image Classification model generally takes images as input and output a list of categories with probabilities.
+
+```
+# Register model
+curl -X POST "http://localhost:8080/models?url=https://resources.djl.ai/demo/pytorch/traced_resnet18.zip&engine=PyTorch"
+
+# Run inference
+curl -O https://resources.djl.ai/images/kitten.jpg
+curl -X POST http://localhost:8080/predictions/traced_resnet18 -T kitten.jpg
+```
+
+The above command specifies the image as binary input. Or you can use multipart/form-data format as below:
+
+```
+curl -X POST http://localhost:8080/predictions/traced_resnet18 -F "[email protected]"
+```
+
+This should return the following result:
+
+```json
+[
+  {
+    "className": "n02123045 tabby, tabby cat",
+    "probability": 0.4021684527397156
+  },
+  {
+    "className": "n02123159 tiger cat",
+    "probability": 0.2915370762348175
+  },
+  {
+    "className": "n02124075 Egyptian cat",
+    "probability": 0.27031460404396057
+  },
+  {
+    "className": "n02123394 Persian cat",
+    "probability": 0.007626926526427269
+  },
+  {
+    "className": "n02127052 lynx, catamount",
+    "probability": 0.004957367666065693
+  }
+]
+```
+
+In the second example, we can load a HuggingFace Bert QA model and make predictions.
+
+```
+# Register model
+curl -X POST "http://localhost:8080/models?url=https://mlrepo.djl.ai/model/nlp/question_answer/ai/djl/huggingface/pytorch/deepset/bert-base-cased-squad2/0.0.1/bert-base-cased-squad2.zip&engine=PyTorch"
+
+# Run inference
+curl -k -X POST http://localhost:8080/predictions/bert_base_cased_squad2 -H "Content-Type: application/json" \
+    -d '{"question": "How is the weather", "paragraph": "The weather is nice, it is beautiful day"}'
+```
+
+The above curl command passes the data to the server using the Content-Type application/json.
+
+This should return the following result:
+
+```
+nice
+```
+
+In the third example, we can try a HuggingFace Fill Mask model. Masked model inputs masked words in a sentence and predicts which words should replace those masks.
+
+```
+# Register model
+curl -X POST "http://localhost:8080/models?url=https://mlrepo.djl.ai/model/nlp/fill_mask/ai/djl/huggingface/pytorch/bert-base-uncased/0.0.1/bert-base-uncased.zip&engine=PyTorch"
+
+# Run inference
+curl -X POST http://localhost:8080/predictions/bert_base_uncased -H "Content-Type: application/json" -d '{"data": "The man worked as a [MASK]."}'
+```
+
+The above curl command passes the data to the server using the Content-Type application/json.
+
+This should return the following result:
+
+```json
+[
+  {
+    "className": "carpenter",
+    "probability": 0.05010193586349487
+  },
+  {
+    "className": "salesman",
+    "probability": 0.027945348992943764
+  },
+  {
+    "className": "mechanic",
+    "probability": 0.02747158892452717
+  },
+  {
+    "className": "cop",
+    "probability": 0.02429874986410141
+  },
+  {
+    "className": "contractor",
+    "probability": 0.024287723004817963
+  }
+]
+```
+
+## Postman
+
+We can also send predict requests in [Postman](https://www.postman.com/) REST Client app.
+
+Refer [here](https://github.com/deepjavalibrary/djl-demo/tree/master/djl-serving/postman-client) to see how to make inference requests using Postman.
+
+## Python
+
+In Python, we'll use the [requests](https://pypi.org/project/requests/) library's POST function to post data via HTTP.
+
+Refer [here](https://github.com/deepjavalibrary/djl-demo/tree/master/djl-serving/python-client) to see how to make inference requests using Python.
+
+## Java
+
+In Java, we'll use the HttpClient to make POST requests to load model and drive model inference.
+
+Refer [here](https://github.com/deepjavalibrary/djl-demo/tree/master/djl-serving/java-client) to see how to make inference requests using Java.