diff --git a/content/en/docs/components/trainer/user-guides/local-execution-mode/docker.md b/content/en/docs/components/trainer/user-guides/local-execution-mode/docker.md
index 9a51358c40..e64f07294f 100644
--- a/content/en/docs/components/trainer/user-guides/local-execution-mode/docker.md
+++ b/content/en/docs/components/trainer/user-guides/local-execution-mode/docker.md
@@ -15,6 +15,30 @@ The Container Backend with Docker enables you to run distributed TrainJobs in is
 
 The Docker backend uses the adapter pattern to provide a unified interface, making it easy to switch between Docker and Podman without code changes.
 
+## Architecture
+
+The Container Backend with Docker uses a local orchestration layer to manage TrainJobs within Docker containers. This ensures environment parity between your local machine and production Kubernetes clusters.
+
+```mermaid
+graph LR
+    User([User Script]) -->|TrainerClient.train| SDK[Kubeflow SDK]
+    
+    SDK -->|1. Pull| Image[Docker Image]
+    SDK -->|2. Net| Net[Bridge Network]
+    SDK -->|3. Run| Daemon[Docker Daemon]
+    
+    subgraph DockerEnv [Local Docker Environment]
+        direction TB
+        Daemon -->|Spawn| C1[Node 0]
+        Daemon -->|Spawn| C2[Node 1]
+        C1 <-->|DDP| C2
+    end
+    
+    C1 -->|4. Logs| Logs[Stream Logs]
+    C2 -->|4. Logs| Logs
+    SDK -.->|"5. Cleanup (if auto_remove)"| Remove[Delete Containers & Network]
+```
+
 ## Prerequisites
 
 ### Required Software
diff --git a/content/en/docs/components/trainer/user-guides/local-execution-mode/local_process.md b/content/en/docs/components/trainer/user-guides/local-execution-mode/local_process.md
index beedb3cf92..e347d21dfb 100644
--- a/content/en/docs/components/trainer/user-guides/local-execution-mode/local_process.md
+++ b/content/en/docs/components/trainer/user-guides/local-execution-mode/local_process.md
@@ -217,6 +217,27 @@ finally:
     client.delete_job(job_name)
 ```
 
+## Architecture
+
+The Local Process Backend operates by orchestrating native OS processes. It bypasses container runtimes like Docker, instead managing the lifecycle of your training script through isolated Python Virtual Environments (venvs).
+
+```mermaid
+graph LR
+    User([User Script]) -->|TrainerClient.train| SDK[Kubeflow SDK]
+    
+    SDK -->|1. Generate| Script[Bash Script]
+    
+    subgraph LocalExec ["Single Subprocess (bash -c)"]
+        direction TB
+        Script -->|2. Execute| Venv[Create Venv + pip]
+        Venv --> Deps[Install Dependencies]
+        Deps --> Train[Run Entrypoint]
+        Train -.-> Clean["Delete Venv (if cleanup_venv)"]
+    end
+    
+    Train -->|3. Logs| Logs[Stream Logs]
+```
+
 ## How It Works
 
 Understanding the internal workflow helps with debugging and optimization:
diff --git a/content/en/docs/components/trainer/user-guides/local-execution-mode/podman.md b/content/en/docs/components/trainer/user-guides/local-execution-mode/podman.md
index f01d3e663e..ff60c0a8c1 100644
--- a/content/en/docs/components/trainer/user-guides/local-execution-mode/podman.md
+++ b/content/en/docs/components/trainer/user-guides/local-execution-mode/podman.md
@@ -168,6 +168,32 @@ backend_config = ContainerBackendConfig(
 )
 ```
 
+## Architecture
+
+The Container Backend with Podman uses a local orchestration layer to manage TrainJobs within Podman containers. This ensures environment parity between your local machine and production Kubernetes clusters.
+
+```mermaid
+graph LR
+    User([User Script]) -->|TrainerClient.train| SDK[Kubeflow SDK]
+    
+    SDK -->|1. Pull| Image[Podman Image]
+    SDK -->|2. Net| Net[DNS-Enabled Bridge Network]
+    SDK -->|3. Run| Podman[Podman Engine]
+    
+    subgraph PodmanEnv [Local Podman Environment]
+        direction TB
+        Podman -->|Spawn| C1[Node 0]
+        Podman -->|Spawn| C2[Node 1]
+        C1 <-->|DDP| C2
+    end
+    
+    C1 -->|4. Logs| Logs[Stream Logs]
+    C2 -->|4. Logs| Logs
+    SDK -.->|"5. Cleanup (if auto_remove)"| Remove[Delete Containers & Network]
+```
+
+
+
 ## Multi-Node Distributed Training
 
 The Podman backend automatically sets up networking and environment variables for distributed training: