Vald Agent Standalone on Kubernetes

This article will show you how to deploy a standalone Vald Agent using Helm and run it on your Kubernetes cluster.

Overview

Vald is made up of multiple microservices. In Get Started, you may use 4 kinds of components to deploy Vald. In this case, you use only 1 component, Vald Agent that is the core component for Vald named vald-agent-ngt, to deploy. The below image shows the architecture image of this case.

Using only Vald Agent, the auto indexing function is not in use.

The 5 steps to Vald Agent Standalone on Kubernetes with Vald:

Check and Satisfy the Requirements
Prepare Kubernetes Cluster
Deploy Vald Agent Standalone on Kubernetes cluster
Run Example Code
Cleanup

Requirements

Kubernetes: v1.19 ~
Go: v1.15 ~
Helm: v3 ~
libhdf5 (only required for tutorial)

Helm is used to deploying Vald on your Kubernetes, and Hdf5 decodes the sample data.
If Helm or HDF5 is not installed, please install Helm and HDF5.

Installation command for Helm

curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash

Installation command for HDF5

# yum
yum install -y hdf5-devel

# apt
apt-get install libhdf5-serial-dev

# homebrew
brew install hdf5

Prepare the Kubernetes Cluster

Prepare Kubernetes cluster

To complete get started, the Kubernetes cluster is required.
Vald will run on Cloud Services such as GKE, AWS. In the sense of trying to "Get-Started", k3d or kind are easy Kubernetes tools to use.

Deploy Vald Agent Standalone on Kubernetes Cluster

This chapter will show you how to deploy a standalone Vald Agent using Helm and run it on your Kubernetes cluster.
This chapter uses NGT as Vald Agent to perform vector insertion operation, indexing, and searching operation.

Clone the repository

To use the deployment yaml for deployment, let's clone vdaas/vald repository.
```
git clone https://github.com/vdaas/vald.git && \
cd vald
```
Deploy Vald Agent Standalone using Helm

There is the values.yaml to deploy standalone Vald Agent. Each component can be disabled by setting the value false to the [component].enabled field. This is useful for deploying standalone Vald Agent NGT pods.
```
helm repo add vald https://vald.vdaas.org/charts && \
helm install vald-agent-ngt vald/vald --values example/helm/values-standalone-agent-ngt.yaml
```

Verify

kubectl get pods

Example output

If the deployment is successful, Vald Agent component should be running.

NAME               READY   STATUS    RESTARTS   AGE
vald-agent-ngt-0   1/1     Running   0          20m
vald-agent-ngt-1   1/1     Running   0          20m
vald-agent-ngt-2   1/1     Running   0          20m
vald-agent-ngt-3   1/1     Running   0          20m

Run Example Code

Port Forward

At first, port-forward the vald-lb-gateway is required to make request from your local environment possible.
```
kubectl port-forward service/vald-agent-ngt 8081:8081
```

Download dataset

Download Fashion-MNIST that is used as a dataset for indexing and search query.

# move to the work directory
cd example/client/agent

# download Fashion-MNIST testing dataset
wget http://ann-benchmarks.com/fashion-mnist-784-euclidean.hdf5

Run Example

We use example/client/agent/main.go to run the example.
This example will insert and index 400 vectors into the Vald from the Fashion-MNIST dataset via gRPC. And then after waiting for indexing, it will request for searching the nearest vector 10 times. You will get the 10 nearest neighbor vectors for each search query.
Run example codes by executing the below command.

# run example
go run main.go

The detailed explanation of example code is here

This will execute 6 steps.

init

Import packages

example code

package main

import (
    "context"
    "encoding/json"
    "flag"
    "time"

    "github.com/kpango/fuid"
    "github.com/kpango/glg"
    agent "github.com/vdaas/vald-client-go/v1/agent/core"
    "github.com/vdaas/vald-client-go/v1/vald"
    "github.com/vdaas/vald-client-go/v1/payload"

    "gonum.org/v1/hdf5"
    "google.golang.org/grpc"
)

Set variables

The constant number of training datasets and test datasets.
example code
```
const (
    insertCount = 400
    testCount = 20
)
```

The variables for configuration.

example code

const (
    datasetPath         string
    grpcServerAddr      string
    indexingWaitSeconds uint
)

Recognition parameters.

example code

func init() {
    flag.StringVar(&datasetPath, "path", "fashion-mnist-784-euclidean.hdf5", "set dataset path")
    flag.StringVar(&grpcServerAddr, "addr", "127.0.0.1:8081", "set gRPC server address")
    flag.UintVar(&indexingWaitSeconds, "wait", 60, "set indexing wait seconds")
    flag.Parse()
}

load
- Loading from Fashion-MNIST dataset and set id for each vector that is loaded. This step will return the training dataset, test dataset, and ids list of ids when loading is completed with success.
  example code
```
ids, train, test, err := load(datasetPath)
if err != nil {
    glg.Fatal(err)
}
```

Create the gRPC connection and Vald client with gRPC connection.

example code

ctx := context.Background()

conn, err := grpc.DialContext(ctx, grpcServerAddr, grpc.WithInsecure())
if err != nil {
    glg.Fatal(err)
}

client := agent.NewAgentClient(conn)

Insert and Index

Insert and Indexing 400 training datasets to the Vald agent.

example code

for i := range ids [:insertCount] {
    if i%10 == 0 {
        glg.Infof("Inserted %d", i)
    }
    _, err := client.Insert(ctx, &payload.Insert_Request{
        Vector: &payload.Object_Vector{
            Id: ids[i],
            Vector: train[i],
        },
        Config: &payload.Insert_Config{
            SkipStrictExistCheck: true,
        },
    })
    if err != nil {
        glg.Fatal(err)
    }
}

Wait until indexing finish.

example code

wt := time.Duration(indexingWaitSeconds) * time.Second
glg.Infof("Wait %s for indexing to finish", wt)
time.Sleep(wt)

[Optional] Indexing manually instead of waiting for auto indexing You can set Agent NGT configuration auto_index_duration_limit and auto_index_check_duration for auto indexing. In this example, you can create index manually using CreateAndSaveIndex() method in the client library
example code
```
```go
_, err = client.CreateAndSaveIndex(ctx, &payload.Control_CreateIndexRequest{
    PoolSize: uint32(insertCount),
})
if err != nil {
    glg.Fatal(err)
}
```

</details>
```

Search

Search 10 neighbor vectors for each 20 test datasets and return a list of neighbor vectors.

When getting approximate vectors, the Vald client sends search config and vector to the server via gRPC.

example code

glg.Infof("Start search %d times", testCount)
for i, vec := range test[:testCount] {
    res, err := client.Search(ctx, &payload.Search_Request){
        Vector: vec,
        Config: &payload.Search_Config{
            Num: 10,
            Radius: -1,
            Epsilon: 0.1,
            Timeout: 100000000,
        }
    }
    if err != nil {
        glg.Fatal(err)
    }

    b, _ := json.MarshalIndent(res.GetResults(), "", " ")
    glg.Infof("%d - Results : %s\n\n", i+1, string(b))
    time.Sleep(1 * time.Second)
}

Remove

- Remove indexed 400 training datasets from the Vald agent.
    <details><summary>example code</summary><br>

    ```go
    for i := range ids [:insertCount] {
        _, err := client.Remove(ctx, &payload.Remove_Request{
            Id: &payload.Object_ID{
                Id: ids[i],
            },
        })
        if err != nil {
            glg.Fatal(err)
        }
        if i%10 == 0 {
            glg.Infof("Removed %d", i)
        }
    }
    ```

    </details>


- Remove from the index manually instead of waiting for auto indexing.
The removed vectors still exist in the NGT graph index before the SaveIndex (or CreateAndSaveIndex) API is called.
If you run the below code, the indexes will be removed completely from the Vald Agent NGT graph and the Backup file.
    <details><summary>example code</summary><br>

    ```go
    _, err = client.SaveIndex(ctx, &payload.Empty{})
    if err != nil {
        glg.Fatal(err)
    }
    ```

    </details>

It would be best to run CreateIndex() after Insert() without waiting for auto-indexing in your client code, even you can wait for the finishing auto createIndex function, which sometimes takes a long time. The backup files (e.g., ngt-meta.kvsdb) will be in your mount directory when vald-agent-ngt finishes indexing.

If you use Go(v1.16~) and catch the error like `missing go.sum entry to add it` when running `go run main.go`, please run `go mod tidy` and retry. This error comes from Go Command Changes of Go 1.16 Release Notes.(Please refer to https://golang.org/doc/go1.16#go-command for more details).

Cleanup

In the last, you can remove all deployed Vald pods by executing the below command.

helm uninstall vald-agent-ngt

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vald-agent-standalone-on-k8s.md

vald-agent-standalone-on-k8s.md

Vald Agent Standalone on Kubernetes

Overview

Requirements

Prepare the Kubernetes Cluster

Deploy Vald Agent Standalone on Kubernetes Cluster

Run Example Code

Cleanup

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Vald Agent Standalone on Kubernetes

Overview

Requirements

Prepare the Kubernetes Cluster

Deploy Vald Agent Standalone on Kubernetes Cluster

Run Example Code

Cleanup

Recommended Documents