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+# Kubernetes Deployments Support
+
+Deploy a model to a kubernetes cluster exposing its prediction endpoints through a service.
+
+## Preparation
+
+- Make sure you have a Kubernetes cluster accessible, with the corresponding kubeconfig file available.
+- The cluster has access to a docker registry so as to pull docker images.
+- Relevant permissions to create resources on the cluster -- deployment, service, etc. are present.
+- Nodes are accessible and reachable, with an external IP address (valid for a NodePort service, more details to come below).
+
+## Description
+
+Deploying to a Kubernetes cluster involves 2 main steps:
+
+1. Build the docker image and upload it to a registry.
+2. Create resources on the Kubernetes cluster -- specifically, a `namespace`, a `deployment` and a `service`.
+
+Once this is done, one can use the usual workflow of [`mlem deployment run`](/doc/command-reference/deployment/run)
+to deploy on Kubernetes.
+
+
+
+You can use [`mlem types deployment kubernetes`](/doc/command-reference/types) to list all the configurable parameters.
+
+
+
+Most of the configurable parameters in the list above come with sensible defaults. But at the least, one needs to follow
+the structure given below:
+
+```cli
+$ mlem deployment run service_name --model model --env kubernetes --conf service_type=loadbalancer
+
+⏳️ Loading model from model.mlem
+💾 Saving deployment to service_name.mlem
+🛠 Creating docker image ml
+ 🛠 Building MLEM wheel file...
+ 💼 Adding model files...
+ 🛠 Generating dockerfile...
+ 💼 Adding sources...
+ 💼 Generating requirements file...
+ 🛠 Building docker image ml:4ee45dc33804b58ee2c7f2f6be447cda...
+ ✅ Built docker image ml:4ee45dc33804b58ee2c7f2f6be447cda
+namespace created. status='{'conditions': None, 'phase': 'Active'}'
+deployment created. status='{'available_replicas': None,
+ 'collision_count': None,
+ 'conditions': None,
+ 'observed_generation': None,
+ 'ready_replicas': None,
+ 'replicas': None,
+ 'unavailable_replicas': None,
+ 'updated_replicas': None}'
+service created. status='{'conditions': None, 'load_balancer': {'ingress': None}}'
+✅ Deployment ml is up in mlem namespace
+```
+
+where:
+- `service_name` is a name of one's own choice, of which corresponding `service_name.mlem` and `service_name.mlem.state` files will be created.
+- `model` denotes the path to model saved via `mlem`.
+- `service_type` is configurable and is passed as `loadbalancer`. The default value is `nodeport` if not passed.
+
+### Checking the docker images
+
+One can check the docker image built via `docker image ls` which should give the following output:
+
+```
+REPOSITORY TAG IMAGE ID CREATED SIZE
+ml 4ee45dc33804b58ee2c7f2f6be447cda 16cf3d92492f 3 minutes ago 778MB
+...
+```
+
+### Checking the kubernetes resources
+
+Pods created can be checked via `kubectl get pods -A` which should have a pod in the `mlem` namespace present as shown below:
+
+```
+NAMESPACE NAME READY STATUS RESTARTS AGE
+kube-system coredns-6d4b75cb6d-xp68b 1/1 Running 7 (12m ago) 7d22h
+...
+kube-system storage-provisioner 1/1 Running 59 (11m ago) 54d
+mlem ml-cddbcc89b-zkfhx 1/1 Running 0 5m58s
+```
+
+By default, all resources are created in the `mlem` namespace. This ofcourse is configurable using `--conf namespace=prod` where `prod`
+is the desired namespace name.
+
+### Making predictions via mlem
+
+One can of course use the [`mlem deployment apply`](/doc/command-reference/deployment/apply) command to ping the deployed endpoint to get the predictions back. An example could be:
+
+```cli
+$ mlem deployment apply service_name data --json
+
+[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
+```
+
+where `data` is the dataset saved via `mlem`.
+
+### Deleting the Kubernetes resources
+
+A model can easily be undeployed using `mlem deploy remove service_name` which will delete the `pods`, `services` and the `namespace` i.e. clear the resources from the cluster. The docker image will still persist in the registry though.
+
+
+
+
+### ⚙️ About which cluster to use
+
+MLEM tries to find the kubeconfig file from the environment variable `KUBECONFIG`
+or the default location `~/.kube/config`.
+
+If you need to use another path, one can pass it with
+
+`--conf kube_config_file_path=...`
+
+
+
+## Case Study: Using EKS cluster with ECR on AWS
+
+The deployment to a cloud managed kubernetes cluster such as EKS is simple and analogous to how it is done in the steps above for a local cluster (such as minikube).
+
+
+
+To setup an EKS cluster, you can simply use [`eksctl`](https://eksctl.io/)
+
+A simple command such as
+
+```cli
+eksctl create cluster --name cluster-name --region us-east-1
+```
+
+will setup an EKS cluster for you with default parameters such as two `m5.large` worker nodes.
+
+Other tools such as [`terraform`](https://learn.hashicorp.com/tutorials/terraform/eks) can also be used.
+
+
+
+The popular docker registry choice to be used with EKS is ECR (Elastic Container Registry). Make sure the EKS cluster has at least read access to ECR.
+
+### ECR
+
+Make sure you have a repository in ECR where docker images can be uploaded. In the sample screenshot below, there exists a `classifier` repository:
+
+
+
+### Using MLEM with ECR and EKS
+
+Provided that the default kubeconfig file (present at `~/.kube/config`) can communicate with EKS, execute the following command:
+
+```cli
+$ mlem deploy run service_name --model model --env kubernetes --conf registry=ecr --conf registry.account=342840881361 --conf registry.region="us-east-1" --conf registry.host="342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier" --conf image_name=classifier --conf service_type=loadbalancer
+
+⏳️ Loading model from model.mlem
+💾 Saving deployment to service_name.mlem
+🛠 Creating docker image classifier
+ 🛠 Building MLEM wheel file...
+ 💼 Adding model files...
+ 🛠 Generating dockerfile...
+ 💼 Adding sources...
+ 💼 Generating requirements file...
+ 🛠 Building docker image 342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier:4ee45dc33804b58ee2c7f2f6be447cda...
+ 🗝 Logged in to remote registry at host 342840881361.dkr.ecr.us-east-1.amazonaws.com
+ ✅ Built docker image 342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier:4ee45dc33804b58ee2c7f2f6be447cda
+ 🔼 Pushing image 342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier:4ee45dc33804b58ee2c7f2f6be447cda to
+342840881361.dkr.ecr.us-east-1.amazonaws.com
+ ✅ Pushed image 342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier:4ee45dc33804b58ee2c7f2f6be447cda to
+342840881361.dkr.ecr.us-east-1.amazonaws.com
+namespace created. status='{'conditions': None, 'phase': 'Active'}'
+deployment created. status='{'available_replicas': None,
+ 'collision_count': None,
+ 'conditions': None,
+ 'observed_generation': None,
+ 'ready_replicas': None,
+ 'replicas': None,
+ 'unavailable_replicas': None,
+ 'updated_replicas': None}'
+service created. status='{'conditions': None, 'load_balancer': {'ingress': None}}'
+✅ Deployment classifier is up in mlem namespace
+```
+
+- Note that the repository name in ECR i.e. `classifier` has to match with the `image_name` supplied through `--conf`
+
+### Checking the docker images
+
+One can check the docker image built via `docker image ls` which should give the following output:
+
+```
+REPOSITORY TAG IMAGE ID CREATED SIZE
+342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier 4ee45dc33804b58ee2c7f2f6be447cda 96afb03ad6f5 2 minutes ago 778MB
+...
+```
+
+This can also be verified in ECR:
+
+
+
+### Checking the kubernetes resources
+
+Pods created can be checked via `kubectl get pods -A` which should have a pod in the `mlem` namespace present as shown below:
+
+```
+NAMESPACE NAME READY STATUS RESTARTS AGE
+kube-system aws-node-pr8cn 1/1 Running 0 11m
+...
