@@ -22,7 +22,7 @@ training_hyperparams:
   resume: ${resume}
 
 
-model_checkpoints_location: local
+
 ckpt_root_dir:
 
 architecture: resnet18_cifar

@@ -78,7 +78,7 @@ checkpoint_params:
 
 experiment_name: ${architecture}_cityscapes
 
-model_checkpoints_location: local
+
 ckpt_root_dir:
 
 multi_gpu: DDP

@@ -44,7 +44,7 @@ arch_params:
   strict_load: no_key_matching
 
 load_checkpoint: False
-model_checkpoints_location: local
+
 ckpt_root_dir:
 
 resume: False

@@ -24,7 +24,7 @@ checkpoint_params:
 architecture: stdc1_seg
 experiment_name: ${architecture}_cityscapes
 
-model_checkpoints_location: local
+
 ckpt_root_dir:
 
 multi_gpu: DDP

@@ -34,7 +34,7 @@ val_dataloader: coco2017_val
 architecture: ssd_lite_mobilenet_v2
 
 data_loader_num_workers: 8
-model_checkpoints_location: local
+
 experiment_suffix: res${dataset_params.train_image_size}
 experiment_name: ${architecture}_coco_${experiment_suffix}
 

@@ -40,7 +40,7 @@ defaults:
 train_dataloader: coco2017_train
 val_dataloader: coco2017_val
 
-model_checkpoints_location: local
+
 
 load_checkpoint: False
 resume: False

@@ -9,7 +9,7 @@
 #   0. Make sure that the data is stored in dataset_params.dataset_dir or add "dataset_params.data_dir=<PATH-TO-DATASET>" at the end of the command below (feel free to check ReadMe)
 #   1. Move to the project root (where you will find the ReadMe and src folder)
 #   2. Run the command:
-#       python src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco_segmentation_shelfnet_lw --model_checkpoints_location=<checkpoint-location>
+#       python src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco_segmentation_shelfnet_lw
 
 
 # /!\ THIS RECIPE IS NOT SUPPORTED AT THE MOMENT /!\

@@ -27,7 +27,7 @@ training_hyperparams:
 
 experiment_name: efficientnet_b0_imagenet
 
-model_checkpoints_location: local
+
 ckpt_root_dir:
 
 multi_gpu: DDP

@@ -25,7 +25,7 @@ arch_params:
 
 data_loader_num_workers: 8
 
-model_checkpoints_location: local
+
 resume: False
 training_hyperparams:
   resume: ${resume}

@@ -8,7 +8,7 @@ defaults:
 train_dataloader: imagenet_train
 val_dataloader: imagenet_val
 
-model_checkpoints_location: local
+
 resume: False
 training_hyperparams:
   resume: ${resume}

@@ -36,7 +36,7 @@ arch_params:
 train_dataloader: imagenet_train
 val_dataloader: imagenet_val
 
-model_checkpoints_location: local
+
 load_checkpoint: False
 resume: False
 training_hyperparams:

@@ -25,7 +25,7 @@ train_dataloader: imagenet_train
 val_dataloader: imagenet_val
 
 
-model_checkpoints_location: local
+
 resume: False
 training_hyperparams:
   resume: ${resume}

@@ -24,7 +24,7 @@ arch_params:
 train_dataloader: imagenet_train
 val_dataloader: imagenet_val
 
-model_checkpoints_location: local
+
 resume: False
 training_hyperparams:
   resume: ${resume}

@@ -66,7 +66,7 @@ student_checkpoint_params:
   pretrained_weights: # a string describing the dataset of the pretrained weights (for example "imagenent").
 
 
-model_checkpoints_location: local
+
 
 
 run_teacher_on_eval: True

@@ -21,7 +21,7 @@ defaults:
 train_dataloader: imagenet_train
 val_dataloader: imagenet_val
 
-model_checkpoints_location: local
+
 
 resume: False
 training_hyperparams:

@@ -1,4 +1,6 @@
 resume: False # whether to continue training from ckpt with the same experiment name.
+resume_path: # Explicit checkpoint path (.pth file) to use to resume training.
+ckpt_name: ckpt_latest.pth  # The checkpoint (.pth file) filename in CKPT_ROOT_DIR/EXPERIMENT_NAME/ to use when resume=True and resume_path=None
 lr_mode: # Learning rate scheduling policy, one of ['step','poly','cosine','function']
 lr_schedule_function: # Learning rate scheduling function to be used when `lr_mode` is 'function'.
 lr_warmup_epochs: 0 # number of epochs for learning rate warm up - see https://arxiv.org/pdf/1706.02677.pdf (Section 2.2).