| from recover_schedules import recover_schedules |
|
|
| params = [ |
| ( |
| {"nb_jobs": nb_jobs, "nb_machines": nb_machines, "top_k": top_k, "wd": wd}, |
| { |
| "testing": False, |
| "seed": 97, |
| "init_mode": "random", |
| "n_optimization_steps": 2000, |
| "epsilon": 0.01, |
| "nb_sinkhorn_iters": 40, |
| "decay_ls": False, |
| "ls_partitions_ratios": [0.66], |
| "init_ls": 2, |
| "max_ls": 10, |
| "min_ls": 0.1, |
| "ls_warmup_iters_ratio": 0.2, |
| "ls_decay_iters_ratio": 0.90, |
| "decay_lr": True, |
| "lr_partitions_ratios": [0.66], |
| "init_lr": 1e-2, |
| "max_lr": 1e-1, |
| "min_lr": 1e-4, |
| "lr_warmup_iters_ratio": 0.1, |
| "lr_decay_iters_ratio": 0.90, |
| "checkpoint_interval": 1, |
| "data_dir": f"../datasets/exhaustive_{nb_jobs}_{nb_machines}/top_{top_k}", |
| "model_dir": f"../datasets/exhaustive_{nb_jobs}_{nb_machines}/top_{top_k}/train_Sm_Wd{wd}", |
| "output_dir": f"../datasets/exhaustive_{nb_jobs}_{nb_machines}/top_{top_k}/train_Sm_Wd{wd}/recover" |
| }, |
| ) |
| for nb_jobs in [7, 8, 9] |
| for nb_machines in [2, 3, 4, 5, 6] |
| for top_k in [0, 1, 2, 3, 4] |
| for wd in ["1e-0", "5e-0", "10e-0"] |
| ] |
|
|
| for config, param in params: |
| print("\n") |
| print("="*80) |
| print(config) |
| print("="*80) |
| recover_schedules(**param) |
