deploy: 3f695ef

FAIR-Chem · Dec 4, 2024 · 7763ea3 · 7763ea3
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diff --git a/_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt b/_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt
diff --git a/_downloads/819e10305ddd6839cd7da05935b17060/mass-inference.txt b/_downloads/819e10305ddd6839cd7da05935b17060/mass-inference.txt
@@ -1,6 +1,6 @@
-2024-11-19 06:22:11 (INFO): Running in local mode without elastic launch (single gpu only)
-2024-11-19 06:22:11 (INFO): Setting env PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
-2024-11-19 06:22:11 (INFO): Project root: /home/runner/work/fairchem/fairchem/src/fairchem
+2024-12-04 00:17:00 (INFO): Running in local mode without elastic launch (single gpu only)
+2024-12-04 00:17:00 (INFO): Setting env PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+2024-12-04 00:17:00 (INFO): Project root: /home/runner/work/fairchem/fairchem/src/fairchem
 /home/runner/work/fairchem/fairchem/src/fairchem/core/models/escn/so3.py:23: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
   _Jd = torch.load(os.path.join(os.path.dirname(__file__), "Jd.pt"))
 /home/runner/work/fairchem/fairchem/src/fairchem/core/models/scn/spherical_harmonics.py:23: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
@@ -15,17 +15,17 @@
   @torch.cuda.amp.autocast(enabled=False)
 /home/runner/work/fairchem/fairchem/src/fairchem/core/models/equiformer_v2/layer_norm.py:357: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
   @torch.cuda.amp.autocast(enabled=False)
-2024-11-19 06:22:12 (INFO): amp: false
+2024-12-04 00:17:01 (INFO): amp: false
 cmd:
-  checkpoint_dir: /home/runner/work/fairchem/fairchem/docs/core/checkpoints/2024-11-19-06-21-52
-  commit: aa298ac
+  checkpoint_dir: /home/runner/work/fairchem/fairchem/docs/core/checkpoints/2024-12-04-00-17-04
+  commit: 3f695ef
   identifier: ''
-  logs_dir: /home/runner/work/fairchem/fairchem/docs/core/logs/tensorboard/2024-11-19-06-21-52
+  logs_dir: /home/runner/work/fairchem/fairchem/docs/core/logs/tensorboard/2024-12-04-00-17-04
   print_every: 10
-  results_dir: /home/runner/work/fairchem/fairchem/docs/core/results/2024-11-19-06-21-52
+  results_dir: /home/runner/work/fairchem/fairchem/docs/core/results/2024-12-04-00-17-04
   seed: 0
-  timestamp_id: 2024-11-19-06-21-52
-  version: 0.1.dev1+gaa298ac
+  timestamp_id: 2024-12-04-00-17-04
+  version: 1.3.0
 dataset: {}
 evaluation_metrics:
   metrics:
@@ -127,24 +127,24 @@ test_dataset:
 trainer: ocp
 val_dataset: {}
 
-2024-11-19 06:22:12 (INFO): Loading model: gemnet_t
-2024-11-19 06:22:13 (INFO): Loaded GemNetT with 31671825 parameters.
-2024-11-19 06:22:13 (WARNING): log_summary for Tensorboard not supported
-2024-11-19 06:22:13 (WARNING): Could not find dataset metadata.npz files in '[PosixPath('data.db')]'
-2024-11-19 06:22:13 (WARNING): Disabled BalancedBatchSampler because num_replicas=1.
-2024-11-19 06:22:13 (WARNING): Failed to get data sizes, falling back to uniform partitioning. BalancedBatchSampler requires a dataset that has a metadata attributed with number of atoms.
-2024-11-19 06:22:13 (INFO): rank: 0: Sampler created...
-2024-11-19 06:22:13 (INFO): Created BalancedBatchSampler with sampler=<fairchem.core.common.data_parallel.StatefulDistributedSampler object at 0x7f0541247e30>, batch_size=16, drop_last=False
-2024-11-19 06:22:13 (INFO): Attemping to load user specified checkpoint at /tmp/fairchem_checkpoints/gndt_oc22_all_s2ef.pt
-2024-11-19 06:22:13 (INFO): Loading checkpoint from: /tmp/fairchem_checkpoints/gndt_oc22_all_s2ef.pt
-/home/runner/work/fairchem/fairchem/src/fairchem/core/trainers/base_trainer.py:602: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+2024-12-04 00:17:01 (INFO): Loading model: gemnet_t
+2024-12-04 00:17:02 (INFO): Loaded GemNetT with 31671825 parameters.
+2024-12-04 00:17:02 (WARNING): log_summary for Tensorboard not supported
+2024-12-04 00:17:02 (WARNING): Could not find dataset metadata.npz files in '[PosixPath('data.db')]'
+2024-12-04 00:17:02 (WARNING): Disabled BalancedBatchSampler because num_replicas=1.
+2024-12-04 00:17:02 (WARNING): Failed to get data sizes, falling back to uniform partitioning. BalancedBatchSampler requires a dataset that has a metadata attributed with number of atoms.
+2024-12-04 00:17:02 (INFO): rank: 0: Sampler created...
+2024-12-04 00:17:02 (INFO): Created BalancedBatchSampler with sampler=<fairchem.core.common.data_parallel.StatefulDistributedSampler object at 0x7fb19944d6d0>, batch_size=16, drop_last=False
+2024-12-04 00:17:03 (INFO): Attemping to load user specified checkpoint at /tmp/fairchem_checkpoints/gndt_oc22_all_s2ef.pt
+2024-12-04 00:17:03 (INFO): Loading checkpoint from: /tmp/fairchem_checkpoints/gndt_oc22_all_s2ef.pt
+/home/runner/work/fairchem/fairchem/src/fairchem/core/trainers/base_trainer.py:604: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
   checkpoint = torch.load(checkpoint_path, map_location=map_location)
-2024-11-19 06:22:14 (INFO): Overwriting scaling factors with those loaded from checkpoint. If you're generating predictions with a pretrained checkpoint, this is the correct behavior. To disable this, delete `scale_dict` from the checkpoint. 
-2024-11-19 06:22:14 (WARNING): Scale factor comment not found in model
-2024-11-19 06:22:14 (INFO): Predicting on test.
-device 0:   0%|                                           | 0/3 [00:00<?, ?it/s]/home/runner/work/fairchem/fairchem/src/fairchem/core/trainers/ocp_trainer.py:461: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
+2024-12-04 00:17:03 (INFO): Overwriting scaling factors with those loaded from checkpoint. If you're generating predictions with a pretrained checkpoint, this is the correct behavior. To disable this, delete `scale_dict` from the checkpoint. 
+2024-12-04 00:17:03 (WARNING): Scale factor comment not found in model
+2024-12-04 00:17:03 (INFO): Predicting on test.
+device 0:   0%|                                           | 0/3 [00:00<?, ?it/s]/home/runner/work/fairchem/fairchem/src/fairchem/core/trainers/ocp_trainer.py:471: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
   with torch.cuda.amp.autocast(enabled=self.scaler is not None):
-device 0:  33%|███████████▋                       | 1/3 [00:02<00:05,  2.99s/it]device 0:  67%|███████████████████████▎           | 2/3 [00:06<00:03,  3.32s/it]device 0: 100%|███████████████████████████████████| 3/3 [00:08<00:00,  2.60s/it]device 0: 100%|███████████████████████████████████| 3/3 [00:08<00:00,  2.77s/it]
-2024-11-19 06:22:22 (INFO): Writing results to /home/runner/work/fairchem/fairchem/docs/core/results/2024-11-19-06-21-52/ocp_predictions.npz
-2024-11-19 06:22:22 (INFO): Total time taken: 8.455804347991943
-Elapsed time = 15.8 seconds
+device 0:  33%|███████████▋                       | 1/3 [00:03<00:06,  3.47s/it]device 0:  67%|███████████████████████▎           | 2/3 [00:06<00:03,  3.35s/it]device 0: 100%|███████████████████████████████████| 3/3 [00:08<00:00,  2.56s/it]device 0: 100%|███████████████████████████████████| 3/3 [00:08<00:00,  2.79s/it]
+2024-12-04 00:17:11 (INFO): Writing results to /home/runner/work/fairchem/fairchem/docs/core/results/2024-12-04-00-17-04/ocp_predictions.npz
+2024-12-04 00:17:11 (INFO): Total time taken: 8.537037134170532
+Elapsed time = 15.7 seconds

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diff --git a/_sources/autoapi/core/_cli_hydra/index.rst b/_sources/autoapi/core/_cli_hydra/index.rst
@@ -62,20 +62,23 @@ Module Contents
       the state so that not to pickle it) and change/remove the initial parameters.
 
 
-   .. py:method:: __call__(dict_config: omegaconf.DictConfig, cli_args: argparse.Namespace) -> None
+   .. py:method:: __call__(dict_config: omegaconf.DictConfig) -> None
 
 
-   .. py:method:: checkpoint(*args, **kwargs)
+   .. py:method:: _init_logger() -> None
+
+
+   .. py:method:: checkpoint(*args, **kwargs) -> submitit.helpers.DelayedSubmission
 
       Resubmits the same callable with the same arguments
 
 
 
-.. py:function:: map_cli_args_to_dist_config(cli_args: argparse.Namespace) -> dict
+.. py:function:: map_cli_args_to_dist_config(cli_args: omegaconf.DictConfig) -> dict
 
 .. py:function:: get_hydra_config_from_yaml(config_yml: str, overrides_args: list[str]) -> omegaconf.DictConfig
 
-.. py:function:: runner_wrapper(config: omegaconf.DictConfig, cli_args: argparse.Namespace)
+.. py:function:: runner_wrapper(config: omegaconf.DictConfig)
 
 .. py:function:: main(args: argparse.Namespace | None = None, override_args: list[str] | None = None)
 
diff --git a/_sources/autoapi/core/common/relaxation/ase_utils/index.rst b/_sources/autoapi/core/common/relaxation/ase_utils/index.rst
@@ -17,6 +17,14 @@ core.common.relaxation.ase_utils
 
 
 
+Attributes
+----------
+
+.. autoapisummary::
+
+   core.common.relaxation.ase_utils.ASE_PROP_RESHAPE
+
+
 Classes
 -------
 
@@ -36,31 +44,30 @@ Functions
 Module Contents
 ---------------
 
-.. py:function:: batch_to_atoms(batch)
+.. py:data:: ASE_PROP_RESHAPE
 
-.. py:class:: OCPCalculator(config_yml: str | None = None, checkpoint_path: str | None = None, model_name: str | None = None, local_cache: str | None = None, trainer: str | None = None, cutoff: int = 6, max_neighbors: int = 50, cpu: bool = True, seed: int | None = None)
+.. py:function:: batch_to_atoms(batch: torch_geometric.data.Batch, results: dict[str, torch.Tensor] | None = None, wrap_pos: bool = True, eps: float = 1e-07) -> list[ase.Atoms]
 
-   Bases: :py:obj:`ase.calculators.calculator.Calculator`
+   Convert a data batch to ase Atoms
 
+   :param batch: data batch
+   :param results: dictionary with predicted result tensors that will be added to a SinglePointCalculator. If no results
+                   are given no calculator will be added to the atoms objects.
+   :param wrap_pos: wrap positions back into the cell.
+   :param eps: Small number to prevent slightly negative coordinates from being wrapped.
 
-   Base-class for all ASE calculators.
+   :returns: list of Atoms
 
-   A calculator must raise PropertyNotImplementedError if asked for a
-   property that it can't calculate.  So, if calculation of the
-   stress tensor has not been implemented, get_stress(atoms) should
-   raise PropertyNotImplementedError.  This can be achieved simply by not
-   including the string 'stress' in the list implemented_properties
-   which is a class member.  These are the names of the standard
-   properties: 'energy', 'forces', 'stress', 'dipole', 'charges',
-   'magmom' and 'magmoms'.
 
+.. py:class:: OCPCalculator(config_yml: str | None = None, checkpoint_path: str | pathlib.Path | None = None, model_name: str | None = None, local_cache: str | None = None, trainer: str | None = None, cpu: bool = True, seed: int | None = None)
 
-   .. py:attribute:: implemented_properties
-      :type:  ClassVar[list[str]]
-      :value: ['energy', 'forces']
+   Bases: :py:obj:`ase.calculators.calculator.Calculator`
 
 
-      Properties calculator can handle (energy, forces, ...)
+   ASE based calculator using an OCP model
+
+
+   .. py:attribute:: _reshaped_props
 
 
    .. py:attribute:: config
@@ -72,44 +79,25 @@ Module Contents
    .. py:attribute:: a2g
 
 
+   .. py:attribute:: implemented_properties
+
+      Properties calculator can handle (energy, forces, ...)
+
+
    .. py:method:: load_checkpoint(checkpoint_path: str, checkpoint: dict | None = None) -> None
 
       Load existing trained model
 
       :param checkpoint_path: string
                               Path to trained model
+      :param checkpoint: dict
+                         A pretrained checkpoint dict
 
 
 
-   .. py:method:: calculate(atoms: ase.Atoms, properties, system_changes) -> None
-
-      Do the calculation.
-
-      properties: list of str
-          List of what needs to be calculated.  Can be any combination
-          of 'energy', 'forces', 'stress', 'dipole', 'charges', 'magmom'
-          and 'magmoms'.
-      system_changes: list of str
-          List of what has changed since last calculation.  Can be
-          any combination of these six: 'positions', 'numbers', 'cell',
-          'pbc', 'initial_charges' and 'initial_magmoms'.
-
-      Subclasses need to implement this, but can ignore properties
-      and system_changes if they want.  Calculated properties should
-      be inserted into results dictionary like shown in this dummy
-      example::
-
-          self.results = {'energy': 0.0,
-                          'forces': np.zeros((len(atoms), 3)),
-                          'stress': np.zeros(6),
-                          'dipole': np.zeros(3),
-                          'charges': np.zeros(len(atoms)),
-                          'magmom': 0.0,
-                          'magmoms': np.zeros(len(atoms))}
+   .. py:method:: calculate(atoms: ase.Atoms | torch_geometric.data.Batch, properties, system_changes) -> None
 
-      The subclass implementation should first call this
-      implementation to set the atoms attribute and create any missing
-      directories.
+      Calculate implemented properties for a single Atoms object or a Batch of them.