# Blue/Green Deployments When you update your endpoint, Amazon SageMaker AI automatically uses a blue/green deployment to maximize the availability of your endpoints. In a blue/green deployment, SageMaker AI provisions a new fleet with the updates (the green fleet). Then, SageMaker AI shifts traffic from the old fleet (the blue fleet) to the green fleet. Once the green fleet operates smoothly for a set evaluation period (called the baking period), SageMaker AI terminates the blue fleet. With the additional capabilities in blue/green deployments, you can utilize traffic shifting modes and auto-rollback monitoring to protect your endpoint from significant production impact. The following list describes the key features of blue/green deployments in SageMaker AI: + **Traffic shifting modes.** The traffic shifting modes for deployment guardrails let you control the volume of traffic and number of traffic-shifting steps between the blue fleet and the green fleet. This capability gives you the ability to progressively evaluate the performance of the green fleet without fully committing to a 100% traffic shift. + **Baking period.** The baking period is a set amount of time to monitor the green fleet before proceeding to the next deployment stage. If any of the pre-specified alarms trip during any baking period, then all endpoint traffic rolls back to the blue fleet. The baking period helps you to build confidence in your update before making the traffic shift permanent. + **Auto-rollbacks.** You can specify Amazon CloudWatch alarms that SageMaker AI uses to monitor the green fleet. If an issue with the updated code trips any of the alarms, SageMaker AI initiates an auto-rollback to the blue fleet in order to maintain availability thereby minimizing risk. ## Traffic Shifting Modes The various traffic shifting modes in blue/green deployments give you more granular control over traffic shifting between the blue fleet and the green fleet. The available traffic shifting modes for blue/green deployments are all at once, canary, and linear. The following table shows a comparison of the options. **Important** For blue/green deployments that involve multiple stage traffic shifting or baking periods, you are billed for both the fleets for the duration of the update, irrespective of the traffic to the fleet. This is in contrast to blue/green deployments with all at once traffic shifting and no baking periods, where you are only billed for one fleet during the course of the update. | Name | What is it? | Pros | Cons | Recommendation | | --- | --- | --- | --- | --- | | All at once | Shifts all of the traffic to the new fleet in a single step. | Minimizes the overall update duration. | Regressive updates affect 100% of the traffic. | Use this option to minimize update time and cost. | | Canary | Traffic shifts in two steps. The first (canary) step shifts a small portion of the traffic followed by the second step, which shifts the remainder of the traffic. | Confines the blast radius of regressive updates to only the canary fleet. | Both fleets are operational in parallel for entire deployment. | Use this option to balance between minimizing the blast radius of regressive updates and minimizing the time that two fleets are operational. | | Linear | A fixed portion of the traffic shifts in a pre-specified number of equally spaced steps. | Minimizes the risk of regressive updates by shifting traffic over several steps. | The update duration and cost are proportional to the number of steps. | Use this option to minimize risk by spreading out deployment across multiple steps. | ## Get Started Once you specify your desired deployment configuration, SageMaker AI handles provisioning new instances, terminating old instances, and shifting traffic for you. You can create and manage your deployment through the existing [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) and [CreateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html) SageMaker API and AWS Command Line Interface commands. Note that if your endpoint uses any of the features listed in the [Exclusions](deployment-guardrails-exclusions.md) page, you cannot use deployment guardrails. See the individual deployment pages for more details on how to set up your deployment: + [ Blue/Green Update with All At Once Traffic Shifting](deployment-guardrails-blue-green-all-at-once.md) + [ Blue/Green Update with Canary Traffic Shifting](deployment-guardrails-blue-green-canary.md) + [ Blue/Green Update with Linear Traffic Shifting](deployment-guardrails-blue-green-linear.md) To follow guided examples that show how to use deployment guardrails, see our example [Jupyter notebooks](https://github.com/aws/amazon-sagemaker-examples/tree/master/sagemaker-inference-deployment-guardrails) for the canary and linear traffic shifting modes. # Use all at once traffic shifting With all at once traffic shifting, you can quickly roll out an endpoint update using the safety guardrails of a blue/green deployment. You can use this traffic shifting option to minimize the update duration while still taking advantage of the availability guarantees of blue/green deployments. The baking period feature helps you to monitor the performance and functionality of your new instances before terminating your old instances, ensuring that your new fleet is fully operational. The following diagram shows how all at once traffic shifting manages the old and new fleets. ![\[A successful 100% traffic shift from the old fleet to the new fleet.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/deployment-guardrails-blue-green-all-at-once.png) When you use all at once traffic shifting, SageMaker AI routes 100% of the traffic to the new fleet (green fleet). Once the green fleet starts receiving traffic, the baking period begins. The baking period is a set amount of time in which pre-specified Amazon CloudWatch alarms monitor the performance of the green fleet. If no alarms trip during the baking period, SageMaker AI terminates the old fleet (blue fleet). If any alarms trip during the baking period, then an auto-rollback initiates and 100% of the traffic shifts back to the blue fleet. ## Prerequisites Before setting up a deployment with all at once traffic shifting, you must create Amazon CloudWatch alarms to watch metrics from your endpoint. If any of the alarms trip during the baking period, then the traffic rolls back to your blue fleet. To learn how to set up CloudWatch alarms on an endpoint, see the prerequisite page [Auto-Rollback Configuration and Monitoring](deployment-guardrails-configuration.md). To learn more about CloudWatch alarms, see [Using Amazon CloudWatch alarms](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/AlarmThatSendsEmail.html) in the *Amazon CloudWatch User Guide*. ## Configure All At Once Traffic Shifting Once you are ready for your deployment and have set up CloudWatch alarms for your endpoint, you can use either the SageMaker AI [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) API or the [update-endpoint](https://docs.aws.amazon.com/cli/latest/reference/sagemaker/update-endpoint.html) command in the AWS Command Line Interface to initiate the deployment. **Topics** + [How to update an endpoint (API)](#deployment-guardrails-blue-green-all-at-once-configure-api-update) + [How to update an endpoint with an existing blue/green update policy (API)](#deployment-guardrails-blue-green-all-at-once-configure-api-existing) + [How to update an endpoint (CLI)](#deployment-guardrails-blue-green-all-at-once-configure-cli-update) ### How to update an endpoint (API) The following example shows how you can update your endpoint with all at once traffic shifting using [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) in the Amazon SageMaker API. ``` import boto3 client = boto3.client("sagemaker") response = client.update_endpoint( EndpointName="", EndpointConfigName="", DeploymentConfig={ "BlueGreenUpdatePolicy": { "TrafficRoutingConfiguration": { "Type": "ALL_AT_ONCE" }, "TerminationWaitInSeconds": 600, "MaximumExecutionTimeoutInSeconds": 1800 }, "AutoRollbackConfiguration": { "Alarms": [ { "AlarmName": "" }, ] } } ) ``` To configure the all at once traffic shifting option, do the following: + For `EndpointName`, use the name of the existing endpoint you want to update. + For `EndpointConfigName`, use the name of the endpoint configuration you want to use. + Under `DeploymentConfig` and `BlueGreenUpdatePolicy`, in `TrafficRoutingConfiguration`, set the `Type` parameter to `ALL_AT_ONCE`. This specifies that the deployment uses the all at once traffic shifting mode. + For `TerminationWaitInSeconds`, use `600`. This parameter tells SageMaker AI to wait for the specified amount of time (in seconds) after your green fleet is fully active before terminating the instances in the blue fleet. In this example, SageMaker AI waits for 10 minutes after the final baking period before terminating the blue fleet. + For `MaximumExecutionTimeoutInSeconds`, use `1800`. This parameter sets the maximum amount of time that the deployment can run before it times out. In the preceding example, your deployment has a limit of 30 minutes to finish. + In `AutoRollbackConfiguration`, within the `Alarms` field, you can add your CloudWatch alarms by name. Create one `AlarmName: ` entry for each alarm you want to use. ### How to update an endpoint with an existing blue/green update policy (API) When you use the [CreateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html) API to