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129 changes: 129 additions & 0 deletions google/cloud/aiplatform/models.py
Original file line number Diff line number Diff line change
Expand Up @@ -1367,6 +1367,9 @@ def deploy(
autoscaling_target_cpu_utilization: Optional[int] = None,
autoscaling_target_accelerator_duty_cycle: Optional[int] = None,
autoscaling_target_request_count_per_minute: Optional[int] = None,
autoscaling_target_dcgm_fi_dev_gpu_util: Optional[int] = None,
autoscaling_target_vllm_gpu_cache_usage_perc: Optional[int] = None,
autoscaling_target_vllm_num_requests_waiting: Optional[int] = None,
autoscaling_target_pubsub_num_undelivered_messages: Optional[int] = None,
autoscaling_pubsub_subscription_labels: Optional[Dict[str, str]] = None,
enable_access_logging=False,
Expand Down Expand Up @@ -1467,6 +1470,13 @@ def deploy(
autoscaling_target_request_count_per_minute (int):
Optional. The target number of requests per minute for autoscaling.
If set, the model will be scaled based on the number of requests it receives.
autoscaling_target_dcgm_fi_dev_gpu_util (int):
Optional. Target DCGM metrics for GPU utilization.
autoscaling_target_vllm_gpu_cache_usage_perc (int):
Optional. Target vLLM metrics for GPU KV cache usage percentage.
autoscaling_target_vllm_num_requests_waiting (int):
Optional. Target vLLM metrics for number of inference requests
currently waiting in the queue.
autoscaling_target_pubsub_num_undelivered_messages (int):
Optional. The target number of pubsub undelivered messages for autoscaling.
If set, the model will be scaled based on the pubsub queue size.
Expand Down Expand Up @@ -1555,6 +1565,9 @@ def deploy(
autoscaling_target_cpu_utilization=autoscaling_target_cpu_utilization,
autoscaling_target_accelerator_duty_cycle=autoscaling_target_accelerator_duty_cycle,
autoscaling_target_request_count_per_minute=autoscaling_target_request_count_per_minute,
autoscaling_target_dcgm_fi_dev_gpu_util=autoscaling_target_dcgm_fi_dev_gpu_util,
autoscaling_target_vllm_gpu_cache_usage_perc=autoscaling_target_vllm_gpu_cache_usage_perc,
autoscaling_target_vllm_num_requests_waiting=autoscaling_target_vllm_num_requests_waiting,
autoscaling_target_pubsub_num_undelivered_messages=autoscaling_target_pubsub_num_undelivered_messages,
autoscaling_pubsub_subscription_labels=autoscaling_pubsub_subscription_labels,
spot=spot,
Expand Down Expand Up @@ -1591,6 +1604,9 @@ def _deploy(
autoscaling_target_cpu_utilization: Optional[int] = None,
autoscaling_target_accelerator_duty_cycle: Optional[int] = None,
autoscaling_target_request_count_per_minute: Optional[int] = None,
autoscaling_target_dcgm_fi_dev_gpu_util: Optional[int] = None,
autoscaling_target_vllm_gpu_cache_usage_perc: Optional[int] = None,
autoscaling_target_vllm_num_requests_waiting: Optional[int] = None,
autoscaling_target_pubsub_num_undelivered_messages: Optional[int] = None,
autoscaling_pubsub_subscription_labels: Optional[Dict[str, str]] = None,
spot: bool = False,
Expand Down Expand Up @@ -1694,6 +1710,13 @@ def _deploy(
autoscaling_target_request_count_per_minute (int):
Optional. The target number of requests per minute for autoscaling.
If set, the model will be scaled based on the number of requests it receives.
autoscaling_target_dcgm_fi_dev_gpu_util (int):
Optional. Target DCGM metrics for GPU utilization.
autoscaling_target_vllm_gpu_cache_usage_perc (int):
Optional. Target vLLM metrics for GPU KV cache usage percentage.
autoscaling_target_vllm_num_requests_waiting (int):
Optional. Target vLLM metrics for number of inference requests
currently waiting in the queue.
autoscaling_target_pubsub_num_undelivered_messages (int):
Optional. The target number of pubsub undelivered messages for autoscaling.
If set, the model will be scaled based on the pubsub queue size.
