add examples for minimal finetuna example

.gitignore

@@ -782,3 +782,4 @@ examples/ex1-ase/
 /*.psp8
 /test-1/
 /test-2/
+*.pt

examples/active_learning/example_finetuna_minimal.py

@@ -0,0 +1,54 @@
+"""A minimal example combining active learning library like Finetuna with SPARC
+
+usage
+First download the checkpoint from the url https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_08/s2ef/gemnet_t_direct_h512_all.pt
+
+python example_finetuna_minimal.py
+"""
+import torch
+import os
+import yaml
+from pathlib import Path
+from ase.io.trajectory import Trajectory
+from ase.optimize import BFGS
+from finetuna.ml_potentials.finetuner_ensemble_calc import FinetunerEnsembleCalc
+from finetuna.online_learner.online_learner import OnlineLearner
+import argparse
+from sparc.calculator import SPARC
+
+from ase.build import molecule
+
+cpu = not torch.cuda.is_available()
+curdir = Path(__file__).parent
+config_file = curdir / "ft_config_gemnet_gpu.yml"
+with open(config_file, "r") as fd:
+    configs = yaml.load(fd, Loader=yaml.FullLoader)
+
+checkpoint = os.environ.get("CHECKPOINT_PATH", None)
+if checkpoint is None:
+    # Use default (relative path)
+    checkpoint = curdir / configs["ocp"]["checkpoint_path_list"][0]
+checkpoint = Path(checkpoint)
+
+if not checkpoint.is_file():
+    raise FileNotFoundError("Cannot found the model checkpoint file!")
+
+finetuner = configs["finetuner"]
+finetuner[0].update(cpu=cpu)
+learner = configs["learner"]
+
+ml_potential = FinetunerEnsembleCalc(
+    checkpoint_paths=[checkpoint],
+    mlp_params=finetuner,
+)
+
+
+init_molecule = molecule("H2O", pbc=False, cell=[8, 8, 8])
+
+sparc_params = {"xc": "pbe", "h": 0.22}
+with SPARC(**sparc_params) as parent_calc:
+    onlinecalc = OnlineLearner(learner, [], ml_potential, parent_calc)
+    init_molecule.calc = onlinecalc
+    dyn = BFGS(init_molecule,
+               maxstep=0.2)
+    dyn.run(fmax=0.03)

examples/active_learning/ft_config_gemnet_gpu.yml

@@ -0,0 +1,73 @@
+finetuner:
+- cpu: false
+  optim:
+    batch_size: 1
+    break_below_lr: 1.0e-07
+    checkpoint_every: 100000
+    eps: 1.0e-08
+    eval_every: 1
+    factor: 0.9
+    force_coefficient: 100
+    lr_initial: 0.0003
+    max_epochs: 400
+    num_workers: 0
+    optimizer_params:
+      eps: 1.0e-08
+      weight_decay: 0
+    patience: 3
+    # print_loss_and_lr: true
+    scheduler_loss: train
+    weight_decay: 0
+  task:
+    primary_metric: loss
+  tuner:
+    num_threads: 8
+    unfreeze_blocks:
+    - out_blocks.3.seq_forces
+    - out_blocks.3.scale_rbf_F
+    - out_blocks.3.dense_rbf_F
+    - out_blocks.3.out_forces
+    - out_blocks.2.seq_forces
+    - out_blocks.2.scale_rbf_F
+    - out_blocks.2.dense_rbf_F
+    - out_blocks.2.out_forces
+    - out_blocks.1.seq_forces
+    - out_blocks.1.scale_rbf_F
+    - out_blocks.1.dense_rbf_F
+    - out_blocks.1.out_forces
+learner:
+  dyn_avg_steps: 15
+  dyn_uncertain_tol: 1000000 # Dynamic uncertainty tolerance
+  # fmax_verify_threshold: 0.03 # Fmax threshold for calling VASP single point calculation
+  # initial_points_to_keep: []
+  # logger:
+  #   pca_quantify: true # Log PCA on wandb
+  #   uncertainty_quantify: false
+  num_initial_points: 0  # Number of VASP calls at the beginning of the relaxation
+  partial_fit: true
+  query_every_n_steps: 30 # K-steps querying strategy: query every 100 steps
+  stat_uncertain_tol: 1000000 # Static uncertainty tolerance
+  tolerance_selection: min
+  # valset_system_id: '1498818'
+  # wandb_init:
+  #   entity: ulissi-group
+  #   project: project
+  #   group: group
+  #   name: name
+  #   notes: notes
+  #   wandb_log: false # Wandb disabled
+# optional_config:
+#   links:
+#     traj: /path/to/vasp/reference/traj
+ocp:
+  model_class_list:
+  - gemnet
+  checkpoint_path_list:
+  - "gemnet_t_direct_h512_all.pt"
+# relaxation:
+#   # fmax: 0.03
+#   max_parent_calls: null
+#   maxstep: 0.2
+#   replay_method: parent_only
+#   steps: null
+#   trajname: oal_relaxation.traj