create an endpoint, you can optionally specify a deployment configuration to reuse for future endpoint updates. You can use the same `DeploymentConfig` options as the previous UpdateEndpoint API example. There are no changes to the CreateEndpoint API behavior. Specifying the deployment configuration does not automatically perform a blue/green update on your endpoint. The option to use a previous deployment configuration happens when using the [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) API to update your endpoint. When updating your endpoint, you can use the `RetainDeploymentConfig` option to keep the deployment configuration you specified when you created the endpoint. When calling the [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) API, set `RetainDeploymentConfig` to `True` to keep the `DeploymentConfig` options from your original endpoint configuration. ``` response = client.update_endpoint( EndpointName="", EndpointConfigName="", RetainDeploymentConfig=True ) ``` ### How to update an endpoint (CLI) If you are using the AWS CLI, the following example shows how to start a blue/green all at once deployment using the [update-endpoint](https://docs.aws.amazon.com/cli/latest/reference/sagemaker/update-endpoint.html) command. ``` update-endpoint --endpoint-name --endpoint-config-name --deployment-config '"BlueGreenUpdatePolicy": {"TrafficRoutingConfiguration": {"Type": "ALL_AT_ONCE"}, "TerminationWaitInSeconds": 600, "MaximumExecutionTimeoutInSeconds": 1800}, "AutoRollbackConfiguration": {"Alarms": [{"AlarmName": ""}]}' ``` To configure the all at once traffic shifting option, do the following: + For `endpoint-name`, use the name of the endpoint you want to update. + For `endpoint-config-name`, use the name of the endpoint configuration you want to use. + For `deployment-config`, use a [BlueGreenUpdatePolicy](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_BlueGreenUpdatePolicy.html) JSON object. **Note** If you would rather save your JSON object in a file, see [Generating AWS CLI skeleton and input parameters](https://docs.aws.amazon.com/cli/latest/userguide/cli-usage-skeleton.html) in the *AWS CLI User Guide*. # Use canary traffic shifting With canary traffic shifting, you can test a portion of your endpoint traffic on the new fleet while the old fleet serves the remainder of the traffic. This testing step is a safety guardrail that validates the new fleet’s functionality before shifting all of your traffic to the new fleet. You still have the benefits of a blue/green deployment, and the added canary feature lets you ensure that your new (green) fleet can serve inference before letting it handle 100% of the traffic. The portion of your green fleet that turns on to receive traffic is called the canary, and you can choose the size of this canary. Note that the canary size should be less than or equal to 50% of the new fleet's capacity. Once the baking period finishes and no pre-specified Amazon CloudWatch alarms trip, the rest of the traffic shifts from the old (blue) fleet to the green fleet. Canary traffic shifting provides you with more safety during your deployment since any issues with the updated model only impact the canary. The following diagram shows how canary traffic shifting manages the distribution of traffic between the blue and green fleets. ![\[A successful two step canary traffic shift from the old fleet to the new fleet.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/deployment-guardrails-blue-green-canary.png) Once SageMaker AI provisions the green fleet, SageMaker AI routes a portion of the incoming traffic (for example, 25%) to the canary. Then the baking period begins, during which your CloudWatch alarms monitor the performance of the green fleet. During this time, both the blue fleet and green fleet are partially active and receiving traffic. If any of the alarms trip during the baking period, then SageMaker AI initiates a rollback and all traffic returns to the blue fleet. If none of the alarms trip, then all of the traffic shifts to the green fleet and there is a final baking period. If the final baking period finishes without tripping any alarms, then the green fleet serves all traffic and SageMaker AI terminates the blue fleet. ## Prerequisites Before setting up a deployment with canary traffic shifting, you must create Amazon CloudWatch alarms to monitor metrics from your endpoint. The alarms are active during the baking period, and if any alarms trip, then all endpoint traffic rolls back to the blue fleet. To learn how to set up CloudWatch alarms on an endpoint, see the prerequisite page [Auto-Rollback Configuration and Monitoring](deployment-guardrails-configuration.md). To learn more about CloudWatch alarms, see [Using Amazon