Expand Down Expand Up @@ -1759,6 +1782,9 @@ def _deploy(
autoscaling_target_cpu_utilization=autoscaling_target_cpu_utilization,
autoscaling_target_accelerator_duty_cycle=autoscaling_target_accelerator_duty_cycle,
autoscaling_target_request_count_per_minute=autoscaling_target_request_count_per_minute,
autoscaling_target_dcgm_fi_dev_gpu_util=autoscaling_target_dcgm_fi_dev_gpu_util,
autoscaling_target_vllm_gpu_cache_usage_perc=autoscaling_target_vllm_gpu_cache_usage_perc,
autoscaling_target_vllm_num_requests_waiting=autoscaling_target_vllm_num_requests_waiting,
autoscaling_target_pubsub_num_undelivered_messages=autoscaling_target_pubsub_num_undelivered_messages,
autoscaling_pubsub_subscription_labels=autoscaling_pubsub_subscription_labels,
spot=spot,
Expand Down Expand Up @@ -1802,6 +1828,9 @@ def _deploy_call(
autoscaling_target_cpu_utilization: Optional[int] = None,
autoscaling_target_accelerator_duty_cycle: Optional[int] = None,
autoscaling_target_request_count_per_minute: Optional[int] = None,
autoscaling_target_dcgm_fi_dev_gpu_util: Optional[int] = None,
autoscaling_target_vllm_gpu_cache_usage_perc: Optional[int] = None,
autoscaling_target_vllm_num_requests_waiting: Optional[int] = None,
autoscaling_target_pubsub_num_undelivered_messages: Optional[int] = None,
autoscaling_pubsub_subscription_labels: Optional[Dict[str, str]] = None,
spot: bool = False,
Expand Down Expand Up @@ -1911,6 +1940,13 @@ def _deploy_call(
A default value of 60 will be used if not specified.
autoscaling_target_request_count_per_minute (int):
Optional. Target request count per minute per instance.
autoscaling_target_dcgm_fi_dev_gpu_util (int):
Optional. Target DCGM metrics for GPU utilization.
autoscaling_target_vllm_gpu_cache_usage_perc (int):
Optional. Target vLLM metrics for GPU KV cache usage percentage.
autoscaling_target_vllm_num_requests_waiting (int):
Optional. Target vLLM metrics for number of inference requests
currently waiting in the queue.
autoscaling_target_pubsub_num_undelivered_messages (int):
Optional. Target pubsub queue size per instance.
autoscaling_pubsub_subscription_labels (Dict[str, str]):
Expand Down Expand Up @@ -2006,6 +2042,9 @@ def _deploy_call(
or autoscaling_target_accelerator_duty_cycle
or autoscaling_target_cpu_utilization
or autoscaling_target_request_count_per_minute
or autoscaling_target_dcgm_fi_dev_gpu_util
or autoscaling_target_vllm_gpu_cache_usage_perc
or autoscaling_target_vllm_num_requests_waiting
or autoscaling_target_pubsub_num_undelivered_messages
or autoscaling_pubsub_subscription_labels
)
Expand All @@ -2017,6 +2056,9 @@ def _deploy_call(
"autoscaling_target_accelerator_duty_cycle, "
"autoscaling_target_cpu_utilization, "
"autoscaling_target_request_count_per_minute, "
"autoscaling_target_dcgm_fi_dev_gpu_util, "
"autoscaling_target_vllm_gpu_cache_usage_perc, "
"autoscaling_target_vllm_num_requests_waiting, "
"autoscaling_target_pubsub_num_undelivered_messages, "
"autoscaling_pubsub_subscription_labels parameters "
"may not be set when `deployment_resource_pool` is "
Expand Down Expand Up @@ -2078,6 +2120,9 @@ def _deploy_call(
or autoscaling_target_accelerator_duty_cycle
or autoscaling_target_cpu_utilization
or autoscaling_target_request_count_per_minute
or autoscaling_target_dcgm_fi_dev_gpu_util
or autoscaling_target_vllm_gpu_cache_usage_perc
or autoscaling_target_vllm_num_requests_waiting
or autoscaling_target_pubsub_num_undelivered_messages
or autoscaling_pubsub_subscription_labels
)
Expand All @@ -2095,6 +2140,9 @@ def _deploy_call(
"autoscaling_target_accelerator_duty_cycle, "
"autoscaling_target_cpu_utilization, "
"autoscaling_target_request_count_per_minute, "
"autoscaling_target_dcgm_fi_dev_gpu_util, "
"autoscaling_target_vllm_gpu_cache_usage_perc, "
"autoscaling_target_vllm_num_requests_waiting, "
"autoscaling_target_pubsub_num_undelivered_messages, "
"autoscaling_pubsub_subscription_labels parameters "
"are ignored."
Expand Down Expand Up @@ -2156,6 +2204,48 @@ def _deploy_call(
[autoscaling_metric_spec]
)

if autoscaling_target_dcgm_fi_dev_gpu_util:
autoscaling_metric_spec = (
gca_machine_resources_compat.AutoscalingMetricSpec(
metric_name=(
"prometheus.googleapis.com/"
"vertex_dcgm_fi_dev_gpu_util"
),
target=autoscaling_target_dcgm_fi_dev_gpu_util,
)
)
dedicated_resources.autoscaling_metric_specs.extend(
[autoscaling_metric_spec]
)

if autoscaling_target_vllm_gpu_cache_usage_perc:
autoscaling_metric_spec = (
gca_machine_resources_compat.AutoscalingMetricSpec(
metric_name=(
"prometheus.googleapis.com/"
"vertex_vllm_gpu_cache_usage_perc"
),
target=autoscaling_target_vllm_gpu_cache_usage_perc,
)
)
dedicated_resources.autoscaling_metric_specs.extend(
[autoscaling_metric_spec]
)

if autoscaling_target_vllm_num_requests_waiting:
autoscaling_metric_spec = (
gca_machine_resources_compat.AutoscalingMetricSpec(
metric_name=(
"prometheus.googleapis.com/"
"vertex_vllm_num_requests_waiting"
),
target=autoscaling_target_vllm_num_requests_waiting,
)
)
dedicated_resources.autoscaling_metric_specs.extend(
[autoscaling_metric_spec]
)

if autoscaling_target_pubsub_num_undelivered_messages:
autoscaling_metric_spec = gca_machine_resources.AutoscalingMetricSpec(
metric_name=(
Expand Down Expand Up @@ -4492,6 +4582,9 @@ def deploy(
autoscaling_target_cpu_utilization: Optional[int] = None,
autoscaling_target_accelerator_duty_cycle: Optional[int] = None,
autoscaling_target_request_count_per_minute: Optional[int] = None,
autoscaling_target_dcgm_fi_dev_gpu_util: Optional[int] = None,
autoscaling_target_vllm_gpu_cache_usage_perc: Optional[int] = None,
autoscaling_target_vllm_num_requests_waiting: Optional[int] = None,
autoscaling_target_pubsub_num_undelivered_messages: Optional[int] = None,
autoscaling_pubsub_subscription_labels: Optional[Dict[str, str]] = None,
) -> None:
Expand Down Expand Up @@ -4673,6 +4766,9 @@ def deploy(
autoscaling_target_cpu_utilization=autoscaling_target_cpu_utilization,
autoscaling_target_accelerator_duty_cycle=autoscaling_target_accelerator_duty_cycle,
autoscaling_target_request_count_per_minute=autoscaling_target_request_count_per_minute,
autoscaling_target_dcgm_fi_dev_gpu_util=autoscaling_target_dcgm_fi_dev_gpu_util,
autoscaling_target_vllm_gpu_cache_usage_perc=autoscaling_target_vllm_gpu_cache_usage_perc,
autoscaling_target_vllm_num_requests_waiting=autoscaling_target_vllm_num_requests_waiting,
autoscaling_target_pubsub_num_undelivered_messages=autoscaling_target_pubsub_num_undelivered_messages,
autoscaling_pubsub_subscription_labels=autoscaling_pubsub_subscription_labels,
)
Expand Down Expand Up @@ -5748,6 +5844,9 @@ def deploy(
autoscaling_target_cpu_utilization: Optional[int] = None,
autoscaling_target_accelerator_duty_cycle: Optional[int] = None,
autoscaling_target_request_count_per_minute: Optional[int] = None,
autoscaling_target_dcgm_fi_dev_gpu_util: Optional[int] = None,
autoscaling_target_vllm_gpu_cache_usage_perc: Optional[int] = None,
autoscaling_target_vllm_num_requests_waiting: Optional[int] = None,
autoscaling_target_pubsub_num_undelivered_messages: Optional[int] = None,
autoscaling_pubsub_subscription_labels: Optional[Dict[str, str]] = None,
enable_access_logging=False,
Expand Down Expand Up @@ -5870,6 +5969,13 @@ def deploy(
autoscaling_target_request_count_per_minute (int):
Optional. The target number of requests per minute for autoscaling.
If set, the model will be scaled based on the number of requests it receives.
autoscaling_target_dcgm_fi_dev_gpu_util (int):
Optional. Target DCGM metrics for GPU utilization.
autoscaling_target_vllm_gpu_cache_usage_perc (int):
Optional. Target vLLM metrics for GPU KV cache usage percentage.
autoscaling_target_vllm_num_requests_waiting (int):
Optional. Target vLLM metrics for number of inference requests
currently waiting in the queue.
autoscaling_target_pubsub_num_undelivered_messages (int):
Optional. The target number of pubsub undelivered messages for autoscaling.
