Vald Agent Standalone on Docker

Vald is designed and implemented based on Cloud-Native architecture. However, there may be cases that want to use only Vald Agent without Kubernetes.

This article will show you how to deploy and run the Vald Agent on Docker. Fashion-mnist is used as an example dataset, same as Get Started.

Requirements

Docker: v19.0 ~
Go: v1.15 ~
libhdf5 (only required for this tutorial.)

HDF5 is required for this tutorial. If hdf5 is not installed, please install HDF5.

[Optional] Install HDF5

# yum
yum install -y hdf5-devel

# apt
apt-get install libhdf5-serial-dev

# homebrew
brew install hdf5

Deploy

This chapter will show you how to deploy Vald Agent on docker.
This chapter will use NGT for the core engine of Vald Agent.

Clone the vdaas/vald repository

git clone https://github.com/vdaas/vald.git

Create directory for setting deploy vald-agent-ngt
```
cd vald
mkdir -p tutorial && cd tutorial
```

Create config.yaml

The configuration of Vald agent for docker is set using config.yaml
You can also check the sample configuration.

cat << EOF > config.yaml
---
version: v0.0.0
time_zone: JST
logging:
  logger: glg
  level: debug
  format: raw
server_config:
  servers:
    - name: agent-grpc
      host: 0.0.0.0
      port: 8081
      mode: GRPC
      probe_wait_time: "3s"
      http:
        shutdown_duration: "5s"
        handler_timeout: ""
        idle_timeout: ""
        read_header_timeout: ""
        read_timeout: ""
        write_timeout: ""
  startup_strategy:
    - agent-grpc
  shutdown_strategy:
    - agent-grpc
  full_shutdown_duration: 600s
  tls:
    enabled: false
    # cert: /path/to/cert
    # key: /path/to/key
    # ca: /path/to/ca
ngt:
  # path to index data
  index_path: "/etc/server/backup"
  # vector dimension
  dimension: 784
  # bulk insert chunk size
  bulk_insert_chunk_size: 10
  # distance_type, which should be "l1", "l2" "angle", "hamming", "cosine", "normalizedangle", "normalizedcosine" or "jaccard"
  distance_type: l2
  # object_type, which should be "float" or "uint8"
  object_type: float
  # creation edge size
  creation_edge_size: 20
  # search edge size
  search_edge_size: 10
  # The limit duration of automatic indexing
  # auto_index_duration_limit should be 30m-6h for production use. Below setting is a just example
  auto_index_duration_limit: 1m
  # Check duration of automatic indexing.
  # auto_index_check_duration be 10m-1h for production use. Below setting is a just example
  auto_index_check_duration: 10s
  # The number of cache to trigger automatic indexing
  auto_index_length: 100
  # The limit duration of auto saving indexing
  # auto_save_index_duration should be 30m-60m for production use. The below setting is a just example.
  auto_save_index_duration: 90s
  # The maximum limit duration for an initial delay
  # initial_delay_max_duration should be 3m-5m for production use. The below setting is a just example.
  initial_delay_max_duration: 60s
  # The default create index batch pool size.
  # When it is too large comparing to machine resource, the docker container will be crash.
  default_pool_size: 500
EOF

Create a backup directory
To avoid removing the indexing data due to any trouble after finishing indexing, we should prepare the path for auto backup.
```
mkdir -p backup
```
Deploy Vald Agent on Docker
To deploy Vald agent on docker with config.yaml, you can run the below command. Note:
- Please check whether there is config.yaml file and backup directory in your current directory.
```
docker run -v $(pwd):/etc/server -u "$(id -u $USER):$(id -g $USER)" -v /etc/passwd:/etc/passwd:ro -v /etc/group:/etc/group:ro -p 8081:8081 --rm -it vdaas/vald-agent-ngt
```

Verify

If the deployment success, you can confirm the output will be similar to below.

2020-07-01 03:02:41	[INFO]:	maxprocs: Leaving GOMAXPROCS=4: CPU quota undefined
2020-07-01 03:02:41	[INFO]:	service agent ngt v0.0.0 starting...
2020-07-01 03:02:41	[INFO]:	executing daemon pre-start function
2020-07-01 03:02:41	[INFO]:	executing daemon start function
2020-07-01 03:02:41	[INFO]:	server agent-grpc executing preStartFunc
2020-07-01 12:02:41	[INFO]:	gRPC server agent-grpc starting on 0.0.0.0:8081

Run using example code

Download dataset

In this tutorial. we use fashion-mnist as a dataset for indexing and search query.

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

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

Running example
Vald provides multiple language client libraries such as Go, Java, Node.js, Python, and so on.
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.
We use example/client/agent/main.go to run the example. The example code is the same as running an example only Vald agent on Kubernetes. If you want to learn the detail of running an example, please refer to the tutorial of Vald Agent Standalone on kubernetes.
```
# run example
go run main.go
```
Note:
- We recommend you to run CreateIndex() after Insert() without waiting for auto indexing.
- When finish indexing completely, the backup files (metadata.json and ngt-meta.kvsdb) can be confirmed in your mount directory.
Warning:
- 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.
Clean Up
Stop the Vald Agent docker container via Ctrl+C.

Vald Agent Standalone on Docker

Requirements

Deploy

Run using example code

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