Local Container bento (works) <-> Yatai build Bento (fails with async issue)

🦄️ Yatai: Model Deployment at Scale on Kubernetes

⚠️ Yatai for BentoML 1.2 is currently under construction. See Yatai 2.0 Proposal for more details.

Yatai (屋台, food cart) is the Kubernetes deployment operator for BentoML.

It let DevOps teams to seamlessly integrate BentoML into their GitOps workflow, for deploying and scaling Machine Learning services on any Kubernetes cluster.

👉 Join our Slack community today!

Why Yatai?

Yatai empowers developers to deploy BentoML on Kubernetes, optimized for CI/CD and DevOps workflow.

Yatai is Cloud native and DevOps friendly. Via its Kubernetes-native workflow, specifically the BentoDeployment CRD (Custom Resource Definition), DevOps teams can easily fit BentoML powered services into their existing workflow.

Getting Started

📖 Documentation - Overview of the Yatai docs and related resources
⚙️ Installation - Hands-on instruction on how to install Yatai for production use
👉 Join Community Slack - Get help from our community and maintainers

Quick Tour

Let's try out Yatai locally in a minikube cluster!

⚙️ Prerequisites:

Install latest minikube: https://minikube.sigs.k8s.io/docs/start/
Install latest Helm: https://helm.sh/docs/intro/install/
Start a minikube Kubernetes cluster: minikube start --cpus 4 --memory 4096, if you are using macOS, you should use hyperkit driver to prevent the macOS docker desktop network limitation
Check that minikube cluster status is "running": minikube status
Make sure your kubectl is configured with minikube context: kubectl config current-context
Enable ingress controller: minikube addons enable ingress

🚧 Install Yatai

Install Yatai with the following script:

bash <(curl -s "https://raw.githubusercontent.com/bentoml/yatai/main/scripts/quick-install-yatai.sh")

This script will install Yatai along with its dependencies (PostgreSQL and MinIO) on your minikube cluster.

Note that this installation script is made for development and testing use only. For production deployment, check out the Installation Guide.

To access Yatai web UI, run the following command and keep the terminal open:

kubectl --namespace yatai-system port-forward svc/yatai 8080:80

In a separate terminal, run:

YATAI_INITIALIZATION_TOKEN=$(kubectl get secret yatai-env --namespace yatai-system -o jsonpath="{.data.YATAI_INITIALIZATION_TOKEN}" | base64 --decode)
echo "Open in browser: http://127.0.0.1:8080/setup?token=$YATAI_INITIALIZATION_TOKEN"

Open the URL printed above from your browser to finish admin account setup.

🍱 Push Bento to Yatai

First, get an API token and login to the BentoML CLI:

Keep the kubectl port-forward command in the step above running
Go to Yatai's API tokens page: http://127.0.0.1:8080/api_tokens
Create a new API token from the UI, making sure to assign "API" access under "Scopes"

Copy the login command upon token creation and run as a shell command, e.g.:

bentoml yatai login --api-token {YOUR_TOKEN} --endpoint http://127.0.0.1:8080

If you don't already have a Bento built, run the following commands from the BentoML Quickstart Project to build a sample Bento:

git clone https://github.com/bentoml/bentoml.git && cd ./examples/quickstart
pip install -r ./requirements.txt
python train.py
bentoml build

Push your newly built Bento to Yatai:

bentoml push iris_classifier:latest

🔧 Install yatai-image-builder component

Yatai's image builder feature comes as a separate component, you can install it via the following script:

bash <(curl -s "https://raw.githubusercontent.com/bentoml/yatai-image-builder/main/scripts/quick-install-yatai-image-builder.sh")

This will install the BentoRequest CRD(Custom Resource Definition) and Bento CRD in your cluster. Similarly, this script is made for development and testing purposes only.

🔧 Install yatai-deployment component

Yatai's Deployment feature comes as a separate component, you can install it via the following script:

bash <(curl -s "https://raw.githubusercontent.com/bentoml/yatai-deployment/main/scripts/quick-install-yatai-deployment.sh")

This will install the BentoDeployment CRD(Custom Resource Definition) in your cluster and enable the deployment UI on Yatai. Similarly, this script is made for development and testing purposes only.

🚢 Deploy Bento!

Once the yatai-deployment component was installed, Bentos pushed to Yatai can be deployed to your Kubernetes cluster and exposed via a Service endpoint.

A Bento Deployment can be created via applying a BentoDeployment resource:

Define your Bento deployment in a my_deployment.yaml file:

apiVersion: resources.yatai.ai/v1alpha1
kind: BentoRequest
metadata:
    name: iris-classifier
    namespace: yatai
spec:
    bentoTag: iris_classifier:3oevmqfvnkvwvuqj  # check the tag by `bentoml list iris_classifier`
---
apiVersion: serving.yatai.ai/v2alpha1
kind: BentoDeployment
metadata:
    name: my-bento-deployment
    namespace: yatai
spec:
    bento: iris-classifier
    ingress:
        enabled: true
    resources:
        limits:
            cpu: "500m"
            memory: "512Mi"
        requests:
            cpu: "250m"
            memory: "128Mi"
    autoscaling:
        maxReplicas: 10
        minReplicas: 2
    runners:
        - name: iris_clf
          resources:
              limits:
                  cpu: "1000m"
                  memory: "1Gi"
              requests:
                  cpu: "500m"
                  memory: "512Mi"
          autoscaling:
              maxReplicas: 4
              minReplicas: 1

Apply the deployment to your minikube cluster:

kubectl apply -f my_deployment.yaml

Now you can check the deployment status via kubectl get BentoDeployment -n my-bento-deployment

Community

To report a bug or suggest a feature request, use GitHub Issues.
For other discussions, use GitHub Discussions under the BentoML repo
To receive release announcements and get support, join us on Slack.

Contributing

There are many ways to contribute to the project:

If you have any feedback on the project, share it with the community in GitHub Discussions under the BentoML repo.
Report issues you're facing and "Thumbs up" on issues and feature requests that are relevant to you.
Investigate bugs and review other developers' pull requests.
Contributing code or documentation to the project by submitting a GitHub pull request. See the development guide.