-

benchmarks/torchvision/main.py

@@ -69,7 +69,7 @@ class SyntheticData:
     def __init__(self, model, device, batch_size, n, fixed_batch):
         self.n = n
         model = model.to(device)
-        
+
         self.inp = torch.randn((batch_size, 3, 224, 224), device=device)
         self.out = torch.rand_like(model(self.inp))
         self.fixed_batch = fixed_batch
@@ -184,7 +184,8 @@ def dataloader(args, model):
     )
 
 def model_optimizer(args, model, device):
-    model.train()
+    if hasattr(model, "train"):
+        model.train()
 
     if "channel_last" in args.optim:
         model = model.to(memory_format=torch.channels_last)
@@ -336,7 +337,8 @@ def iobench(args):
 
 
 def train_epoch(args, model, criterion, optimizer, loader, device, dtype, scaler=None):
-    model.train()
+    if hasattr(model, "train"):
+        model.train()
 
     set_to_none = "set_grad_none" in args.optim
 

config/resnet50.yaml

@@ -53,33 +53,49 @@ resnet-matrix-noio:
       --fixed-batch: true
 
 
-resnet18-matrix:
+# resnet18-matrix:
+#   matrix:
+#     worker: [8]
+#     batch-size: [256, 512, 1024]
+#     loader: ['pytorch', 'dali']
+
+#   job:
+#     name: 'resnet18-w{worker}-bs{batch-size}-{loader}'
+#     inherits: _resnet50
+#     argv:
+#       --model: resnet18
+#       --batch-size: '{batch-size}'
+#       --num-workers: '{worker}'
+#       --loader: '{loader}'
+
+resnet-optim:
   matrix:
     worker: [8]
-    batch-size: [256, 512, 1024]
-    loader: ['pytorch', 'dali']
+    batch-size: [256]
+    loader: ['dali']
+    optim: ["trace", "inductor", "script", "channel_last", "set_grad_none"]
 
   job:
-    name: 'resnet18-w{worker}-bs{batch-size}-{loader}'
+    name: 'resnet50-w{worker}-bs{batch-size}-{loader}-{optim}'
     inherits: _resnet50
     argv:
-      --model: resnet18
       --batch-size: '{batch-size}'
       --num-workers: '{worker}'
       --loader: '{loader}'
+      --optim: '{optim}'
 
-resnet-optim:
+
+image-loading:
   matrix:
-    worker: [8]
-    batch-size: [256]
-    loader: ['dali']
-    optim: ["trace", "inductor", "script", "channel_last", "set_grad_none"]
+    worker: [2, 4, 8, 16, 24, 32, 64]
+    batch-size: [32, 64, 128, 256, 512, 1024]
+    loader: ['pytorch', 'dali']
 
   job:
-    name: 'resnet50-w{worker}-bs{batch-size}-{loader}-{optim}'
+    name: 'imageloading-w{worker}-bs{batch-size}-{loader}'
     inherits: _resnet50
     argv:
       --batch-size: '{batch-size}'
       --num-workers: '{worker}'
       --loader: '{loader}'
-      --optim: '{optim}'
+      --iobench: true