Refactor documentation

Signed-off-by: Manjunath-A-C <[email protected]>

Author: manju956

examples/tritonserver/Containerfile

@@ -1,4 +1,4 @@
-FROM na.artifactory.swg-devops.com/sys-pcloud-docker-local/devops/pim/base
+FROM quay.io/<account id>/pim:base
 
 COPY tritonserver_config.sh /usr/bin/
 COPY tritonserver_config.service /etc/systemd/system

examples/tritonserver/README.md

@@ -3,31 +3,39 @@
 Triton inference server can be used to serve machine learning or deep learning models like classification, regression etc on CPU/GPU platforms.
 Triton inference server is built on top of base image [here](../../base-image/)
 
-## Steps to setup e2e inference flow
+## Build PIM triton server
+**Step 1: Build Base image**
+Follow the steps provided [here](../../base-image/README.md) to build the base image.
+
+**Step 2: Build triton server PIM image**
+Ensure to replace the `FROM` image in [Containerfile](Containerfile) with the base image you have built before building this image.
 
-## Step 1: Building the images
-Build container image for AI example application covered in [ai-demos](https://github.com/PDeXchange/ai-demos) using [build-steps](app/README.md)
-To reuse the built container image, push the built image to container registry.
 ```shell
-podman push <registry>/build_env
+podman build -t <your_registry>/pim-triton-server
+
+podman push <your_registry>/pim-triton-server
 ```
 
-## Step2: Train the model
-Model with ONNX runtime can be trained by running the container image built in Step 1. Follow the [training steps](app/README.md)
-After the successful training completion, model(mode.onnx) and config(config.pbtxt) files will be available in path **<current_dir>/app/model_repository/fraud**
+## Steps to setup e2e inference flow
 
-### Setting up PIM partition
-Follow this [deployer section](../../README.md#deployer-steps) to setup PIM cli, configuring your AI partition and launching it.
+### Step 1: Preparing the model and config file
+As mentioned earlier triton inference server can be used to serve any machine learning models with their respective model and configuration files stored in model repository. You can build your model and config file for your use case. 
+To show case the e2e flow of triton inference server deployment from PIM, we will be utilising the existing application [fraud-detection](https://github.com/PDeXchange/ai-demos/tree/main/02_Fraud_Detection). Please follow below steps to build the model and config file.
 
-Regarding configuration of AI application served from triton server, user need to provide generated model artifacts like model file and config file to the PIM partition as shown below in `ai.config-json` section.
-```ini
-  config-json = """
-  {
-    "modelSource": "http://<Host/IP>/fraud_detection/model.onnx",
-    "configSource": "http://<Host/IP>/fraud_detection/config.pbtxt"
-  }
-```
-Both of the model files will be available in `<current_dir>/model_repository/fraud` dir on the machine wher you have trained the model. Store these files in a simple HTTP server and pass the URI path to the PIM partition like above.
+#### Step I: Building the image
+To easily train the model with the provided python application, we have provided a Containerfile with the necessary packages, environment and tools to run the python application which can train the model for you. the source files for the python application will be volume mounted during training.
+
+Build the container image for AI example application covered in [ai-demos](https://github.com/PDeXchange/ai-demos) using [build-steps](app/README.md)
+
+To reuse the built container image, push the built image to container registry.
+`podman push <registry>/build_env`
+
+#### Step II: Train the model
+Model with ONNX runtime can be trained by running the container image built in Step I. Follow the [training steps](app/README.md)
+After the successful training completion, model(mode.onnx) and config(config.pbtxt) files will be available in path **<current_dir>/app/model_repository/fraud**
+
+### Step 2: Store model artifacts in a model repository
+Store both model file(model.onnx) and config file(config.pbtxt) in a simple HTTP server
 
 #### Steps to start http server and copy the model artifacts
 ```shell
@@ -41,9 +49,23 @@ cp <current_dir>/model_repository/fraud/config.pbtxt /var/www/html/fraud_detecti
 cp <current_dir>/model_repository/fraud/1/model.onnx /var/www/html/fraud_detection/
 ```
 
