docs: clean up toctree navigation and add disaggregated serving guide (#6024)

Signed-off-by: Dan Gil <dagil@nvidia.com>
Co-authored-by: Cursor <cursoragent@cursor.com>

docs/_sections/fault_tolerance.rst

-Fault Tolerance
-===============
-
-.. toctree::
-   :maxdepth: 1
-
-   Overview <../fault_tolerance/README.md>
-   Request Migration <../fault_tolerance/request_migration.md>
-   Request Cancellation <../fault_tolerance/request_cancellation.md>
-   Graceful Shutdown <../fault_tolerance/graceful_shutdown.md>
-   Request Rejection <../fault_tolerance/request_rejection.md>
-   Testing <../fault_tolerance/testing.md>

docs/_sections/k8s_deployment.rst

-Deployment Guide
-================
-
-.. toctree::
-   :hidden:
-
-   Kubernetes Quickstart <../kubernetes/README>
-   Detailed Installation Guide <../kubernetes/installation_guide>
-   Dynamo Operator <../kubernetes/dynamo_operator>
-   Service Discovery <../kubernetes/service_discovery>
-   Webhooks <../kubernetes/webhooks>
-   Minikube Setup <../kubernetes/deployment/minikube>
-   Managing Models with DynamoModel <../kubernetes/deployment/dynamomodel-guide>
-   Autoscaling <../kubernetes/autoscaling>
-   Checkpointing <../kubernetes/chrek/README>

docs/_sections/k8s_multinode.rst

-Multinode
-=========
-
-.. toctree::
-   :hidden:
-
-   Multinode Deployments <../kubernetes/deployment/multinode-deployment>
-   Grove <../kubernetes/grove>

docs/_sections/k8s_observability.rst

-Observability
-=============
-
-.. toctree::
-   :hidden:
-
-   Metrics <../kubernetes/observability/metrics>
-   Logging <../kubernetes/observability/logging>
-   Operator Metrics <../kubernetes/observability/operator-metrics>

docs/_sections/observability.rst

-Observability
-=============
-
-.. toctree::
-   :hidden:
-
-   Overview <../observability/README>
-   Prometheus + Grafana Setup <../observability/prometheus-grafana>
-   Metrics <../observability/metrics>
-   Metrics Developer Guide <../observability/metrics-developer-guide>
-   Health Checks <../observability/health-checks>
-   Tracing <../observability/tracing>
-   Logging <../observability/logging>

docs/components/frontend/README.md

@@ -79,3 +79,9 @@ See the [Frontend Guide](frontend_guide.md) for full configuration options.
 |----------|-------------|
 | [Frontend Guide](frontend_guide.md) | KServe gRPC configuration and integration |
 | [Router Documentation](../router/README.md) | KV-aware routing configuration |
+
+```{toctree}
+:hidden:
+
+frontend_guide
+```

docs/components/kvbm/README.md

@@ -74,3 +74,9 @@ KVBM has three primary logical layers:
 - **[FlexKV Integration](../../integrations/flexkv_integration.md)** — Use FlexKV for KV cache management
 - **[SGLang HiCache](../../integrations/sglang_hicache.md)** — Enable SGLang's hierarchical cache with NIXL
 - **[NIXL Documentation](https://github.com/ai-dynamo/nixl/blob/main/docs/nixl.md)** — NIXL communication library details
+
+```{toctree}
+:hidden:
+
+kvbm_guide
+```

docs/components/planner/README.md

@@ -134,3 +134,10 @@ The planner queries the frontend's `/metrics` endpoint via Prometheus. Required
 - Request count and duration
 - TTFT and ITL distributions
 - Input/output sequence lengths
+
+```{toctree}
+:hidden:
+
+planner_guide
+planner_examples
+```

docs/components/router/README.md

@@ -101,3 +101,10 @@ For basic model registration without KV routing, use `--router-mode round-robin`
 - **[Router Guide](router_guide.md)**: Deep dive into KV cache routing, configuration, disaggregated serving, and tuning
 - **[Router Examples](router_examples.md)**: Python API usage, K8s examples, and custom routing patterns
 - **[Router Design](../../design_docs/router_design.md)**: Architecture details, algorithms, and event transport modes
+
+```{toctree}
+:hidden:
+
+router_guide
+router_examples
+```

docs/diagrams/arch_comparison.d2

+direction: right
+
+aggregated: Aggregated {
+  width: 600
+  height: 450
+
+  frontend: Frontend {
+    width: 180
+    height: 60
+    style.font-size: 20
+  }
+  router: Router {
+    width: 180
+    height: 60
+    style.font-size: 20
+  }
+  w1: "W1 (TP2)" {
+    width: 180
+    height: 60
+    style.font-size: 20
+  }
+  w2: "W2 (TP2)" {
+    width: 180
+    height: 60
+    style.font-size: 20
+  }
+  w3: "W3 (TP2)" {
+    width: 180
+    height: 60
+    style.font-size: 20
+  }
+  w4: "W4 (TP2)" {
+    width: 180
+    height: 60
+    style.font-size: 20
+  }
+
+  frontend -> router
+  router -> w1
+  router -> w2
+  router -> w3
+  router -> w4
+
+  note: |md
+    Each worker handles both prefill and decode.
+  |
+  note.style.font-size: 18
+}
+
+disaggregated: Disaggregated {
+  width: 600
+  height: 450
+
+  frontend: Frontend {
+    width: 180
+    height: 60
+    style.font-size: 20
+  }
+  router: Router {
+    width: 180
+    height: 60
+    style.font-size: 20
+  }
+  p1: "Prefill 1 (TP2)" {
+    width: 220
+    height: 60
+    style.font-size: 20
+  }
+  p2: "Prefill 2 (TP2)" {
+    width: 220
+    height: 60
+    style.font-size: 20
+  }
+  decode: "Decode (TP4)" {
+    width: 220
+    height: 60
+    style.font-size: 20
+  }
+
+  frontend -> router
+  router -> p1
+  router -> p2
+  p1 -> decode: "KV Cache via RDMA"
+  p2 -> decode: "KV Cache via RDMA"
+
+  note: |md
+    Prefill and decode on separate workers.
+  |
+  note.style.font-size: 18
+}
+
+aggregated.style.font-size: 24
+disaggregated.style.font-size: 24

docs/diagrams/decision_flowchart.d2

+direction: down
+
+q1: "AIC shows disagg > agg\nthroughput?" {
+  shape: diamond
+}
+
+q2: "RDMA available\nin cluster?" {
+  shape: diamond
+}
+
+q3: "ISL/OSL ratio > 8:1?" {
+  shape: diamond
+}
+
+q4: "Disagg > 20%\nfaster?" {
+  shape: diamond
+}
+
+agg: "AGGREGATED\nSimpler, no RDMA needed" {
+  shape: rectangle
+  width: 380
+  height: 80
+}
+
+disagg: "DISAGGREGATED\nHigher throughput, needs RDMA" {
+  shape: rectangle
+  width: 380
+  height: 80
+}
+
+q1 -> q2: Yes
+q1 -> agg: No
+q2 -> q3: Yes
+q2 -> agg: No
+q3 -> disagg: Yes
+q3 -> q4: No
+q4 -> disagg: Yes
+q4 -> agg: No

docs/diagrams/e2e_workflow.d2

+direction: right
+
+install: {
+  label: "Install"
+  shape: rectangle
+  install_detail: |md
+    pip3 install
+    aiconfigurator
+  |
+}
+
+configure: {
+  label: "Configure"
+  shape: rectangle
+  configure_detail: |md
+    Model, GPUs,
+    SLA targets
+  |
+}
+
+compare: {
+  label: "Compare"
+  shape: rectangle
+  compare_detail: |md
+    Agg vs disagg
+    rankings
+  |
+}
+
+deploy: {
+  label: "Deploy"
+  shape: rectangle
+  deploy_detail: |md
+    Apply DGD
+    manifest
+  |
+}
+
+validate: {
+  label: "Validate"
+  shape: rectangle
+  validate_detail: |md
+    Benchmark
+    with AIPerf
+  |
+}
+
+install -> configure
+configure -> compare
+compare -> deploy
+deploy -> validate

docs/diagrams/param_mapping.d2

+direction: right
+
+aic: "AIC Output" {
+  shape: rectangle
+  c1: "concurrency: 56 (=14x4)" {
+    width: 280
+  }
+  c2: "ISL: 4000, OSL: 500" {
+    width: 280
+  }
+  c3: "Model: Qwen3-32B-FP8" {
+    width: 280
+  }
+  c4: "concurrency x ~14" {
+    width: 280
+  }
+  c5: "(best practice)" {
+    width: 280
+  }
+}
+
+aiperf: "AIPerf Argument" {
+  shape: rectangle
+  a1: "--concurrency 56" {
+    width: 320
+  }
+  a2: "--isl 4000 --osl 500" {
+    width: 320
+  }
+  a3: "-m Qwen/Qwen3-32B-FP8" {
+    width: 320
+  }
+  a4: "--num-requests 800" {
+    width: 320
+  }
+  a5: "--extra-inputs \"ignore_eos:true\"" {
+    width: 320
+  }
+}
+
+aic.c1 -> aiperf.a1
+aic.c2 -> aiperf.a2
+aic.c3 -> aiperf.a3
+aic.c4 -> aiperf.a4
+aic.c5 -> aiperf.a5

docs/fault_tolerance/README.md

@@ -140,3 +140,13 @@ See [Fault Tolerance Testing](testing.md) for details.
 - [Observability](../observability/README.md) - Metrics and monitoring
 - [Distributed Runtime](../design_docs/distributed_runtime.md) - Service discovery architecture
 - [Event Plane](../design_docs/event_plane.md) - etcd and NATS coordination
+
+```{toctree}
+:hidden:
+
+Request Migration <request_migration>
+Request Cancellation <request_cancellation>
+Graceful Shutdown <graceful_shutdown>
+Request Rejection <request_rejection>
+Testing <testing>
+```

docs/features/disaggregated_serving/README.md

+<!--
+SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: Apache-2.0
+-->
+
+# Disaggregated Serving Guide
+
+[AIConfigurator](https://github.com/ai-dynamo/aiconfigurator/tree/main) is a performance optimization tool that helps you find the optimal configuration for deploying LLMs with Dynamo. It automatically determines the best number of prefill and decode workers, parallelism settings, and deployment parameters to meet your SLA targets while maximizing throughput.
+
+## Why Use AIConfigurator?
+
+When deploying LLMs with Dynamo, you need to make several critical decisions:
+- **Aggregated vs Disaggregated**: Which architecture gives better performance for your workload?
+- **Worker Configuration**: How many prefill and decode workers to deploy?
+- **Parallelism Settings**: What tensor/pipeline parallel configuration to use?
+- **SLA Compliance**: How to meet your TTFT and TPOT targets?
+
+AIConfigurator answers these questions in seconds, providing:
+- Recommended configurations that meet your SLA requirements
+- Ready-to-deploy Dynamo configuration files (including Kubernetes manifests)
+- Performance comparisons between different deployment strategies
+- Up to 1.7x better throughput compared to manual configuration
+
+### End-to-End Workflow
+
+![AIConfigurator end-to-end workflow](../../images/e2e_workflow.svg)
+
+### Aggregated vs Disaggregated Architecture
+
+AIConfigurator evaluates two deployment architectures and recommends the best one for your workload:
+
+![Aggregated vs Disaggregated architecture comparison](../../images/arch_comparison.svg)
+
+### When to Use Each Architecture
+
+![Decision flowchart for choosing aggregated vs disaggregated](../../images/decision_flowchart.svg)
+
+## Quick Start
+
+```bash
+# Install
+pip3 install aiconfigurator
+
+# Find optimal configuration for vLLM backend
+aiconfigurator cli default \
+  --model_path Qwen/Qwen3-32B-FP8 \
+  --total_gpus 8 \
+  --system h200_sxm \
+  --backend vllm \
+  --backend_version 0.12.0 \
+  --isl 4000 \
+  --osl 500 \
+  --ttft 600 \
+  --tpot 16.67 \
+  --save_dir ./results_vllm
+
+# Deploy on Kubernetes
+kubectl apply -f ./results_vllm/agg/top1/agg/k8s_deploy.yaml
+```
+
+## Complete Walkthrough: vLLM on H200
+
+This section walks through a validated example deploying Qwen3-32B-FP8 on 8× H200 GPUs using vLLM.
+
+### Step 1: Run AIConfigurator
+
+```bash
+aiconfigurator cli default \
+  --model_path Qwen/Qwen3-32B-FP8 \
+  --system h200_sxm \
+  --total_gpus 8 \
+  --isl 4000 \
+  --osl 500 \
+  --ttft 600 \
+  --tpot 16.67 \
+  --backend vllm \
+  --backend_version 0.12.0 \
+  --save_dir ./results_vllm
+```
+
+**Parameters explained:**
+- `--model_path`: HuggingFace model ID or local path (e.g., `Qwen/Qwen3-32B-FP8`)
+- `--system`: GPU system type (`h200_sxm`, `h100_sxm`, `a100_sxm`)
+- `--total_gpus`: Number of GPUs available for deployment
+- `--isl` / `--osl`: Input/Output sequence lengths in tokens
+- `--ttft` / `--tpot`: SLA targets - Time To First Token (ms) and Time Per Output Token (ms)
+- `--backend`: Inference backend (`vllm`, `trtllm`, or `sglang`)
+- `--backend_version`: Backend version (e.g., `0.12.0` for vLLM)
+- `--save_dir`: Directory to save generated deployment configs
+
+### Step 2: Review the Results
+
+AIConfigurator outputs a comparison of aggregated vs disaggregated deployment strategies:
+
+```text
+********************************************************************************
+*                     Dynamo aiconfigurator Final Results                      *
+********************************************************************************
+  ----------------------------------------------------------------------------
+  Input Configuration & SLA Target:
+    Model: Qwen/Qwen3-32B-FP8 (is_moe: False)
+    Total GPUs: 8
+    Best Experiment Chosen: disagg at 521.77 tokens/s/gpu
+  ----------------------------------------------------------------------------
+  Overall Best Configuration:
+    - Best Throughput: 4,174.16 tokens/s
+    - Per-GPU Throughput: 521.77 tokens/s/gpu
+    - Per-User Throughput: 76.96 tokens/s/user
+    - TTFT: 388.11ms
+    - TPOT: 12.99ms
+  ----------------------------------------------------------------------------
+```
+
+AIC evaluates both aggregated and disaggregated architectures and outputs ranked configurations for each:
+
+```text
+agg Top Configurations: (Sorted by tokens/s/gpu)
++------+--------------+---------------+--------+-----------------+-------------+-------------------+----------+----------+----+
+| Rank | tokens/s/gpu | tokens/s/user |  TTFT  | request_latency | concurrency | total_gpus (used) | replicas | parallel | bs |
++------+--------------+---------------+--------+-----------------+-------------+-------------------+----------+----------+----+
+|  1   |    397.31    |     60.66     | 509.14 |     8734.68     | 56 (=14x4)  |    8 (8=4x2)      |    4     |  tp2pp1  | 14 |
+|  2   |    349.90    |     60.98     | 412.58 |     8596.28     | 48 (=24x2)  |    8 (8=2x4)      |    2     |  tp4pp1  | 24 |
+|  3   |    235.62    |     62.71     | 482.57 |     8439.41     | 32 (=32x1)  |    8 (8=1x8)      |    1     |  tp8pp1  | 32 |
++------+--------------+---------------+--------+-----------------+-------------+-------------------+----------+----------+----+
+
+disagg Top Configurations: (Sorted by tokens/s/gpu)
++------+--------------+---------------+--------+-----------------+-------------+-------------------+----------+-------------+----------+
+| Rank | tokens/s/gpu | tokens/s/user |  TTFT  | request_latency | concurrency | total_gpus (used) | replicas | (p)parallel | (d)parallel |
++------+--------------+---------------+--------+-----------------+-------------+-------------------+----------+-------------+----------+
+|  1   |    521.77    |     76.96     | 388.11 |     6871.61     | 60 (=60x1)  |    8 (8=1x8)      |    1     |   tp2pp1    |  tp4pp1  |
+|  2   |    521.77    |     63.29     | 388.11 |     8272.31     | 80 (=40x2)  |    8 (8=2x4)      |    2     |   tp2pp1    |  tp2pp1  |
+|  3   |    260.89    |     62.81     | 388.11 |     8332.18     | 42 (=42x1)  |    8 (8=1x8)      |    1     |   tp2pp1    |  tp1pp1  |
++------+--------------+---------------+--------+-----------------+-------------+-------------------+----------+-------------+----------+
+```
+
+**Reading the output:**
+- **tokens/s/gpu**: Overall throughput efficiency — higher is better
+- **tokens/s/user**: Per-request generation speed (inverse of TPOT)
+- **TTFT**: Predicted time to first token
+- **concurrency**: Total concurrent requests across all replicas (e.g., `56 (=14x4)` means batch size 14 × 4 replicas)
+- **agg Rank 1** recommends TP2 with 4 replicas — simpler to deploy
+- **disagg Rank 1** recommends 2 prefill workers (TP2) + 1 decode worker (TP4) — higher throughput but requires RDMA
+
+### Step 3: Deploy on Kubernetes
+
+The `--save_dir` generates ready-to-use Kubernetes manifests:
+
+```
+results_vllm/
+├── agg/
+│   └── top1/
+│       └── agg/
+│           ├── k8s_deploy.yaml    # Kubernetes DynamoGraphDeployment
+│           └── agg_config.yaml    # Engine configuration
+├── disagg/
+│   └── top1/
+│       └── disagg/
+│           ├── k8s_deploy.yaml
+│           ├── prefill_config.yaml
+│           └── decode_config.yaml
+└── pareto_frontier.png
+```
+
+#### Prerequisites
+
+Before deploying, ensure you have:
+
+1. **HuggingFace Token Secret** (for gated models):
+   ```bash
+   kubectl create secret generic hf-token-secret \
+     -n your-namespace \
+     --from-literal=HF_TOKEN="your-huggingface-token"
+   ```
+
+2. **Model Cache PVC** (recommended for faster restarts):
+   ```yaml
+   apiVersion: v1
+   kind: PersistentVolumeClaim
+   metadata:
+     name: model-cache
+     namespace: your-namespace
+   spec:
+     accessModes:
+       - ReadWriteMany
+     resources:
+       requests:
+         storage: 100Gi
+   ```
+
+#### Deploy the Configuration
+
+The generated `k8s_deploy.yaml` provides a starting point. You'll typically need to customize it for your environment:
+
+```bash
+kubectl apply -f ./results_vllm/agg/top1/agg/k8s_deploy.yaml
+```
+
+**Complete deployment example** with model cache and production settings:
+
+```yaml
+apiVersion: nvidia.com/v1alpha1
+kind: DynamoGraphDeployment
+metadata:
+  name: dynamo-agg
+  namespace: your-namespace
+spec:
+  backendFramework: vllm
+  pvcs:
+    - name: model-cache
+      create: false           # Use existing PVC
+  services:
+    Frontend:
+      componentType: frontend
+      replicas: 1
+      volumeMounts:
+        - name: model-cache
+          mountPoint: /opt/models
+      envs:
+        - name: HF_HOME
+          value: /opt/models
+      extraPodSpec:
+        mainContainer:
+          image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.8.0
+          imagePullPolicy: IfNotPresent
+
+    VLLMWorker:
+      envFromSecret: hf-token-secret
+      componentType: worker
+      replicas: 4
+      resources:
+        limits:
+          gpu: "2"
+      sharedMemory:
+        size: 16Gi            # Required for vLLM
+      volumeMounts:
+        - name: model-cache
+          mountPoint: /opt/models
+      envs:
+        - name: HF_HOME
+          value: /opt/models
+      extraPodSpec:
+        mainContainer:
+          image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.8.0
+          workingDir: /workspace
+          imagePullPolicy: IfNotPresent
+          command:
+            - python3
+            - -m
+            - dynamo.vllm
+          args:
+            - --model
+            - "Qwen/Qwen3-32B-FP8"
+            - "--no-enable-prefix-caching"
+            - "--tensor-parallel-size"
+            - "2"
+            - "--pipeline-parallel-size"
+            - "1"
+            - "--data-parallel-size"
+            - "1"
+            - "--kv-cache-dtype"
+            - "fp8"
+            - "--max-model-len"
+            - "6000"
+            - "--max-num-seqs"
+            - "1024"
+```
+
+**Key deployment settings:**
+
+| Setting | Purpose | Notes |
+|---------|---------|-------|
+| `backendFramework: vllm` | Tells Dynamo which runtime to use | Required at spec level |
+| `pvcs` + `volumeMounts` | Caches model weights across restarts | Mount at `/opt/models` (not `/root/`) |
+| `HF_HOME` env var | Points HuggingFace to cache location | Must match `mountPoint` |
+| `sharedMemory.size: 16Gi` | IPC memory for vLLM | 16Gi for vLLM, 80Gi for TRT-LLM |
+| `envFromSecret` | Injects HF_TOKEN | Required for gated models |
+
+### Step 4: Validate with AIPerf
+
+After deployment, validate the predictions against actual performance using [AIPerf](https://github.com/ai-dynamo/aiperf).
+
+> **Tip**: Run AIPerf **inside the cluster** to avoid network latency affecting measurements. Use a Kubernetes Job:
+
+#### Deriving AIPerf Parameters from AIC Output
+
+To use AIPerf to benchmark an AIC-recommended configuration, you'll need to translate AIC parameters into AIPerf profiling arguments (we are working to automate this):
+
+![AIC-to-AIPerf parameter mapping](../../images/param_mapping.svg)
+
+| AIC Output | AIPerf Parameter | Notes |
+|------------|-----------------|-------|
+| `concurrency: 56 (=14x4)` | `--concurrency 56` | Use total concurrency when benchmarking via the frontend |
+| ISL/OSL targets | `--isl 4000 --osl 500` | Match your AIC inputs |
+| - | `--num-requests 800` | Use `concurrency × 40` minimum for statistical stability |
+| - | `--extra-inputs "ignore_eos:true"` | Ensures exact OSL tokens generated |
+
+> **Note on concurrency**: AIC reports concurrency as `total (=bs × replicas)`. When benchmarking through the frontend (which routes to all replicas), use the total value. If benchmarking a single replica directly, use the per-replica `bs` value instead.
+
+```yaml
+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: aiperf-benchmark
+  namespace: your-namespace
+spec:
+  template:
+    spec:
+      restartPolicy: Never
+      containers:
+      - name: aiperf
+        image: python:3.10
+        command:
+        - /bin/bash
+        - -c
+        - |
+          pip install aiperf
+          aiperf profile \
+            -m Qwen/Qwen3-32B-FP8 \
+            --endpoint-type chat \
+            -u http://dynamo-agg-frontend:8000 \
+            --isl 4000 --isl-stddev 0 \
+            --osl 500 --osl-stddev 0 \
+            --num-requests 800 \
+            --concurrency 56 \
+            --streaming \
+            --extra-inputs "ignore_eos:true" \
+            --num-warmup-requests 40 \
+            --ui-type simple
+```
+
+```bash
+kubectl apply -f aiperf-job.yaml
+kubectl logs -f -l job-name=aiperf-benchmark
+```
+
+**Validated results** (Qwen3-32B-FP8, 8× H200, TP2×4 replicas, aggregated):
+
+| Metric | AIC Prediction | Actual (avg) | Status |
+|--------|---------------|--------------|--------|
+| TTFT (ms) | 509 | 209 | Better than target |
+| ITL/TPOT (ms) | 16.49 | 15.06 | Within 10% |
+| Throughput (req/s) | ~6.3 | 6.9 | Within 10% |
+| Total Output TPS | ~3,178 | 3,462 | Within 10% |
+
+> **Note**: Actual throughput typically reaches ~85-90% of AIC predictions, with ITL/TPOT being the most accurate metric. Expect some variance between benchmark runs; running multiple times is recommended. Enable prefix caching (`--enable-prefix-caching`) for additional TTFT improvements with repeated prompts.
+
+## Fine-Tuning Your Deployment
+
+AIConfigurator provides a strong starting point. Here's how to iterate for production:
+
+### Adjusting for Actual Workload
+
+If your real workload differs from the benchmark parameters:
+
+```bash
+# For longer outputs (chat/code generation):
+# increase OSL, relax TTFT target
+aiconfigurator cli default \
+  --model_path Qwen/Qwen3-32B-FP8 \
+  --total_gpus 8 \
+  --system h200_sxm \
+  --backend vllm \
+  --backend_version 0.12.0 \
+  --isl 2000 \
+  --osl 2000 \
+  --ttft 1000 \
+  --tpot 10 \
+  --save_dir ./results_long_output
+```
+
+### Exploring Alternative Configurations
+
+Use `exp` mode to compare custom configurations:
+
+```yaml
+# custom_exp.yaml
+exps:
+  - exp_tp2
+  - exp_tp4
+
+exp_tp2:
+  mode: "patch"
+  serving_mode: "agg"
+  model_path: "Qwen/Qwen3-32B-FP8"
+  total_gpus: 8
+  system_name: "h200_sxm"
+  backend_name: "vllm"
+  backend_version: "0.12.0"
+  isl: 4000
+  osl: 500
+  ttft: 600
+  tpot: 16.67
+  config:
+    agg_worker_config:
+      tp_list: [2]
+
+exp_tp4:
+  mode: "patch"
+  serving_mode: "agg"
+  model_path: "Qwen/Qwen3-32B-FP8"
+  total_gpus: 8
+  system_name: "h200_sxm"
+  backend_name: "vllm"
+  backend_version: "0.12.0"
+  isl: 4000
+  osl: 500
+  ttft: 600
+  tpot: 16.67
+  config:
+    agg_worker_config:
+      tp_list: [4]
+```
+
+```bash
+aiconfigurator cli exp --yaml_path custom_exp.yaml --save_dir ./results_custom
+```
+
+> **Critical**: Disaggregated deployments **require RDMA** for KV cache transfer. Without RDMA, performance degrades by **40x** (TTFT increases from 355ms to 10+ seconds). See the Disaggregated Deployment section below.
+
+### Deploying Disaggregated (RDMA Required)
+
+Disaggregated deployments transfer KV cache between prefill and decode workers. **Without RDMA, this transfer becomes a severe bottleneck**, causing 40x performance degradation.
+
+#### Prerequisites for Disaggregated
+
+1. **RDMA-capable network** (InfiniBand or RoCE)
+2. **RDMA device plugin** installed on the cluster (provides `rdma/ib` resources)
+3. **ETCD and NATS** deployed (for coordination)
+
+#### Disaggregated DGD with RDMA
+
+```yaml
+apiVersion: nvidia.com/v1alpha1
+kind: DynamoGraphDeployment
+metadata:
+  name: dynamo-disagg
+  namespace: your-namespace
+spec:
+  backendFramework: vllm
+  pvcs:
+    - name: model-cache
+      create: false
+  services:
+    Frontend:
+      componentType: frontend
+      replicas: 1
+      volumeMounts:
+        - name: model-cache
+          mountPoint: /opt/models
+      envs:
+        - name: HF_HOME
+          value: /opt/models
+      extraPodSpec:
+        mainContainer:
+          image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.8.0
+          imagePullPolicy: IfNotPresent
+
+    VLLMPrefillWorker:
+      envFromSecret: hf-token-secret
+      componentType: worker
+      subComponentType: prefill
+      replicas: 2
+      resources:
+        limits:
+          gpu: "2"
+      sharedMemory:
+        size: 16Gi
+      volumeMounts:
+        - name: model-cache
+          mountPoint: /opt/models
+      envs:
+        - name: HF_HOME
+          value: /opt/models
+        - name: UCX_TLS
+          value: "rc_x,rc,dc_x,dc,cuda_copy,cuda_ipc"  # Enable RDMA transports
+        - name: UCX_RNDV_SCHEME
+          value: "get_zcopy"
+        - name: UCX_RNDV_THRESH
+          value: "0"
+      extraPodSpec:
+        mainContainer:
+          image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.8.0
+          workingDir: /workspace
+          imagePullPolicy: IfNotPresent
+          securityContext:
+            capabilities:
+              add: ["IPC_LOCK"]  # Required for RDMA memory registration
+          resources:
+            limits:
+              rdma/ib: "2"      # Request RDMA resources
+            requests:
+              rdma/ib: "2"
+          command: ["python3", "-m", "dynamo.vllm"]
+          args:
+            - --model
+            - "Qwen/Qwen3-32B-FP8"
+            - "--tensor-parallel-size"
+            - "2"
+            - "--kv-cache-dtype"
+            - "fp8"
+            - "--max-num-seqs"
+            - "1"               # Prefill workers use batch size 1
+            - --is-prefill-worker
+
+    VLLMDecodeWorker:
+      envFromSecret: hf-token-secret
+      componentType: worker
+      subComponentType: decode
+      replicas: 1
+      resources:
+        limits:
+          gpu: "4"
+      sharedMemory:
+        size: 16Gi
+      volumeMounts:
+        - name: model-cache
+          mountPoint: /opt/models
+      envs:
+        - name: HF_HOME
+          value: /opt/models
+        - name: UCX_TLS
+          value: "rc_x,rc,dc_x,dc,cuda_copy,cuda_ipc"
+        - name: UCX_RNDV_SCHEME
+          value: "get_zcopy"
+        - name: UCX_RNDV_THRESH
+          value: "0"
+      extraPodSpec:
+        mainContainer:
+          image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.8.0
+          workingDir: /workspace
+          imagePullPolicy: IfNotPresent
+          securityContext:
+            capabilities:
+              add: ["IPC_LOCK"]
+          resources:
+            limits:
+              rdma/ib: "4"
+            requests:
+              rdma/ib: "4"
+          command: ["python3", "-m", "dynamo.vllm"]
+          args:
+            - --model
+            - "Qwen/Qwen3-32B-FP8"
+            - "--tensor-parallel-size"
+            - "4"
+            - "--kv-cache-dtype"
+            - "fp8"
+            - "--max-num-seqs"
+            - "1024"            # Decode workers handle high concurrency
+            - --is-decode-worker
+```
+
+**Critical RDMA settings:**
+
+| Setting | Purpose |
+|---------|---------|
+| `rdma/ib: "N"` | Request N RDMA resources (match TP size) |
+| `IPC_LOCK` capability | Required for RDMA memory registration |
+| `UCX_TLS` env var | Enables RDMA transports (rc_x, dc_x) |
+| `UCX_RNDV_SCHEME=get_zcopy` | Zero-copy RDMA transfers |
+
+#### Verifying RDMA is Active
+
+After deployment, check the worker logs for UCX initialization:
+
+```bash
+kubectl logs <prefill-worker-pod> | grep -i "UCX\|NIXL"
+```
+
+You should see:
+```
+NIXL INFO Backend UCX was instantiated
+```
+
+If you see only TCP transports, RDMA is not active - check your RDMA device plugin and resource requests.
+
+### Tuning vLLM-Specific Parameters
+
+Override vLLM engine parameters with `--generator-set`:
+
+```bash
+aiconfigurator cli default \
+  --model_path Qwen/Qwen3-32B-FP8 \
+  --total_gpus 8 \
+  --system h200_sxm \
+  --backend vllm \
+  --backend_version 0.12.0 \
+  --isl 4000 --osl 500 \
+  --ttft 600 --tpot 16.67 \
+  --save_dir ./results_tuned \
+  --generator-set Workers.agg.kv_cache_free_gpu_memory_fraction=0.85 \
+  --generator-set Workers.agg.max_num_seqs=2048
+```
+
+Run `aiconfigurator cli default --generator-help` to see all available parameters.
+
+### Prefix Caching Considerations
+
+For workloads with repeated prefixes (e.g., system prompts):
+
+- **Enable prefix caching** when you have high prefix hit rates
+- **Disable prefix caching** (`--no-enable-prefix-caching`) for diverse prompts
+
+AIConfigurator's default predictions assume no prefix caching. Enable it post-deployment if your workload benefits.
+
+## Supported Configurations
+
+### Backends and Versions
+
+| Backend | Versions | Status |
+|---------|----------|--------|
+| TensorRT-LLM | 1.0.0rc3, 1.2.0rc5 | Production |
+| vLLM | 0.12.0 | Production |
+| SGLang | 0.5.6.post2 | Production |
+
+### Systems
+
+| GPU System | TensorRT-LLM | vLLM | SGLang |
+|------------|--------------|------|--------|
+| H200 SXM | Yes | Yes | Yes |
+| H100 SXM | Yes | Yes | Yes |
+| A100 SXM | Yes | Yes | -- |
+| B200 SXM | Yes | -- | Yes |
+| GB200 SXM | Yes | -- | -- |
+
+### Models
+
+- **Dense**: GPT, LLAMA2/3, QWEN2.5/3
+- **MoE**: Mixtral, DEEPSEEK_V3
+
+## Common Use Cases
+
+```bash
+# Strict latency SLAs (real-time chat)
+aiconfigurator cli default \
+  --model_path meta-llama/Llama-3.1-70B \
+  --total_gpus 16 \
+  --system h200_sxm \
+  --backend vllm \
+  --backend_version 0.12.0 \
+  --ttft 200 --tpot 8
+
+# High throughput (batch processing)
+aiconfigurator cli default \
+  --model_path Qwen/Qwen3-32B-FP8 \
+  --total_gpus 32 \
+  --system h200_sxm \
+  --backend trtllm \
+  --ttft 2000 --tpot 50
+
+# Request latency constraint (end-to-end SLA)
+aiconfigurator cli default \
+  --model_path Qwen/Qwen3-32B-FP8 \
+  --total_gpus 16 \
+  --system h200_sxm \
+  --backend vllm \
+  --backend_version 0.12.0 \
+  --request_latency 12000 \
+  --isl 4000 --osl 500
+```
+
+## Additional Options
+
+```bash
+# Web interface for interactive exploration
+pip3 install aiconfigurator[webapp]
+aiconfigurator webapp  # Visit http://127.0.0.1:7860
+
+# Quick config generation (no parameter sweep)
+aiconfigurator cli generate \
+  --model_path Qwen/Qwen3-32B-FP8 \
+  --total_gpus 8 \
+  --system h200_sxm \
+  --backend vllm
+
+# Check model/system support
+aiconfigurator cli support \
+  --model_path Qwen/Qwen3-32B-FP8 \
+  --system h200_sxm \
+  --backend vllm
+```
+
+## Troubleshooting
+
+### AIConfigurator Issues
+
+**Model not found**: Use the full HuggingFace path (e.g., `Qwen/Qwen3-32B-FP8` not `QWEN3_32B`)
+
+**Backend version mismatch**: Check supported versions with `aiconfigurator cli support --model_path <model> --system <system> --backend <backend>`
+
+### Deployment Issues
+
+**Pods crash with "Permission denied" on cache directory**:
+- Mount the PVC at `/opt/models` instead of `/root/.cache/huggingface`
+- Set `HF_HOME=/opt/models` environment variable
+- Ensure the PVC has `ReadWriteMany` access mode
+
+**Workers stuck in CrashLoopBackOff**:
+- Check logs: `kubectl logs <pod-name> --previous`
+- Verify `sharedMemory.size` is set (16Gi for vLLM, 80Gi for TRT-LLM)
+- Ensure HuggingFace token secret exists and is named correctly
+
+**Model download slow on every restart**:
+- Add PVC for model caching (see deployment example above)
+- Verify `volumeMounts` and `HF_HOME` are configured on workers
+
+**"Context stopped or killed" errors (disaggregated only)**:
+- Deploy ETCD and NATS infrastructure (required for KV cache transfer)
+- See [Dynamo Kubernetes Guide](/docs/kubernetes/README.md) for platform setup
+
+### Performance Issues
+
+**OOM errors**: Reduce `--max-num-seqs` or increase tensor parallelism
+
+**Performance below predictions**:
+- Verify warmup requests are sufficient (40+ recommended)
+- Check for competing workloads on the cluster
+- Ensure KV cache memory fraction is optimized
+- Run benchmarks from inside the cluster to eliminate network latency
+
+**Disaggregated TTFT extremely high (10+ seconds)**:
+This is almost always caused by **missing RDMA configuration**. Without RDMA, KV cache transfer falls back to TCP and becomes a severe bottleneck.
+
+To diagnose:
+```bash
+# Check if RDMA resources are allocated
+kubectl get pod <worker-pod> -o yaml | grep -A5 "resources:"
+
+# Check UCX transport in logs
+kubectl logs <worker-pod> | grep -i "UCX\|transport"
+```
+
+To fix:
+1. Ensure your cluster has RDMA device plugin installed
+2. Add `rdma/ib` resource requests to worker pods
+3. Add `IPC_LOCK` capability to security context
+4. Add UCX environment variables (see Disaggregated Deployment section)
+
+**Disaggregated working but throughput lower than aggregated**:
+For balanced workloads (ISL/OSL ratio between 2:1 and 10:1), aggregated is often better. Disaggregated shines for:
+- Very long inputs (ISL > 8000) with short outputs
+- Workloads needing independent prefill/decode scaling
+
+## Learn More
+
+- [AIConfigurator CLI Guide](https://github.com/ai-dynamo/aiconfigurator/blob/main/docs/cli_user_guide.md)
+- [Dynamo Deployment Guide](https://github.com/ai-dynamo/aiconfigurator/blob/main/docs/dynamo_deployment_guide.md)
+- [Dynamo Installation Guide](/docs/kubernetes/installation_guide.md)
+- [Benchmarking Guide](/docs/benchmarks/benchmarking.md)