If set, the model will be scaled based on the pubsub queue size.
Expand Down Expand Up @@ -5929,6 +6035,13 @@ def deploy(
autoscaling_target_request_count_per_minute (int):
Optional. The target number of requests per minute for autoscaling.
If set, the model will be scaled based on the number of requests it receives.
autoscaling_target_dcgm_fi_dev_gpu_util (int):
Optional. Target DCGM metrics for GPU utilization.
autoscaling_target_vllm_gpu_cache_usage_perc (int):
Optional. Target vLLM metrics for GPU KV cache usage percentage.
autoscaling_target_vllm_num_requests_waiting (int):
Optional. Target vLLM metrics for number of inference requests
currently waiting in the queue.
autoscaling_target_pubsub_num_undelivered_messages (int):
Optional. The target number of pubsub undelivered messages for autoscaling.
If set, the model will be scaled based on the pubsub queue size.
Expand Down Expand Up @@ -6001,6 +6114,9 @@ def deploy(
autoscaling_target_cpu_utilization=autoscaling_target_cpu_utilization,
autoscaling_target_accelerator_duty_cycle=autoscaling_target_accelerator_duty_cycle,
autoscaling_target_request_count_per_minute=autoscaling_target_request_count_per_minute,
autoscaling_target_dcgm_fi_dev_gpu_util=autoscaling_target_dcgm_fi_dev_gpu_util,
autoscaling_target_vllm_gpu_cache_usage_perc=autoscaling_target_vllm_gpu_cache_usage_perc,
autoscaling_target_vllm_num_requests_waiting=autoscaling_target_vllm_num_requests_waiting,
autoscaling_target_pubsub_num_undelivered_messages=autoscaling_target_pubsub_num_undelivered_messages,
autoscaling_pubsub_subscription_labels=autoscaling_pubsub_subscription_labels,
spot=spot,
Expand Down Expand Up @@ -6047,6 +6163,9 @@ def _deploy(
autoscaling_target_cpu_utilization: Optional[int] = None,
autoscaling_target_accelerator_duty_cycle: Optional[int] = None,
autoscaling_target_request_count_per_minute: Optional[int] = None,
autoscaling_target_dcgm_fi_dev_gpu_util: Optional[int] = None,
autoscaling_target_vllm_gpu_cache_usage_perc: Optional[int] = None,
autoscaling_target_vllm_num_requests_waiting: Optional[int] = None,
autoscaling_target_pubsub_num_undelivered_messages: Optional[int] = None,
autoscaling_pubsub_subscription_labels: Optional[Dict[str, str]] = None,
spot: bool = False,
Expand Down Expand Up @@ -6171,6 +6290,13 @@ def _deploy(
autoscaling_target_request_count_per_minute (int):
Optional. The target number of requests per minute for autoscaling.
If set, the model will be scaled based on the number of requests it receives.
autoscaling_target_dcgm_fi_dev_gpu_util (int):
Optional. Target DCGM metrics for GPU utilization.
autoscaling_target_vllm_gpu_cache_usage_perc (int):
Optional. Target vLLM metrics for GPU KV cache usage percentage.
autoscaling_target_vllm_num_requests_waiting (int):
Optional. Target vLLM metrics for number of inference requests
currently waiting in the queue.
autoscaling_target_pubsub_num_undelivered_messages (int):
Optional. The target number of pubsub undelivered messages for autoscaling.
If set, the model will be scaled based on the pubsub queue size.
Expand Down Expand Up @@ -6267,6 +6393,9 @@ def _deploy(
autoscaling_target_cpu_utilization=autoscaling_target_cpu_utilization,
autoscaling_target_accelerator_duty_cycle=autoscaling_target_accelerator_duty_cycle,
autoscaling_target_request_count_per_minute=autoscaling_target_request_count_per_minute,
autoscaling_target_dcgm_fi_dev_gpu_util=autoscaling_target_dcgm_fi_dev_gpu_util,
autoscaling_target_vllm_gpu_cache_usage_perc=autoscaling_target_vllm_gpu_cache_usage_perc,
autoscaling_target_vllm_num_requests_waiting=autoscaling_target_vllm_num_requests_waiting,
autoscaling_target_pubsub_num_undelivered_messages=autoscaling_target_pubsub_num_undelivered_messages,
autoscaling_pubsub_subscription_labels=autoscaling_pubsub_subscription_labels,
spot=spot,
Expand Down
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