Triton Server accelerates distribution of models based on Dragonfly
CNCF projects highlighted in this post, and migrated by mingcheng.
Project post by Yufei Chen, Miao Hao, and Min Huang, Dragonfly project
This document will help you experience how to use dragonfly with TritonServe. During the downloading of models, the file size is large and there are many services downloading the files at the same time. The bandwidth of the storage will reach the limit and the download will be slow.
Dragonfly can be used to eliminate the bandwidth limit of the storage through P2P technology, thereby accelerating file downloading.
Installation
By integrating Dragonfly Repository Agent into Triton, download traffic through Dragonfly to pull models stored in S3, OSS, GCS, and ABS, and register models in Triton. The Dragonfly Repository Agent is in the dragonfly-repository-agent repository.
Prerequisites
| Name | Version | Document |
|---|---|---|
| Kubernetes cluster | 1.20+ | kubernetes.io |
| Helm | 3.8.0+ | helm.sh |
| Triton Server | 23.08-py3 | Triton Server |
Notice: Kind is recommended if no kubernetes cluster is available for testing.
Dragonfly Kubernetes Cluster Setup
For detailed installation documentation, please refer to quick-start-kubernetes.
Prepare Kubernetes Cluster
Create kind multi-node cluster configuration file kind-config.yaml, configuration content is as follows:
kind: ClusterapiVersion: kind.x-k8s.io/v1alpha4nodes: - role: control-plane - role: worker - role: worker
Create a kind multi-node cluster using the configuration file:
kind create cluster --config kind-config.yaml
Switch the context of kubectl to kind cluster:
kubectl config use-context kind-kind
Kind loads dragonfly image
Pull dragonfly latest images:
docker pull dragonflyoss/scheduler:latestdocker pull dragonflyoss/manager:latestdocker pull dragonflyoss/dfdaemon:latest
Kind cluster loads dragonfly latest images:
kind load docker-image dragonflyoss/scheduler:latestkind load docker-image dragonflyoss/manager:latestkind load docker-image dragonflyoss/dfdaemon:latest
Create dragonfly cluster based on helm charts
Create helm charts configuration file charts-config.yamland set dfdaemon.config.agents.regx to match the download path of the object storage. Example: add regx:.*models.* to match download request from object storage bucket models. Configuration content is as follows:
scheduler: image: dragonflyoss/scheduler tag: latest replicas: 1 metrics: enable: true config: verbose: true pprofPort: 18066seedPeer: image: dragonflyoss/dfdaemon tag: latest replicas: 1 metrics: enable: true config: verbose: true pprofPort: 18066dfdaemon: image: dragonflyoss/dfdaemon tag: latest metrics: enable: true config: verbose: true pprofPort: 18066 proxy: defaultFilter: 'Expires&Signature&ns' security: insecure: true cacert: '' cert: '' key: '' tcpListen: namespace: '' port: 65001 registryMirror: url: https://index.docker.io insecure: true certs: [] direct: false proxies: - regx: blobs/sha256.* # Proxy all http downlowd requests of model bucket path. - regx: .*models.*manager: image: dragonflyoss/manager tag: latest replicas: 1 metrics: enable: true config: verbose: true pprofPort: 18066jaeger: enable: true
Create a dragonfly cluster using the configuration file:
helm repo add dragonfly https://dragonflyoss.github.io/helm-charts/
helm install --wait --create-namespace --namespace dragonfly-system dragonfly dragonfly/dragonfly -f charts-config.yaml
Example output:
LAST DEPLOYED: Wed Nov 29 21:23:48 2023
NAMESPACE: dragonfly-system
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
1. Get the scheduler address by running these commands:
export SCHEDULER_POD_NAME=$(kubectl get pods --namespace dragonfly-system -l "app=dragonfly,release=dragonfly,component=scheduler" -o jsonpath={.items[0].metadata.name})
export SCHEDULER_CONTAINER_PORT=$(kubectl get pod --namespace dragonfly-system $SCHEDULER_POD_NAME -o jsonpath="{.spec.containers[0].ports[0].containerPort}")
kubectl --namespace dragonfly-system port-forward $SCHEDULER_POD_NAME 8002:$SCHEDULER_CONTAINER_PORT
echo "Visit http://127.0.0.1:8002 to use your scheduler"
2. Get the dfdaemon port by running these commands:
export DFDAEMON_POD_NAME=$(kubectl get pods --namespace dragonfly-system -l "app=dragonfly,release=dragonfly,component=dfdaemon" -o jsonpath={.items[0].metadata.name})
export DFDAEMON_CONTAINER_PORT=$(kubectl get pod --namespace dragonfly-system $DFDAEMON_POD_NAME -o jsonpath="{.spec.containers[0].ports[0].containerPort}")
You can use $DFDAEMON_CONTAINER_PORT as a proxy port in Node.
