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

# Checkpoint/Restore for Fast Pod Startup

> ⚠️ **Experimental Feature**: ChReK is currently in **beta/preview**. It requires privileged mode for restore operations. See [Limitations](#limitations) for details.

Reduce cold start times for LLM inference workers from ~3 minutes to ~30 seconds using container checkpointing.

## Overview

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** | ~3 min | Download model, load to GPU, initialize engine |
| **Warm Start** (checkpoint) | ~30 sec | Restore from checkpoint tar |

## Prerequisites

- Dynamo Platform installed (v0.4.0+)
- ChReK Helm chart installed (separate from platform)
- GPU nodes with CRIU support
- 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 service:

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
metadata:
  name: my-llm
spec:
  services:
    VllmWorker:
      replicas: 1
      extraPodSpec:
        mainContainer:
          image: nvcr.io/nvidia/ai-dynamo/dynamo-vllm:latest
          args:
            - python3 -m dynamo.vllm --model meta-llama/Llama-3-8B
      resources:
        limits:
          nvidia.com/gpu: "1"

      # Checkpoint configuration
      checkpoint:
        enabled: true
        mode: auto  # Automatically create checkpoint if not found
        identity:
          model: "meta-llama/Llama-3-8B"
          backendFramework: "vllm"
          tensorParallelSize: 1
          dtype: "bfloat16"
```

### 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

## Storage Backends

### PVC (Currently Supported)

Use when you have RWX storage available (e.g., NFS, EFS, Filestore).

```yaml
checkpoint:
  storage:
    type: pvc
    pvc:
      pvcName: "chrek-pvc"
      basePath: "/checkpoints"
```

**Requirements:**
- RWX (ReadWriteMany) PVC for multi-node access
- Sufficient storage (checkpoints are ~10-50GB per model)

### S3 / MinIO (Planned - Not Yet Implemented)

> ⚠️ **Note:** S3 storage backend is defined in the API but not yet fully implemented.

Object storage support is planned for a future release. The configuration will look like:

```yaml
checkpoint:
  storage:
    type: s3  # Not yet supported
    s3:
      # AWS S3
      uri: "s3://my-bucket/checkpoints"

      # Or MinIO / custom S3
      uri: "s3://minio.example.com/my-bucket/checkpoints"

      # Optional: credentials secret
      credentialsSecretRef: "s3-creds"
```

### OCI Registry (Planned - Not Yet Implemented)

> ⚠️ **Note:** OCI registry storage backend is defined in the API but not yet fully implemented.

Container registry storage support is planned for a future release. The configuration will look like:

```yaml
checkpoint:
  storage:
    type: oci  # Not yet supported
    oci:
      uri: "oci://myregistry.io/checkpoints"
      credentialsSecretRef: "registry-creds"  # Docker config secret
```

## 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"
    tensorParallelSize: 1
```

### 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` | ✓ | ✓ | `vllm`, `sglang`, `trtllm` |
| `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

⚠️ **Important**: ChReK has significant limitations that impact production readiness:

### Security Considerations
- **🔴 Privileged mode required**: Restore pods **must run in privileged mode** for CRIU to function
- Privileged containers have elevated host access, which may violate security policies in many production environments
- This requirement applies to all worker pods that restore from checkpoints

### Technical Limitations
- **vLLM backend only**: Currently only the vLLM backend supports checkpoint/restore. SGLang and TensorRT-LLM support is planned.
- **Single-node only**: Checkpoints must be created and restored on the same node
- **Single-GPU only**: Multi-GPU configurations are not yet supported
- **Network state**: Active TCP connections are closed during restore (handled with `tcp-close` CRIU option)
- **Storage**: Only PVC backend currently implemented (S3/OCI planned)

### Recommendation
ChReK is **experimental/beta** and best suited for:
- ✅ Development and testing environments
- ✅ Research and experimentation
- ✅ Controlled production environments with appropriate security controls
- ❌ Security-sensitive production workloads without proper risk assessment

## Troubleshooting

### Checkpoint Not Creating

1. Check the checkpoint job:
   ```bash
   kubectl get jobs -l nvidia.com/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/

   # For S3
   aws s3 ls s3://my-bucket/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.

## Best Practices

1. **Use RWX PVCs** for multi-node deployments (currently the only supported backend)
2. **Pre-warm checkpoints** before scaling up
3. **Monitor checkpoint size** - large models create large checkpoints
4. **Clean up old checkpoints** to save storage

## Environment Variables

| Variable | Description |
|----------|-------------|
| `DYN_CHECKPOINT_STORAGE_TYPE` | Backend: `pvc`, `s3`, `oci` |
| `DYN_CHECKPOINT_LOCATION` | Source location (URI) |
| `DYN_CHECKPOINT_PATH` | Local path to tar file |
| `DYN_CHECKPOINT_HASH` | Identity hash (debugging) |
| `DYN_CHECKPOINT_SIGNAL_FILE` | Signal file (creation mode only) |

## 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:latest
            command: ["python3", "-m", "dynamo.vllm"]
            args:
              - "--model"
              - "meta-llama/Meta-Llama-3-8B-Instruct"
              - "--tensor-parallel-size"
              - "1"
              - "--dtype"
              - "bfloat16"
            env:
              - name: HF_TOKEN
                valueFrom:
                  secretKeyRef:
                    name: hf-token-secret
                    key: HF_TOKEN
            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:
    VllmWorker:
      replicas: 2
      extraPodSpec:
        mainContainer:
          image: nvcr.io/nvidia/ai-dynamo/dynamo-vllm:latest
      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 Standalone Usage Guide](standalone.md) - Use ChReK without Dynamo Platform
- [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

