Update models

PATCH /v1/ml/models/{name}

Applies a JSON merge patch to one of the caller org's deployed models and answers the updated resource — the way to change a model's image, replica count or resource requests without tearing the deployment down.

The body is relayed to Kubernetes VERBATIM. That is deliberate and it is why this route is not a typed op: re-encoding a merge patch changes what it means, because an integer that round-trips through a generic decoder comes back a float. Merge-patch semantics apply as written — a null removes a field, and a list is replaced whole rather than merged.

Scoped to the caller's own tenant namespace, resolved from the validated org and project; a name the caller's tenant does not hold is a 404, never another tenant's resource. An empty body is refused, and a patch Kubernetes rejects comes back 422 with its reason rather than being silently dropped.

Request

1 field.

Response

The document declares no response body for this operation. It answers 200 on success and the platform error shape on failure — see Errors.

Examples

hanzo ml models update <name>

import { Configuration, MlApi } from 'hanzoai';

const api = new MlApi(new Configuration({ accessToken: process.env.HANZO_API_KEY }));
const { data } = await api.patchMlModelsByName({ name: 'name' });

from hanzoai.cloud import ApiClient, Configuration
from hanzoai.cloud.api import MlApi

client = ApiClient(Configuration(access_token=os.environ["HANZO_API_KEY"]))
result = MlApi(client).patch_ml_models_by_name(name='name')

cfg := hanzoai.NewConfiguration()
cfg.AddDefaultHeader("Authorization", "Bearer "+os.Getenv("HANZO_API_KEY"))
client := hanzoai.NewAPIClient(cfg)

resp, _, err := client.MlAPI.PatchMlModelsByName(context.Background()).Execute()
if err != nil {
	return err
}

use hanzo_client::apis::{configuration::Configuration, ml_api};

let mut cfg = Configuration::new();
cfg.bearer_access_token = std::env::var("HANZO_API_KEY").ok();

let result = ml_api::patch_ml_models_by_name(&cfg, Default::default()).await?;

import ai.hanzo.cloud.ApiClient;
import ai.hanzo.cloud.api.MlApi;

ApiClient client = new ApiClient();
client.setBearerToken(System.getenv("HANZO_API_KEY"));

var result = new MlApi(client).patchMlModelsByName();

curl -X PATCH https://api.hanzo.ai/v1/ml/models/<name> \
  -H "Authorization: Bearer $HANZO_API_KEY"

curl -X POST https://api.hanzo.ai/v1/mcp \
  -H "Content-Type: application/json" \
  -d '{
       "jsonrpc": "2.0",
       "id": 1,
       "method": "tools/call",
       "params": {
         "name": "describe",
         "arguments": {
           "op": "list_ml_models"
         }
       }
     }'

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