---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
title: Integration with Dynamo
---

> ⚠️ **Experimental Feature**: ChReK is currently in **beta/preview**. The ChReK DaemonSet runs in privileged mode to perform CRIU operations. See [Limitations](#limitations) for details.

Checkpointing captures the complete state of a running worker pod (including GPU memory) and saves it to storage. New pods can restore from this checkpoint instead of performing a full cold start.

| Startup Type | Time | What Happens |
|--------------|------|--------------|
| **Cold Start** | ~1 min | Download model, load to GPU, initialize engine |
| **Warm Start** (checkpoint) | < 10 sec | Restore from checkpoint tar |

## Prerequisites

- Dynamo Platform installed (v0.4.0+) on k8s cluster with GPU nodes
- ChReK Helm chart installed (separate from platform)
- RWX PVC storage (PVC is currently the only supported backend)

## Quick Start

### 1. Install ChReK Infrastructure

First, install the ChReK Helm chart in each namespace where you need checkpointing:

```bash
# Install ChReK infrastructure
helm install chrek nvidia/chrek \
  --namespace my-team \
  --create-namespace \
  --set storage.pvc.size=100Gi
```

This creates:
- A PVC for checkpoint storage (`chrek-pvc`)
- A DaemonSet for CRIU operations (`chrek-agent`)

### 2. Configure Operator Values

Update your Helm values to point to the ChReK infrastructure:

```yaml
# values.yaml
dynamo-operator:
  checkpoint:
    enabled: true
    storage:
      type: pvc  # Only PVC is currently supported (S3/OCI planned)
      pvc:
        pvcName: "chrek-pvc"  # Must match ChReK chart
        basePath: "/checkpoints"
      signalHostPath: "/var/lib/chrek/signals"  # Must match ChReK chart
```

### 2. Configure Your DGD

Add checkpoint configuration to your worker service. Both vLLM and SGLang are supported — use the appropriate `backendFramework`, command, and CLI flags.

#### vLLM Example

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
metadata:
  name: my-llm
spec:
  services:
    worker:
      replicas: 1
      extraPodSpec:
        mainContainer:
          image: nvcr.io/nvidia/ai-dynamo/dynamo-vllm-placeholder:latest
          command: ["python3"]
          args:
            - "-m"
            - "dynamo.vllm"
            - "--model"
            - "meta-llama/Llama-3-8B"
            - "--max-model-len"
            - "4096"
            - "--gpu-memory-utilization"
            - "0.90"
          env:
            # Required for cross-node checkpoint/restore
            - name: GLOO_SOCKET_IFNAME
              value: "lo"
            - name: NCCL_SOCKET_IFNAME
              value: "lo"
      resources:
        limits:
          nvidia.com/gpu: "1"
      checkpoint:
        enabled: true
        mode: auto
        identity:
          model: "meta-llama/Llama-3-8B"
          backendFramework: "vllm"
          tensorParallelSize: 1
          dtype: "bfloat16"
          maxModelLen: 4096
```

#### SGLang Example

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
metadata:
  name: my-sglang-llm
spec:
  services:
    worker:
      replicas: 1
      extraPodSpec:
        mainContainer:
          image: nvcr.io/nvidia/ai-dynamo/dynamo-sglang-placeholder:latest
          command: ["python3"]
          args:
            - "-m"
            - "dynamo.sglang"
            - "--model"
            - "meta-llama/Llama-3-8B"
            - "--mem-fraction-static"
            - "0.90"
          env:
            # Required for cross-node checkpoint/restore
            - name: GLOO_SOCKET_IFNAME
              value: "lo"
            - name: NCCL_SOCKET_IFNAME
              value: "lo"
      resources:
        limits:
          nvidia.com/gpu: "1"
      checkpoint:
        enabled: true
        mode: auto
        identity:
          model: "meta-llama/Llama-3-8B"
          backendFramework: "sglang"
          tensorParallelSize: 1
          dtype: "bfloat16"
          maxModelLen: 4096
```

