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Automatically detect CPU, GPU, accelerator_type for AWS (#11147)

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This commit is contained in:
Ameer Haj Ali
2020-10-02 21:16:43 -07:00
committed by GitHub
parent 6974cea0cd
commit 6b86d4d280
6 changed files with 172 additions and 12 deletions
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doc/source/cluster/autoscaling.rst
+11 -6
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@@ -48,7 +48,7 @@ Unmanaged nodes **must have 0 resources**.
If you are using the `available_node_types` field, you should create a custom node type with `resources: {}`, and `max_workers: 0` when configuring the autoscaler.
The autoscaler will not attempt to start, stop, or update unmanaged nodes. The user is responsible for properly setting up and cleaning up unmanaged nodes.
The autoscaler will not attempt to start, stop, or update unmanaged nodes. The user is responsible for properly setting up and cleaning up unmanaged nodes.
Multiple Node Type Autoscaling
@@ -71,7 +71,9 @@ An example of configuring multiple node types is as follows `(full example) <htt
cpu_4_ondemand:
node_config:
InstanceType: m4.xlarge
resources: {"CPU": 4}
# For AWS instances, autoscaler will automatically add the available
# CPUs/GPUs/accelerator_type ({"CPU": 4} for m4.xlarge) in "resources".
# resources: {"CPU": 4}
min_workers: 1
max_workers: 5
cpu_16_spot:
@@ -79,19 +81,22 @@ An example of configuring multiple node types is as follows `(full example) <htt
InstanceType: m4.4xlarge
InstanceMarketOptions:
MarketType: spot
resources: {"CPU": 16, "Custom1": 1, "is_spot": 1}
# Autoscaler will auto fill the CPU resources below.
resources: {"Custom1": 1, "is_spot": 1}
max_workers: 10
gpu_1_ondemand:
node_config:
InstanceType: p2.xlarge
resources: {"CPU": 4, "GPU": 1, "Custom2": 2}
# Autoscaler will auto fill the CPU/GPU resources below.
resources: {"Custom2": 2}
max_workers: 4
worker_setup_commands:
- pip install tensorflow-gpu # Example command.
gpu_8_ondemand:
node_config:
InstanceType: p2.8xlarge
resources: {"CPU": 32, "GPU": 8}
InstanceType: p3.8xlarge
# Autoscaler autofills the "resources" below.
# resources: {"CPU": 32, "GPU": 4, "accelerator_type:V100": 1}
max_workers: 2
worker_setup_commands:
- pip install tensorflow-gpu # Example command.