# Model Caching with Fluid: Cloud-Native Data Orchestration and Acceleration

Fluid is an open-source, cloud-native data orchestration and acceleration platform for Kubernetes. It virtualizes and accelerates data access from various sources (object storage, distributed file systems, cloud storage), making it ideal for AI, machine learning, and big data workloads.

## Key Features

- **Data Caching and Acceleration:** Cache remote data close to compute workloads for faster access.
- **Unified Data Access:** Access data from S3, HDFS, NFS, and more through a single interface.
- **Kubernetes Native:** Integrates with Kubernetes using CRDs for data management.
- **Scalability:** Supports large-scale data and compute clusters.

## Installation

You can install Fluid on any Kubernetes cluster using Helm.

**Prerequisites:**
- Kubernetes >= 1.18
- `kubectl` >= 1.18
- `Helm` >= 3.5

**Quick Install:**
```sh
kubectl create ns fluid-system
helm repo add fluid https://fluid-cloudnative.github.io/charts
helm repo update
helm install fluid fluid/fluid -n fluid-system
```
For advanced configuration, see the [Fluid Installation Guide](https://fluid-cloudnative.github.io/docs/get-started/installation).

## Pre-deployment Steps

1. Install Fluid (see [Installation](#installation)).
2. Create a Dataset and Runtime (see [the following example](#webufs-example)).
3. Mount the resulting PVC in your workload.


## Mounting Data Sources

### WebUFS Example

WebUFS allows mounting HTTP/HTTPS sources as filesystems.

```yaml
# Mount a public HTTP directory as a Fluid Dataset
apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: webufs-model
spec:
  mounts:
    - mountPoint: https://myhost.org/path_to_my_model  # Replace with your HTTP source
      name: webufs-model
---
apiVersion: data.fluid.io/v1alpha1
kind: AlluxioRuntime
metadata:
  name: webufs-model
spec:
  replicas: 2
  tieredstore:
    levels:
      - mediumtype: MEM
        path: /dev/shm
        quota: 2Gi
        high: "0.95"
        low: "0.7"
```
After applying, Fluid creates a PersistentVolumeClaim (PVC) named `webufs-model` containing the files.

### S3 Example

Mount an S3 bucket as a Fluid Dataset.

```yaml
# Mount an S3 bucket as a Fluid Dataset
apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: s3-model
spec:
  mounts:
    - mountPoint: s3://<your-bucket>  # Replace with your bucket name
      options:
        alluxio.underfs.s3.endpoint: http://minio:9000  # S3 endpoint (e.g., MinIO)
        alluxio.underfs.s3.disable.dns.buckets: "true"
        aws.secretKey: "<your-secret>"
        aws.accessKeyId: "<your-access-key>"
---
apiVersion: data.fluid.io/v1alpha1
kind: AlluxioRuntime
metadata:
  name: s3-model
spec:
  replicas: 1
  tieredstore:
    levels:
      - mediumtype: MEM
        path: /dev/shm
        quota: 1Gi
        high: "0.95"
        low: "0.7"
---
apiVersion: data.fluid.io/v1alpha1
kind: DataLoad
metadata:
  name: s3-model-loader
spec:
  dataset:
    name: s3-model
    namespace: <your-namespace>  # Replace with your namespace
  loadMetadata: true
  target:
    - path: "/"
      replicas: 1
```

The resulting PVC is named `s3-model`.

## Using HuggingFace Models with Fluid

**Limitations:**
- HuggingFace models are not exposed as simple filesystems or buckets.
- No native integration exists between Fluid and the HuggingFace Hub API.

**Workaround: Download and Upload to S3/MinIO**

1. Download the model using the HuggingFace CLI or SDK.
2. Upload the model files to a supported storage backend (S3, GCS, NFS).
3. Mount that backend using Fluid.

**Example Pod to Download and Upload:**
```yaml
apiVersion: v1
kind: Pod
metadata:
  name: download-hf-to-minio
spec:
  restartPolicy: Never
  containers:
    - name: downloader
      image: python:3.10-slim
      command: ["sh", "-c"]
      args:
        - |
          set -eux
          pip install --no-cache-dir huggingface_hub awscli
          BUCKET_NAME=hf-models
          ENDPOINT_URL=http://minio:9000
          MODEL_NAME=deepseek-ai/DeepSeek-R1-Distill-Llama-8B
          LOCAL_DIR=/tmp/model
          if ! aws --endpoint-url $ENDPOINT_URL s3 ls "s3://$BUCKET_NAME" > /dev/null 2>&1; then
            aws --endpoint-url $ENDPOINT_URL s3 mb "s3://$BUCKET_NAME"
          fi
          huggingface-cli download $MODEL_NAME --local-dir $LOCAL_DIR --local-dir-use-symlinks False
          aws --endpoint-url $ENDPOINT_URL s3 cp $LOCAL_DIR s3://$BUCKET_NAME/$MODEL_NAME --recursive
      env:
        - name: AWS_ACCESS_KEY_ID
          value: "<your-access-key>"
        - name: AWS_SECRET_ACCESS_KEY
          value: "<your-secret>"
      volumeMounts:
        - name: tmp-volume
          mountPath: /tmp/model
  volumes:
    - name: tmp-volume
      emptyDir: {}
```

You can then use `s3://hf-models/deepseek-ai/DeepSeek-R1-Distill-Llama-8B` as your Dataset mount.

## Usage with Dynamo

Mount the Fluid-generated PVC in your DynamoGraphDeployment:

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
metadata:
  name: model-caching
spec:
  envs:
    - name: HF_HOME
      value: /model
    - name: DYN_DEPLOYMENT_CONFIG
      value: '{"Common": {"model": "/model", ...}}'
  services:
    VllmWorker:
      pvc:
        name: s3-model
        mountPoint: /model
    Processor:
      pvc:
        name: s3-model
        mountPoint: /model
```


## Full example with llama3.3 70B

### Performance

When deploying LLaMA 3.3 70B using Fluid as the caching layer, we observed the best performance by configuring a single-node cache that holds 100% of the model files locally. By ensuring that the vllm worker pod is scheduled on the same node as the Fluid cache, we were able to eliminate network I/O bottlenecks, which resulted in the fastest model startup time and the highest inference efficiency during our tests.

| Cache Configuration                          | vLLM Pod Placement               | Startup Time    |
|----------------------------------------------|----------------------------------|-----------------|
| ❌ No Cache (Download from HuggingFace)      | N/A                              | ~9 minutes      |
| 🟡 Multi-Node Cache (100% Model Cached)      | Not on Cache Node                | ~18 minutes     |
| 🟡 Multi-Node Cache (100% Model Cached)      | On Cache Node                    | ~10 minutes     |
| ✅ Single-Node Cache (100% Model Cached)     | On Cache Node                    | ~80 seconds     |


### Resources

```yaml
# dataset.yaml
apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: llama-3-3-70b-instruct-model
  namespace: my-namespace
spec:
  mounts:
    - mountPoint: s3://hf-models/meta-llama/Llama-3.3-70B-Instruct
      options:
        alluxio.underfs.s3.endpoint: http://minio:9000
        alluxio.underfs.s3.disable.dns.buckets: "true"
        aws.secretKey: "minioadmin"
        aws.accessKeyId: "minioadmin"
        alluxio.underfs.s3.streaming.upload.enabled: "true"
        alluxio.underfs.s3.multipart.upload.threads: "20"
        alluxio.underfs.s3.socket.timeout: "50s"
        alluxio.underfs.s3.request.timeout: "60s"
---
# runtime.yaml
apiVersion: data.fluid.io/v1alpha1
kind: AlluxioRuntime
metadata:
  name: llama-3-3-70b-instruct-model
  namespace: my-namespace
spec:
  replicas: 1
  properties:
    alluxio.user.file.readtype.default: CACHE_PROMOTE
    alluxio.user.file.write.type.default: CACHE_THROUGH
    alluxio.user.block.size.bytes.default: 128MB
  tieredstore:
    levels:
      - mediumtype: MEM
        path: /dev/shm
        quota: 300Gi
        high: "1.0"
        low: "0.7"
---
# DataLoad - Preloads the model into cache
apiVersion: data.fluid.io/v1alpha1
kind: DataLoad
metadata:
  name: llama-3-3-70b-instruct-model-loader
spec:
  dataset:
    name: llama-3-3-70b-instruct-model
    namespace: my-namespace
  loadMetadata: true
  target:
    - path: "/"
      replicas: 1
```

and the associated DynamoGraphDeployment with pod affinity to schedule the vllm worker on the same node than the Alluxio cache worker

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
metadata:
  name: my-hello-world
spec:
  envs:
  - name: DYN_LOG
    value: "debug"
  - name: DYN_DEPLOYMENT_CONFIG
    value: '{"Common": {"model": "/model", "block-size": 64, "max-model-len": 16384},
      "Frontend": {"served_model_name": "meta-llama/Llama-3.3-70B-Instruct", "endpoint":
      "dynamo.Processor.chat/completions", "port": 8000}, "Processor": {"router":
      "round-robin", "router-num-threads": 4, "common-configs": ["model", "block-size",
      "max-model-len"]}, "VllmWorker": {"tensor-parallel-size": 4, "enforce-eager": true, "max-num-batched-tokens":
      16384, "enable-prefix-caching": true, "ServiceArgs": {"workers": 1, "resources":
      {"gpu": "4", "memory": "40Gi"}}, "common-configs": ["model", "block-size", "max-model-len"]},
      "Planner": {"environment": "kubernetes", "no-operation": true}}'
  services:
    Processor:
      pvc:
        mountPoint: /model
        name: llama-3-3-70b-instruct-model
    VllmWorker:
      pvc:
        mountPoint: /model
        name: llama-3-3-70b-instruct-model
      extraPodSpec:
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: fluid.io/s-alluxio-my-namespace-llama-3-3-70b-instruct-model
                    operator: In
                    values:
                      - "true"
```


## Troubleshooting & FAQ

- **PVC not created?** Check Fluid and AlluxioRuntime pod logs.
- **Model not found?** Ensure the model was uploaded to the correct bucket/path.
- **Permission errors?** Verify S3/MinIO credentials and bucket policies.

## Resources

- [Fluid Documentation](https://fluid-cloudnative.github.io/)
- [Alluxio Documentation](https://docs.alluxio.io/)
- [MinIO Documentation](https://docs.min.io/)
- [Hugging Face Hub](https://huggingface.co/docs/hub/index)
- [Dynamo README](https://github.com/ai-dynamo/dynamo/blob/main/.devcontainer/README.md)
- [Dynamo Documentation](https://docs.nvidia.com/dynamo/latest/index.html)