CloudWatch alarms](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/AlarmThatSendsEmail.html) in the *Amazon CloudWatch User Guide*. ## Configure Canary Traffic Shifting Once you are ready for your deployment and have set up Amazon CloudWatch alarms for your endpoint, you can use either the Amazon SageMaker AI [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) API or the [update-endpoint](https://docs.aws.amazon.com/cli/latest/reference/sagemaker/update-endpoint.html) command in the AWS CLI to initiate the deployment. **Topics** + [How to update an endpoint (API)](#deployment-guardrails-blue-green-canary-configure-api-update) + [How to update an endpoint with an existing blue/green update policy (API)](#deployment-guardrails-blue-green-canary-configure-api-existing) + [How to update an endpoint (CLI)](#deployment-guardrails-blue-green-canary-configure-cli-update) ### How to update an endpoint (API) The following example of the [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) API shows how you can update an endpoint with canary traffic shifting. ``` import boto3 client = boto3.client("sagemaker") response = client.update_endpoint( EndpointName="", EndpointConfigName="", DeploymentConfig={ "BlueGreenUpdatePolicy": { "TrafficRoutingConfiguration": { "Type": "CANARY", "CanarySize": { "Type": "CAPACITY_PERCENT", "Value": 30 }, "WaitIntervalInSeconds": 600 }, "TerminationWaitInSeconds": 600, "MaximumExecutionTimeoutInSeconds": 1800 }, "AutoRollbackConfiguration": { "Alarms": [ { "AlarmName": "" } ] } } ) ``` To configure the canary traffic shifting option, do the following: + For `EndpointName`, use the name of the existing endpoint you want to update. + For `EndpointConfigName`, use the name of the endpoint configuration you want to use. + Under `DeploymentConfig` and `BlueGreenUpdatePolicy`, in `TrafficRoutingConfiguration`, set the `Type` parameter to `CANARY`. This specifies that the deployment uses canary traffic shifting. + In the `CanarySize` field, you can change the size of the canary by modifying the `Type` and `Value` parameters. For `Type`, use `CAPACITY_PERCENT`, meaning the percentage of your green fleet you want to use as the canary, and then set `Value` to `30`. In this example, you use 30% of the green fleet’s capacity as the canary. Note that the canary size should be equal to or less than 50% of the green fleet's capacity. + For `WaitIntervalInSeconds`, use `600`. The parameter tells SageMaker AI to wait for the specified amount of time (in seconds) between each interval shift. This interval is the duration of the canary baking period. In the preceding example, SageMaker AI waits for 10 minutes after the canary shift and then completes the second and final traffic shift. + For `TerminationWaitInSeconds`, use `600`. This parameter tells SageMaker AI to wait for the specified amount of time (in seconds) after your green fleet is fully active before terminating the instances in the blue fleet. In this example, SageMaker AI waits for 10 minutes after the final baking period before terminating the blue fleet. + For `MaximumExecutionTimeoutInSeconds`, use `1800`. This parameter sets the maximum amount of time that the deployment can run before it times out. In the preceding example, your deployment has a limit of 30 minutes to finish. + In `AutoRollbackConfiguration`, within the `Alarms` field, you can add your CloudWatch alarms by name. Create one `AlarmName: ` entry for each alarm you want to use. ### How to update an endpoint with an existing blue/green update policy (API) When you use the [CreateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html) API to create an endpoint, you can optionally specify a deployment configuration to reuse for future endpoint updates. You can use the same `DeploymentConfig` options as the previous UpdateEndpoint API example. There are no changes to the CreateEndpoint API behavior. Specifying the deployment configuration does not automatically perform a blue/green update on your endpoint. The option to use a previous deployment configuration happens when using the [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) API to update your endpoint. When updating your endpoint, you can use the `RetainDeploymentConfig` option to keep the deployment configuration you specified when you created the endpoint. When calling the [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) API, set `RetainDeploymentConfig` to `True` to keep the `DeploymentConfig` options from your original endpoint configuration. ``` response = client.update_endpoint( EndpointName="", EndpointConfigName="", RetainDeploymentConfig=True ) ``` ### How to update an endpoint (CLI) If you are using the AWS CLI, the following example shows how to start a blue/green