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[autoscaler] Replace cluster yaml validation with json schema v… (#7261)
* replace manual cluster yaml validation with json schema - improved error message - support for intellisense in VSCode (or other IDEs) - run linting - moved schema to ray/autoscaler - fixed typo - remove importlib dependency * Update python/ray/autoscaler/autoscaler.py * read * restrict allowed properties * added unit test for invalid yaml added ray[test] package (remove pytest from default dependencies) * updated autoscaler test to use ValidationError exception * add missing dependency * added pytest * replace manual cluster yaml validation with json schema - improved error message - support for intellisense in VSCode (or other IDEs) - run linting - moved schema to ray/autoscaler - fixed typo - remove importlib dependency * Update python/ray/autoscaler/autoscaler.py * read * restrict allowed properties * added unit test for invalid yaml added ray[test] package (remove pytest from default dependencies) * updated autoscaler test to use ValidationError exception * add missing dependency * added pytest * removed parameterized dependency reverted ray[test] intro * removed parameterized * fix_tests * format Co-authored-by: Ubuntu <marcozo@mc-ray-jumpbox.chcbtljllnieveqhw3e4c1ducc.xx.internal.cloudapp.net> Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
This commit is contained in:
Markus Cozowicz
@@ -0,0 +1 @@
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include ray/python/ray/autoscaler/ray-schema.json
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@@ -1,18 +1,18 @@
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from collections import defaultdict
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import copy
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import hashlib
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import json
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import jsonschema
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import logging
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import math
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import numpy as np
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import os
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import subprocess
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import threading
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import time
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from collections import defaultdict
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import numpy as np
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import ray.services as services
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import yaml
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from ray.worker import global_worker
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import ray
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from ray.autoscaler.docker import dockerize_if_needed
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from ray.autoscaler.node_provider import get_node_provider, \
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get_default_config
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@@ -25,131 +25,15 @@ from ray.ray_constants import AUTOSCALER_MAX_NUM_FAILURES, \
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AUTOSCALER_MAX_LAUNCH_BATCH, AUTOSCALER_MAX_CONCURRENT_LAUNCHES, \
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AUTOSCALER_UPDATE_INTERVAL_S, AUTOSCALER_HEARTBEAT_TIMEOUT_S, \
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AUTOSCALER_RESOURCE_REQUEST_CHANNEL, MEMORY_RESOURCE_UNIT_BYTES
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from six import string_types
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import ray.services as services
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from ray.worker import global_worker
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from six.moves import queue
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logger = logging.getLogger(__name__)
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REQUIRED, OPTIONAL = True, False
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# For (a, b), if a is a dictionary object, then
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# no extra fields can be introduced.
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CLUSTER_CONFIG_SCHEMA = {
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# An unique identifier for the head node and workers of this cluster.
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"cluster_name": (str, REQUIRED),
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# The minimum number of workers nodes to launch in addition to the head
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# node. This number should be >= 0.
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"min_workers": (int, OPTIONAL),
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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.
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"max_workers": (int, REQUIRED),
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# The number of workers to launch initially, in addition to the head node.
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"initial_workers": (int, OPTIONAL),
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# The mode of the autoscaler e.g. default, aggressive
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"autoscaling_mode": (str, OPTIONAL),
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# The autoscaler will scale up the cluster to this target fraction of
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# resources usage. For example, if a cluster of 8 nodes is 100% busy
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# and target_utilization was 0.8, it would resize the cluster to 10.
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"target_utilization_fraction": (float, OPTIONAL),
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# If a node is idle for this many minutes, it will be removed.
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"idle_timeout_minutes": (int, OPTIONAL),
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# Cloud-provider specific configuration.
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"provider": (
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{
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"type": (str, REQUIRED), # e.g. aws
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"region": (str, OPTIONAL), # e.g. us-east-1
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"availability_zone": (str, OPTIONAL), # e.g. us-east-1a
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"module": (str,
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OPTIONAL), # module, if using external node provider
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"project_id": (None, OPTIONAL), # gcp project id, if using gcp
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"head_ip": (str, OPTIONAL), # local cluster head node
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"worker_ips": (list, OPTIONAL), # local cluster worker nodes
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"use_internal_ips": (bool, OPTIONAL), # don't require public ips
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"namespace": (str, OPTIONAL), # k8s namespace, if using k8s
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# k8s autoscaler permissions, if using k8s
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"autoscaler_service_account": (dict, OPTIONAL),
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"autoscaler_role": (dict, OPTIONAL),
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"autoscaler_role_binding": (dict, OPTIONAL),
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"extra_config": (dict, OPTIONAL), # provider-specific config
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# Whether to try to reuse previously stopped nodes instead of
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# launching nodes. This will also cause the autoscaler to stop
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# nodes instead of terminating them. Only implemented for AWS.
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"cache_stopped_nodes": (bool, OPTIONAL),
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},
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REQUIRED),
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# How Ray will authenticate with newly launched nodes.
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"auth": (
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{
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"ssh_user": (str, OPTIONAL), # e.g. ubuntu
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"ssh_private_key": (str, OPTIONAL),
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},
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OPTIONAL),
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# Docker configuration. If this is specified, all setup and start commands
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# will be executed in the container.
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"docker": (
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{
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"image": (str, OPTIONAL), # e.g. tensorflow/tensorflow:1.5.0-py3
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"container_name": (str, OPTIONAL), # e.g., ray_docker
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"pull_before_run": (bool, OPTIONAL), # run `docker pull` first
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# shared options for starting head/worker docker
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"run_options": (list, OPTIONAL),
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# image for head node, takes precedence over "image" if specified
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"head_image": (str, OPTIONAL),
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# head specific run options, appended to run_options
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"head_run_options": (list, OPTIONAL),
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# analogous to head_image
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"worker_image": (str, OPTIONAL),
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# analogous to head_run_options
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"worker_run_options": (list, OPTIONAL),
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},
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OPTIONAL),
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# Provider-specific config for the head node, e.g. instance type.
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"head_node": (dict, OPTIONAL),
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# Provider-specific config for worker nodes. e.g. instance type.
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"worker_nodes": (dict, OPTIONAL),
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# Map of remote paths to local paths, e.g. {"/tmp/data": "/my/local/data"}
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"file_mounts": (dict, OPTIONAL),
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# List of commands that will be run before `setup_commands`. If docker is
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# enabled, these commands will run outside the container and before docker
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# is setup.
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"initialization_commands": (list, OPTIONAL),
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# List of common shell commands to run to setup nodes.
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"setup_commands": (list, OPTIONAL),
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# Commands that will be run on the head node after common setup.
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"head_setup_commands": (list, OPTIONAL),
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# Commands that will be run on worker nodes after common setup.
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"worker_setup_commands": (list, OPTIONAL),
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# Command to start ray on the head node. You shouldn't need to modify this.
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"head_start_ray_commands": (list, OPTIONAL),
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# Command to start ray on worker nodes. You shouldn't need to modify this.
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"worker_start_ray_commands": (list, OPTIONAL),
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# Whether to avoid restarting the cluster during updates. This field is
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# controlled by the ray --no-restart flag and cannot be set by the user.
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"no_restart": (None, OPTIONAL),
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}
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RAY_SCHEMA_PATH = os.path.join(
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os.path.dirname(ray.autoscaler.__file__), "ray-schema.json")
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class LoadMetrics:
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@@ -764,61 +648,17 @@ class StandardAutoscaler:
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len(nodes)))
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def typename(v):
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if isinstance(v, type):
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return v.__name__
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else:
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return type(v).__name__
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def check_required(config, schema):
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# Check required schema entries
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if not isinstance(config, dict):
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raise ValueError("Config is not a dictionary")
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for k, (v, kreq) in schema.items():
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if v is None:
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continue # None means we don't validate the field
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if kreq is REQUIRED:
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if k not in config:
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type_str = typename(v)
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raise ValueError(
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"Missing required config key `{}` of type {}".format(
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k, type_str))
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if not isinstance(v, type):
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check_required(config[k], v)
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def check_extraneous(config, schema):
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"""Make sure all items of config are in schema"""
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if not isinstance(config, dict):
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raise ValueError("Config {} is not a dictionary".format(config))
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for k in config:
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if k not in schema:
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raise ValueError("Unexpected config key `{}` not in {}".format(
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k, list(schema.keys())))
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v, kreq = schema[k]
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if v is None:
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continue
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elif isinstance(v, type):
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if not isinstance(config[k], v):
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if v is str and isinstance(config[k], string_types):
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continue
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raise ValueError(
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"Config key `{}` has wrong type {}, expected {}".format(
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k,
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type(config[k]).__name__, v.__name__))
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else:
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check_extraneous(config[k], v)
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def validate_config(config, schema=CLUSTER_CONFIG_SCHEMA):
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def validate_config(config):
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"""Required Dicts indicate that no extra fields can be introduced."""