**Key differences between backends:**

| Setting | vLLM | SGLang |
|---------|------|--------|
| Module | `dynamo.vllm` | `dynamo.sglang` |
| Max context (optional) | `--max-model-len` | `--context-length` |
| GPU memory | `--gpu-memory-utilization` | `--mem-fraction-static` |
| Placeholder image | `dynamo-vllm-placeholder` | `dynamo-sglang-placeholder` |
| Identity `backendFramework` | `"vllm"` | `"sglang"` |

> **Note:** Do **not** set `DYN_READY_FOR_CHECKPOINT_FILE` or `DYN_CHECKPOINT_READY_FILE` in the DGD worker env vars. These are injected automatically by the operator's checkpoint controller into checkpoint job pods only. Setting them on worker pods causes all workers to enter checkpoint mode instead of cold-starting normally.

### 3. Deploy

```bash
kubectl apply -f my-llm.yaml -n dynamo-system
```

On first deployment:
1. A checkpoint job runs to create the checkpoint
2. Worker pods start with cold start (checkpoint not ready yet)
3. Once checkpoint is ready, new pods (scale-up, restarts) restore from checkpoint

## Checkpoint Modes

### Auto Mode (Recommended)

The operator automatically creates a `DynamoCheckpoint` CR if one doesn't exist:

```yaml
checkpoint:
  enabled: true
  mode: auto
  identity:
    model: "meta-llama/Llama-3-8B"
    backendFramework: "vllm"  # or "sglang"
    tensorParallelSize: 1
    dtype: "bfloat16"
    maxModelLen: 4096
```

### Reference Mode

Reference an existing `DynamoCheckpoint` CR by its 16-character hash using `checkpointRef`:

```yaml
checkpoint:
  enabled: true
  checkpointRef: "e5962d34ba272638"  # 16-char hash of DynamoCheckpoint CR
```

This is useful when:
- You want to **pre-warm checkpoints** before creating DGDs
- You want to **explicit control** over which checkpoint to use

**Flow:**
1. Create a `DynamoCheckpoint` CR (see [DynamoCheckpoint CRD](#dynamocheckpoint-crd) section)
2. Wait for it to become `Ready`
3. Reference it in your DGD using `checkpointRef` with the hash

```bash
# Check checkpoint status (using 16-char hash name)
kubectl get dynamocheckpoint e5962d34ba272638 -n dynamo-system
NAME                MODEL                   BACKEND  PHASE  HASH              AGE
e5962d34ba272638    meta-llama/Llama-3-8B  vllm     Ready  e5962d34ba272638  5m

# Now create DGD referencing it
kubectl apply -f my-dgd.yaml
```

## Checkpoint Identity

Checkpoints are uniquely identified by a **16-character SHA256 hash** (64 bits) of configuration that affects runtime state:

| Field | Required | Affects Hash | Example |
|-------|----------|-------------|---------|
| `model` | ✓ | ✓ | `meta-llama/Llama-3-8B` |
| `framework` | ✓ | ✓ | `sglang`, `trtllm`, `vllm` |
| `dynamoVersion` | | ✓ | `0.9.0`, `1.0.0` |
| `tensorParallelSize` | | ✓ | `1`, `2`, `4`, `8` (default: 1) |
| `pipelineParallelSize` | | ✓ | `1`, `2` (default: 1) |
| `dtype` | | ✓ | `float16`, `bfloat16`, `fp8` |
| `maxModelLen` | | ✓ | `4096`, `8192` |
| `extraParameters` | | ✓ | Custom key-value pairs |

**Not included in hash** (don't invalidate checkpoint):
- `replicas`
- `nodeSelector`, `affinity`, `tolerations`
- `resources` (requests/limits)
- Logging/observability config

**Example with all fields:**
```yaml
checkpoint:
  enabled: true
  mode: auto
  identity:
    model: "meta-llama/Llama-3-8B"
    backendFramework: "vllm"
    dynamoVersion: "0.9.0"
    tensorParallelSize: 1
    pipelineParallelSize: 1
    dtype: "bfloat16"
    maxModelLen: 8192
    extraParameters:
      enableChunkedPrefill: "true"
      quantization: "awq"
```

**Checkpoint Naming:** The `DynamoCheckpoint` CR is automatically named using the 16-character identity hash (e.g., `e5962d34ba272638`).

**Checkpoint Sharing:** Multiple DGDs with the same identity automatically share the same checkpoint.

## DynamoCheckpoint CRD

The `DynamoCheckpoint` (shortname: `dckpt`) is a Kubernetes Custom Resource that manages checkpoint lifecycle.

**When to create a DynamoCheckpoint directly:**
- **Pre-warming:** Create checkpoints before deploying DGDs for instant startup
- **Explicit control:** Manage checkpoint lifecycle independently from DGDs

**Note:** With the new hash-based naming, checkpoint names are automatically generated (16-character hash). The operator handles checkpoint discovery and reuse automatically in `auto` mode.

**Create a checkpoint:**

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoCheckpoint
metadata:
  name: e5962d34ba272638  # Use the computed 16-char hash
spec:
  identity:
    model: "meta-llama/Llama-3-8B"
    backendFramework: "vllm"
    tensorParallelSize: 1
    dtype: "bfloat16"

  job:
    activeDeadlineSeconds: 3600
    podTemplateSpec:
      spec:
        containers:
          - name: main
            image: nvcr.io/nvidia/ai-dynamo/dynamo-vllm:latest
            command: ["python3", "-m", "dynamo.vllm"]
            args: ["--model", "meta-llama/Llama-3-8B"]
            resources:
              limits:
                nvidia.com/gpu: "1"
            env:
              - name: HF_TOKEN
                valueFrom:
                  secretKeyRef:
                    name: hf-token-secret
                    key: HF_TOKEN
```

**Note:** You can compute the hash yourself, or use `auto` mode to let the operator create it.

**Check status:**

```bash
# List all checkpoints
kubectl get dynamocheckpoint -n dynamo-system
# Or use shortname
kubectl get dckpt -n dynamo-system

NAME                MODEL                          BACKEND  PHASE    HASH              AGE
e5962d34ba272638    meta-llama/Llama-3-8B         vllm     Ready    e5962d34ba272638  5m
a7b4f89c12de3456    meta-llama/Llama-3-70B        vllm     Creating a7b4f89c12de3456  2m
```

**Phases:**
| Phase | Description |
|-------|-------------|
| `Pending` | CR created, waiting for job to start |
| `Creating` | Checkpoint job is running |
| `Ready` | Checkpoint available for use |
| `Failed` | Checkpoint creation failed |

**Detailed status:**

```bash
kubectl describe dckpt e5962d34ba272638 -n dynamo-system
```

```yaml
Status:
  Phase: Ready
  IdentityHash: e5962d34ba272638
  Location: /checkpoints/e5962d34ba272638
  StorageType: pvc
  CreatedAt: 2026-01-29T10:05:00Z
```

**Reference from DGD:**

Once the checkpoint is `Ready`, you can reference it by hash:

```yaml
spec:
  services:
    VllmWorker:
      checkpoint:
        enabled: true
        checkpointRef: "e5962d34ba272638"  # 16-char hash
```

Or use `auto` mode and the operator will find/create it automatically.

## Limitations

- **vLLM and SGLang backends only**: TensorRT-LLM support is planned.
- **LLM workers only**: Checkpoint/restore supports LLM decode and prefill workers. Specialized workers (multimodal, embedding, diffusion) are not supported.
- **Single-GPU only**: Multi-GPU configurations are not yet supported (planned)
- **Network state**: Active TCP connections are closed during restore (handled with `tcp-close` CRIU option)
- **Storage**: Only PVC backend currently implemented (S3/OCI planned)
- **Security**: ChReK runs as a **privileged DaemonSet** which is required to run CRIU

## Troubleshooting

### Checkpoint Not Creating

1. Check the checkpoint job:
   ```bash
   kubectl get jobs -l nvidia.com/chrek-is-checkpoint-source=true -n dynamo-system
   kubectl logs job/checkpoint-<name> -n dynamo-system
   ```

2. Check the DaemonSet:
   ```bash
   kubectl logs daemonset/chrek-agent -n dynamo-system
   ```

3. Verify storage access:
   ```bash
   kubectl exec -it <checkpoint-agent-pod> -- ls -la /checkpoints
   ```

### Restore Failing

1. Check pod logs:
   ```bash
   kubectl logs <worker-pod> -n dynamo-system
   ```

2. Verify checkpoint file exists:
   ```bash
   # For PVC
   kubectl exec -it <any-pod-with-pvc> -- ls -la /checkpoints/
   ```

3. Check environment variables:
   ```bash
   kubectl exec <worker-pod> -- env | grep DYN_CHECKPOINT
   ```

### Cold Start Despite Checkpoint

Pods fall back to cold start if:
- Checkpoint file doesn't exist yet (still being created)
- Checkpoint file is corrupted
- CRIU restore fails

Check logs for "Falling back to cold start" message.

## Environment Variables

| Variable | Description |
|----------|-------------|
| `DYN_CHECKPOINT_STORAGE_TYPE` | Backend: `pvc`, `s3`, `oci` (`s3` and `oci` are currently no-ops) |
| `DYN_CHECKPOINT_LOCATION` | Full checkpoint location (checkpoint jobs) |
| `DYN_CHECKPOINT_PATH` | Base checkpoint directory (restore pods, PVC) |
| `DYN_CHECKPOINT_HASH` | Identity hash |
| `DYN_READY_FOR_CHECKPOINT_FILE` | Ready-for-checkpoint file path (checkpoint jobs) |

## Complete Example

Create a checkpoint and use it in a DGD:

```yaml
# 1. Create the DynamoCheckpoint CR
apiVersion: nvidia.com/v1alpha1
kind: DynamoCheckpoint
metadata:
  name: e5962d34ba272638  # 16-char hash (computed from identity)
  namespace: dynamo-system
spec:
  identity:
    model: "meta-llama/Meta-Llama-3-8B-Instruct"
    backendFramework: "vllm"
    tensorParallelSize: 1
    dtype: "bfloat16"
  job:
    activeDeadlineSeconds: 3600
    backoffLimit: 3
    podTemplateSpec:
      spec:
        containers:
          - name: main
            image: nvcr.io/nvidia/ai-dynamo/dynamo-vllm-placeholder:latest
            command: ["python3"]
            args:
              - "-m"
              - "dynamo.vllm"
              - "--model"
              - "meta-llama/Meta-Llama-3-8B-Instruct"
              - "--max-model-len"
              - "4096"
              - "--gpu-memory-utilization"
              - "0.90"
            env:
              - name: HF_TOKEN
                valueFrom:
                  secretKeyRef:
                    name: hf-token-secret
                    key: HF_TOKEN
              - name: GLOO_SOCKET_IFNAME
                value: "lo"
              - name: NCCL_SOCKET_IFNAME
                value: "lo"
            resources:
              limits:
                nvidia.com/gpu: "1"
        restartPolicy: Never
---
# 2. Wait for Ready: kubectl get dckpt e5962d34ba272638 -n dynamo-system -w
---
# 3. Reference the checkpoint in your DGD
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
metadata:
  name: my-llm
  namespace: dynamo-system
spec:
  services:
    worker:
      replicas: 2
      extraPodSpec:
        mainContainer:
          image: nvcr.io/nvidia/ai-dynamo/dynamo-vllm-placeholder:latest
          command: ["python3"]
          args:
            - "-m"
            - "dynamo.vllm"
            - "--model"
            - "meta-llama/Meta-Llama-3-8B-Instruct"
            - "--max-model-len"
            - "4096"
            - "--gpu-memory-utilization"
            - "0.90"
          env:
            - name: GLOO_SOCKET_IFNAME
              value: "lo"
            - name: NCCL_SOCKET_IFNAME
              value: "lo"
      resources:
        limits:
          nvidia.com/gpu: "1"
      checkpoint:
        enabled: true
        checkpointRef: "e5962d34ba272638"  # Reference by hash
```

## Related Documentation

- [ChReK Overview](README.md) - ChReK architecture and use cases
- [ChReK Helm Chart README](https://github.com/ai-dynamo/dynamo/tree/main/deploy/helm/charts/chrek/README.md) - Chart configuration
- [Installation Guide](../installation-guide.md) - Platform installation
- [API Reference](../api-reference.md) - Complete CRD specifications