docs/hidden_toctree.rst

@@ -12,6 +12,8 @@
    :hidden:
 
    development/runtime-guide.md
+   development/jail_stream.md
+
    api/nixl_connect/connector.md
    api/nixl_connect/descriptor.md
    api/nixl_connect/device.md
@@ -26,48 +28,20 @@
 
    kubernetes/api_reference.md
    kubernetes/deployment/create_deployment.md
-   kubernetes/deployment/dynamomodel-guide.md
-   kubernetes/chrek/README.md
    kubernetes/chrek/dynamo.md
    kubernetes/chrek/standalone.md
-
    kubernetes/fluxcd.md
-   kubernetes/grove.md
    kubernetes/model_caching_with_fluid.md
-   kubernetes/README.md
-   reference/cli.md
-   observability/metrics.md
-   integrations/kv_events_custom_engines.md
-   agents/tool-calling.md
-   development/jail_stream.md
 
-   components/planner/README.md
-   components/planner/planner_guide.md
-   components/planner/planner_examples.md
-   components/kvbm/README.md
-   components/kvbm/kvbm_guide.md
-   components/router/README.md
-   components/router/router_guide.md
-   components/router/router_examples.md
-   components/frontend/frontend_guide.md
-   design_docs/kvbm_design.md
-   integrations/flexkv_integration.md
-   integrations/sglang_hicache.md
-   fault_tolerance/README.md
-   fault_tolerance/request_migration.md
-   fault_tolerance/request_cancellation.md
-   fault_tolerance/graceful_shutdown.md
-   fault_tolerance/request_rejection.md
-   fault_tolerance/testing.md
-   design_docs/request_plane.md
-   design_docs/event_plane.md
+   reference/cli.md
+   reference/glossary.md
+   performance/tuning.md
 
-   backends/trtllm/multinode/multinode-examples.md
-   backends/trtllm/llama4_plus_eagle.md
-   backends/trtllm/kv-cache-transfer.md
-   backends/trtllm/gemma3_sliding_window_attention.md
-   backends/trtllm/gpt-oss.md
-   backends/trtllm/prometheus.md
+   backends/vllm/deepseek-r1.md
+   backends/vllm/gpt-oss.md
+   backends/vllm/multi-node.md
+   backends/vllm/prometheus.md
+   backends/vllm/prompt-embeddings.md
 
    backends/sglang/expert-distribution-eplb.md
    backends/sglang/gpt-oss.md
@@ -76,25 +50,19 @@
    backends/sglang/sglang-disaggregation.md
    backends/sglang/prometheus.md
 
-   examples/README.md
-   examples/runtime/hello_world/README.md
-
-   design_docs/distributed_runtime.md
-   design_docs/dynamo_flow.md
-
-   backends/vllm/deepseek-r1.md
-   backends/vllm/gpt-oss.md
-   integrations/lmcache_integration.md
-   backends/vllm/multi-node.md
-   backends/vllm/prometheus.md
-   backends/vllm/prompt-embeddings.md
+   backends/trtllm/multinode/multinode-examples.md
+   backends/trtllm/llama4_plus_eagle.md
+   backends/trtllm/kv-cache-transfer.md
+   backends/trtllm/gemma3_sliding_window_attention.md
+   backends/trtllm/gpt-oss.md
+   backends/trtllm/prometheus.md
 
    features/speculative_decoding/README.md
    features/speculative_decoding/speculative_decoding_vllm.md
 
+   examples/README.md
+   examples/runtime/hello_world/README.md
+
    benchmarks/kv-router-ab-testing.md
 
    mocker/mocker.md
-
-..   TODO: architecture/distributed_runtime.md and architecture/dynamo_flow.md
-     have some outdated names/references and need a refresh.

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+}
+@font-face {
+	font-family: d2-3076236447-font-semibold;
+	src: 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+}
+.d2-3076236447 .text-mono {
+	font-family: "d2-3076236447-font-mono";
+}
+@font-face {
+	font-family: d2-3076236447-font-mono;
+	src: 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+}
+.d2-3076236447 .text-mono-bold {
+	font-family: "d2-3076236447-font-mono-bold";
+}
+@font-face {
+	font-family: d2-3076236447-font-mono-bold;
+	src: 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+}
+.d2-3076236447 .text-mono-italic {
+	font-family: "d2-3076236447-font-mono-italic";
+}
+@font-face {
+	font-family: d2-3076236447-font-mono-italic;
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+}
+.d2-1215148716 .text-mono-bold {
+	font-family: "d2-1215148716-font-mono-bold";
+}
+@font-face {
+	font-family: d2-1215148716-font-mono-bold;
+	src: 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+}
+.d2-1215148716 .text-mono-italic {
+	font-family: "d2-1215148716-font-mono-italic";
+}
+@font-face {
+	font-family: d2-1215148716-font-mono-italic;
+	src: 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+}
+    @media (prefers-color-scheme: dark) {
+      .d2-1215148716 .fill-N1 { fill: #E8E8E8; }
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+}
+@font-face {
+	font-family: d2-2903904730-font-semibold;
+	src: 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+}
+.d2-2903904730 .text-mono {
+	font-family: "d2-2903904730-font-mono";
+}
+@font-face {
+	font-family: d2-2903904730-font-mono;
+	src: 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+}
+    @media (prefers-color-scheme: dark) {
+      /* Text and connection colors: near-black -> near-white */
+      .d2-2903904730 .fill-N1 { fill: #E8E8E8; }
+      .d2-2903904730 .fill-N2 { fill: #CCCCCC; }
+      .d2-2903904730 .fill-N3 { fill: #999999; }
+      .d2-2903904730 .fill-B1 { fill: #E8E8E8; }
+      .d2-2903904730 .fill-B2 { fill: #7BACFF; }
+      .d2-2903904730 .fill-B3 { fill: #6AAFDC; }
+      /* Container and node fills: light -> dark */
+      .d2-2903904730 .fill-N4 { fill: #3A3A44; }
+      .d2-2903904730 .fill-N5 { fill: #2E2E38; }
+      .d2-2903904730 .fill-N6 { fill: #252530; }
+      .d2-2903904730 .fill-N7 { fill: transparent; }
+      .d2-2903904730 .fill-B4 { fill: #2A2A34; }
+      .d2-2903904730 .fill-B5 { fill: #1E1E28; }
+      .d2-2903904730 .fill-B6 { fill: #16161E; }
+      /* Strokes: near-black -> near-white */
+      .d2-2903904730 .stroke-N1 { stroke: #E8E8E8; }
+      .d2-2903904730 .stroke-N2 { stroke: #CCCCCC; }
+      .d2-2903904730 .stroke-N3 { stroke: #999999; }
+      .d2-2903904730 .stroke-N4 { stroke: #555555; }
+      .d2-2903904730 .stroke-N5 { stroke: #444444; }
+      .d2-2903904730 .stroke-N6 { stroke: #333333; }
+      .d2-2903904730 .stroke-N7 { stroke: transparent; }
+      .d2-2903904730 .stroke-B1 { stroke: #E8E8E8; }
+      .d2-2903904730 .stroke-B2 { stroke: #7BACFF; }
+      .d2-2903904730 .stroke-B3 { stroke: #6AAFDC; }
+      .d2-2903904730 .stroke-B4 { stroke: #3A3A44; }
+      .d2-2903904730 .stroke-B5 { stroke: #2E2E38; }
+      .d2-2903904730 .stroke-B6 { stroke: #252530; }
+      /* Text colors */
+      .d2-2903904730 .color-N1 { color: #E8E8E8; }
+      .d2-2903904730 .color-N2 { color: #CCCCCC; }
+      .d2-2903904730 .color-N3 { color: #999999; }
+      /* Connection marker fills (arrowheads) */
+      .d2-2903904730 .connection.fill-B1 { fill: #E8E8E8; }
+    }
+    ]]></style><style type="text/css"><![CDATA[.shape {
+  shape-rendering: geometricPrecision;
+  stroke-linejoin: round;
+}
+.connection {
+  stroke-linecap: round;
+  stroke-linejoin: round;
+}
+.blend {
+  mix-blend-mode: multiply;
+  opacity: 0.5;
+}
+
+		.d2-2903904730 .fill-N1{fill:#000410;}
+		.d2-2903904730 .fill-N2{fill:#0000B8;}
+		.d2-2903904730 .fill-N3{fill:#9499AB;}
+		.d2-2903904730 .fill-N4{fill:#CFD2DD;}
+		.d2-2903904730 .fill-N5{fill:#C3DEF3;}
+		.d2-2903904730 .fill-N6{fill:#EEF1F8;}
+		.d2-2903904730 .fill-N7{fill:#FFFFFF;}
+		.d2-2903904730 .fill-B1{fill:#000410;}
+		.d2-2903904730 .fill-B2{fill:#0000E4;}
+		.d2-2903904730 .fill-B3{fill:#5AA4DC;}
+		.d2-2903904730 .fill-B4{fill:#E7E9EE;}
+		.d2-2903904730 .fill-B5{fill:#F5F6F9;}
+		.d2-2903904730 .fill-B6{fill:#FFFFFF;}
+		.d2-2903904730 .fill-AA2{fill:#008566;}
+		.d2-2903904730 .fill-AA4{fill:#45BBA5;}
+		.d2-2903904730 .fill-AA5{fill:#7ACCBD;}
+		.d2-2903904730 .fill-AB4{fill:#F1C759;}
+		.d2-2903904730 .fill-AB5{fill:#F9E088;}
+		.d2-2903904730 .stroke-N1{stroke:#000410;}
+		.d2-2903904730 .stroke-N2{stroke:#0000B8;}
+		.d2-2903904730 .stroke-N3{stroke:#9499AB;}
+		.d2-2903904730 .stroke-N4{stroke:#CFD2DD;}
+		.d2-2903904730 .stroke-N5{stroke:#C3DEF3;}
+		.d2-2903904730 .stroke-N6{stroke:#EEF1F8;}
+		.d2-2903904730 .stroke-N7{stroke:#FFFFFF;}
+		.d2-2903904730 .stroke-B1{stroke:#000410;}
+		.d2-2903904730 .stroke-B2{stroke:#0000E4;}
+		.d2-2903904730 .stroke-B3{stroke:#5AA4DC;}
+		.d2-2903904730 .stroke-B4{stroke:#E7E9EE;}
+		.d2-2903904730 .stroke-B5{stroke:#F5F6F9;}
+		.d2-2903904730 .stroke-B6{stroke:#FFFFFF;}
+		.d2-2903904730 .stroke-AA2{stroke:#008566;}
+		.d2-2903904730 .stroke-AA4{stroke:#45BBA5;}
+		.d2-2903904730 .stroke-AA5{stroke:#7ACCBD;}
+		.d2-2903904730 .stroke-AB4{stroke:#F1C759;}
+		.d2-2903904730 .stroke-AB5{stroke:#F9E088;}
+		.d2-2903904730 .background-color-N1{background-color:#000410;}
+		.d2-2903904730 .background-color-N2{background-color:#0000B8;}
+		.d2-2903904730 .background-color-N3{background-color:#9499AB;}
+		.d2-2903904730 .background-color-N4{background-color:#CFD2DD;}
+		.d2-2903904730 .background-color-N5{background-color:#C3DEF3;}
+		.d2-2903904730 .background-color-N6{background-color:#EEF1F8;}
+		.d2-2903904730 .background-color-N7{background-color:#FFFFFF;}
+		.d2-2903904730 .background-color-B1{background-color:#000410;}
+		.d2-2903904730 .background-color-B2{background-color:#0000E4;}
+		.d2-2903904730 .background-color-B3{background-color:#5AA4DC;}
+		.d2-2903904730 .background-color-B4{background-color:#E7E9EE;}
+		.d2-2903904730 .background-color-B5{background-color:#F5F6F9;}
+		.d2-2903904730 .background-color-B6{background-color:#FFFFFF;}
+		.d2-2903904730 .background-color-AA2{background-color:#008566;}
+		.d2-2903904730 .background-color-AA4{background-color:#45BBA5;}
+		.d2-2903904730 .background-color-AA5{background-color:#7ACCBD;}
+		.d2-2903904730 .background-color-AB4{background-color:#F1C759;}
+		.d2-2903904730 .background-color-AB5{background-color:#F9E088;}
+		.d2-2903904730 .color-N1{color:#000410;}
+		.d2-2903904730 .color-N2{color:#0000B8;}
+		.d2-2903904730 .color-N3{color:#9499AB;}
+		.d2-2903904730 .color-N4{color:#CFD2DD;}
+		.d2-2903904730 .color-N5{color:#C3DEF3;}
+		.d2-2903904730 .color-N6{color:#EEF1F8;}
+		.d2-2903904730 .color-N7{color:#FFFFFF;}
+		.d2-2903904730 .color-B1{color:#000410;}
+		.d2-2903904730 .color-B2{color:#0000E4;}
+		.d2-2903904730 .color-B3{color:#5AA4DC;}
+		.d2-2903904730 .color-B4{color:#E7E9EE;}
+		.d2-2903904730 .color-B5{color:#F5F6F9;}
+		.d2-2903904730 .color-B6{color:#FFFFFF;}
+		.d2-2903904730 .color-AA2{color:#008566;}
+		.d2-2903904730 .color-AA4{color:#45BBA5;}
+		.d2-2903904730 .color-AA5{color:#7ACCBD;}
+		.d2-2903904730 .color-AB4{color:#F1C759;}
+		.d2-2903904730 .color-AB5{color:#F9E088;}.appendix text.text{fill:#000410}.md{--color-fg-default:#000410;--color-fg-muted:#0000B8;--color-fg-subtle:#9499AB;--color-canvas-default:#FFFFFF;--color-canvas-subtle:#EEF1F8;--color-border-default:#000410;--color-border-muted:#0000E4;--color-neutral-muted:#EEF1F8;--color-accent-fg:#0000E4;--color-accent-emphasis:#0000E4;--color-attention-subtle:#0000B8;--color-danger-fg:red;}.sketch-overlay-B1{fill:url(#streaks-darker-d2-2903904730);mix-blend-mode:lighten}.sketch-overlay-B2{fill:url(#streaks-darker-d2-2903904730);mix-blend-mode:lighten}.sketch-overlay-B3{fill:url(#streaks-normal-d2-2903904730);mix-blend-mode:color-burn}.sketch-overlay-B4{fill:url(#streaks-bright-d2-2903904730);mix-blend-mode:darken}.sketch-overlay-B5{fill:url(#streaks-bright-d2-2903904730);mix-blend-mode:darken}.sketch-overlay-B6{fill:url(#streaks-bright-d2-2903904730);mix-blend-mode:darken}.sketch-overlay-AA2{fill:url(#streaks-dark-d2-2903904730);mix-blend-mode:overlay}.sketch-overlay-AA4{fill:url(#streaks-normal-d2-2903904730);mix-blend-mode:color-burn}.sketch-overlay-AA5{fill:url(#streaks-normal-d2-2903904730);mix-blend-mode:color-burn}.sketch-overlay-AB4{fill:url(#streaks-normal-d2-2903904730);mix-blend-mode:color-burn}.sketch-overlay-AB5{fill:url(#streaks-normal-d2-2903904730);mix-blend-mode:color-burn}.sketch-overlay-N1{fill:url(#streaks-darker-d2-2903904730);mix-blend-mode:lighten}.sketch-overlay-N2{fill:url(#streaks-darker-d2-2903904730);mix-blend-mode:lighten}.sketch-overlay-N3{fill:url(#streaks-normal-d2-2903904730);mix-blend-mode:color-burn}.sketch-overlay-N4{fill:url(#streaks-normal-d2-2903904730);mix-blend-mode:color-burn}.sketch-overlay-N5{fill:url(#streaks-normal-d2-2903904730);mix-blend-mode:color-burn}.sketch-overlay-N6{fill:url(#streaks-bright-d2-2903904730);mix-blend-mode:darken}.sketch-overlay-N7{fill:url(#streaks-bright-d2-2903904730);mix-blend-mode:darken}.light-code{display: block}.dark-code{display: none}
+    @media (prefers-color-scheme: dark) {
+      /* Text and connection colors: near-black -> near-white */
+      .d2-2903904730 .fill-N1 { fill: #E8E8E8; }
+      .d2-2903904730 .fill-N2 { fill: #CCCCCC; }
+      .d2-2903904730 .fill-N3 { fill: #999999; }
+      .d2-2903904730 .fill-B1 { fill: #E8E8E8; }
+      .d2-2903904730 .fill-B2 { fill: #7BACFF; }
+      .d2-2903904730 .fill-B3 { fill: #6AAFDC; }
+      /* Container and node fills: light -> dark */
+      .d2-2903904730 .fill-N4 { fill: #3A3A44; }
+      .d2-2903904730 .fill-N5 { fill: #2E2E38; }
+      .d2-2903904730 .fill-N6 { fill: #252530; }
+      .d2-2903904730 .fill-N7 { fill: transparent; }
+      .d2-2903904730 .fill-B4 { fill: #2A2A34; }
+      .d2-2903904730 .fill-B5 { fill: #1E1E28; }
+      .d2-2903904730 .fill-B6 { fill: #16161E; }
+      /* Strokes: near-black -> near-white */
