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I was running your example code after upgrading to ray 2.5.0, but now I get the following error.
TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
Downgrade the protobuf package to 3.20.x or lower.
Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).
I don't understand why I have to downgrade protobuf ti much older versions. Any suggestion? This error disappear if I use older versions of Ray
Versions / Dependencies
ray = {extras = ["tune"], version = "^2.5.0"}
python = "^3.10"
Medium: It is a significant difficulty but I can work around it.
The text was updated successfully, but these errors were encountered:
FrancescoMandru
added
bug
Something that is supposed to be working; but isn't
triage
Needs triage (eg: priority, bug/not-bug, and owning component)
labels
Jun 14, 2023
krfricke
added
P1
Issue that should be fixed within a few weeks
core
Issues that should be addressed in Ray Core
and removed
tune
Tune-related issues
air
labels
Jun 21, 2023
I was running your example code after upgrading to ray 2.5.0, but now I get the following error.
TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
I don't understand why I have to downgrade protobuf ti much older versions. Any suggestion? This error disappear if I use older versions of Ray
Versions / Dependencies
ray = {extras = ["tune"], version = "^2.5.0"}
python = "^3.10"
Reproduction script
from ray import tune
def objective(config): # ①
score = config["a"] ** 2 + config["b"]
return {"score": score}
search_space = { # ②
"a": tune.grid_search([0.001, 0.01, 0.1, 1.0]),
"b": tune.choice([1, 2, 3]),
}
tuner = tune.Tuner(objective, param_space=search_space) # ③
results = tuner.fit()
print(results.get_best_result(metric="score", mode="min").config)
Issue Severity
Medium: It is a significant difficulty but I can work around it.
The text was updated successfully, but these errors were encountered: