documentation: Remove redundant curl options in example (#336)

* documentation: fixed issues in installation and quick start in README.md

* documentation: Reverting conda related doc changes from PR#286

Fixes #297

* remove minus y (#311)

update pip install instructions

* fix-doc

* fix remaining "-X POST"s

Co-authored-by: eslesar-aws <eslesar@amazon.com>
Co-authored-by: Aaqib Ansari <maaquib@gmail.com>
Co-authored-by: Geeta Chauhan <4461127+chauhang@users.noreply.github.com>
Co-authored-by: Aaron Markham <markhama@amazon.com>

README.md

@@ -186,7 +186,7 @@ The following code completes all three steps:
 
 ```bash
 curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg
-curl -X POST http://127.0.0.1:8080/predictions/densenet161 -T kitten.jpg
+curl http://127.0.0.1:8080/predictions/densenet161 -T kitten.jpg
 ```
 
 The predict endpoint returns a prediction response in JSON. It will look something like the following result:

docs/batch_inference_with_ts.md

@@ -135,7 +135,7 @@ $ curl localhost:8081/models/resnet-152
   * Run inference to test the model.
 
     ```text
-      $ curl -X POST localhost/predictions/resnet-152 -T kitten.jpg
+      $ curl localhost/predictions/resnet-152 -T kitten.jpg
       {
         "probability": 0.7148938179016113,
         "class": "n02123045 tabby, tabby cat"

docs/inference_api.md

@@ -47,11 +47,11 @@ To get predictions from the default version of each loaded model, make a REST ca
 ```bash
 curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg
 
-curl -X POST http://localhost:8080/predictions/resnet-18 -T kitten.jpg
+curl http://localhost:8080/predictions/resnet-18 -T kitten.jpg
 
 or:
 
-curl -X POST http://localhost:8080/predictions/resnet-18 -F "data=@kitten.jpg"
+curl http://localhost:8080/predictions/resnet-18 -F "data=@kitten.jpg"
 ```
 
 To get predictions from a specific version of each loaded model, make a REST call to `/predictions/{model_name}/{version}`:
@@ -63,11 +63,11 @@ To get predictions from a specific version of each loaded model, make a REST cal
 ```bash
 curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg
 
-curl -X POST http://localhost:8080/predictions/resnet-18/2.0 -T kitten.jpg
+curl http://localhost:8080/predictions/resnet-18/2.0 -T kitten.jpg
 
 or:
 
-curl -X POST http://localhost:8080/predictions/resnet-18/2.0 -F "data=@kitten.jpg"
+curl http://localhost:8080/predictions/resnet-18/2.0 -F "data=@kitten.jpg"
 ```
 
 The result is JSON that tells you that the image is most likely a tabby cat. The highest prediction was:

examples/Huggingface_Transformers/README.md

@@ -84,4 +84,4 @@ torchserve --start --model-store model_store --models my_tc=RobertaQA.mar
 
 ```
 
-- To run the inference using our registered model, open a new terminal and run: `curl -X POST http://127.0.0.1:8080/predictions/my_tc -T ./sample_text.txt`
+- To run the inference using our registered model, open a new terminal and run: `curl http://127.0.0.1:8080/predictions/my_tc -T ./sample_text.txt`

examples/image_classifier/README.md

@@ -22,7 +22,7 @@
     mkdir model_store
     mv densenet161.mar model_store/
     torchserve --start --model-store model_store --models densenet161=densenet161.mar
-    curl -X POST http://127.0.0.1:8080/predictions/densenet161 -T examples/image_classifier/kitten.jpg
+    curl http://127.0.0.1:8080/predictions/densenet161 -T examples/image_classifier/kitten.jpg
 
 
 #### TorchScript example using DenseNet161 image classifier:
@@ -57,7 +57,7 @@
     mkdir model_store
     mv densenet161_ts.mar model_store/
     torchserve --start --model-store model_store --models densenet161=densenet161_ts.mar
-    curl -X POST http://127.0.0.1:8080/predictions/densenet161 -T examples/image_classifier/kitten.jpg
+    curl http://127.0.0.1:8080/predictions/densenet161 -T examples/image_classifier/kitten.jpg
     ```
 #### TorchScript example using custom model and custom handler:
 

examples/image_classifier/mnist/README.md

@@ -31,6 +31,6 @@ Run the commands given in following steps from the parent directory of the root
     mkdir model_store
     mv mnist.mar model_store/
     torchserve --start --model-store model_store --models mnist=mnist.mar
-    curl -X POST http://127.0.0.1:8080/predictions/mnist -T ./serve/examples/image_classifier/mnist/test_data/0.png
+    curl http://127.0.0.1:8080/predictions/mnist -T ./serve/examples/image_classifier/mnist/test_data/0.png
     ```
 

examples/image_classifier/resnet_152_batch/README.md

@@ -20,9 +20,9 @@ The above commands will create the mar file and register the resnet152 model wit
 To test batch inference execute the following commands within the specified max_batch_delay time :
 
 ```bash
-curl -X POST http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/croco.jpg &
-curl -X POST http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/dog.jpg &
-curl -X POST http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/kitten.jpg &
+curl http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/croco.jpg &
+curl http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/dog.jpg &
+curl http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/kitten.jpg &
 ```
 
 #### TorchScript example using Resnet152 batch image classifier:
@@ -63,7 +63,7 @@ curl -X POST http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/ima
 * To test batch inference execute the following commands within the specified max_batch_delay time :
 
 ```bash
-curl -X POST http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/croco.jpg &
-curl -X POST http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/dog.jpg &
-curl -X POST http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/kitten.jpg &
+curl http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/croco.jpg &
+curl http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/dog.jpg &
+curl http://127.0.0.1:8080/predictions/resnet152 -T ./serve/examples/image_classifier/resnet_152_batch/images/kitten.jpg &
 ```

examples/image_classifier/resnet_18/README.md

@@ -8,7 +8,7 @@ torch-model-archiver --model-name resnet-18 --version 1.0 --model-file ./serve/e
 mkdir model_store
 mv resnet-18.mar model_store/
 torchserve --start --model-store model_store --models resnet-18=resnet-18.mar
-curl -X POST http://127.0.0.1:8080/predictions/resnet-18 -T ./serve/examples/image_classifier/kitten.jpg
+curl http://127.0.0.1:8080/predictions/resnet-18 -T ./serve/examples/image_classifier/kitten.jpg
 ```
 
 #### TorchScript example using Resnet18 image classifier:
@@ -43,5 +43,5 @@ curl -X POST http://127.0.0.1:8080/predictions/resnet-18 -T ./serve/examples/ima
     mkdir model_store
     mv resnet-18.mar model_store/
     torchserve --start --model-store model_store --models resnet-18=resnet-18.mar
-    curl -X POST http://127.0.0.1:8080/predictions/resnet-18 -T ./serve/examples/image_classifier/kitten.jpg
+    curl http://127.0.0.1:8080/predictions/resnet-18 -T ./serve/examples/image_classifier/kitten.jpg
     ```

examples/image_segmenter/README.md

@@ -15,7 +15,7 @@ wget https://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pth
     mkdir model_store
     mv fcn_resnet_101.mar model_store/
     torchserve --start --model-store model_store --models fcn=fcn_resnet_101.mar
-    curl -X POST http://127.0.0.1:8080/predictions/fcn -T examples/image_segmenter/fcn/persons.jpg
+    curl http://127.0.0.1:8080/predictions/fcn -T examples/image_segmenter/fcn/persons.jpg
     ```
 * Output
 

examples/object_detector/fast-rcnn/README.md

@@ -15,7 +15,7 @@ wget https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.p
     mkdir model_store
     mv fastrcnn.mar model_store/
     torchserve --start --model-store model_store --models fastrcnn=fastrcnn.mar
-    curl -X POST http://127.0.0.1:8080/predictions/fastrcnn -T serve/examples/object_detector/persons.jpg
+    curl http://127.0.0.1:8080/predictions/fastrcnn -T serve/examples/object_detector/persons.jpg
     ```
 * Output
 

examples/object_detector/maskrcnn/README.md

@@ -15,7 +15,7 @@ wget https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
     mkdir model_store
     mv maskrcnn.mar model_store/
     torchserve --start --model-store model_store --models maskrcnn=maskrcnn.mar
-    curl -X POST http://127.0.0.1:8080/predictions/maskrcnn -T serve/examples/object_detector/persons.jpg
+    curl http://127.0.0.1:8080/predictions/maskrcnn -T serve/examples/object_detector/persons.jpg
     ```
 * Output
 

examples/text_classification/README.md

@@ -30,5 +30,5 @@ The above command generated the model's state dict as model.pt and the vocab use
     mkdir model_store
     mv my_text_classifier.mar model_store/
     torchserve --start --model-store model_store --models my_tc=my_text_classifier.mar
-    curl -X POST http://127.0.0.1:8080/predictions/my_tc -T examples/text_classification/sample_text.txt
+    curl http://127.0.0.1:8080/predictions/my_tc -T examples/text_classification/sample_text.txt
     ```

examples/text_classification/Transformers_README.md

@@ -41,6 +41,5 @@ torchserve --start --model-store model_store --models my_tc=BertSeqClassificatio
 #### Get predictions from a model:
 
 ```
-curl -X POST http://127.0.0.1:8080/predictions/my_tc -T ./sample_text.txt
+curl http://127.0.0.1:8080/predictions/my_tc -T ./sample_text.txt
 ```
-

examples/text_to_speech_synthesizer/README.md

@@ -34,7 +34,7 @@ pip install librosa --user
     ```
   * Run inference and download audio output using curl command : 
     ```bash
-    curl -X POST http://127.0.0.1:8080/predictions/waveglow_synthesizer -T sample_text.txt -o audio.wav
+    curl http://127.0.0.1:8080/predictions/waveglow_synthesizer -T sample_text.txt -o audio.wav
     ```
 
   * Run inference and download audio output using python script :