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if not isinstance(config, dict):
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raise ValueError("Config {} is not a dictionary".format(config))
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check_required(config, schema)
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check_extraneous(config, schema)
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with open(RAY_SCHEMA_PATH) as f:
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schema = json.load(f)
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try:
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jsonschema.validate(config, schema)
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except jsonschema.ValidationError as e:
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raise jsonschema.ValidationError(message=e.message) from None
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def fillout_defaults(config):
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@@ -0,0 +1,243 @@
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{
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"$schema": "http://json-schema.org/draft-07/schema#",
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"$id": "http://github.com/ray-project/ray/python/ray/autoscaler/ray-schema.json",
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"title": "Ray AutoScaler",
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"description": "Ray autoscaler schema",
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"type": "object",
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"definitions": {
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"commands": {
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"type": "array",
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"items": {
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"type": "string",
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"description": "shell command"
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}
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}
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},
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"required": [
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"cluster_name",
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"provider"
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],
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"additionalProperties": false,
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"properties": {
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"cluster_name": {
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"description": "An unique identifier for the head node and workers of this cluster.",
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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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"type": "integer",
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"minimum": 0
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},
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"max_workers": {
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"description": "The maximum number of workers nodes to launch in addition to the head node. This takes precedence over min_workers.",
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"type": "integer",
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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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"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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"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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"type": "number",
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"minimum": 0,
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"maximum": 1
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},
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"idle_timeout_minutes": {
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"description": "If a node is idle for this many minutes, it will be removed.",
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"type": "integer",
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"minimum": 0
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},
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"provider": {
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"type": "object",
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"description": "Cloud-provider specific configuration.",
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"required": [ "type" ],
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"additionalProperties": false,
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"properties": {
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"type": {
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"type": "string",
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"description": "e.g. aws, azure, gcp,..."
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},
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"region": {
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"type": "string",
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"description": "e.g. us-east-1"
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},
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"module": {
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"type": "string",
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"description": "module, if using external node provider"
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},
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"head_ip": {
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"type": "string",
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"description": "gcp project id, if using gcp"
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},
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"worker_ips": {
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"type": "array",
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"description": "local cluster head node"
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},
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"use_internal_ips": {
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"type": "boolean",
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"description": "don't require public ips"
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},
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"namespace": {
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"type": "string",
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"description": "k8s namespace, if using k8s"
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},
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"location": {
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"type": "string",
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"description": "Azure location"
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},
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"resource_group": {
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"type": "string",
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"description": "Azure resource group"
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},
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"tags": {
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"type": "object",
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"description": "Azure user-defined tags"
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},
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"subscription_id": {
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"type": "string",
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"description": "Azure subscription id"
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},
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"msi_identity_id": {
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"type": "string",
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"description": "User-defined managed identity (generated by config)"
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},
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"msi_identity_principal_id": {
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"type": "string",
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"description": "User-defined managed identity principal id (generated by config)"
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},
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"subnet_id": {
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"type": "string",
|
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"description": "Network subnet id"
|
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},
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"autoscaler_service_account": {
|
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"type": "object",
|
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"description": "k8s autoscaler permissions, if using k8s"
|
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},
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"autoscaler_role": {
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"type": "object",
|
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"description": "k8s autoscaler permissions, if using k8s"
|
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},
|
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"autoscaler_role_binding": {
|
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"type": "object",
|
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"description": "k8s autoscaler permissions, if using k8s"
|
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},
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"extra_config": {
|
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"type": "object",
|
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"description": "provider-specific config"
|
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},
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"cache_stopped_nodes": {
|
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"type": "boolean",
|
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"description": " Whether to try to reuse previously stopped nodes instead of launching nodes. This will also cause the autoscaler to stop nodes instead of terminating them. Only implemented for AWS."