-### Validate AI application functionality
-To verify AI example application served from Triton server, Apply below speicifed configurations in [config.ini](../../config.ini).  
-Sample JSON payload is provided for fraud detection usecase. Feed the appropriate JSON payload specific to AI example app to be served from triton.
+### Step 3: Setting up PIM partition
+Follow this [deployer section](../../README.md#deployer-steps) to setup PIM cli, configuring your AI partition and launching it.
+
+Regarding configuration of AI application served from triton server, user need to provide generated model artifacts like model file and config file to the PIM partition as shown below in `ai.config-json` section.
+```ini
+  config-json = """
+  {
+    "modelSource": "http://<Host/IP>/fraud_detection/model.onnx",
+    "configSource": "http://<Host/IP>/fraud_detection/config.pbtxt"
+    "aiApp": "fraud_detection"
+  }
+```
+modelSource and configSource are the URI path to the model artifacts stored on the model repository covered in Step 2. Specify name of the AI application for which model and config files need to be pulled from model repository.
+
+### Step 4: Validate AI application functionality
+To verify AI example application served from Triton server, feed the ai.validation section with application specific REST schema like URL, headers and payload. If you have built and trained model for fraud detection usecase, apply below speicifed configurations in [config.ini](../../config.ini).  
+
 
 ```ini
   [[validation]]
@@ -114,16 +136,3 @@ Once PIM partition is deployed with triton server serving the model of configure
   }]
 }
 ```
-
-### Build PIM triton server
-**Step 1: Build Base image**
-Follow the steps provided [here](../../base-image/README.md) to build the base image.
-
-**Step 2: Build triton server PIM image**
-Ensure to replace the `FROM` image in [Containerfile](Containerfile) with the base image you have built before building this image.
-
-```shell
-podman build -t <your_registry>/pim-triton-server
-
-podman push <your_registry>/pim-triton-server
-```

examples/tritonserver/app/README.md

@@ -4,6 +4,12 @@
 Users can deploy AI workloads of their choice of model and configuration by supplying the trained model file(model.onnx) and configuration file (config.pbtxt) to http server to be used by Triton server when its run on a PIM partition.
 
 ## Fraud detection usecase with ONNX runtime
+### Pre-requisites
+Below mentioned pre-requisites are needed to build container image for fraud detection example
+- podman
+- container registry to push the built fraud detection container image
+- protobuf
+
 ### Build fraud detection container image
 The [script](build_and_train.sh) builds the base container image for the AI example applications given in [ai-demos](https://github.com/PDeXchange/ai-demos). AI application name for which container image to be built is given as an argument to the script.
 ```shell

examples/tritonserver/tritonserver.container

@@ -9,7 +9,7 @@ RestartSec=60
 EnvironmentFile=/etc/pim/env.conf
 
 [Container]
-Image=na.artifactory.swg-devops.com/sys-linux-power-team-ftp3distro-docker-images-docker-local/tritonserver:latest
+Image=quay.io/powercloud/tritonserver:latest
 ContainerName=tritonserver
 EnvironmentFile=/etc/pim/tritonserver.conf
 Network=host

examples/tritonserver/tritonserver_config.sh

@@ -4,22 +4,20 @@ set -x
 
 [ -f /etc/pim/tritonserver.conf ] || touch /etc/pim/tritonserver.conf
 
-# List of AI applications to be served from tritonserver
-ai_apps=("fraud")
+AI_APP=$(jq -r '.aiApp' /etc/pim/pim_config.json)
+echo "Application: ${AI_APP}"
 
-for app in "${ai_apps[@]}"; do
-    mkdir -p /var/models/model_repository/${app}/1
+mkdir -p /var/models/model_repository/${AI_APP}/1
 
-    ONNX_MODEL_SOURCE=$(jq -r '.modelSource' /etc/pim/pim_config.json)
-    if [[ -n "$ONNX_MODEL_SOURCE" ]]; then
-    curl "$ONNX_MODEL_SOURCE" --output /var/models/model_repository/${app}/1/model.onnx
-    fi
+ONNX_MODEL_SOURCE=$(jq -r '.modelSource' /etc/pim/pim_config.json)
+if [[ -n "$ONNX_MODEL_SOURCE" ]]; then
+curl "$ONNX_MODEL_SOURCE" --output /var/models/model_repository/${AI_APP}/1/model.onnx
+fi
 
-    CONFIG_FILE=$(jq -r '.configSource' /etc/pim/pim_config.json)
-    if [[ -n "$CONFIG_FILE" ]]; then
-        curl "$CONFIG_FILE" --output /var/models/model_repository/${app}/config.pbtxt
-    fi
-done
+CONFIG_FILE=$(jq -r '.configSource' /etc/pim/pim_config.json)
+if [[ -n "$CONFIG_FILE" ]]; then
+    curl "$CONFIG_FILE" --output /var/models/model_repository/${AI_APP}/config.pbtxt
+fi
 
 var_to_add=MODEL_REPOSITORY=/var/models/model_repository
 sed -i "/^MODEL_REPOSITORY=.*/d"  /etc/pim/tritonserver.conf && echo "$var_to_add" >> /etc/pim/tritonserver.conf