canary deployment using the [update-endpoint](https://docs.aws.amazon.com/cli/latest/reference/sagemaker/update-endpoint.html) command. ``` update-endpoint --endpoint-name --endpoint-config-name --deployment-config '"BlueGreenUpdatePolicy": {"TrafficRoutingConfiguration": {"Type": "CANARY", "CanarySize": {"Type": "CAPACITY_PERCENT", "Value": 30}, "WaitIntervalInSeconds": 600}, "TerminationWaitInSeconds": 600, "MaximumExecutionTimeoutInSeconds": 1800}, "AutoRollbackConfiguration": {"Alarms": [{"AlarmName": ""}]}' ``` To configure the canary traffic shifting option, do the following: + For `endpoint-name`, use the name of the endpoint you want to update. + For `endpoint-config-name`, use the name of the endpoint configuration you want to use. + For `deployment-config`, use a [BlueGreenUpdatePolicy](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_BlueGreenUpdatePolicy.html) JSON object. **Note** If you would rather save your JSON object in a file, see [Generating AWS CLI skeleton and input parameters](https://docs.aws.amazon.com/cli/latest/userguide/cli-usage-skeleton.html) in the *AWS CLI User Guide*. # Use linear traffic shifting Linear traffic shifting enables you to gradually shift traffic from your old fleet (blue fleet) to your new fleet (green fleet). With linear traffic shifting, you can shift traffic in multiple steps, minimizing the chance of a disruption to your endpoint. This blue/green deployment option gives you the most granular control over traffic shifting. You can choose either the number of instances or the percentage of the green fleet’s capacity to activate during each step. Each linear step should only be between 10-50% of the green fleet's capacity. For each step, there is a baking period during which your pre-specified Amazon CloudWatch alarms monitor metrics on the green fleet. Once the baking period finishes and no alarms trip, the active portion of your green fleet continues receiving traffic and a new step begins. If alarms trip during any of the baking periods, 100% of the endpoint traffic rolls back to the blue fleet. The following diagram shows how linear traffic shifting routes traffic to the blue and green fleets. ![\[A successful three step linear traffic shift from the old fleet to the new fleet.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/deployment-guardrails-blue-green-linear.png) Once SageMaker AI provisions the new fleet, the first portion of the green fleet turns on and receives traffic. SageMaker AI deactivates the same size portion of the blue fleet, and the baking period begins. If any alarms trip, all of the endpoint traffic rolls back to the blue fleet. If the baking period finishes, then the next step begins. Another portion of the green fleet activates and receives traffic, part of the blue fleet deactivates, and another baking period begins. The same process repeats until the blue fleet is fully deactivated and the green fleet is fully active and receiving all traffic. If an alarm goes off at any point, SageMaker AI terminates the shifting process and 100% of the traffic rolls back to the blue fleet. ## Prerequisites Before setting up a deployment with linear traffic shifting, you must create CloudWatch alarms to monitor metrics from your endpoint. The alarms are active during the baking period, and if any alarms trip, then all endpoint traffic rolls back to the blue fleet. To learn how to set up CloudWatch alarms on an endpoint, see the prerequisite page [Auto-Rollback Configuration and Monitoring](deployment-guardrails-configuration.md). To learn more about CloudWatch alarms, see [Using Amazon CloudWatch alarms](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/AlarmThatSendsEmail.html) in the *Amazon CloudWatch User Guide*. ## Configure Linear Traffic Shifting Once you are ready for your deployment and have set up CloudWatch alarms for your endpoint, you can use either the Amazon SageMaker AI [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) API or the [update-endpoint](https://docs.aws.amazon.com/cli/latest/reference/sagemaker/update-endpoint.html) command in the AWS CLI to initiate the deployment. **Topics** + [How to update an endpoint (API)](#deployment-guardrails-blue-green-linear-configure-api-update) + [How to update an endpoint with an existing blue/green update policy (API)](#deployment-guardrails-blue-green-linear-configure-api-existing) + [How to update an endpoint (CLI)](#deployment-guardrails-blue-green-canary-configure-cli-update) ### How to update an endpoint (API) The following example of the [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) API shows how you can update an endpoint with linear traffic shifting. ``` import boto3 client = boto3.client("sagemaker") response = client.update_endpoint( EndpointName="", EndpointConfigName="", DeploymentConfig={ "BlueGreenUpdatePolicy": { "TrafficRoutingConfiguration": { "Type": "LINEAR", "LinearStepSize": { "Type": "CAPACITY_PERCENT", "Value": 20 }, "WaitIntervalInSeconds": 300 }, "TerminationWaitInSeconds": 300, "MaximumExecutionTimeoutInSeconds": 3600 }, "AutoRollbackConfiguration": { "Alarms": [ { "AlarmName": "" } ] } } ) ``` To configure the linear traffic shifting option, do the following: + For `EndpointName`, use the name of the existing endpoint you want to update. + For `EndpointConfigName`, use the name of the endpoint configuration you want to use. + Under `DeploymentConfig` and `BlueGreenUpdatePolicy`, in `TrafficRoutingConfiguration`, set the `Type` parameter to `LINEAR`. This specifies that the deployment uses linear traffic shifting. + In the `LinearStepSize` field, you can change the size of the steps by modifying the `Type` and `Value` parameters. For `Type`, use `CAPACITY_PERCENT`, meaning the percentage of your green fleet you want to use as the step size, and then set `Value` to `20`. In this example, you turn on 20% of the green fleet’s capacity for each traffic shifting step. Note that when customizing your linear step size, you should only use steps that are 10-50% of the green fleet's capacity. + For `WaitIntervalInSeconds`, use `300`. The parameter tells SageMaker AI to wait for the specified amount of time (in seconds) between each traffic shift. This interval is the duration of the baking period between each linear step. In the preceding example, SageMaker AI waits for 5 minutes between each traffic shift. + For `TerminationWaitInSeconds`, use `300`. This parameter tells SageMaker AI to wait for the specified amount of time (in seconds) after your green fleet is fully active before terminating the instances in the blue fleet. In this example, SageMaker AI waits for 5 minutes after the final baking period before terminating the blue fleet. + For `MaximumExecutionTimeoutInSeconds`, use `3600`. This parameter sets the maximum amount of time that the deployment can run before it times out. In the preceding example, your deployment has a limit of 1 hour to finish. + In `AutoRollbackConfiguration`, within the `Alarms` field, you can add your CloudWatch alarms by name. Create one `AlarmName: ` entry for each alarm you want to use. ### How to update an endpoint with an existing blue/green update policy (API) When you use the [CreateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html) API to create an endpoint, you can optionally specify a deployment configuration to reuse for future endpoint updates. You can use the same `DeploymentConfig` options as the previous UpdateEndpoint API example. There are no changes to the CreateEndpoint API behavior. Specifying the deployment configuration does not automatically perform a blue/green update on your endpoint. The option to use a previous deployment configuration happens when using the [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) API to update your endpoint. When updating your endpoint, you can use the `RetainDeploymentConfig` option to keep the deployment configuration you specified when you created the endpoint. When calling the [UpdateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) API, set `RetainDeploymentConfig` to `True` to keep the `DeploymentConfig` options from your original endpoint configuration. ``` response = client.update_endpoint( EndpointName="", EndpointConfigName="", RetainDeploymentConfig=True ) ``` ### How to update an endpoint (CLI) If you are using the AWS CLI, the following example shows how to start a blue/green linear deployment using the [update-endpoint](https://docs.aws.amazon.com/cli/latest/reference/sagemaker/update-endpoint.html) command. ``` update-endpoint --endpoint-name --endpoint-config-name --deployment-config '{"BlueGreenUpdatePolicy": {"TrafficRoutingConfiguration": {"Type": "LINEAR", "LinearStepSize": {"Type": "CAPACITY_PERCENT", "Value": 20}, "WaitIntervalInSeconds": 300}, "TerminationWaitInSeconds": 300, "MaximumExecutionTimeoutInSeconds": 3600}, "AutoRollbackConfiguration": {"Alarms": [{"AlarmName": ""}]}' ``` To configure the linear traffic shifting option, do the following: + For `endpoint-name`, use the name of the endpoint you want to update. + For `endpoint-config-name`, use the name of the endpoint configuration you want to use. + For `deployment-config`, use a [BlueGreenUpdatePolicy](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_BlueGreenUpdatePolicy.html) JSON object. **Note** If you would rather save your JSON object in a file, see [Generating AWS CLI skeleton and input parameters](https://docs.aws.amazon.com/cli/latest/userguide/cli-usage-skeleton.html) in the *AWS CLI User Guide*.