optim_alg="SGD",
learning_rate=0.1,
lr_scheduler="MultiStepLR",
lr_milestones=[60, 120, 160],
lr_gamma=0.2,
weight_decay=0.0005,
momentum=0.9,
nesterov_momentum=True,
# ---- optimizer related
hebbian_prune_perc=None,
weight_prune_perc=0.2,
pruning_early_stop=2,
hebbian_grow=False,
)
# run
tune_config = dict(
name="comparison_iterative_pruning_2",
num_samples=1,
local_dir=os.path.expanduser("~/nta/results"),
checkpoint_freq=0,
checkpoint_at_end=False,
resources_per_trial={
"cpu": 1,
"gpu": 1
},
verbose=2,
)
run_ray(tune_config, exp_config, fix_seed=True)
lr_scheduler="StepLR",
lr_step_size=1,
lr_gamma=0.9825,
# ---- Model ----
model=ray.tune.grid_search(["StochasticSynapsesModel"]),
# debug:
use_tqdm=True,
test_noise=False,
debug_weights=True,
debug_sparse=True,
)
# ray configurations
tune_config = dict(
name=os.path.basename(__file__).replace(".py", ""),
num_samples=1,
local_dir=os.path.expanduser("~/nta/results"),
checkpoint_freq=0,
checkpoint_at_end=False,
stop={"training_iteration": 100},
resources_per_trial={
# 1 GPU per trial
"cpu": os.cpu_count() / cuda_device_count,
"gpu": 1},
loggers=DEFAULT_LOGGERS,
verbose=0,
)
run_ray(tune_config, base_exp_config)
model="SparseModel",
data_dir="~/nta/data",
on_perc=0.2,
batch_size_train=10,
batch_size_test=10,
debug_sparse=True,
name="test2",
)
exp_config["elasticsearch_index"] = __file__.replace(".py", "") + "_eval"
# run
tune_config = dict(
name="test",
num_samples=3,
local_dir=os.path.expanduser("~/nta/results"),
# checkpoint_freq=0,
# checkpoint_at_end=True,
stop={"training_iteration": 1},
resources_per_trial={
"cpu": 1,
"gpu": 1
},
verbose=2,
loggers=DEFAULT_LOGGERS + (ElasticsearchLogger, ),
)
run_ray(tune_config, exp_config)
# df = mlflow.search_runs([experiment_id])