|
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},
|
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"availability_zone": {
|
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"type": "string",
|
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"description": "GCP availability zone"
|
||||
},
|
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"project_id": {
|
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"type": ["string", "null"],
|
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"description": "GCP globally unique project id"
|
||||
}
|
||||
}
|
||||
},
|
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"auth": {
|
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"type": "object",
|
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"description": "How Ray will authenticate with newly launched nodes.",
|
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"additionalProperties": false,
|
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"properties": {
|
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"ssh_user": {
|
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"type": "string",
|
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"default": "ubuntu"
|
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},
|
||||
"ssh_private_key": {
|
||||
"type": "string"
|
||||
}
|
||||
}
|
||||
},
|
||||
"docker": {
|
||||
"type": "object",
|
||||
"description": "Docker configuration. If this is specified, all setup and start commands will be executed in the container.",
|
||||
"additionalProperties": false,
|
||||
"properties": {
|
||||
"image": {
|
||||
"type": "string",
|
||||
"description": "the docker image name",
|
||||
"default": "tensorflow/tensorflow:1.5.0-py3"
|
||||
},
|
||||
"container_name": {
|
||||
"type": "string",
|
||||
"default": "ray_docker"
|
||||
},
|
||||
"pull_before_run": {
|
||||
"type": "boolean",
|
||||
"description": "run `docker pull` first"
|
||||
},
|
||||
"run_options": {
|
||||
"type": "array",
|
||||
"description": "shared options for starting head/worker docker"
|
||||
},
|
||||
"head_image": {
|
||||
"type": "string",
|
||||
"description": "image for head node, takes precedence over 'image' if specified"
|
||||
},
|
||||
"head_run_options": {
|
||||
"type": "array",
|
||||
"description": "head specific run options, appended to run_options"
|
||||
},
|
||||
"worker_image": {
|
||||
"type": "string",
|
||||
"description": "analogous to head_image"
|
||||
},
|
||||
"worker_run_options": {
|
||||
"type": "array",
|
||||
"description": "analogous to head_run_options"
|
||||
}
|
||||
}
|
||||
},
|
||||
"head_node": {
|
||||
"type": "object",
|
||||
"description": "Provider-specific config for the head node, e.g. instance type."
|
||||
},
|
||||
"worker_nodes": {
|
||||
"type": "object",
|
||||
"description": "Provider-specific config for worker nodes. e.g. instance type."
|
||||
},
|
||||
"file_mounts": {
|
||||
"type": "object",
|
||||
"description": "Map of remote paths to local paths, e.g. {\"/tmp/data\": \"/my/local/data\"}"
|
||||
},
|
||||
"initialization_commands": {
|
||||
"$ref": "#/definitions/commands",
|
||||
"description": "List of commands that will be run before `setup_commands`. If docker is enabled, these commands will run outside the container and before docker is setup."
|
||||
},
|
||||
"setup_commands": {
|
||||
"$ref": "#/definitions/commands",
|
||||
"description": "List of common shell commands to run to setup nodes."
|
||||
},
|
||||
"head_setup_commands": {
|
||||
"$ref": "#/definitions/commands",
|
||||
"description": "Commands that will be run on the head node after common setup."
|
||||
},
|
||||
"worker_setup_commands": {
|
||||
"$ref": "#/definitions/commands",
|
||||
"description": "Commands that will be run on worker nodes after common setup."
|
||||
},
|
||||
"head_start_ray_commands": {
|
||||
"$ref": "#/definitions/commands",
|
||||
"description": "Command to start ray on the head node. You shouldn't need to modify this."
|
||||
},
|
||||
"worker_start_ray_commands": {
|
||||
"$ref": "#/definitions/commands",
|
||||
"description": "Command to start ray on worker nodes. You shouldn't need to modify this."
|
||||
},
|
||||
"no_restart": {
|
||||
"description": "Whether to avoid restarting the cluster during updates. This field is controlled by the ray --no-restart flag and cannot be set by the user."
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,30 @@
|
||||
# An unique identifier for the head node and workers of this cluster.
|
||||
cluster_name: minimal
|
||||
hi: 1
|
||||
# The maximum number of workers nodes to launch in addition to the head
|
||||
# node. This takes precedence over min_workers. min_workers default to 0.
|
||||
min_workers: 0
|
||||
max_workers: 0
|
||||
|
||||
provider:
|
||||
type: aws
|
||||
region: us-west-2
|
||||
availability_zone: us-west-2b
|
||||
|
||||
# How Ray will authenticate with newly launched nodes.
|
||||
auth:
|
||||
ssh_user: ubuntu
|
||||
|
||||
head_node:
|
||||
InstanceType: m4.10xlarge
|
||||
ImageId: ami-3b6bce43 # Amazon Deep Learning AMI (Ubuntu)
|
||||
|
||||
setup_commands:
|
||||
- error me
|
||||
# - echo 'export PATH="$HOME/anaconda3/envs/tensorflow_p36/bin:$PATH"' >> ~/.bashrc
|
||||
# - source activate tensorflow_p27 && pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp27-cp27mu-manylinux1_x86_64.whl
|
||||
|
||||
# # Command to start ray on the head node. You don't need to change this.
|
||||
head_start_ray_commands:
|
||||
- ray stop
|
||||
- ray start --head --redis-port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml
|
||||
@@ -5,6 +5,7 @@ import time
|
||||
import unittest
|
||||
import yaml
|
||||
import copy
|
||||
from jsonschema.exceptions import ValidationError
|
||||
|
||||
import ray
|
||||
import ray.services as services
|
||||
@@ -323,17 +324,17 @@ class AutoscalingTest(unittest.TestCase):
|
||||
self.fail("Test config did not pass validation test!")
|
||||
|
||||
config["blah"] = "blah"
|
||||
with pytest.raises(ValueError):
|
||||
with pytest.raises(ValidationError):
|
||||
validate_config(config)
|
||||
del config["blah"]
|
||||
|
||||
config["provider"]["blah"] = "blah"
|
||||
with pytest.raises(ValueError):
|
||||
with pytest.raises(ValidationError):
|
||||
validate_config(config)
|
||||
del config["provider"]["blah"]
|
||||
|
||||
del config["provider"]
|
||||
with pytest.raises(ValueError):
|
||||
with pytest.raises(ValidationError):
|
||||
validate_config(config)
|
||||
|
||||
def testValidateDefaultConfig(self):
|
||||
@@ -346,7 +347,7 @@ class AutoscalingTest(unittest.TestCase):
|
||||
config = fillout_defaults(config)
|
||||
try:
|
||||
validate_config(config)
|
||||
except Exception:
|
||||
except ValidationError:
|
||||
self.fail("Default config did not pass validation test!")
|
||||
|
||||
def testScaleUp(self):
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
import jsonschema
|
||||
import os
|
||||
import unittest
|
||||
import yaml
|
||||
@@ -15,7 +16,6 @@ CONFIG_PATHS += recursive_fnmatch(
|
||||
|
||||
class AutoscalingConfigTest(unittest.TestCase):
|
||||
def testValidateDefaultConfig(self):
|
||||
|
||||
for config_path in CONFIG_PATHS:
|
||||
with open(config_path) as f:
|
||||
config = yaml.safe_load(f)
|
||||
@@ -25,6 +25,18 @@ class AutoscalingConfigTest(unittest.TestCase):
|
||||
except Exception:
|
||||
self.fail("Config did not pass validation test!")
|
||||
|
||||
def _test_invalid_config(self, config_path):
|
||||
with open(os.path.join(RAY_PATH, config_path)) as f:
|
||||
config = yaml.safe_load(f)
|
||||
try:
|
||||
validate_config(config)
|
||||
self.fail("Expected validation to fail for {}".format(config_path))
|
||||
except jsonschema.ValidationError:
|
||||
pass
|
||||
|
||||
def testInvalidConfig(self):
|
||||
self._test_invalid_config("tests/additional_property.yaml")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import pytest
|
||||
|
||||
+2
-1
@@ -75,7 +75,7 @@ extras = {
|
||||
"debug": [],
|
||||
"dashboard": [],
|
||||
"serve": ["uvicorn", "pygments", "werkzeug", "flask", "pandas", "blist"],
|
||||
"tune": ["tabulate", "tensorboardX"],
|
||||
"tune": ["tabulate", "tensorboardX"]
|
||||
}
|
||||
|
||||
extras["rllib"] = extras["tune"] + [
|
||||
@@ -178,6 +178,7 @@ requires = [
|
||||
"packaging",
|
||||
"pytest",
|
||||
"pyyaml",
|
||||
"jsonschema",
|
||||
"redis>=3.3.2",
|
||||
# NOTE: Don't upgrade the version of six! Doing so causes installation
|
||||
# problems. See https://github.com/ray-project/ray/issues/4169.
|
||||
|
||||
Reference in New Issue
Block a user