Jina AI Cloud Hosting#

https://docs.jina.ai/_images/jcloud-banner.png
../../_images/jcloud-banner.png

After building a Jina project, the next step is to deploy and host it on the cloud. Jina AI Cloud is Jina’s reliable, scalable and production-ready cloud-hosting solution that manages your project lifecycle without surprises or hidden development costs.

Tip

Are you ready to unlock the power of AI with Jina AI Cloud? Take a look at our pricing options now!

In addition to deploying Flows, jcloud supports the creation of secrets and jobs which are created in the Flow’s namespace.

Basics#

Jina AI Cloud provides a CLI that you can use via jina cloud from the terminal (or jcloud or simply jc for minimalists.)

Hint

You can also install just the JCloud CLI without installing the Jina package.

pip install jcloud
jc -h

If you installed the JCloud CLI individually, all of its commands fall under the jc or jcloud executable.

In case the command jc is already occupied by another tool, use jcloud instead. If your pip install doesn’t register bash commands for you, you can run python -m jcloud -h.

For the rest of this section, we use jc or jcloud. But again they are interchangeable with jina cloud.

Flows#

Deploy#

In Jina’s idiom, a project is a Flow, which represents an end-to-end task such as indexing, searching or recommending. In this document, we use “project” and “Flow” interchangeably.

A Flow can have two types of file structure: a single YAML file or a project folder.

Single YAML file#

A self-contained YAML file, consisting of all configuration at the Flow-level and Executor-level.

All Executors’ uses must follow the format jinaai+docker://<username>/MyExecutor (from Executor Hub) to avoid any local file dependencies:

# flow.yml
jtype: Flow
executors:
  - name: sentencizer
    uses: jinaai+docker://jina-ai/Sentencizer

To deploy:

jc flow deploy flow.yml

Caution

When jcloud deploys a flow it automatically appends the following global arguments to the flow.yml, if not present:

jcloud:
  version: jina-version
  docarray: docarray-version

The jina and docarray corresponds to your development environment’s jina and docarray versions.

Tip

We recommend testing locally before deployment:

jina flow --uses flow.yml

Project folder#

Tip

The best practice for creating a Jina AI Cloud project is to use:

jc new

This ensures the correct project structure that is accepted by Jina AI Cloud.

Just like a regular Python project, you can have sub-folders of Executor implementations and a flow.yml on the top-level to connect all Executors together.

You can create an example local project using jc new hello. The default structure looks like:

hello/
├── .env
├── executor1
│   ├── config.yml
│   ├── executor.py
│   └── requirements.txt
└── flow.yml

Where:

  • hello/ is your top-level project folder.

  • executor1 directory has all Executor related code/configuration. You can read the best practices for file structures. Multiple Executor directories can be created.

  • flow.yml Your Flow YAML.

  • .env All environment variables used during deployment.

To deploy:

jc flow deploy hello

The Flow is successfully deployed when you see:

../../_images/deploy.png

You will get a Flow ID, say merry-magpie-82b9c0897f. This ID is required to manage, view logs and remove the Flow.

As this Flow is deployed with the default gRPC gateway (feel free to change it to http or websocket), you can use jina.Client to access it:

from jina import Client, Document

print(
    Client(host='grpcs://merry-magpie-82b9c0897f.wolf.jina.ai').post(
        on='/', inputs=Document(text='hello')
    )
)

Get status#

To get the status of a Flow:

jc flow status merry-magpie-82b9c0897f
../../_images/status.png

Monitoring#

Basic monitoring is provided to Flows deployed on Jina AI Cloud.

To access the Grafana-powered dashboard, first get the status of the Flow. The Grafana Dashboard link is displayed at the bottom of the pane. Visit the URL to find basic metrics like ‘Number of Request Gateway Received’ and ‘Time elapsed between receiving a request and sending back the response’:

../../_images/monitoring.png

List Flows#

To list all of your “Starting”, “Serving”, “Failed”, “Updating”, and “Paused” Flows:

jc flows list
../../_images/list.png

You can also filter your Flows by passing a phase:

jc flows list --phase Deleted
../../_images/list_deleted.png

Or see all Flows:

jc flows list --phase all
../../_images/list_all.png

Remove Flows#

You can remove a single Flow, multiple Flows or even all Flows by passing different identifiers.

