deploy: b90c171

_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt

@@ -1,17 +1,17 @@
-2024-04-14 01:18:42 (INFO): Project root: /home/runner/work/ocp/ocp
+2024-04-14 13:46:21 (INFO): Project root: /home/runner/work/ocp/ocp
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-2024-04-14 01:18:44 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
-2024-04-14 01:18:44 (INFO): amp: true
+2024-04-14 13:46:23 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+2024-04-14 13:46:23 (INFO): amp: true
 cmd:
-  checkpoint_dir: fine-tuning/checkpoints/2024-04-14-01-18-56-ft-oxides
-  commit: 42bd551
+  checkpoint_dir: fine-tuning/checkpoints/2024-04-14-13-45-36-ft-oxides
+  commit: b90c171
   identifier: ft-oxides
-  logs_dir: fine-tuning/logs/wandb/2024-04-14-01-18-56-ft-oxides
+  logs_dir: fine-tuning/logs/wandb/2024-04-14-13-45-36-ft-oxides
   print_every: 10
-  results_dir: fine-tuning/results/2024-04-14-01-18-56-ft-oxides
+  results_dir: fine-tuning/results/2024-04-14-13-45-36-ft-oxides
   seed: 0
-  timestamp_id: 2024-04-14-01-18-56-ft-oxides
+  timestamp_id: 2024-04-14-13-45-36-ft-oxides
 dataset:
   a2g_args:
     r_energy: true

_sources/core/inference.md

@@ -47,11 +47,6 @@ with ase.db.connect('full_data.db') as full_db:
 
 You have to choose a checkpoint to start with. The newer checkpoints may require too much memory for this environment. 
 
-```{code-cell} ipython3
-import torch
-torch.set_num_threads(4)
-```
-
 ```{code-cell} ipython3
 from ocpmodels.models.model_registry import available_pretrained_models
 print(available_pretrained_models)

_sources/tutorials/NRR/NRR_example.md

@@ -18,10 +18,6 @@ Using OCP to enumerate adsorbates on alloy catalyst surfaces
 
 In the previous example, we constructed slab models of adsorbates on desired sites. Here we leverage code to automate this process. The goal in this section is to generate candidate structures, compute energetics, and then filter out the most relevant ones.
 
-```{code-cell} ipython3
-import torch
-torch.set_num_threads(4)
-```
 
 ```{code-cell} ipython3
 from ocpmodels.common.relaxation.ase_utils import OCPCalculator
@@ -433,7 +429,3 @@ ax2.set_ylabel("dE *H literature [eV]")
 f.set_figwidth(15)
 f.set_figheight(7)
 ```
-
-# Next steps
-
-Next we consider [fine-tuning](../fine-tuning/fine-tuning-oxides).

_sources/tutorials/advanced/embeddings.md

@@ -49,14 +49,15 @@ import os
 checkpoint_path = model_name_to_local_file('GemNet-OCOC20+OC22', local_cache='/tmp/ocp_checkpoints/')
 
 calc = OCPCalculator(checkpoint_path=checkpoint_path)
+calc.trainer._unwrapped_model.return_embedding = True
+
 ```
 
 ## Bulk Cu equation of state example
 
 Here we simply compute an equation of state by varying the lattice constant. You will see a small unphysical feature near 3.7 angstroms. We will investigate why that happens.
 
 ```{code-cell} ipython3
-#calc.trainer._unwrapped_model.return_embedding = False
 a0 = 3.63
 E = []
 
@@ -78,7 +79,7 @@ plt.xlabel('Lattice constant (A)')
 plt.ylabel('Energy (eV)');
 ```
 
-Something is a little off in this equation of state, there is an unphysical bump in it. We now rerun this and get the embeddings. You simply call the `calc.embed` method. We need a reference configuration to compare too. We choose a lattice constant of 3.63 angstroms and compute three different embeddings.
+Something is a little off in this equation of state, there is an unphysical bump in it. We now rerun this and get the embeddings. You simply set `calc.trainer._unwrapped_model.return_embedding = True`  and the calculator will return useful embeddings if possible (e.g. for GemNet-OC). We need a reference configuration to compare too. We choose a lattice constant of 3.63 angstroms and compute three different embeddings.
 
 ```{code-cell} ipython3
 a0 = 3.63
@@ -91,9 +92,9 @@ atoms = atoms.repeat((2, 2, 2))
 atoms.set_tags(np.ones(len(atoms)))
 atoms.calc = calc
 
-out = calc.embed(atoms)
+atoms.get_potential_energy()
 
-x1, x2, x3 = out['h sum'], out['x_E sum'], out['x_F sum']
+x1, x2, x3 = atoms.calc.results['h sum'], atoms.calc.results['x_E sum'], atoms.calc.results['x_F sum']
 ```
 
 Next, we loop over a grid of lattice constants, and we compare the cosine similarity of the embeddings for each one to the reference embeddings above. A similarity of 1 means they are the same, and as the similarity decreases it means the embbedings are more and more different (and so is the energy).
@@ -111,12 +112,12 @@ for a in LC:
                  pbc=True)
     atoms = atoms.repeat((2, 2, 2))
     atoms.set_tags(np.ones(len(atoms)))
+    atoms.get_potential_energy()
 
-    out = calc.embed(atoms)
     