3. Configure runtime to use dragonfly:
https://d7y.io/docs/getting-started/quick-start/kubernetes/
4. Get Jaeger query URL by running these commands:
export JAEGER_QUERY_PORT=$(kubectl --namespace dragonfly-system get services dragonfly-jaeger-query -o jsonpath="{.spec.ports[0].port}")
kubectl --namespace dragonfly-system port-forward service/dragonfly-jaeger-query 16686:$JAEGER_QUERY_PORT
echo "Visit http://127.0.0.1:16686/search?limit=20&lookback=1h&maxDuration&minDuration&service=dragonfly to query download events"
Check that dragonfly is deployed successfully:
kubectl get pods -n dragonfly-systemNAME READY STATUS RESTARTS AGEdragonfly-dfdaemon-8qcpd 1/1 Running 4 (118s ago) 2m45sdragonfly-dfdaemon-qhkn8 1/1 Running 4 (108s ago) 2m45sdragonfly-jaeger-6c44dc44b9-dfjfv 1/1 Running 0 2m45sdragonfly-manager-549cd546b9-ps5tf 1/1 Running 0 2m45sdragonfly-mysql-0 1/1 Running 0 2m45sdragonfly-redis-master-0 1/1 Running 0 2m45sdragonfly-redis-replicas-0 1/1 Running 0 2m45sdragonfly-redis-replicas-1 1/1 Running 0 2m7sdragonfly-redis-replicas-2 1/1 Running 0 101sdragonfly-scheduler-0 1/1 Running 0 2m45sdragonfly-seed-peer-0 1/1 Running 1 (52s ago) 2m45s
Expose the Proxy service port
Create the dfstore.yaml configuration file to expose the port on which the Dragonfly Peer’s HTTP proxy listens. The default port is 65001 and settargetPort to 65001.
kind: ServiceapiVersion: v1metadata: name: dfstorespec: selector: app: dragonfly component: dfdaemon release: dragonfly ports: - protocol: TCP port: 65001 targetPort: 65001 type: NodePort
Create service:
kubectl --namespace dragonfly-system apply -f dfstore.yaml
Forward request to Dragonfly Peer’s HTTP proxy:
kubectl --namespace dragonfly-system port-forward service/dfstore 65001:65001
Install Dragonfly Repository Agent
Set Dragonfly Repository Agent configuration
Create the dragonfly_config.jsonconfiguration file, the configuration is as follows:
{
"proxy": "http://127.0.0.1:65001",
"header": {},
"filter": [
"X-Amz-Algorithm",
"X-Amz-Credential",
"X-Amz-Date",
"X-Amz-Expires",
"X-Amz-SignedHeaders",
"X-Amz-Signature"
]
}
- proxy: The address of Dragonfly Peer’s HTTP Proxy.
- header: Adds a request header to the request.
- filter: Used to generate unique tasks and filter unnecessary query parameters in the URL.
In the filter of the configuration, set different values when using different object storage:
| Type | Value |
|---|---|
| OSS | ["Expires","Signature","ns"] |
| S3 | ["X-Amz-Algorithm", "X-Amz-Credential", "X-Amz-Date", "X-Amz-Expires", "X-Amz-SignedHeaders", "X-Amz-Signature"] |
| OBS | ["X-Amz-Algorithm", "X-Amz-Credential", "X-Amz-Date", "X-Obs-Date", "X-Amz-Expires", "X-Amz-SignedHeaders", "X-Amz-Signature"] |
Set Model Repository configuration
Create cloud_credential.json cloud storage credential, the configuration is as follows:
```json
{
"gs": {
"": "PATH_TO_GOOGLE_APPLICATION_CREDENTIALS",
"gs://gcs-bucket-002": "PATH_TO_GOOGLE_APPLICATION_CREDENTIALS_2"
},
"s3": {
"": {
"secret_key": "AWS_SECRET_ACCESS_KEY",
"key_id": "AWS_ACCESS_KEY_ID",
"region": "AWS_DEFAULT_REGION",
"session_token": "",
"profile": ""
},
"s3://s3-bucket-002": {
"secret_key": "AWS_SECRET_ACCESS_KEY_2",
"key_id": "AWS_ACCESS_KEY_ID_2",
"region": "AWS_DEFAULT_REGION_2",
"session_token": "AWS_SESSION_TOKEN_2",
"profile": "AWS_PROFILE_2"
}
},
"as": {
"": {
"account_str": "AZURE_STORAGE_ACCOUNT",
"account_key": "AZURE_STORAGE_KEY"
},
"as://Account-002/Container": {
"account_str": "",
"account_key": ""
}
}
}
In order to pull the model through Dragonfly, the model configuration file needs to be added following code in config.pbtxt file:
model_repository_agents{ agents [ { name: "dragonfly", } ]}
The densenet_onnx example contains modified configuration and model file. Modified config.pbtxt such as:
name: "densenet_onnx"platform: "onnxruntime_onnx"max_batch_size : 0input [ { name: "data_0" data_type: TYPE_FP32 format: FORMAT_NCHW dims: [ 3, 224, 224 ] reshape { shape: [ 1, 3, 224, 224 ] } }]output [ { name: "fc6_1" data_type: TYPE_FP32 dims: [ 1000 ] reshape { shape: [ 1, 1000, 1, 1 ] } label_filename: "densenet_labels.txt" }]model_repository_agents{ agents [ { name: "dragonfly", } ]}
Triton Server integrates Dragonfly Repository Agent plugin
Install Triton Server with Docker
Pull dragonflyoss/dragonfly-repository-agent image which is integrated Dragonfly Repository Agent plugin in Triton Server, refer to Dockerfile.
docker pull dragonflyoss/dragonfly-repository-agent:latest
Run the container and mount the configuration directory:
docker run --network host --rm \ -v ${path-to-config-dir}:/home/triton/ \ dragonflyoss/dragonfly-repository-agent:latest tritonserver \ --model-repository=${model-repository-path}
- path-to-config-dir: The files path of dragonfly_config.json&cloud_credential.json.
