Skip to content

ComputeQuotaResourceConfig

Source code in docs_src/capo_sagemaker/types/compute_quota_resource_config.py 
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
class ComputeQuotaResourceConfig(TypedDict, closed=True):
    instance_type: NotRequired[
        "capo_sagemaker.types.cluster_instance_type.ClusterInstanceType"
    ]
    """<p>The instance type of the instance group for the cluster.</p>"""
    count: NotRequired["capo_sagemaker.types.instance_count.InstanceCount"]
    """<p>The number of instances to add to the instance group of a SageMaker HyperPod cluster.</p>"""
    accelerators: NotRequired[
        "capo_sagemaker.types.accelerators_amount.AcceleratorsAmount"
    ]
    """<p>The number of accelerators to allocate. If you don't specify a value for vCPU and MemoryInGiB, SageMaker AI automatically allocates ratio-based values for those parameters based on the number of accelerators you provide. For example, if you allocate 16 out of 32 total accelerators, SageMaker AI uses the ratio of 0.5 and allocates values to vCPU and MemoryInGiB.</p>"""
    v_cpu: NotRequired["capo_sagemaker.types.v_cpu_amount.VCpuAmount"]
    """<p>The number of vCPU to allocate. If you specify a value only for vCPU, SageMaker AI automatically allocates ratio-based values for MemoryInGiB based on this vCPU parameter. For example, if you allocate 20 out of 40 total vCPU, SageMaker AI uses the ratio of 0.5 and allocates values to MemoryInGiB. Accelerators are set to 0.</p>"""
    memory_in_gi_b: NotRequired[
        "capo_sagemaker.types.memory_in_gi_b_amount.MemoryInGiBAmount"
    ]
    """<p>The amount of memory in GiB to allocate. If you specify a value only for this parameter, SageMaker AI automatically allocates a ratio-based value for vCPU based on this memory that you provide. For example, if you allocate 200 out of 400 total memory in GiB, SageMaker AI uses the ratio of 0.5 and allocates values to vCPU. Accelerators are set to 0.</p>"""
    accelerator_partition: NotRequired[
        "capo_sagemaker.types.accelerator_partition_config.AcceleratorPartitionConfig"
    ]
    """<p>The accelerator partition configuration for fractional GPU allocation.</p>"""

accelerator_partition instance-attribute 

accelerator_partition: NotRequired[AcceleratorPartitionConfig]

The accelerator partition configuration for fractional GPU allocation.

accelerators instance-attribute 

accelerators: NotRequired[AcceleratorsAmount]

The number of accelerators to allocate. If you don't specify a value for vCPU and MemoryInGiB, SageMaker AI automatically allocates ratio-based values for those parameters based on the number of accelerators you provide. For example, if you allocate 16 out of 32 total accelerators, SageMaker AI uses the ratio of 0.5 and allocates values to vCPU and MemoryInGiB.

count instance-attribute 

count: NotRequired[InstanceCount]

The number of instances to add to the instance group of a SageMaker HyperPod cluster.

instance_type instance-attribute 

instance_type: NotRequired[ClusterInstanceType]

The instance type of the instance group for the cluster.

memory_in_gi_b instance-attribute 

memory_in_gi_b: NotRequired[MemoryInGiBAmount]

The amount of memory in GiB to allocate. If you specify a value only for this parameter, SageMaker AI automatically allocates a ratio-based value for vCPU based on this memory that you provide. For example, if you allocate 200 out of 400 total memory in GiB, SageMaker AI uses the ratio of 0.5 and allocates values to vCPU. Accelerators are set to 0.

v_cpu instance-attribute 

v_cpu: NotRequired[VCpuAmount]

The number of vCPU to allocate. If you specify a value only for vCPU, SageMaker AI automatically allocates ratio-based values for MemoryInGiB based on this vCPU parameter. For example, if you allocate 20 out of 40 total vCPU, SageMaker AI uses the ratio of 0.5 and allocates values to MemoryInGiB. Accelerators are set to 0.