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

> ⚠️ **Experimental Feature**: Dynamo Snapshot is currently in **preview** and may only be functional in some k8s cluster setups. The Dynamo Snapshot DaemonSet runs in privileged mode to perform CRIU operations. See [Limitations](#limitations) for details.

**Dynamo Snapshot** is an experimental infrastructure for fast-starting GPU applications in Kubernetes using CRIU (Checkpoint/Restore in User-space) and NVIDIA's cuda-checkpoint utility. Dynamo Snapshot dramatically reduces cold-start times for large models from minutes to seconds by capturing initialized application state and restoring it on-demand.

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

> ⚠️ Restore time may vary depending on cluster configuration (storage bandwidth, GPU model, etc.)

## Prerequisites

- Dynamo Platform/Operator installed on a k8s cluster with **x86_64 (amd64)** GPU nodes
- NVIDIA driver 580.xx or newer on the target GPU nodes
- `ReadWriteMany` storage if you need cross-node restore
- vLLM or SGLang backend (TensorRT-LLM is not supported yet)
- Security clearance to run a privileged DaemonSet

## Quick Start

This guide assumes a normal Dynamo deployment workflow is already present on your Kubernetes cluster.

### 1. Build and push a placeholder image

Snapshot-enabled workers must use a placeholder image that wraps the normal runtime image with the restore tooling. If you do not already have one, build it with the snapshot placeholder target and push it to a registry your cluster can pull from:

```bash
export RUNTIME_IMAGE=registry.example.com/dynamo/vllm-runtime:1.0.0
export PLACEHOLDER_IMAGE=registry.example.com/dynamo/vllm-placeholder:1.0.0

cd deploy/snapshot

make docker-build-placeholder \
  PLACEHOLDER_BASE_IMG="${RUNTIME_IMAGE}" \
  PLACEHOLDER_IMG="${PLACEHOLDER_IMAGE}"

make docker-push-placeholder \
  PLACEHOLDER_IMG="${PLACEHOLDER_IMAGE}"
```

This flow is defined in [deploy/snapshot/Makefile](https://github.com/ai-dynamo/dynamo/blob/main/deploy/snapshot/Makefile) and [deploy/snapshot/Dockerfile](https://github.com/ai-dynamo/dynamo/blob/main/deploy/snapshot/Dockerfile). The placeholder image preserves the base runtime entrypoint and command contract, and adds the CRIU, `cuda-checkpoint`, and `nsrestore` tooling needed for restore.

### 2. Enable checkpointing in the platform and verify it

Whether you are installing or upgrading `dynamo-platform`, the operator must have checkpointing enabled and must point at the same storage that the snapshot chart will use:

```yaml
dynamo-operator:
  checkpoint:
    enabled: true
    storage:
      type: pvc
      pvc:
        pvcName: snapshot-pvc
        basePath: /checkpoints
```

If the platform is already installed, verify that the operator config contains the checkpoint block:

```bash
OPERATOR_CONFIG=$(kubectl get deploy -n "${PLATFORM_NAMESPACE}" \
  -l app.kubernetes.io/name=dynamo-operator,app.kubernetes.io/component=manager \
  -o jsonpath='{.items[0].spec.template.spec.volumes[?(@.name=="operator-config")].configMap.name}')

kubectl get configmap "${OPERATOR_CONFIG}" -n "${PLATFORM_NAMESPACE}" \
  -o jsonpath='{.data.config\.yaml}' | sed -n '/^checkpoint:/,/^[^[:space:]]/p'
```

Verify that the rendered config includes `enabled: true` and the same PVC name and base path you plan to use for the snapshot chart.

For the full platform/operator configuration surface, see [deploy/helm/charts/platform/README.md](https://github.com/ai-dynamo/dynamo/blob/main/deploy/helm/charts/platform/README.md) and [deploy/helm/charts/platform/components/operator/values.yaml](https://github.com/ai-dynamo/dynamo/blob/main/deploy/helm/charts/platform/components/operator/values.yaml).

### 3. Install the snapshot chart

```bash
helm upgrade --install snapshot ./deploy/helm/charts/snapshot \
  --namespace ${NAMESPACE} \
  --create-namespace \
  --set storage.pvc.create=true
```

Cross-node restore requires `ReadWriteMany` storage. The chart defaults to that mode.

For better restore times, use a fast `ReadWriteMany` StorageClass for the checkpoint PVC. If you are reusing an existing checkpoint PVC, do not set `storage.pvc.create=true`; install the chart with `storage.pvc.create=false` and point `storage.pvc.name` at the existing PVC instead.

Verify that the PVC and DaemonSet are ready:

```bash
kubectl get pvc snapshot-pvc -n ${NAMESPACE}
kubectl rollout status daemonset/snapshot-agent -n ${NAMESPACE}
```

For the full snapshot chart configuration surface, see [deploy/helm/charts/snapshot/README.md](https://github.com/ai-dynamo/dynamo/blob/main/deploy/helm/charts/snapshot/README.md) and [deploy/helm/charts/snapshot/values.yaml](https://github.com/ai-dynamo/dynamo/blob/main/deploy/helm/charts/snapshot/values.yaml).

### 4. Apply a snapshot-compatible `DynamoGraphDeployment`

This example is adapted from [examples/backends/vllm/deploy/agg.yaml](https://github.com/ai-dynamo/dynamo/blob/main/examples/backends/vllm/deploy/agg.yaml). The worker must use the placeholder image from step 1, and the checkpoint identity must describe the runtime state you want to reuse.

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
metadata:
  name: vllm-snapshot-demo
spec:
  services:
    Frontend:
      componentType: frontend
      replicas: 1
      extraPodSpec:
        mainContainer:
          image: registry.example.com/dynamo/vllm-runtime:1.0.0

    VllmDecodeWorker:
      componentType: worker
      replicas: 1
      resources:
        limits:
          gpu: "1"
      readinessProbe:
        httpGet:
          path: /live
          port: system
        periodSeconds: 1
        timeoutSeconds: 4
        failureThreshold: 3
      checkpoint:
        enabled: true
        mode: Auto
        identity:
          model: Qwen/Qwen3-0.6B
          backendFramework: vllm
      extraPodSpec:
        mainContainer:
          image: registry.example.com/dynamo/vllm-placeholder:1.0.0
          command:
            - python3
            - -m
            - dynamo.vllm
          args:
            - --model
            - Qwen/Qwen3-0.6B
          env:
            - name: NCCL_DEBUG
              value: ERROR
            - name: TORCH_CPP_LOG_LEVEL
              value: ERROR
            - name: TORCH_DISTRIBUTED_DEBUG
              value: "OFF"
```

For SGLang, use `dynamo.sglang`, an SGLang placeholder image, `backendFramework: sglang`, and the matching CLI flags.

Apply the manifest:

```bash
kubectl apply -f vllm-snapshot-demo.yaml -n ${NAMESPACE}
```

On the first rollout, the worker cold-starts, the operator resolves the checkpoint identity hash, and the checkpoint Job writes a new checkpoint directory into `snapshot-pvc`.

### 5. Wait for the checkpoint to become ready

Auto mode resolves checkpoints by identity hash. It may create `checkpoint-<hash>` or reuse an existing checkpoint with a different CR name. For the sample identity above, the hash is `73e74442beb109ed`:

```bash
kubectl get dckpt -n ${NAMESPACE}

CKPT_NAME=$(kubectl get dckpt -n ${NAMESPACE} \
  -l nvidia.com/snapshot-checkpoint-hash=73e74442beb109ed \
  -o jsonpath='{.items[0].metadata.name}')
kubectl wait \
  --for=jsonpath='{.status.phase}'=Ready \
  "dynamocheckpoint/${CKPT_NAME}" \
  -n ${NAMESPACE} \
  --timeout=5m
```

If you change the checkpoint identity, the hash changes and so does the checkpoint selected by Auto mode.

### 6. Trigger restore

Once the checkpoint is ready, scale the worker replicas from `1` to `2`:

