Further optimize the disk usage
See original GitHub issueI’m conducting a Hyperparameter search on large parameter space using Hyperband. Im experiencing Diskspace issues (+700GB), because of all the save Trails. I would like to delete discarded Trails, which have been discarded by successive halving.
How can I lookup discarded trails that will not be used in futer trails?
Which trails are safe for me to delete. Is it safe to delete all trails that are listed in “past_id” in the Brackets, which can be found in self.oracle.get_state()
(see example below)?
Thanks alot! Great Repo!
Example: This is a current state of a small Hyperparameter space after serveral trails:
{
"brackets": [
{
"bracket_num": 2,
"rounds": [
[
{
"id": "e1822fa866ee7b337a6ce32e154a81e7",
"past_id": null
},
{
"id": "a83404db6388d841da8adfffe6c574d6",
"past_id": null
},
{
"id": "7b35e7e6e19a6cb906ff0ec4dd00b0d7",
"past_id": null
},
{
"id": "4bcb8b7e9e868f2c29b5205c8ece21f6",
"past_id": null
},
{
"id": "e32d7a054796e970dca5484336501b48",
"past_id": null
},
{
"id": "1f955d99321824edb51f34065fd6bf6d",
"past_id": null
},
{
"id": "9fe445228a1608fee0454a0d783b7183",
"past_id": null
},
{
"id": "331329bda15420cbc26346cab29b664b",
"past_id": null
},
{
"id": "135e840913f8a796b15dc5e0a9f4c72c",
"past_id": null
},
{
"id": "2656c6528a0dff80e1fb619d5db78d72",
"past_id": null
},
{
"id": "a38341d5327dab6f2b2c113b4486e060",
"past_id": null
},
{
"id": "8f6ec3b93f4cdd17d91daed2ce270d61",
"past_id": null
}
],
[
{
"id": "7a0c454d30b87ed7ba7577383c1e66db",
"past_id": "1f955d99321824edb51f34065fd6bf6d"
},
{
"id": "4595fc148424315a5e718464ad4c6806",
"past_id": "8f6ec3b93f4cdd17d91daed2ce270d61"
},
{
"id": "443291ecac85d951672d74098aba5d80",
"past_id": "7b35e7e6e19a6cb906ff0ec4dd00b0d7"
}
],
[]
]
}
],
"current_bracket": 2,
"current_iteration": 0,
"factor": 3,
"hyperband_iterations": 4,
"hyperparameters": {
"space": [
{
"class_name": "Choice",
"config": {
"conditions": [],
"default": 64,
"name": "n_hidden",
"ordered": true,
"values": [
64,
512,
128
]
}
},
{
"class_name": "Choice",
"config": {
"conditions": [],
"default": 48,
"name": "n_dense",
"ordered": true,
"values": [
48,
56,
128
]
}
},
{
"class_name": "Choice",
"config": {
"conditions": [],
"default": 0.2,
"name": "dropout",
"ordered": true,
"values": [
0.2,
0.5
]
}
},
{
"class_name": "Choice",
"config": {
"conditions": [],
"default": 0.1,
"name": "dropout_dense",
"ordered": true,
"values": [
0.1
]
}
},
{
"class_name": "Choice",
"config": {
"conditions": [],
"default": 0.6,
"name": "momentum",
"ordered": true,
"values": [
0.6
]
}
},
{
"class_name": "Choice",
"config": {
"conditions": [],
"default": 0.001,
"name": "learning_rate",
"ordered": true,
"values": [
0.001
]
}
},
{
"class_name": "Choice",
"config": {
"conditions": [],
"default": "LSTM",
"name": "mode",
"ordered": false,
"values": [
"LSTM"
]
}
},
{
"class_name": "Choice",
"config": {
"conditions": [],
"default": "elu",
"name": "activation_rnn",
"ordered": false,
"values": [
"elu"
]
}
},
{
"class_name": "Choice",
"config": {
"conditions": [],
"default": "sigmoid",
"name": "recurrent_activation_rnn",
"ordered": false,
"values": [
"sigmoid"
]
}
},
{
"class_name": "Choice",
"config": {
"conditions": [],
"default": "elu",
"name": "activation_dense",
"ordered": false,
"values": [
"elu"
]
}
},
{
"class_name": "Choice",
"config": {
"conditions": [],
"default": "categorical_crossentropy",
"name": "loss",
"ordered": false,
"values": [
"categorical_crossentropy"
]
}
}
],
"values": {
"activation_dense": "elu",
"activation_rnn": "elu",
"dropout": 0.2,
"dropout_dense": 0.1,
"learning_rate": 0.001,
"loss": "categorical_crossentropy",
"mode": "LSTM",
"momentum": 0.6,
"n_dense": 48,
"n_hidden": 64,
"recurrent_activation_rnn": "sigmoid"
}
},
"max_epochs": 20,
"min_epochs": 1,
"ongoing_trials": {
"tuner0": "443291ecac85d951672d74098aba5d80"
},
"seed": 9306,
"seed_state": 9438,
"tried_so_far": [
"999ad17860a2835f685acaffad22a8e9",
"83912e964572c3e5471b9c1956ac8fc4",
"c53014e5e1523fa1499a3162f6f21221",
"04b5f4035d0d8e3eeee393bd63563f5a",
"3697a835f3f1144b3b6120d9c11521eb",
"43647cb41652f1ec535a14a749d731e9",
"88d5a1ad41730b8854f697f9efbd68ca",
"b5357efc9ad057e78cd58b8aa2861dc9",
"4973018c5f6b84338d88249fc851f8d5",
"7c86520a8761f167ee528223d9450c58",
"9a6e1847de8a317f7e107c96a74ebd88",
"1a7559c42715ecc6df85a76a7cb10e64"
]
}
Issue Analytics
- State:
- Created 3 years ago
- Reactions:1
- Comments:10 (5 by maintainers)
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It only saves the best epoch per model training now.
I don’t know about the internals of Keras Tuner, but I feel save-points remain of trails (or points of trails), which will never be touched again. But I don’t know how to select those save-points and remove them.