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47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
import os
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from ray.ray_constants import ( # noqa F401
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AUTOSCALER_RESOURCE_REQUEST_CHANNEL, LOGGER_FORMAT,
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MEMORY_RESOURCE_UNIT_BYTES, RESOURCES_ENVIRONMENT_VARIABLE)
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def env_integer(key, default):
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if key in os.environ:
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return int(os.environ[key])
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return default
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# Abort autoscaling if more than this number of errors are encountered. This
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# is a safety feature to prevent e.g. runaway node launches.
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AUTOSCALER_MAX_NUM_FAILURES = env_integer("AUTOSCALER_MAX_NUM_FAILURES", 5)
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# The maximum number of nodes to launch in a single request.
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# Multiple requests may be made for this batch size, up to
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# the limit of AUTOSCALER_MAX_CONCURRENT_LAUNCHES.
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AUTOSCALER_MAX_LAUNCH_BATCH = env_integer("AUTOSCALER_MAX_LAUNCH_BATCH", 5)
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# Max number of nodes to launch at a time.
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AUTOSCALER_MAX_CONCURRENT_LAUNCHES = env_integer(
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"AUTOSCALER_MAX_CONCURRENT_LAUNCHES", 10)
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# Interval at which to perform autoscaling updates.
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AUTOSCALER_UPDATE_INTERVAL_S = env_integer("AUTOSCALER_UPDATE_INTERVAL_S", 5)
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# The autoscaler will attempt to restart Ray on nodes it hasn't heard from
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# in more than this interval.
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AUTOSCALER_HEARTBEAT_TIMEOUT_S = env_integer("AUTOSCALER_HEARTBEAT_TIMEOUT_S",
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30)
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# The maximum allowed resource demand vector size to guarantee the resource
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# demand scheduler bin packing algorithm takes a reasonable amount of time
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# to run.
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AUTOSCALER_MAX_RESOURCE_DEMAND_VECTOR_SIZE = 1000
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# Max number of retries to AWS (default is 5, time increases exponentially)
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BOTO_MAX_RETRIES = env_integer("BOTO_MAX_RETRIES", 12)
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# Max number of retries to create an EC2 node (retry different subnet)
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BOTO_CREATE_MAX_RETRIES = env_integer("BOTO_CREATE_MAX_RETRIES", 5)
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# ray home path in the container image
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RAY_HOME = "/home/ray"
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