```bash
kubectl patch dgd vllm-snapshot-demo -n ${NAMESPACE} --type=merge \
  -p '{"spec":{"services":{"VllmDecodeWorker":{"replicas":2}}}}'
```

New worker pods for `VllmDecodeWorker` will restore from the ready checkpoint automatically.

## Checkpoint Configuration

### Auto Mode (Recommended)

The operator computes the checkpoint identity hash, looks up an existing `DynamoCheckpoint` by that hash, and creates a new `DynamoCheckpoint` only when no matching checkpoint already exists:

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

The `DynamoGraphDeployment` mirrors checkpoint resolution state under `.status.checkpoints`, including the resolved checkpoint CR name, identity hash, and whether the checkpoint was visible to the worker when it started:

```bash
kubectl get dgd vllm-snapshot-demo -n ${NAMESPACE} \
  -o jsonpath='{.status.checkpoints.VllmDecodeWorker.checkpointName}{"\n"}{.status.checkpoints.VllmDecodeWorker.identityHash}{"\n"}'
```

### Manual Management and `checkpointRef`

Use `checkpointRef` when you want a service to restore from a specific `DynamoCheckpoint` CR:

```yaml
checkpoint:
  enabled: true
  checkpointRef: "qwen3-06b-bf16"
```

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

`checkpointRef` resolves by `DynamoCheckpoint.metadata.name`. Use a readable CR name when you want an explicit checkpoint that operators can reference directly.

If you are managing checkpoint CRs yourself, set `mode: Manual` on the service to prevent the operator from creating a new `DynamoCheckpoint` when identity-based lookup does not find one.

```bash
# Check checkpoint status by CR name
kubectl get dynamocheckpoint qwen3-06b-bf16 -n ${NAMESPACE}

# Now create DGD referencing it
kubectl apply -f my-dgd.yaml -n ${NAMESPACE}
```

`mode: Auto` still resolves checkpoints by identity hash. The operator backfills `status.identityHash` and the `nvidia.com/snapshot-checkpoint-hash` label on each `DynamoCheckpoint` so auto lookup and uniqueness checks do not depend on the CR name.

## 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` |
| `backendFramework` | ✓ | ✓ | `sglang`, `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: "1.0.0"
    tensorParallelSize: 1
    pipelineParallelSize: 1
    dtype: "bfloat16"
    maxModelLen: 8192
    extraParameters:
      enableChunkedPrefill: "true"
      quantization: "awq"
```

## 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

The operator requires `spec.identity` and `spec.job.podTemplateSpec`. The pod template should match the worker container you want checkpointed, including image, command, args, secrets, volumes, and resource limits. You do not need to set checkpoint-control plumbing manually; the operator injects the checkpoint-ready signal path for checkpoint Jobs and adds the restore metadata consumed by restored pods and the node-local controller inside the `snapshot-agent` DaemonSet.
`spec.job.backoffLimit` is deprecated and ignored. Checkpoint Jobs are always single-attempt.

**Create a checkpoint:**

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoCheckpoint
metadata:
  name: qwen3-06b-bf16
spec:
  identity:
    model: Qwen/Qwen3-0.6B
    backendFramework: vllm
    tensorParallelSize: 1
    dtype: bfloat16
    maxModelLen: 4096

  job:
    activeDeadlineSeconds: 3600
    ttlSecondsAfterFinished: 300
    podTemplateSpec:
      spec:
        restartPolicy: Never
        containers:
          - name: main
            image: registry.example.com/dynamo/vllm-placeholder:1.0.0
            command:
              - python3
              - -m
              - dynamo.vllm
            args:
              - --model
              - Qwen/Qwen3-0.6B
            env:
              - name: NCCL_DEBUG
                value: ERROR
              - name: TORCH_CPP_LOG_LEVEL
                value: ERROR
              - name: TORCH_DISTRIBUTED_DEBUG
                value: "OFF"
            resources:
              limits:
                nvidia.com/gpu: "1"
```

For this example identity, the operator computes a deterministic identity hash and stores it in `status.identityHash`. Auto mode uses that hash, not the CR name, when it decides whether to reuse or create a checkpoint.

**Check status:**

```bash
# List all checkpoints
kubectl get dynamocheckpoint -n ${NAMESPACE}
# Or use shortname
kubectl get dckpt -n ${NAMESPACE}

