mirror of
https://github.com/wassname/ray.git
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deprecate useless fields in the cluster yaml. (#13637)
* prepare for head node * move command runner interface outside _private * remove space * Eric * flake * min_workers in multi node type * fixing edge cases * eric not idle * fix target_workers to consider min_workers of node types * idle timeout * minor * minor fix * test * lint * eric v2 * eric 3 * min_workers constraint before bin packing * Update resource_demand_scheduler.py * Revert "Update resource_demand_scheduler.py" This reverts commit 818a63a2c86d8437b3ef21c5035d701c1d1127b5. * reducing diff * make get_nodes_to_launch return a dict * merge * weird merge fix * auto fill instance types for AWS * Alex/Eric * Update doc/source/cluster/autoscaling.rst * merge autofill and input from user * logger.exception * make the yaml use the default autofill * docs Eric * remove test_autoscaler_yaml from windows tests * lets try changing the test a bit * return test * lets see * edward * Limit max launch concurrency * commenting frac TODO * move to resource demand scheduler * use STATUS UP TO DATE * Eric * make logger of gc freed refs debug instead of info * add cluster name to docker mount prefix directory * grrR * fix tests * moving docker directory to sdk * move the import to prevent circular dependency * smallf fix * ian * fix max launch concurrency bug to assume failing nodes as pending and consider only load_metric's connected nodes as running * small fix * deflake test_joblib * lint * placement groups bypass * remove space * Eric * first ocmmit * lint * exmaple * documentation * hmm * file path fix * fix test * some format issue in docs * modified docs * joblib strikes again on windows * add ability to not start autoscaler/monitor * a * remove worker_default * Remove default pod type from operator * Remove worker_default_node_type from rewrite_legacy_yaml_to_availble_node_types * deprecate useless fields Co-authored-by: Ameer Haj Ali <ameerhajali@ameers-mbp.lan> Co-authored-by: Alex Wu <alex@anyscale.io> Co-authored-by: Alex Wu <itswu.alex@gmail.com> Co-authored-by: Eric Liang <ekhliang@gmail.com> Co-authored-by: Ameer Haj Ali <ameerhajali@Ameers-MacBook-Pro.local> Co-authored-by: root <root@ip-172-31-56-188.us-west-2.compute.internal> Co-authored-by: Dmitri Gekhtman <dmitri.m.gekhtman@gmail.com>
This commit is contained in:
Ameer Haj Ali
@@ -24,7 +24,7 @@
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"type": "string"
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},
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"min_workers": {
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"description": "The minimum number of workers nodes to launch in addition to the head node. This number should be >= 0",
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"description": "DEPRECATED. Use the per node_type min_workers field instead.",
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"type": "integer",
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"minimum": 0
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},
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@@ -34,17 +34,17 @@
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"minimum": 0
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},
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"initial_workers": {
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"description": "The number of workers to launch initially, in addition to the head node.",
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"description": "DEPRECATED.",
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"type": "integer",
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"minimum": 0
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},
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"autoscaling_mode": {
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"description": "The mode of the autoscaler e.g. default, aggressive",
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"description": "DEPRECATED. Use upscaling_speed instead.",
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"type": "string",
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"enum": [ "default", "aggressive" ]
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},
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"target_utilization_fraction": {
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"description": "The autoscaler will scale up the cluster to this target fraction of resources usage. For example, if a cluster of 8 nodes is 100% busy # and target_utilization was 0.8, it would resize the cluster to 10.",
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"description": "DEPRECATED. Use upscaling_speed instead.",
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"type": "number",
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"minimum": 0,
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"maximum": 1
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@@ -254,6 +254,10 @@
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"type": "string",
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"description": "If using multiple node types, specifies the head node type."
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},
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"worker_default_node_type": {
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"type": "string",
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"description": "DEPRECATED."
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},
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"head_node": {
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"type": "object",
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"description": "Provider-specific config for the head node, e.g. instance type."
