Define device early to make run on Mac possible. Also, include timest…

…amp in run name

notebooks/cvrptw/run.py

@@ -6,6 +6,15 @@
 from rl4co.models.zoo.am import AttentionModel
 from rl4co.utils.trainer import RL4COTrainer
 
+device_str = (
+    "cuda"
+    if torch.cuda.is_available()
+    else "mps"
+    if (torch.backends.mps.is_available() and torch.backends.mps.is_built())
+    else "cpu"
+)
+device = torch.device(device_str)
+
 env_cvrptw = CVRPTWEnv(
     num_loc=30,
     min_loc=0,
@@ -17,6 +26,7 @@
     min_time=0,
     max_time=480,
     scale=True,
+    device=device_str,
 )
 
 env = env_cvrptw
@@ -34,7 +44,7 @@
     ### --- random policy --- ###
     reward, td, actions = rollout(
         env=env,
-        td=env.reset(batch_size=[batch_size]),
+        td=env.reset(batch_size=[batch_size]).to(device),
         policy=random_policy,
         max_steps=1000,
     )
@@ -43,7 +53,7 @@
     env.get_reward(td, actions)
     CVRPTWEnv.check_solution_validity(td, actions)
 
-    env.render(td, actions)
+    # env.render(td, actions)
 
     ### --- AM --- ###
     # Model: default is AM with REINFORCE and greedy rollout baseline
@@ -55,17 +65,22 @@
     )
 
     # Greedy rollouts over untrained model
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     td_init = env.reset(batch_size=[3]).to(device)
     model = model.to(device)
     out = model(td_init.clone(), phase="test", decode_type="greedy", return_actions=True)
 
     ### --- Logging --- ###
+    from datetime import date, datetime
     import wandb
     from lightning.pytorch.loggers import WandbLogger
 
+    date_time_str = datetime.now().strftime("%Y/%m/%d_%H:%M:%S")
+
     wandb.login()
-    logger = WandbLogger(project="routefinder", name="cvrptw-am")
+    logger = WandbLogger(
+        project="routefinder",
+        name=f"cvrptw-am_{date_time_str}",
+    )
 
     ### --- Training --- ###
     # The RL4CO trainer is a wrapper around PyTorch Lightning's `Trainer` class which adds some functionality and more efficient defaults
@@ -81,3 +96,6 @@
 
     ### --- Testing --- ###
     trainer.test(model)
+
+    ### --- Saving --- ###
+    trainer.save_checkpoint(f"data/cvrptw_{date_time_str}.ckpt")