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https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
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Enable the use of dedicated Endpoints in instructpix2pix notebook
PiperOrigin-RevId: 751641417
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Copybara-Service
parent
f4b1b277cf
commit
feea47a206
@@ -178,7 +178,10 @@
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"# The machine and accelerator specs for model deployment.\n",
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"accelerator_type = \"NVIDIA_L4\"\n",
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"machine_type = \"g2-standard-12\"\n",
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"accelerator_count = 1"
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"accelerator_count = 1\n",
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"\n",
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"# @markdown Set use_dedicated_endpoint to False if you don't want to use [dedicated endpoint](https://cloud.google.com/vertex-ai/docs/general/deployment#create-dedicated-endpoint). Note that [dedicated endpoint does not support VPC Service Controls](https://cloud.google.com/vertex-ai/docs/predictions/choose-endpoint-type), uncheck the box if you are using VPC-SC.\n",
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"use_dedicated_endpoint = True # @param {type:\"boolean\"}"
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]
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},
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{
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@@ -200,6 +203,7 @@
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" machine_type=machine_type,\n",
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" accelerator_type=accelerator_type,\n",
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" accelerator_count=accelerator_count,\n",
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" use_dedicated_endpoint=use_dedicated_endpoint,\n",
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" accept_eula=True, # Accept the End User License Agreement (EULA) on the model card before deploy. Otherwise, the deployment will be forbidden.\n",
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")"
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]
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@@ -226,9 +230,13 @@
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" machine_type: str,\n",
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" accelerator_type: str,\n",
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" accelerator_count: int,\n",
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" use_dedicated_endpoint: bool = False,\n",
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"):\n",
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" model_name = \"instruct-pix2pix\"\n",
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" endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
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" endpoint = aiplatform.Endpoint.create(\n",
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" display_name=f\"{model_name}-endpoint\",\n",
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" dedicated_endpoint_enabled=use_dedicated_endpoint,\n",
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" )\n",
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" serving_env = {\n",
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" \"MODEL_ID\": model_id,\n",
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" \"TASK\": task,\n",
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@@ -272,6 +280,7 @@
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" machine_type=machine_type,\n",
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" accelerator_type=accelerator_type,\n",
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" accelerator_count=accelerator_count,\n",
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" use_dedicated_endpoint=use_dedicated_endpoint,\n",
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")"
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]
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},
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@@ -302,7 +311,9 @@
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" \"num_inference_steps\": num_inference_steps,\n",
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" },\n",
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"]\n",
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"response = endpoints[LABEL].predict(instances=instances)\n",
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"response = endpoints[LABEL].predict(\n",
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" instances=instances, use_dedicated_endpoint=use_dedicated_endpoint\n",
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")\n",
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"images = [common_util.base64_to_image(image) for image in response.predictions]\n",
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"common_util.image_grid([init_image, images[0]], rows=1, cols=2)"
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]
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