To remove a single Flow:

jc flow remove merry-magpie-82b9c0897f

To remove multiple Flows:

jc flow remove merry-magpie-82b9c0897f wondrous-kiwi-b02db6a066

To remove all Flows:

jc flow remove all

By default, removing multiple or all Flows is an interactive process where you must give confirmation before each Flow is deleted. To make it non-interactive, set the below environment variable before running the command:

export JCLOUD_NO_INTERACTIVE=1

Update a Flow#

You can update a Flow by providing an updated YAML.

To update a Flow:

jc flow update super-mustang-c6cf06bc5b flow.yml
../../_images/update_flow.png

Pause / Resume Flow#

You have the option to pause a Flow that is not currently in use but may be needed later. This will allow the Flow to be resumed later when it is needed again by using resume.

To pause a Flow:

jc flow pause super-mustang-c6cf06bc5b
../../_images/pause_flow.png

To resume a Flow:

jc flow resume super-mustang-c6cf06bc5b
../../_images/resume_flow.png

Restart Flow, Executor or Gateway#

If you need to restart a Flow, there are two options: restart all Executors and the Gateway associated with the Flow, or selectively restart only a specific Executor or the Gateway.

To restart a Flow:

jc flow restart super-mustang-c6cf06bc5b
../../_images/restart_flow.png

To restart the Gateway:

jc flow restart super-mustang-c6cf06bc5b --gateway
../../_images/restart_gateway.png

To restart an Executor:

jc flow restart super-mustang-c6cf06bc5b --executor executor0
../../_images/restart_executor.png

Recreate a Deleted Flow#

To recreate a deleted Flow:

jc flow recreate profound-rooster-eec4b17c73
../../_images/recreate_flow.png

Scale an Executor#

You can also manually scale any Executor.

jc flow scale good-martin-ca6bfdef84 --executor executor0 --replicas 2
../../_images/scale_executor.png

Normalize a Flow#

To normalize a Flow:

jc flow normalize flow.yml

Hint

Normalizing a Flow is the process of building the Executor image and pushing the image to Hubble.

Get Executor or Gateway logs#

To get the Gateway logs:

jc flow logs --gateway central-escargot-354a796df5
../../_images/gateway_logs.png

To get the Executor logs:

jc flow logs --executor executor0 central-escargot-354a796df5
../../_images/executor_logs.png

Secrets#

Create a Secret#

To create a Secret for a Flow:

jc secret create mysecret rich-husky-af14064067 --from-literal "{'env-name': 'secret-value'}"

Tip

You can optionally pass the --update flag to automatically update the Flow spec with the updated secret information. This flag will update the Flow which is hosted on the cloud. Finally, you can also optionally pass a Flow’s yaml file path with --path to update the yaml file locally. Refer to this section for more information.

Caution

If the --update flag is not passed then you have to manually update the flow with jc update flow rich-husky-af14064067 updated-flow.yml

List Secrets#

To list all the Secrets created in a Flow’s namespace:

jc secret list rich-husky-af14064067
../../_images/list_secrets.png

Get a Secret#

To retrieve a Secret’s details:

jc secret get mysecret rich-husky-af14064067
../../_images/get_secret.png

Remove Secret#

jc secret remove rich-husky-af14064067 mysecret

Update a Secret#

You can update a Secret for a Flow.

jc secret update rich-husky-af14064067 mysecret --from-literal "{'env-name': 'secret-value'}"

Tip

You can optionally pass the --update flag to automatically update the Flow spec with the updated secret information. This flag will update the Flow which is hosted on the cloud. Finally, you can also optionally pass a Flow’s yaml file path with --path to update the yaml file locally. Refer to this section for more information.

Caution

Updating a Secret automatically restarts a Flow.

Jobs#

Create a Job#

To create a Job for a Flow:

jc job create job-name rich-husky-af14064067 image 'job entrypoint' --timeout 600 --backofflimit 2

Tip

image can be any Executor image passed to a Flow’s Executor uses or any normal docker image prefixed with docker://

List Jobs#

To listg all Jobs created in a Flow’s namespace:

jc jobs list rich-husky-af14064067
../../_images/list_jobs.png

Get a Job#

To retrieve a Job’s details:

jc job get myjob1 rich-husky-af14064067
../../_images/get_job.png

Remove Job#

jc job remove rich-husky-af14064067 myjob1

Get Job Logs#

To get the Job logs:

jc job logs myjob1 -f rich-husky-af14064067
../../_images/job_logs.png

Deployments#

Deploy#

Caution

When jcloud deploys a deployment it automatically appends the following global arguments to the deployment.yml, if not present:

jcloud:
  version: jina-version
  docarray: docarray-version

Single YAML file#

A self-contained YAML file, consisting of all configuration information at the Deployment-level and Executor-level.

A Deployment’s uses parameter must follow the format jinaai+docker://<username>/MyExecutor (from Executor Hub) to avoid any local file dependencies:

# deployment.yml
jtype: Deployment
with:
  protocol: grpc
  uses: jinaai+docker://jina-ai/Sentencizer

To deploy:

jc deployment deploy ./deployment.yaml

The Deployment is successfully deployed when you see:

../../_images/deploy1.png

You will get a Deployment ID, for example pretty-monster-130a5ac952. This ID is required to manage, view logs, and remove the Deployment.

Since this Deployment is deployed with the default gRPC protocol (feel free to change it to http), you can use jina.Client to access it:

from jina import Client, Document

print(
    Client(host='grpcs://executor-pretty-monster-130a5ac952.wolf.jina.ai').post(
        on='/', inputs=Document(text='hello')
    )
)

Get status#

To get the status of a Deployment:

jc deployment status pretty-monster-130a5ac952
../../_images/status1.png

List Deployments#

To list all of your “Starting”, “Serving”, “Failed”, “Updating”, and “Paused” Deployments:

jc deployment list
../../_images/list1.png

You can also filter your Deployments by passing a phase:

jc deployment list --phase Deleted
../../_images/list_deleted1.png

Or see all Deployments:

jc deployment list --phase all
../../_images/list_all1.png

Remove Deployments#

You can remove a single Deployment, multiple Deployments, or even all Deployments by passing different commands to the jc executable at the command line.

To remove a single Deployment:

jc deployment remove pretty-monster-130a5ac952

To remove multiple Deployments:

jc deployment remove pretty-monster-130a5ac952 artistic-tuna-ab154c4dcc

To remove all Deployments:

jc deployment remove all

By default, removing all or multiple Deployments is an interactive process where you must give confirmation before each Deployment is deleted. To make it non-interactive, set the below environment variable before running the command:

export JCLOUD_NO_INTERACTIVE=1

Update a Deployment#

You can update a Deployment by providing an updated YAML.

To update a Deployment:

jc deployment update pretty-monster-130a5ac952 deployment.yml
../../_images/update.png

Pause / Resume Deployment#

You have the option to pause a Deployment that is not currently in use but may be needed later. This will allow the Deployment to be resumed later when it is needed again by using resume.

To pause a Deployment:

jc deployment pause pretty-monster-130a5ac952
../../_images/pause.png

To resume a Deployment:

jc eployment resume pretty-monster-130a5ac952
../../_images/resume.png

Restart Deployment#

To restart a Deployment:

jc deployment restart pretty-monster-130a5ac952
../../_images/restart.png

Recreate a Deleted Deployment#

To recreate a deleted Deployment:

jc deployment recreate pretty-monster-130a5ac952
../../_images/recreate.png

Scale a Deployment#

You can also manually scale any Deployment.

jc deployment scale pretty-monster-130a5ac952 --replicas 2
../../_images/scale.png

Get Deployment logs#

To get the Deployment logs:

jc deployment logs pretty-monster-130a5ac952
../../_images/logs.png

Configuration#

Please refer to Configuration for configuring the Flow on Jina AI Cloud.

Restrictions#

Jina AI Cloud scales according to your needs. You can demand different instance types with GPU/memory/CPU predefined based on the needs of your Flows and Executors. If you have specific resource requirements, please contact us on Discord or raise a GitHub issue.

Restrictions

  • Deployments are only supported in the us-east region.