-    cossim1.append(torch.cosine_similarity(x1, out["h sum"]).item())
-    cossim2.append(torch.cosine_similarity(x2, out["x_E sum"]).item())
-    cossim3.append(torch.cosine_similarity(x3, out["x_F sum"]).item())
+    cossim1.append(torch.cosine_similarity(x1, atoms.calc.results["h sum"]).item())
+    cossim2.append(torch.cosine_similarity(x2, atoms.calc.results["x_E sum"]).item())
+    cossim3.append(torch.cosine_similarity(x3, atoms.calc.results["x_F sum"]).item())
     E += [out['energy']]
 ```
 
@@ -152,28 +153,29 @@ We use this example to show that we can cluster structures by embedding similari
 from ase.build import bulk
 from ase.cluster import Octahedron
 
-calc.trainer._unwrapped_model.return_embedding = True
-
 embeddings = []
 labels = []
 
 oct = Octahedron('Cu', 2)
 oct.set_tags(np.ones(len(oct)))
+oct.calc = calc
 
 for i in range(20):
     oct.rattle(0.01)
-    embeddings += [calc.embed(oct)['x_E sum'][0].numpy()]
+    oct.get_potential_energy()
+    embeddings += [oct.calc.results['x_E sum'][0].numpy()]
     labels += [0]
 ```
 
 ```{code-cell} ipython3
 b = bulk('Cu')
 b = b.repeat((2, 2, 2))
 b.set_tags(np.ones(len(b)))
+b.calc = calc
 
 for i in range(40):
     b.rattle(0.01)
-    embeddings += [calc.embed(b)['x_E sum'][0].numpy()]
+    embeddings += [b.calc.results['x_E sum'][0].numpy()]
     labels += [1]
 ```
 
@@ -211,26 +213,28 @@ energies = []
 
 oct = Octahedron('Cu', 2)
 oct.set_tags(np.ones(len(oct)))
-
+oct.calc = calc
 for i in range(20):
     oct.rattle(0.01)
-    out = calc.embed(oct)
-    for a in out['h'][0]:
+    oct.get_potential_energy()
+
+    for a in oct.calc.results['h'][0]:
         embeddings += [a.numpy()]
         labels += [0]
-        energies += [out['energy']]
+        energies += [oct.calc.results['energy']]
 
 b = bulk('Cu')
 b = b.repeat((2, 2, 2))
 b.set_tags(np.ones(len(b)))
 
 for i in range(20):
     b.rattle(0.01)
-    out = calc.embed(b)
-    for a in out['h'][0]:
+    b.get_potential_energy()
+
+    for a in b.calc.results['h'][0]:
         embeddings += [a.numpy()]
         labels += [1]
-        energies += [out['energy']]
+        energies += [b.calc.results['energy']]
         
 embeddings = np.array(embeddings)
 
@@ -266,30 +270,35 @@ data = vdict(space='cosine')
 
 ethanol = molecule('CH3CH2OH')
 ethanol.set_tags(np.ones(len(ethanol)))
-ethanol_emb = calc.embed(ethanol)
+ethanol.calc = calc
+ethanol.get_potential_energy()
+
+for i, atom in enumerate(ethanol):
+    data[ethanol.calc.results['x_E'][0][i].numpy()] = [i, ethanol]
+    
 
 methane = molecule('C2H6')
 methane.set_tags(np.ones(len(methane)))
-methane_emb = calc.embed(methane)
+methane.calc = calc
+methane.get_potential_energy()
 
-for i, atom in enumerate(ethanol):
-    data[ethanol_emb['x_E'][0][i].numpy()] = [i, ethanol]
-    
 for i, atom in enumerate(methane):
-    data[methane_emb['x_E'][0][i].numpy()] = [i, methane]
+    data[methane.calc.results['x_E'][0][i].numpy()] = [i, methane]
 ```
 
 Now we construct our "query". We inspect the Atoms object, see that the C atom is the first one, and then extract the embedding for that atom and save it in a variable.
 
 ```{code-cell} ipython3
 methanol = molecule('CH3OH')
 methanol.set_tags(np.ones(len(methanol)))
-methanol_emb = calc.embed(methanol)
+methanol.calc = calc
+methanol.get_potential_energy()
+
 methanol
 ```
 
 ```{code-cell} ipython3
-query = methanol_emb['x_E'][0][0].numpy()
+query = methanol.calc.results['x_E'][0][0].numpy()
 ```
 
 We run our search with the syntax like a dictionary. It returns the closest found match.

autoapi/index.html

@@ -251,7 +251,6 @@
 
 
 
-
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autoapi/ocpmodels/common/data_parallel/index.html

@@ -251,7 +251,6 @@
 
 
 
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@@ -251,7 +251,6 @@
 
 
 
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autoapi/ocpmodels/common/flags/index.html

@@ -251,7 +251,6 @@
 
 
 
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autoapi/ocpmodels/common/gp_utils/index.html

@@ -251,7 +251,6 @@
 
 
 
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autoapi/ocpmodels/common/hpo_utils/index.html

@@ -251,7 +251,6 @@
 
 
 
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autoapi/ocpmodels/common/index.html

@@ -251,7 +251,6 @@
 
 
 
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autoapi/ocpmodels/common/logger/index.html

@@ -251,7 +251,6 @@
 
 
 
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autoapi/ocpmodels/common/registry/index.html

@@ -251,7 +251,6 @@
 
 
 
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autoapi/ocpmodels/common/relaxation/ase_utils/index.html

@@ -251,7 +251,6 @@
 
 
 
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autoapi/ocpmodels/common/relaxation/index.html

@@ -251,7 +251,6 @@
 
 
 
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autoapi/ocpmodels/common/relaxation/ml_relaxation/index.html

@@ -251,7 +251,6 @@
 
 
 
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autoapi/ocpmodels/common/relaxation/optimizers/index.html

@@ -251,7 +251,6 @@
 
 
 
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@@ -251,7 +251,6 @@
 
 
 
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@@ -253,7 +253,6 @@
 
 
 
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@@ -251,7 +251,6 @@
 
 
 
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@@ -251,7 +251,6 @@
 
 
 
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@@ -251,7 +251,6 @@
 
 
 
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