Commit
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b8fda63
1
Parent(s):
6caf480
Config head node runtime via IP (#234)
Browse files- Config head node runtime via IP (748f48ac03faa90488220eb2f9b5a76e128a6c06)
- Update minor formatting (c48e37c67945c09e6593d45c40c4cbe0df7cc757)
Co-authored-by: Madhavan Venkatesh <[email protected]>
examples/hyperparam_optimiz_for_disease_classifier.py
CHANGED
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@@ -18,10 +18,42 @@ import ray
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from ray import tune
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from ray.tune import ExperimentAnalysis
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from ray.tune.suggest.hyperopt import HyperOptSearch
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runtime_env = {"conda": "base",
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"env_vars": {"LD_LIBRARY_PATH": "/path/to/miniconda3/lib:/path/to/sw/lib:/path/to/sw/lib"}}
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ray.init(runtime_env=runtime_env)
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import datetime
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import numpy as np
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import pandas as pd
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@@ -123,6 +155,7 @@ def model_init():
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return model
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# define metrics
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def compute_metrics(pred):
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labels = pred.label_ids
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preds = pred.predictions.argmax(-1)
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from ray import tune
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from ray.tune import ExperimentAnalysis
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from ray.tune.suggest.hyperopt import HyperOptSearch
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ray.shutdown() #engage new ray session
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runtime_env = {"conda": "base",
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"env_vars": {"LD_LIBRARY_PATH": "/path/to/miniconda3/lib:/path/to/sw/lib:/path/to/sw/lib"}}
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ray.init(runtime_env=runtime_env)
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def initialize_ray_with_check(ip_address):
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"""
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Initialize Ray with a specified IP address and check its status and accessibility.
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Args:
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- ip_address (str): The IP address (with port) to initialize Ray.
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Returns:
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- bool: True if initialization was successful and dashboard is accessible, False otherwise.
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"""
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try:
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ray.init(address=ip_address)
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print(ray.nodes())
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services = ray.get_webui_url()
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if not services:
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raise RuntimeError("Ray dashboard is not accessible.")
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else:
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print(f"Ray dashboard is accessible at: {services}")
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return True
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except Exception as e:
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print(f"Error initializing Ray: {e}")
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return False
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# Usage:
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ip = 'your_ip:xxxx' # Replace with your actual IP address and port
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if initialize_ray_with_check(ip):
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print("Ray initialized successfully.")
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else:
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print("Error during Ray initialization.")
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import datetime
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import numpy as np
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import pandas as pd
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return model
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# define metrics
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# note: macro f1 score recommended for imbalanced multiclass classifiers
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def compute_metrics(pred):
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labels = pred.label_ids
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preds = pred.predictions.argmax(-1)
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