refactor: move backend deploy, launch and slurm files from components to examples (#3849)

Signed-off-by: Anant Sharma <anants@nvidia.com>

components/backends/trtllm/deploy/disagg-multinode.yaml → examples/backends/trtllm/deploy/disagg-multinode.yaml

@@ -96,7 +96,7 @@ spec:
       extraPodSpec:
         mainContainer:
           image: my-registry/trtllm-runtime:my-tag
-          workingDir: /workspace/components/backends/trtllm
+          workingDir: /workspace/examples/backends/trtllm
           command:
           - python3
           - -m

components/backends/trtllm/deploy/disagg_planner.yaml → examples/backends/trtllm/deploy/disagg_planner.yaml

@@ -17,7 +17,7 @@ spec:
       extraPodSpec:
         mainContainer:
           image: my-registry/trtllm-runtime:my-tag
-          workingDir: /workspace/components/backends/trtllm
+          workingDir: /workspace/examples/backends/trtllm
           command:
             - python3
           args:

components/backends/trtllm/performance_sweeps/README.md → examples/backends/trtllm/performance_sweeps/README.md

@@ -49,7 +49,7 @@ For more finer grained details on how to launch TRTLLM backend workers with Deep
 
 Before running the scripts, ensure you have:
 1. Access to a SLURM cluster
-2. Container image of Dynamo with TensorRT-LLM built using instructions from [here](https://github.com/ai-dynamo/dynamo/tree/main/components/backends/trtllm#build-docker).
+2. Container image of Dynamo with TensorRT-LLM built using instructions from [here](https://github.com/ai-dynamo/dynamo/tree/main/docs/backends/trtllm/README.md#build-container).
 3. Model files accessible on the cluster
 4. Required environment variables set
 
@@ -69,7 +69,7 @@ export SLURM_JOB_NAME=""
 
 # NOTE: IMAGE must be set manually for now
 # To build an iamge, see the steps here:
-# https://github.com/ai-dynamo/dynamo/tree/main/components/backends/trtllm#build-docker
+# https://github.com/ai-dynamo/dynamo/tree/main/docs/backends/trtllm/README.md#build-container
 export IMAGE="<dynamo_trtllm_image>"
 
 # NOTE: In general, Deepseek R1 is very large, so it is recommended to