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@@ -1,13 +1,10 @@
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cluster_name: default
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min_workers: 5
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max_workers: 5
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initial_workers: 5
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autoscaling_mode: default
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docker:
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image: 'anyscale/ray-ml:latest'
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container_name: ray_container
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pull_before_run: true
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target_utilization_fraction: 0.8
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idle_timeout_minutes: 5
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provider:
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type: aws
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@@ -12,7 +12,6 @@ head_start_ray_commands:
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- ray stop
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- ray start --head --autoscaling-config=~/ray_bootstrap_config.yaml
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idle_timeout_minutes: 5
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initial_workers: 1
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initialization_commands:
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- echo init
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max_workers: 2
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@@ -27,7 +26,6 @@ setup_commands:
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- echo a
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- echo b
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- echo ${echo hi}
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target_utilization_fraction: 0.9
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worker_nodes:
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ImageId: latest_dlami
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InstanceType: t1.micro
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@@ -17,7 +17,6 @@ head_start_ray_commands:
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- ray stop
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- ray start --head --autoscaling-config=~/ray_bootstrap_config.yaml
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idle_timeout_minutes: 5
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initial_workers: 1
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initialization_commands:
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- echo init
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max_workers: 2
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@@ -32,7 +31,6 @@ setup_commands:
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- echo a
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- echo b
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- echo ${echo hi}
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target_utilization_fraction: 0.9
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worker_nodes:
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ImageId: latest_dlami
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InstanceType: t3a.small
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@@ -52,7 +52,6 @@ class OnPremCoordinatorServerTest(unittest.TestCase):
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"cluster_name": "random_name",
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"min_workers": 0,
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"max_workers": 0,
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"initial_workers": 0,
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"provider": {
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"type": "local",
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"head_ip": "0.0.0.0:2",
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@@ -154,7 +153,6 @@ class OnPremCoordinatorServerTest(unittest.TestCase):
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"cluster_name": "random_name",
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"min_workers": 0,
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"max_workers": 0,
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"initial_workers": 0,
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"provider": {
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"type": "local",
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"coordinator_address": self.coordinator_address,
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@@ -4,11 +4,8 @@ cluster_name: sgd-tf
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# The maximum number of workers nodes to launch in addition to the head
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# node. This takes precedence over min_workers. min_workers default to 0.
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min_workers: 3
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initial_workers: 3
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max_workers: 3
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target_utilization_fraction: 0.9
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 20
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# docker:
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@@ -104,7 +104,6 @@ You can specify the number of nodes you want to use with the following configura
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# The maximum number of workers nodes to launch in addition to the head
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# node. This takes precedence over min_workers. min_workers default to 0.
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min_workers: <NUMBER_OF_NODES> # Change this to a custom quantity
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initial_workers: <NUMBER_OF_NODES> # same as above
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max_workers: <NUMBER_OF_NODES> # same as above
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You may want to install FP16 support for PyTorch with the following configuration in the YAML file:
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@@ -4,11 +4,8 @@ cluster_name: horovod-pytorch
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# The maximum number of workers nodes to launch in addition to the head
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# node. This takes precedence over min_workers. min_workers default to 0.
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min_workers: 1
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initial_workers: 1
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max_workers: 1
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target_utilization_fraction: 0.9
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 50
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# docker:
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@@ -4,11 +4,8 @@ cluster_name: sgd-pytorch
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# The maximum number of workers nodes to launch in addition to the head
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# node. This takes precedence over min_workers. min_workers default to 0.
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min_workers: 3
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initial_workers: 3
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max_workers: 3
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target_utilization_fraction: 0.9
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 20
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# docker:
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@@ -4,11 +4,8 @@ cluster_name: sgd-pytorch-imagenet
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# The maximum number of workers nodes to launch in addition to the head
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# node. This takes precedence over min_workers. min_workers default to 0.
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min_workers: 1
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initial_workers: 1
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max_workers: 1
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target_utilization_fraction: 0.9
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 10
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# docker:
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@@ -4,10 +4,8 @@ cluster_name: sgd-coco-pytorch
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# The maximum number of workers nodes to launch in addition to the head
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# node. This takes precedence over min_workers. min_workers default to 0.
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min_workers: 1
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initial_workers: 1
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max_workers: 1
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target_utilization_fraction: 0.9
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# Cloud-provider specific configuration.
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provider:
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type: aws
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@@ -4,11 +4,8 @@ cluster_name: sgd-pytorch
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# The maximum number of workers nodes to launch in addition to the head
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# node. This takes precedence over min_workers. min_workers default to 0.
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min_workers: 2
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initial_workers: 2
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max_workers: 2
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target_utilization_fraction: 0.9
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 10
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# docker:
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@@ -4,10 +4,8 @@ cluster_name: transformer-cluster
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# The maximum number of workers nodes to launch in addition to the head
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# node. This takes precedence over min_workers. min_workers default to 0.
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min_workers: 3
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initial_workers: 3
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max_workers: 3
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target_utilization_fraction: 0.9
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# Cloud-provider specific configuration.
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provider:
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type: aws
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