# Manage custom model resources
URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-resource-mgmt.html

> Configure the resources the model consumes to facilitate smooth deployment and minimize potential environment errors in production.

# Manage custom model resources

After creating a custom inference model, you can configure the resources the model consumes to facilitate smooth deployment and minimize potential environment errors in production.

To configure the resource allocation and access settings:

1. Navigate toModel Registry > Custom Model Workshop.
2. On theModelstab, click the model you want to manage and then click theAssembletab.
3. On the custom model'sAssemble Modelpage, under the deployment status, configure theResource Settings: NoteYou can also see these settings in the custom model's model package on theModel Registry > Model Packagespage. Click the custom model package, and then, on thePackage Infotab, scroll down to theResource Allocationsection.
4. Click the edit iconand configure the custom model's resource allocation and network access settings in theUpdate resource settingsdialog box: Resource settings accessUsers can determine the maximum memory allocated for a model, but onlyorganization adminscan configure additional resource settings. Imbalanced memory settingsDataRobot recommends configuring resource settings only when necessary. When you configure theMemorysetting below, you set the Kubernetes memory "limit" (the maximum allowed memory allocation); however, you can't set the memory "request" (the minimum guaranteed memory allocation). For this reason, it is possible to set the "limit" value too far above the default "request" value. An imbalance between the memory "request" and the memory usage allowed by the increased "limit" can result in the custom model exceeding the memory consumption limit. As a result, you may experience unstable custom model execution due to frequent eviction and relaunching of the custom model. If you require an increasedMemorysetting, you can mitigate this issue by increasing the "request" at the Organization level; for more information, contact DataRobot Support. SettingDescriptionMemoryDetermines the maximum amount of memory that may be allocated for a custom inference model. If a model allocates more than the configured maximum memory value, it is evicted by the system. If this occurs during testing, the test is marked as a failure. If this occurs when the model is deployed, the model is automatically launched again by Kubernetes.ReplicasSets the number of replicas executed in parallel to balance workloads when a custom model is running. Increasing the number of replicas may not result in better performance, depending on the custom model's speed.Network accessPremium feature. Configures the egress traffic of the custom model:Public: The default setting. The custom model can access any fully qualified domain name (FQDN) in a public network to leverage third-party services.None: The custom model is isolated from the public network and cannot access third party services.When public network access is enabled, your custom model can use theDATAROBOT_ENDPOINTandDATAROBOT_API_TOKENenvironment variables. These environment variables are available for any custom model using adrop-in environmentor acustom environmentbuilt onDRUM. Premium feature: Network accessEverynewcustom model you create haspublic network accessby default; however, when you create new versions of any custom model created before October 2023, those new versions remain isolated from public networks (access set toNone) until you enable public access for a new version (access set toPublic). From this point on, each subsequent version inherits the public access definition from the previous version.
5. Once you have configured the resource settings for the custom model, clickSave. This creates a new minor version of the custom model with edited resource settings applied.

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