+kube-system kube-proxy-dfxsv 1/1 Running 0 11m
+mlem classifier-687655f977-h7wsl 1/1 Running 0 83s
+```
+
+By default, all resources are created in the `mlem` namespace. This ofcourse is configurable using `--conf namespace=prod` where `prod`
+is the desired namespace name.
+
+Services created can be checked via `kubectl get svc -A` which should look like the following:
+
+```
+NAMESPACE NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
+default kubernetes ClusterIP 10.100.0.1 443/TCP 20m
+kube-system kube-dns ClusterIP 10.100.0.10 53/UDP,53/TCP 20m
+mlem classifier LoadBalancer 10.100.87.16 a069daf48f9f244338a4bf5c60c6b823-1734837081.us-east-1.elb.amazonaws.com 8080:32067/TCP 2m32s
+```
+
+### Making predictions via mlem or otherwise
+
+One can clearly visit the External IP of the service `classifier` created by `mlem` i.e.
+
+**a069daf48f9f244338a4bf5c60c6b823-1734837081.us-east-1.elb.amazonaws.com:8080**
+
+using the browser and see the usual FastAPI docs page:
+
+
+
+But one can also use the [`mlem deployment apply`](/doc/command-reference/deployment/apply) command to ping the deployed endpoint to get the predictions back. An example could be:
+
+```cli
+$ mlem deployment apply service_name data --json
+
+[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
+```
+
+i.e. `mlem` knows how to calculate the externally reachable endpoint given the service type.
+
+### A note about NodePort Service
+
+
+
+While the example discussed above deploys a LoadBalancer Service Type, but one can also use NodePort (which is the default) OR via `--conf service_type=nodeport`
+
+While `mlem` knows how to calculate externally reachable IP address, make sure the EC2 machine running the pod has external traffic allowed to it. This can be configured in the inbound rules of the node's security group.
+
+This can be seen as the last rule being added below:
+
+
+
+
+
+## Swapping the model in deployment
+
+If you want to change the model that is currently under deployment, simply run
+
+```cli
+$ mlem deploy run service_name --model other-model
+```
+
+This will build a new docker image corresponding to the `other-model` and will terminate the existing pod and create a new one, thereby replacing it, without downtime.
+
+This can be seen below:
+
+### Checking the docker images
+
+```
+REPOSITORY TAG IMAGE ID CREATED SIZE
+342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier d57e4cacec82ebd72572d434ec148f1d 9bacd4cd9cc0 11 minutes ago 2.66GB
+342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier 4ee45dc33804b58ee2c7f2f6be447cda 26cb86b55bc4 About an hour ago 778MB
+...
+```
+
+Notice how a new docker image with the tag `d57e4cacec82ebd72572d434ec148f1d` is built.
+
+### Checking the deployment process
+
+```
+⏳️ Loading deployment from service_name.mlem
+⏳️ Loading model from other-model.mlem
+🛠 Creating docker image classifier
+ 🛠 Building MLEM wheel file...
+ 💼 Adding model files...
+ 🛠 Generating dockerfile...
+ 💼 Adding sources...
+ 💼 Generating requirements file...
+ 🛠 Building docker image 342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier:d57e4cacec82ebd72572d434ec148f1d...
+ 🗝 Logged in to remote registry at host 342840881361.dkr.ecr.us-east-1.amazonaws.com
+ ✅ Built docker image 342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier:d57e4cacec82ebd72572d434ec148f1d
+ 🔼 Pushing image 342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier:d57e4cacec82ebd72572d434ec148f1d to 342840881361.dkr.ecr.us-east-1.amazonaws.com
+ ✅ Pushed image 342840881361.dkr.ecr.us-east-1.amazonaws.com/classifier:d57e4cacec82ebd72572d434ec148f1d to 342840881361.dkr.ecr.us-east-1.amazonaws.com
+✅ Deployment classifier is up in mlem namespace
+```
+
+Here, an existing deployment i.e. `service_name` is used but with a newer model. Hence, details of registry need not be passed again. The contents of `service_name` can be checked by inspecting the `service_name.mlem` file.
+
+### Checking the kubernetes resources
+
+We can see the existing pod being terminated and the new one running in its place below:
+
+```
+NAMESPACE NAME READY STATUS RESTARTS AGE
+kube-system aws-node-pr8cn 1/1 Running 0 90m
+...
