# -*- coding: utf-8 -*- # # Copyright 2020 Google LLC. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utilities for validating parameters.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from googlecloudsdk.calliope import exceptions from googlecloudsdk.command_lib.ai import constants def ValidateDisplayName(display_name): """Validates the display name.""" if display_name is not None and not display_name: raise exceptions.InvalidArgumentException( '--display-name', 'Display name can not be empty.' ) def ValidateRegion(region, available_regions=constants.SUPPORTED_REGION): """Validates whether a given region is among the available ones.""" if region not in available_regions: raise exceptions.InvalidArgumentException( 'region', 'Available values are [{}], but found [{}].'.format( ', '.join(available_regions), region ), ) def ValidateGpuPartitionSize(gpu_partition_size): """Validates the gpu partition size.""" if gpu_partition_size is not None and not isinstance(gpu_partition_size, str): raise exceptions.InvalidArgumentException( '--gpu-partition-size', 'Required string, but found [{}].'.format(gpu_partition_size), ) def GetAndValidateKmsKey(args): """Parse CMEK resource arg, and check if the arg was partially specified.""" if hasattr(args.CONCEPTS, 'kms_key'): kms_ref = args.CONCEPTS.kms_key.Parse() if kms_ref: return kms_ref.RelativeName() else: for keyword in ['kms_key', 'kms_keyring', 'kms_location', 'kms_project']: if getattr(args, keyword, None): raise exceptions.InvalidArgumentException( '--kms-key', 'Encryption key not fully specified.' ) def ValidateAutoscalingMetricSpecs(specs): """Value validation for autoscaling metric specs target name and value.""" if specs is None: return for key, value in specs.items(): if key not in constants.OP_AUTOSCALING_METRIC_NAME_MAPPER: raise exceptions.InvalidArgumentException( '--autoscaling-metric-specs', """Autoscaling metric name can only be one of the following: {}.""".format( ', '.join([ "'{}'".format(c) for c in sorted( constants.OP_AUTOSCALING_METRIC_NAME_MAPPER.keys() ) ]) ), ) if key == 'request-counts-per-minute': if value <= 0: raise exceptions.InvalidArgumentException( '--autoscaling-metric-specs', 'Metric target for request-counts-per-minute must be a positive' ' value.', ) elif value <= 0 or value > 100: raise exceptions.InvalidArgumentException( '--autoscaling-metric-specs', 'Metric target value {} for {} is not between 0 and 100.'.format( value, key ), ) def ValidateRequiredReplicaCount(required_replica_count, min_replica_count): """Value validation for required replica count.""" if required_replica_count is not None: min_replica_count = min_replica_count or 1 if required_replica_count > min_replica_count: raise exceptions.InvalidArgumentException( '--required-replica-count', 'Value must be less than or equal to min-replica-count.' ) def ValidateScaleToZeroArgs( min_replica_count=None, initial_replica_count=None, max_replica_count=None, min_scaleup_period=None, idle_scaledown_period=None, ): """Value validation for scale-to-zero args.""" # Validation for initial replica count. if initial_replica_count is not None: if min_replica_count is None: raise exceptions.InvalidArgumentException( '--initial-replica-count', """Cannot set initial-replica-count without explicitly setting min-replica-count to 0 to enable scale-to-zero.""", ) if min_replica_count > 0: raise exceptions.InvalidArgumentException( '--initial-replica-count', """Cannot set initial-replica-count when min-replica-count > 0 as scale-to-zero will not be enabled.""", ) if ( max_replica_count is not None and max_replica_count < initial_replica_count ): raise exceptions.InvalidArgumentException( '--initial-replica-count', """Initial-replica-count must be smaller than max replica count.""", ) # Validation for STZConfig args with min replica count > 0. if min_scaleup_period is not None: if min_replica_count is None: raise exceptions.InvalidArgumentException( '--min-scaleup-period', """Cannot set min-scaleup-period without explicitly setting min-replica-count to 0 to enable scale-to-zero.""", ) if min_replica_count > 0: raise exceptions.InvalidArgumentException( '--min-scaleup-period', """Cannot set min-scaleup-period when min-replica-count > 0 as scale-to-zero will not be enabled.""", ) if idle_scaledown_period is not None: if min_replica_count is None: raise exceptions.InvalidArgumentException( '--idle-scaledown-period', """Cannot set idle-scaledown-period without explicitly setting min-replica-count to 0 to enable scale-to-zero.""", ) if min_replica_count > 0: raise exceptions.InvalidArgumentException( '--idle-scaledown-period', """Cannot set idle-scaledown-period when min-replica-count > 0 as scale-to-zero will not be enabled.""", ) def ValidateSharedResourceArgs( shared_resources_ref=None, machine_type=None, accelerator_dict=None, min_replica_count=None, max_replica_count=None, required_replica_count=None, autoscaling_metric_specs=None, ): """Value validation for dedicated resource args while making a shared resource command call. Args: shared_resources_ref: str or None, the shared deployment resource pool full name the model should use, formatted as the full URI machine_type: str or None, the type of the machine to serve the model. accelerator_dict: dict or None, the accelerator attached to the deployed model from args. min_replica_count: int or None, the minimum number of replicas the deployed model will be always deployed on. max_replica_count: int or None, the maximum number of replicas the deployed model may be deployed on. required_replica_count: int or None, the required number of replicas the deployed model will be considered successfully deployed. autoscaling_metric_specs: dict or None, the metric specification that defines the target resource utilization for calculating the desired replica count. """ if shared_resources_ref is None: return if machine_type is not None: raise exceptions.InvalidArgumentException( '--machine-type', """Cannot use machine type and shared resources in the same command.""", ) if accelerator_dict is not None: raise exceptions.InvalidArgumentException( '--accelerator', """Cannot use accelerator and shared resources in the same command.""", ) if min_replica_count is not None: raise exceptions.InvalidArgumentException( '--min-replica-count', """Cannot use min replica count and shared resources in the same command.""", ) if max_replica_count is not None: raise exceptions.InvalidArgumentException( '--max-replica-count', """Cannot use max replica count and shared resources in the same command.""", ) if required_replica_count is not None: raise exceptions.InvalidArgumentException( '--required-replica-count', """Cannot use required replica count and shared resources in the same command.""", ) if autoscaling_metric_specs is not None: raise exceptions.InvalidArgumentException( '--autoscaling-metric-specs', """Cannot use autoscaling metric specs and shared resources in the same command.""", ) def ValidateEndpointArgs(network=None, public_endpoint_enabled=None): """Validates the network and public_endpoint_enabled.""" if network is not None and public_endpoint_enabled: raise exceptions.InvalidArgumentException( 'Please either set --network for private endpoint, or set' ' --public-endpoint-enabled', 'for public enpdoint.', ) def ValidateModelGardenModelArgs(args): """Validates the model garden model args.""" if args.model is not None and not args.model: raise exceptions.InvalidArgumentException( '--model', 'Model name should not be empty.', ) if ( len(args.model.split('/')) != 2 or len(args.model.split('/')[1].split('@')) > 2 ): raise exceptions.InvalidArgumentException( '--model', 'Model name should be in the format of Model Garden, e.g.' ' `{publisher_name}/{model_name}@{model_version_name}, e.g.' ' `google/gemma2@gemma-2-2b` or in the format of Hugging Face' ' convention, e.g. `meta-llama/Meta-Llama-3-8B`. You can use the' ' `gcloud ai model-garden models list` command to find supported' ' models.', )