Clean up configs.

PiperOrigin-RevId: 306883491

official/vision/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b0-gpu.yaml

@@ -3,8 +3,6 @@
 # Reaches ~76.1% within 350 epochs.
 # Note: This configuration uses a scaled per-replica batch size based on the number of devices.
 runtime:
-  model_dir: null
-  mode: 'train_and_eval'
   distribution_strategy: 'mirrored'
   num_gpus: 1
 train_dataset:

official/vision/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b0-tpu.yaml

@@ -3,8 +3,6 @@
 # Reaches ~76.1% within 350 epochs.
 # Note: This configuration uses a scaled per-replica batch size based on the number of devices.
 runtime:
-  model_dir: null
-  mode: 'train_and_eval'
   distribution_strategy: 'tpu'
 train_dataset:
   name: 'imagenet2012'

official/vision/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b1-gpu.yaml

 # Note: This configuration uses a scaled per-replica batch size based on the number of devices.
 runtime:
-  model_dir: null
-  mode: 'train_and_eval'
   distribution_strategy: 'mirrored'
   num_gpus: 1
 train_dataset:

official/vision/image_classification/configs/examples/efficientnet/imagenet/efficientnet-b1-tpu.yaml

@@ -2,8 +2,6 @@
 # Takes ~3 minutes, 15 seconds per epoch for v3-32.
 # Note: This configuration uses a scaled per-replica batch size based on the number of devices.
 runtime:
-  model_dir: null
-  mode: 'train_and_eval'
   distribution_strategy: 'tpu'
 train_dataset:
   name: 'imagenet2012'

official/vision/image_classification/configs/examples/resnet/imagenet/gpu.yaml

@@ -2,8 +2,6 @@
 # Reaches > 76.1% within 90 epochs.
 # Note: This configuration uses a scaled per-replica batch size based on the number of devices.
 runtime:
-  model_dir: null
-  mode: 'train_and_eval'
   distribution_strategy: 'mirrored'
   num_gpus: 1
 train_dataset:
@@ -33,7 +31,7 @@ validation_dataset:
   mean_subtract: True
   standardize: True
 model:
-  model_name: 'resnet'
+  name: 'resnet'
   model_params:
     rescale_inputs: False
   optimizer:

official/vision/image_classification/configs/examples/resnet/imagenet/tpu.yaml

@@ -3,8 +3,6 @@
 # Reaches > 76.1% within 90 epochs.
 # Note: This configuration uses a scaled per-replica batch size based on the number of devices.
 runtime:
-  model_dir: null
-  mode: 'train_and_eval'
   distribution_strategy: 'tpu'
 train_dataset:
   name: 'imagenet2012'
@@ -35,7 +33,7 @@ validation_dataset:
   standardize: True
   dtype: 'bfloat16'
 model:
-  model_name: 'resnet'
+  name: 'resnet'
   model_params:
     rescale_inputs: False
   optimizer: