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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: 0.05 | |
| test_ratio: 0.15 | |
| #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} | |