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config_old.yml
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config_old.yml
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Job:
run_mode: "Training"
#{Training, Predict, Repeat, CV, Hyperparameter, Ensemble, Analysis}
Training:
job_name: "my_train_job"
reprocess: "False"
model: CGCNN_demo
load_model: "False"
save_model: "True"
model_path: "my_model.pth"
write_output: "True"
parallel: "True"
#seed=0 means random initalization
seed: 0
Predict:
job_name: "my_predict_job"
reprocess: "False"
model_path: "my_model.pth"
write_output: "True"
seed: 0
Repeat:
job_name: "my_repeat_job"
reprocess: "False"
model: CGCNN_demo
model_path: "my_model.pth"
write_output: "False"
parallel: "True"
seed: 0
###specific options
#number of repeat trials
repeat_trials: 5
CV:
job_name: "my_CV_job"
reprocess: "False"
model: CGCNN_demo
write_output: "True"
parallel: "True"
seed: 0
###specific options
#number of folds for n-fold CV
cv_folds: 5
Hyperparameter:
job_name: "my_hyperparameter_job"
reprocess: "False"
model: CGCNN_demo
seed: 0
###specific options
hyper_trials: 10
#number of concurrent trials (can be greater than number of GPUs)
hyper_concurrency: 8
#frequency of checkpointing and update (default: 1)
hyper_iter: 1
#resume a previous hyperparameter optimization run
hyper_resume: "True"
#Verbosity of ray tune output; available: (1, 2, 3)
hyper_verbosity: 1
#Delete processed datasets
hyper_delete_processed: "True"
Ensemble:
job_name: "my_ensemble_job"
reprocess: "False"
save_model: "False"
model_path: "my_model.pth"
write_output: "Partial"
parallel: "True"
seed: 0
###specific options
#List of models to use: (Example: "CGCNN_demo,MPNN_demo,SchNet_demo,MEGNet_demo" or "CGCNN_demo,CGCNN_demo,CGCNN_demo,CGCNN_demo")
ensemble_list: "CGCNN_demo,CGCNN_demo,CGCNN_demo,CGCNN_demo,CGCNN_demo"
Analysis:
job_name: "my_job"
reprocess: "False"
model: CGCNN_demo
model_path: "my_model.pth"
write_output: "True"
seed: 0
Processing:
#Whether to use "inmemory" or "large" format for pytorch-geometric dataset. Reccomend inmemory unless the dataset is too large
dataset_type: "inmemory"
#Path to data files
data_path: "/data"
#Path to target file within data_path
target_path: "targets.csv"
#Method of obtaining atom idctionary: available:(provided, default, blank, generated)
dictionary_source: "default"
#Path to atom dictionary file within data_path
dictionary_path: "atom_dict.json"
#Format of data files (limit to those supported by ASE)
data_format: "json"
#Print out processing info
verbose: "True"
#graph specific settings
graph_max_radius : 8.0
graph_max_neighbors : 12
voronoi: "False"
edge_features: "True"
graph_edge_length : 50
#SM specific settings
SM_descriptor: "False"
#SOAP specific settings
SOAP_descriptor: "False"
SOAP_rcut : 8.0
SOAP_nmax : 6
SOAP_lmax : 4
SOAP_sigma : 0.3
Training:
#Index of target column in targets.csv
target_index: 0
#Loss functions (from pytorch) examples: l1_loss, mse_loss, binary_cross_entropy
loss: "l1_loss"
#Ratios for train/val/test split out of a total of 1
train_ratio: 0.8
val_ratio: 0.1
test_ratio: 0.1
#Training print out frequency (print per n number of epochs)
verbosity: 5
Models:
CGCNN_demo:
model: CGCNN
dim1: 100
dim2: 150
pre_fc_count: 1
gc_count: 4
post_fc_count: 3
pool: "global_mean_pool"
pool_order: "early"
batch_norm: "True"
batch_track_stats: "True"
act: "relu"
dropout_rate: 0.0
epochs: 250
lr: 0.002
batch_size: 100
optimizer: "AdamW"
optimizer_args: {}
scheduler: "ReduceLROnPlateau"
scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002}
MPNN_demo:
model: MPNN
dim1: 100
dim2: 100
dim3: 100
pre_fc_count: 1
gc_count: 4
post_fc_count: 3
pool: "global_mean_pool"
pool_order: "early"
batch_norm: "True"
batch_track_stats: "True"
act: "relu"
dropout_rate: 0.0
epochs: 250
lr: 0.001
batch_size: 100
optimizer: "AdamW"
optimizer_args: {}
scheduler: "ReduceLROnPlateau"
scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002}
SchNet_demo:
model: SchNet
dim1: 100
dim2: 100
dim3: 150
cutoff: 8
pre_fc_count: 1
gc_count: 4
post_fc_count: 3
pool: "global_mean_pool"
pool_order: "early"
batch_norm: "True"
batch_track_stats: "True"
act: "relu"
dropout_rate: 0.0
epochs: 250
lr: 0.0005
batch_size: 100
optimizer: "AdamW"
optimizer_args: {}
scheduler: "ReduceLROnPlateau"
scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002}
MEGNet_demo:
model: MEGNet
dim1: 100
dim2: 100
dim3: 100
pre_fc_count: 1
gc_count: 4
gc_fc_count: 1
post_fc_count: 3
pool: "global_mean_pool"
pool_order: "early"
batch_norm: "True"
batch_track_stats: "True"
act: "relu"
dropout_rate: 0.0
epochs: 250
lr: 0.0005
batch_size: 100
optimizer: "AdamW"
optimizer_args: {}
scheduler: "ReduceLROnPlateau"
scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002}
GCN_demo:
model: GCN
dim1: 100
dim2: 150
pre_fc_count: 1
gc_count: 4
post_fc_count: 3
pool: "global_mean_pool"
pool_order: "early"
batch_norm: "True"
batch_track_stats: "True"
act: "relu"
dropout_rate: 0.0
epochs: 250
lr: 0.002
batch_size: 100
optimizer: "AdamW"
optimizer_args: {}
scheduler: "ReduceLROnPlateau"
scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002}
SM_demo:
model: SM
dim1: 100
fc_count: 2
epochs: 200
lr: 0.002
batch_size: 100
optimizer: "AdamW"
optimizer_args: {}
scheduler: "ReduceLROnPlateau"
scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002}
SOAP_demo:
model: SOAP
dim1: 100
fc_count: 2
epochs: 200
lr: 0.002
batch_size: 100
optimizer: "AdamW"
optimizer_args: {}
scheduler: "ReduceLROnPlateau"
scheduler_args: {"mode":"min", "factor":0.8, "patience":10, "min_lr":0.00001, "threshold":0.0002}