+      .d2-2903904730 .stroke-N1 { stroke: #E8E8E8; }
+      .d2-2903904730 .stroke-N2 { stroke: #CCCCCC; }
+      .d2-2903904730 .stroke-N3 { stroke: #999999; }
+      .d2-2903904730 .stroke-N4 { stroke: #555555; }
+      .d2-2903904730 .stroke-N5 { stroke: #444444; }
+      .d2-2903904730 .stroke-N6 { stroke: #333333; }
+      .d2-2903904730 .stroke-N7 { stroke: transparent; }
+      .d2-2903904730 .stroke-B1 { stroke: #E8E8E8; }
+      .d2-2903904730 .stroke-B2 { stroke: #7BACFF; }
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+      .d2-2903904730 .stroke-B5 { stroke: #2E2E38; }
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+}
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+}
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+}
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+  font-size: 2em;
+  border-bottom: 1px solid var(--color-border-muted);
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  color: var(--color-fg-default);
+  vertical-align: middle;
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+  border: solid 1px var(--color-neutral-muted);
+  border-bottom-color: var(--color-neutral-muted);
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  margin-top: 16px;
+}
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+}
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+}
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+  font-style: italic;
+  font-family: "d2-2903904730-font-semibold";
+}
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+}
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+}
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+  border: 1px solid var(--color-border-default);
+}
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+  border-top: 1px solid var(--color-border-muted);
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  clear: both;
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+}
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+}
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+}
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+}
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+}
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+  margin: 13px 0 0;
+  overflow: hidden;
+  text-align: right;
+}
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+  text-align: right;
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  word-break: normal;
+}
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+  overflow: auto;
+  font-size: 85%;
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+  background-color: var(--color-canvas-subtle);
+  border-radius: 6px;
+}
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+.d2-2903904730 .md pre code,
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+  margin: 0;
+  overflow: visible;
+  line-height: inherit;
+  word-wrap: normal;
+  background-color: transparent;
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+}
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+  white-space: nowrap;
+}
+
+.d2-2903904730 .md .csv-data .blob-num {
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+  text-align: right;
+  background: var(--color-canvas-default);
+  border: 0;
+}
+
+.d2-2903904730 .md .csv-data tr {
+  border-top: 0;
+}
+
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+  font-family: "d2-2903904730-font-semibold";
+  background: var(--color-canvas-subtle);
+  border-top: 0;
+}
+
+.d2-2903904730 .md .footnotes {
+  font-size: 12px;
+  color: var(--color-fg-muted);
+  border-top: 1px solid var(--color-border-default);
+}
+
+.d2-2903904730 .md .footnotes ol {
+  padding-left: 16px;
+}
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+.d2-2903904730 .md .footnotes li {
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+}
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+  right: -8px;
+  bottom: -8px;
+  left: -24px;
+  pointer-events: none;
+  content: "";
+  border: 2px solid var(--color-accent-emphasis);
+  border-radius: 6px;
+}
+
+.d2-2903904730 .md .footnotes li:target {
+  color: var(--color-fg-default);
+}
+
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+}
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+}
+
+.d2-2903904730 .md .task-list-item.enabled label {
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+}
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+.d2-2903904730 .md .task-list-item + .task-list-item {
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+}
+
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+  display: none;
+}
+
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+  vertical-align: middle;
+}
+
+.d2-2903904730 .md .contains-task-list:dir(rtl) .task-list-item-checkbox {
+  margin: 0 -1.6em 0.25em 0.2em;
+}
+</style><style type="text/css"><![CDATA[
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+	fill: url(#dots-d2-2903904730);
+	mix-blend-mode: multiply;
+}
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+      .d2-2903904730 .fill-N2 { fill: #CCCCCC; }
+      .d2-2903904730 .fill-N3 { fill: #999999; }
+      .d2-2903904730 .fill-B1 { fill: #E8E8E8; }
+      .d2-2903904730 .fill-B2 { fill: #7BACFF; }
+      .d2-2903904730 .fill-B3 { fill: #6AAFDC; }
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+      .d2-2903904730 .fill-N4 { fill: #3A3A44; }
+      .d2-2903904730 .fill-N5 { fill: #2E2E38; }
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+      .d2-2903904730 .fill-N7 { fill: transparent; }
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+      .d2-2903904730 .fill-B5 { fill: #1E1E28; }
+      .d2-2903904730 .fill-B6 { fill: #16161E; }
+      /* Strokes: near-black -> near-white */
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+      .d2-2903904730 .stroke-N2 { stroke: #CCCCCC; }
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+}
+.d2-2851460652 .text-mono-bold {
+	font-family: "d2-2851460652-font-mono-bold";
+}
+@font-face {
+	font-family: d2-2851460652-font-mono-bold;
+	src: 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+}
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+      .d2-2851460652 .color-N1 { color: #E8E8E8; }
+      .d2-2851460652 .color-N2 { color: #CCCCCC; }
+      .d2-2851460652 .color-N3 { color: #999999; }
+      .d2-2851460652 .connection.fill-B1 { fill: #E8E8E8; }
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+}
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+  stroke-linejoin: round;
+}
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+}
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+		.d2-2851460652 .background-color-B2{background-color:#0000E4;}
+		.d2-2851460652 .background-color-B3{background-color:#5AA4DC;}
+		.d2-2851460652 .background-color-B4{background-color:#E7E9EE;}
+		.d2-2851460652 .background-color-B5{background-color:#F5F6F9;}
+		.d2-2851460652 .background-color-B6{background-color:#FFFFFF;}
+		.d2-2851460652 .background-color-AA2{background-color:#008566;}
+		.d2-2851460652 .background-color-AA4{background-color:#45BBA5;}
+		.d2-2851460652 .background-color-AA5{background-color:#7ACCBD;}
+		.d2-2851460652 .background-color-AB4{background-color:#F1C759;}
+		.d2-2851460652 .background-color-AB5{background-color:#F9E088;}
+		.d2-2851460652 .color-N1{color:#000410;}
+		.d2-2851460652 .color-N2{color:#0000B8;}
+		.d2-2851460652 .color-N3{color:#9499AB;}
+		.d2-2851460652 .color-N4{color:#CFD2DD;}
+		.d2-2851460652 .color-N5{color:#C3DEF3;}
+		.d2-2851460652 .color-N6{color:#EEF1F8;}
+		.d2-2851460652 .color-N7{color:#FFFFFF;}
+		.d2-2851460652 .color-B1{color:#000410;}
+		.d2-2851460652 .color-B2{color:#0000E4;}
+		.d2-2851460652 .color-B3{color:#5AA4DC;}
+		.d2-2851460652 .color-B4{color:#E7E9EE;}
+		.d2-2851460652 .color-B5{color:#F5F6F9;}
+		.d2-2851460652 .color-B6{color:#FFFFFF;}
+		.d2-2851460652 .color-AA2{color:#008566;}
+		.d2-2851460652 .color-AA4{color:#45BBA5;}
+		.d2-2851460652 .color-AA5{color:#7ACCBD;}
+		.d2-2851460652 .color-AB4{color:#F1C759;}
+		.d2-2851460652 .color-AB5{color:#F9E088;}.appendix text.text{fill:#000410}.md{--color-fg-default:#000410;--color-fg-muted:#0000B8;--color-fg-subtle:#9499AB;--color-canvas-default:#FFFFFF;--color-canvas-subtle:#EEF1F8;--color-border-default:#000410;--color-border-muted:#0000E4;--color-neutral-muted:#EEF1F8;--color-accent-fg:#0000E4;--color-accent-emphasis:#0000E4;--color-attention-subtle:#0000B8;--color-danger-fg:red;}.sketch-overlay-B1{fill:url(#streaks-darker-d2-2851460652);mix-blend-mode:lighten}.sketch-overlay-B2{fill:url(#streaks-darker-d2-2851460652);mix-blend-mode:lighten}.sketch-overlay-B3{fill:url(#streaks-normal-d2-2851460652);mix-blend-mode:color-burn}.sketch-overlay-B4{fill:url(#streaks-bright-d2-2851460652);mix-blend-mode:darken}.sketch-overlay-B5{fill:url(#streaks-bright-d2-2851460652);mix-blend-mode:darken}.sketch-overlay-B6{fill:url(#streaks-bright-d2-2851460652);mix-blend-mode:darken}.sketch-overlay-AA2{fill:url(#streaks-dark-d2-2851460652);mix-blend-mode:overlay}.sketch-overlay-AA4{fill:url(#streaks-normal-d2-2851460652);mix-blend-mode:color-burn}.sketch-overlay-AA5{fill:url(#streaks-normal-d2-2851460652);mix-blend-mode:color-burn}.sketch-overlay-AB4{fill:url(#streaks-normal-d2-2851460652);mix-blend-mode:color-burn}.sketch-overlay-AB5{fill:url(#streaks-normal-d2-2851460652);mix-blend-mode:color-burn}.sketch-overlay-N1{fill:url(#streaks-darker-d2-2851460652);mix-blend-mode:lighten}.sketch-overlay-N2{fill:url(#streaks-darker-d2-2851460652);mix-blend-mode:lighten}.sketch-overlay-N3{fill:url(#streaks-normal-d2-2851460652);mix-blend-mode:color-burn}.sketch-overlay-N4{fill:url(#streaks-normal-d2-2851460652);mix-blend-mode:color-burn}.sketch-overlay-N5{fill:url(#streaks-normal-d2-2851460652);mix-blend-mode:color-burn}.sketch-overlay-N6{fill:url(#streaks-bright-d2-2851460652);mix-blend-mode:darken}.sketch-overlay-N7{fill:url(#streaks-bright-d2-2851460652);mix-blend-mode:darken}.light-code{display: block}.dark-code{display: none}
+    @media (prefers-color-scheme: dark) {
+      .d2-2851460652 .fill-N1 { fill: #E8E8E8; }
+      .d2-2851460652 .fill-N2 { fill: #CCCCCC; }
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