Feat/get recent

README.md

@@ -41,18 +41,14 @@ We show that this approach is not only sufficiently expressive to reproduce lega
             * `polynomial.py` higher order polynomials.
 
 
-# Example: Deploy espaloma 0.2.0 pretrained force field to arbitrary MM system
+# Example: Deploy latest espaloma pretrained force field to arbitrary MM system
 
 ```python  
 # imports
 import os
 import torch
 import espaloma as esp
 
-# grab pretrained model
-if not os.path.exists("espaloma_model.pt"):
-    os.system("wget http://data.wangyq.net/espaloma_model.pt")
-
 # define or load a molecule of interest via the Open Force Field toolkit
 from openff.toolkit.topology import Molecule
 molecule = Molecule.from_smiles("CN1C=NC2=C1C(=O)N(C(=O)N2C)C")
@@ -61,7 +57,7 @@ molecule = Molecule.from_smiles("CN1C=NC2=C1C(=O)N(C(=O)N2C)C")
 molecule_graph = esp.Graph(molecule)
 
 # apply a trained espaloma model to assign parameters
-espaloma_model = torch.load("espaloma_model.pt")
+espaloma_model = esp.get_latest_model()
 espaloma_model(molecule_graph.heterograph)
 
 # create an OpenMM System for the specified molecule

espaloma/__init__.py

@@ -8,6 +8,7 @@
 from .graphs.graph import Graph
 from .metrics import GraphMetric
 from .mm.geometry import *
+from .graphs.deploy import get_latest_model
 
 # Add imports here
 # import espaloma

espaloma/graphs/deploy.py

@@ -24,10 +24,16 @@
 OPENMM_BOND_K_UNIT = OPENMM_ENERGY_UNIT / (OPENMM_LENGTH_UNIT**2)
 OPENMM_ANGLE_K_UNIT = OPENMM_ENERGY_UNIT / (OPENMM_ANGLE_UNIT**2)
 
+LATEST_URL = "https://github.com/choderalab/espaloma/"
+"releases/latest/download/espaloma-latest.pt"
+
 # =============================================================================
 # MODULE FUNCTIONS
 # =============================================================================
-
+def get_latest_model():
+    model = torch.utils.model_zoo.load_url(LATEST_URL, map_location="cpu")
+    model.eval()
+    return model
 
 def load_forcefield(forcefield="openff_unconstrained-2.0.0"):
     # get a forcefield

espaloma/graphs/tests/test_deploy.py

@@ -22,15 +22,12 @@ def test_butane_charge_nn():
     the nn charge method"""
     import torch
     # Download serialized espaloma model
-    url = f'https://github.com/choderalab/espaloma/releases/download/0.3.0/espaloma-0.3.0rc1.pt'
-    espaloma_model_filepath = f'espaloma-0.3.0rc1.pt'
-    urllib.request.urlretrieve(url, filename=espaloma_model_filepath)
     # Test deployment
     ff = esp.graphs.legacy_force_field.LegacyForceField("openff-1.2.0")
     g = esp.Graph("CCCC")
     g = ff.parametrize(g)
     # apply a trained espaloma model to assign parameters
-    net = torch.load(espaloma_model_filepath, map_location=torch.device('cpu'))
+    net = esp.get_latest_model()
     net.eval()
     net(g.heterograph)
     esp.graphs.deploy.openmm_system_from_graph(g, suffix="_ref", charge_method="nn")