NAME               MODEL                                BACKEND  PHASE     HASH              AGE
qwen3-06b-bf16     Qwen/Qwen3-0.6B                      vllm     Ready     3bff874d069f0ed5  5m
llama3-8b-bf16     meta-llama/Meta-Llama-3-8B-Instruct  vllm     Creating  9be4f5574b5a285d  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 |

Other useful status fields are:

| Field | Meaning |
|-------|---------|
| `status.identityHash` | Deterministic hash of `spec.identity` used for auto lookup and reuse |
| `status.jobName` | Name of the checkpoint Job |
| `status.location` | Checkpoint location in the configured storage backend |
| `status.storageType` | Storage backend type (`pvc`, `s3`, or `oci`) |
| `status.createdAt` | Timestamp recorded when the checkpoint becomes ready |
| `status.message` | Failure or progress message when available |

`status.conditions` is deprecated for `DynamoCheckpoint`. The legacy condition types `JobCreated` and `JobCompleted` are kept for compatibility only. Prefer `status.phase`, `status.jobName`, and `status.message` when checking checkpoint progress.

**Detailed status:**

```bash
kubectl describe dckpt qwen3-06b-bf16 -n ${NAMESPACE}
```

```yaml
Status:
  Phase: Ready
  IdentityHash: 3bff874d069f0ed5
  JobName: checkpoint-job-3bff874d069f0ed5
  Location: /checkpoints/3bff874d069f0ed5
  StorageType: pvc
  CreatedAt: 2026-01-29T10:05:00Z
```

**Reference from DGD:**

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

```yaml
spec:
  services:
    VllmDecodeWorker:
      checkpoint:
        enabled: true
        checkpointRef: "qwen3-06b-bf16"
```

Or use `mode: Auto` with the same identity, and the operator will reuse the same deterministic checkpoint object automatically.

## Limitations

- **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 may work in very basic hardware configurations, but are not officially supported yet.
- **Network state**: Restore is sensitive to live TCP socket state. Loopback bootstrap/control sockets can work with the supported CRIU TCP policies, but non-loopback or pod-IP-bound connections can still break restore.
- **Security**: Dynamo Snapshot runs as a **privileged DaemonSet** which is required to run CRIU and cuda-checkpoint. However, workload pods do not need to be privileged.

## Troubleshooting

### Checkpoint Not Ready

1. Check the checkpoint job:
   ```bash
   kubectl get dckpt -n ${NAMESPACE}
   kubectl describe dckpt <checkpoint-name> -n ${NAMESPACE}
   JOB_NAME=$(kubectl get dckpt <checkpoint-name> -n ${NAMESPACE} -o jsonpath='{.status.jobName}')
   if [ -n "${JOB_NAME}" ]; then
     kubectl logs job/"${JOB_NAME}" -n ${NAMESPACE}
   fi
   ```

2. Check the DaemonSet:
   ```bash
   kubectl logs daemonset/snapshot-agent -n ${NAMESPACE} --all-containers
   ```

3. Verify that platform and chart storage settings match:
   ```bash
   kubectl get dckpt <checkpoint-name> -n ${NAMESPACE} -o yaml
   ```

### Restore Failing

1. Check pod logs:
   ```bash
   kubectl logs <worker-pod> -n ${NAMESPACE}
   ```

2. Describe the restore target pod:
   ```bash
   kubectl describe pod <worker-pod> -n ${NAMESPACE}
   ```

3. Confirm the referenced checkpoint is still `Ready`:
   ```bash
   kubectl get dckpt <checkpoint-name> -n ${NAMESPACE}
   ```

## Planned Features

- TensorRT-LLM backend support
- S3/MinIO storage backend
- OCI registry storage backend
- Multi-GPU checkpoints

## Related Documentation

- [Dynamo Snapshot Helm Chart README](https://github.com/ai-dynamo/dynamo/blob/main/deploy/helm/charts/snapshot/README.md) - Chart configuration
- [Installation Guide](installation-guide.md) - Platform installation
- [API Reference](api-reference.md) - Complete CRD specifications