+kube-system kube-proxy-dfxsv 1/1 Running 0 90m
+mlem classifier-66b9588df5-wmc2v 1/1 Running 0 99s
+mlem classifier-687655f977-bm4w8 1/1 Terminating 0 60m
+```
+
+## Requirements
+
+```bash
+pip install mlem[kubernetes]
+# or
+pip install kubernetes docker
+```
+
+## Examples
+
+```python
+
+```
+
+## Implementation reference
+
+### `class K8sYamlBuilder`
+
+**MlemABC parent type**: `builder`
+
+**MlemABC type**: `kubernetes`
+
+ MlemBuilder implementation for building Kubernetes manifests/yamls
+
+**Fields**:
+
+- `target: str` _(required)_ - Target path for the manifest/yaml
+
+- `namespace: str = "mlem"` - Namespace to create kubernetes resources such as
+ pods, service in
+
+- `image_name: str = "ml"` - Name of the docker image to be deployed
+
+- `image_uri: str = "ml:latest"` - URI of the docker image to be deployed
+
+- `image_pull_policy: ImagePullPolicy = "Always"` - Image pull policy for the
+ docker image to be deployed
+
+- `port: int = 8080` - Port where the service should be available
+
+- `service_type: ServiceType = NodePortService()` - Type of service by which
+ endpoints of the model are exposed
+
+---
+
+### `class K8sDeploymentState`
+
+**MlemABC parent type**: `deploy_state`
+
+**MlemABC type**: `kubernetes`
+
+ DeployState implementation for Kubernetes deployments
+
+**Fields**:
+
+- `model_hash: str` - hash of deployed model meta
+
+- `image: DockerImage` - Docker Image being used for Deployment
+
+- `deployment_name: str` - Name of Deployment
+
+---
+
+### `class K8sDeployment`
+
+**MlemABC parent type**: `deployment`
+
+**MlemABC type**: `kubernetes`
+
+ MlemDeployment implementation for Kubernetes deployments
+
+**Fields**:
+
+- `namespace: str = "mlem"` - Namespace to create kubernetes resources such as
+ pods, service in
+
+- `image_name: str = "ml"` - Name of the docker image to be deployed
+
+- `image_uri: str = "ml:latest"` - URI of the docker image to be deployed
+
+- `image_pull_policy: ImagePullPolicy = "Always"` - Image pull policy for the
+ docker image to be deployed
+
+- `port: int = 8080` - Port where the service should be available
+
+- `service_type: ServiceType = NodePortService()` - Type of service by which
+ endpoints of the model are exposed
+
+- `state_manager: StateManager` - State manager used
+
+- `server: Server` - Type of Server to use, with options such as FastAPI,
+ RabbitMQ etc.
+
+- `registry: DockerRegistry = DockerRegistry()` - Docker registry
+
+- `daemon: DockerDaemon = host=''` - Docker daemon
+
+- `kube_config_file_path: str` - Path for kube config file of the cluster
+
+---
+
+### `class K8sEnv`
+
+**MlemABC parent type**: `env`
+
+**MlemABC type**: `kubernetes`
+
+ MlemEnv implementation for Kubernetes Environments
+
+**Fields**:
+
+- `registry: DockerRegistry` - Docker registry
+
+---
+
+### `class ClusterIPService`
+
+**MlemABC parent type**: `k8s_service_type`
+
+**MlemABC type**: `clusterip`
+
+ ClusterIP Service implementation for service inside a Kubernetes
+ Cluster
+
+**No fields**
+
+---
+
+### `class LoadBalancerService`
+
+**MlemABC parent type**: `k8s_service_type`
+
+**MlemABC type**: `loadbalancer`
+
+ LoadBalancer Service implementation for service inside a Kubernetes
+ Cluster
+
+**No fields**
+
+---
+
+### `class NodePortService`
+
+**MlemABC parent type**: `k8s_service_type`
+
+**MlemABC type**: `nodeport`
+
+ NodePort Service implementation for service inside a Kubernetes Cluster
+
+**No fields**
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