- model-repository-path: The path of remote model repository.
The correct output is as follows:
=============================== Triton Inference Server ===============================
successfully loaded 'densenet_onnx'
I1130 09:43:22.595672 1 server.cc:604]
+------------------+------------------------------------------------------------------------+
| Repository Agent | Path |
+------------------+------------------------------------------------------------------------+
| dragonfly | /opt/tritonserver/repoagents/dragonfly/libtritonrepoagent_dragonfly.so |
+------------------+------------------------------------------------------------------------+
I1130 09:43:22.596011 1 server.cc:631]
+-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Backend | Path | Config |
+-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
| pytorch | /opt/tritonserver/backends/pytorch/libtriton_pytorch.so | {} |
| onnxruntime | /opt/tritonserver/backends/onnxruntime/libtriton_onnxruntime.so | {"cmdline":{"auto-complete-config":"true","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","default-max-batch-size":"4"}} |
+-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
I1130 09:43:22.596112 1 server.cc:674]
+---------------+---------+--------+
| Model | Version | Status |
+---------------+---------+--------+
| densenet_onnx | 1 | READY |
+---------------+---------+--------+
I1130 09:43:22.598318 1 metrics.cc:703] Collecting CPU metrics
I1130 09:43:22.599373 1 tritonserver.cc:2435]
+----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Option | Value |
+----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| server_id | triton |
| server_version | 2.37.0 |
| server_extensions | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor_data parameters statistics trace logging |
| model_repository_path[0] | s3://192.168.36.128:9000/models |
| model_control_mode | MODE_NONE |
| strict_model_config | 0 |
| rate_limit | OFF |
| pinned_memory_pool_byte_size | 268435456 |
| min_supported_compute_capability | 6.0 |
| strict_readiness | 1 |
| exit_timeout | 30 |
| cache_enabled | 0 |
+----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
I1130 09:43:22.610334 1 grpc_server.cc:2451] Started GRPCInferenceService at 0.0.0.0:8001
I1130 09:43:22.612623 1 http_server.cc:3558] Started HTTPService at 0.0.0.0:8000
I1130 09:43:22.695843 1 http_server.cc:187] Started Metrics Service at 0.0.0.0:8002
Execute the following command to check the Dragonfly logs:
kubectl exec -it -n dragonfly-system dragonfly-dfdaemon-<id> -- tail -f /var/log/dragonfly/daemon/core.log
Check downloaded successfully through Dragonfly:
{"level":"info","ts":"2024-02-02 05:28:02.631","caller":"peer/peertask_conductor.go:1349","msg":"peer task done, cost: 352ms","peer":"10.244.2.3-1-4398a429-d780-423a-a630-57d765f1ccfc","task":"974aaf56d4877cc65888a4736340fb1d8fecc93eadf7507f531f9fae650f1b4d","component":"PeerTask","trace":"4cca9ce80dbf5a445d321cec593aee65"}
Verify
Call inference API：
docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:23.08-py3-sdk /workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg
Check the response successful:
Request 01Image '/workspace/images/mug.jpg': 15.349563 (504) = COFFEE MUG 13.227461 (968) = CUP 10.424893 (505) = COFFEEPOT
Performance testing
Test the performance of single-machine model download by Triton API after the integration of Dragonfly P2P. Due to the influence of the network environment of the machine itself, the actual download time is not important, but The proportion of download speed in different scenarios is more meaningful:
- Triton API: Use signed URL provided by Object Storage to download the model directly.
- Triton API & Dragonfly Cold Boot: Use Triton Serve API to download model via Dragonfly P2P network and no cache hits.
- Hit Remote Peer: Use Triton Serve API to download model via Dragonfly P2P network and hit the remote peer cache.
- Hit Local Peer: Use Triton Serve API to download model via Dragonfly P2P network and hit the local peer cache.
Test results show Triton and Dragonfly integration. It can effectively reduce the file download time. Note that this test was a single-machine test, which means that in the case of cache hits, the performance limitation is on the disk. If Dragonfly is deployed on multiple machines for P2P download, the models download speed will be faster.
Resources
Dragonfly Community
- Website: https://d7y.io/
- Github Repo: https://github.com/dragonflyoss/dragonfly
- Dragonfly Repository Agent Github Repo: https://github.com/dragonflyoss/dragonfly-repository-agent
- Slack Channel: #dragonfly on CNCF Slack
- Discussion Group: dragonfly-discuss@googlegroups.com
- Twitter: @dragonfly_oss