Dashboard (beta)

Dashboard offers the insight of running tasks in Jina. With dashboard, one can analyze logs, design flows and view Jina Hub images.

🌟 Features:

• Log streaming, real-time chart on log-level.
• Grouping logs by Pods, Executors, Levels. Full text search on logs.
• Drag & drop flow design, setting properties of each Pod via a webform.
• Prebuilt pod menu for easy flow design.
• Flow can be imported from/exported to YAML.
• Browse hub images, use your GitHub account to rate and review images.

Contents

• Monitor Jina "Hello, World!" with Dashboard
• Getting started
  • 1. Start the log server
  • 2. Connect the Dashboard to your log server
  • Customize the endpoints
• Self-host Dashboard
  • Run in the debug mode
  • Run in the live mode
• Contributing
• License

Monitor Jina "Hello, World!" with Dashboard

Jina "Hello, World" is a simple demo of image neural search for Fashion-MNIST.

Make sure to have Fluentd installed - gem install fluentd --no-doc

Run: fluentd -c jina/resources/fluent.conf (file is in https://github.com/jina-ai/jina)

1. Run the following command in the console
   • (Python >=3.7 required)

     JINA_LOG_PROFILING=1 jina hello-world --logserver

   • ...or if you use Docker (no install required)

     docker run -e "JINA_LOG_PROFILING=1" -p 5000:5000 jinaai/jina:devel hello-world --logserver
     

2. Open https://dashboard.jina.ai in your browser

It should start streaming. If not, please refer to this step.

Getting started

1. Start the log server

Set environment variable JINA_LOG_PROFILING to 1 to enable Task tab in the dashboard.

Log server is a helper thread in Jina flow. It exposes HTTP endpoints to the public which the dashboard can use to fetch logs, visualize the flow.

By default the log server is disabled. To enable it you can,

If you use Flow API in Python,	from jina.flow import Flow f = (Flow(logserver=True) .add(...) .add(...)) with f.build() as fl: fl.index(...)
...or write a Flow from YAML	# myflow.yml !Flow with: logserver: true pods: ... f = Flow.load_config('myflow.yml') with f.build() as fl: fl.index(...)
...or start a Flow from CLI	jina flow --logserver --yaml-path myflow.yml
...or start a Flow with Docker Container	docker run -p 5000:5000 jina flow --logserver ...

Either way, if you see the following logs show up in the console, then your log server is successfully running. You can now move to the next step.

2. Connect the Dashboard to your log server

Go to: https://dashboard.jina.ai

Make sure your settings are configured correctly to point to your Jina instance.

If you have a running Jina instance, it should connect automatically.

If you start a new Jina instance, click "try again" and it will connect.

Note: make sure you wait for the logserver to start before attempting to connect dashboard.

Connection status will be indicated by the globe icon. A red X means the dashboard is not connected to any Jina instance, a green checkmark means there is an active connection.

You should now see the log stream and flow visualization.

Customize the endpoints

By default the configurations of the log server are as follows:

host: 0.0.0.0
port: 5000
endpoints:
  log: /stream/log # fetching log in SSE stream
  profile: /stream/profile # fetching profiling log in SSE stream
  yaml: /data/yaml # get the YAML spec of a flow
  shutdown: /action/shutdown # shutdown the log server
  ready: /status/ready # tell if the log server is ready, return 200 if yes

You can customize the endpoints of the log server via YAML, say mylogserver.yml. Then pass it to the Flow API via

If you use Flow API in Python,	f = Flow(logserver=True, logserver_config='mylogserver.yml')
...or write a Flow from YAML	!Flow with: logserver: true logserver_config: mylogserver.yml
...or start a Flow from CLI	jina flow --logserver --logserver-config mylogserver.yml ...
...or start a Flow with Docker Container	docker run -p PORT:PORT -v "$(PWD)/mylogserver.yml:/mylogserver.yml" jina flow --logserver --logserver-config /mylogserver.yml ...

Don't forget to update endpoint in the dashboard accordingly.

Self-host Dashboard

One can self-host a dashboard locally.

1. git clone https://github.com/jina-ai/dashboard.git && cd dashboard.
2. Install dependencies using command npm i.
3. Run dashboard via the following ways .

Note: features like the hub and GitHub login will not work when running locally as they are restricted to the dashboard.jina.ai origin. They are not necessary to view logs or interact with flows. If you would like to browse the hub do so from dashboard.jina.ai.

Run in the debug mode

1. cd dev-resources
2. node testServer
3. testServer will be running on http://localhost:5000 by default
4. npm start
5. dashboard will be served on http://localhost:3000 by default

Run in the live mode

1. npm build
2. node dashboard
3. dashboard will be served on http://localhost:3030 by default

Contributing

We welcome all kinds of contributions from the open-source community, individuals and partners. Without your active involvement, Jina can't be successful.

The following resources help you to make a good first contribution:

• Contributing guidelines
• Release cycles and development stages

License

Copyright (c) 2020 Jina AI Limited. All rights reserved.

Jina is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.