@@ -443,12 +443,12 @@ jobs:
             python3.8 -m pip install -r requirements.txt
             python3.8 -m pip install .
             python3.8 -m pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY600 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY800 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_repvgg dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_resnet50 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_vit_base dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_kd_recipe_example/train_from_kd_recipe.py --config-name=imagenet_resnet50_kd dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=8 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY600 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY800 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_repvgg dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_resnet50 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_vit_base dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_kd_recipe_example/train_from_kd_recipe.py --config-name=imagenet_resnet50_kd dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=8 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
 
       - run:
           name: Remove new environment when failed
@@ -478,12 +478,12 @@ jobs:
             python3.8 -m pip install -r requirements.txt
             python3.8 -m pip install .
             python3.8 -m pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_efficientnet dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_mobilenetv2 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_mobilenetv3_large dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_mobilenetv3_small dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY200 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY400 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_efficientnet dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_mobilenetv2 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_mobilenetv3_large dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_mobilenetv3_small dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY200 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=imagenet_regnetY architecture=regnetY400 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
       - run:
           name: Remove new environment when failed
           command: "rm -r << parameters.sg_new_env_name >>"
@@ -515,18 +515,18 @@ jobs:
             python3.8 -m pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
             wget  -O $(pwd)/checkpoints/ddrnet23_slim_bb_imagenet.pth https://deci-pretrained-models.s3.amazonaws.com/ddrnet/imagenet_pt_backbones/ddrnet23_slim_bb_imagenet.pth
             wget  -O $(pwd)/checkpoints/ddrnet23_bb_imagenet.pth https://deci-pretrained-models.s3.amazonaws.com/ddrnet/imagenet_pt_backbones/ddrnet23_bb_imagenet.pth
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py  --config-name=cityscapes_ddrnet checkpoint_params.checkpoint_path=$(pwd)/checkpoints/ddrnet23_bb_imagenet.pth dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py  --config-name=cityscapes_ddrnet architecture=ddrnet_23_slim checkpoint_params.checkpoint_path=$(pwd)/checkpoints/ddrnet23_slim_bb_imagenet.pth dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py  --config-name=cityscapes_ddrnet checkpoint_params.checkpoint_path=$(pwd)/checkpoints/ddrnet23_bb_imagenet.pth dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpu
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py  --config-name=cityscapes_ddrnet architecture=ddrnet_23_slim checkpoint_params.checkpoint_path=$(pwd)/checkpoints/ddrnet23_slim_bb_imagenet.pth dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_
             wget  -O $(pwd)/checkpoints/stdc1_imagenet_pretrained.pth https://deci-pretrained-models.s3.amazonaws.com/stdc_backbones/stdc1_imagenet_pretrained.pth
             wget  -O $(pwd)/checkpoints/stdc2_imagenet_pretrained.pth https://deci-pretrained-models.s3.amazonaws.com/stdc_backbones/stdc2_imagenet_pretrained.pth
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_pplite_seg50 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc1_imagenet_pretrained.pth architecture=pp_lite_t_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_pplite_seg50 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc2_imagenet_pretrained.pth architecture=pp_lite_b_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_pplite_seg75 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc1_imagenet_pretrained.pth architecture=pp_lite_t_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_pplite_seg75 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc2_imagenet_pretrained.pth architecture=pp_lite_b_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg50 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc1_imagenet_pretrained.pth dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg50 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc2_imagenet_pretrained.pth architecture=stdc2_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg75 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc1_imagenet_pretrained.pth dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg75 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc2_imagenet_pretrained.pth architecture=stdc2_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_pplite_seg50 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc1_imagenet_pretrained.pth architecture=pp_lite_t_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_va
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_pplite_seg50 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc2_imagenet_pretrained.pth architecture=pp_lite_b_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_va
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_pplite_seg75 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc1_imagenet_pretrained.pth architecture=pp_lite_t_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_va
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_pplite_seg75 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc2_imagenet_pretrained.pth architecture=pp_lite_b_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_va
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg50 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc1_imagenet_pretrained.pth dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg50 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc2_imagenet_pretrained.pth architecture=stdc2_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_ba
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg75 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc1_imagenet_pretrained.pth dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=cityscapes_stdc_seg75 checkpoint_params.checkpoint_path=$(pwd)/checkpoints/stdc2_imagenet_pretrained.pth architecture=stdc2_seg dataset_params.train_dataloader_params.batch_size=3 dataset_params.val_dataloader_params.batch_size=3 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_ba
       - run:
           name: Remove new environment when failed
           command: "rm -r << parameters.sg_new_env_name >>"
@@ -556,12 +556,12 @@ jobs:
             python3.8 -m pip install -r requirements.txt
             python3.8 -m pip install .
             python3.8 -m pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_ssd_lite_mobilenet_v2 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_n dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_t dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_s dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_m dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=8 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_l dataset_params.train_dataloader_params.batch_size=4 dataset_params.val_dataloader_params.batch_size=8 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_ssd_lite_mobilenet_v2 dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_n dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_t dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_s dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=16 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_m dataset_params.train_dataloader_params.batch_size=8 dataset_params.val_dataloader_params.batch_size=8 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+            python3.8 src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolox architecture=yolox_l dataset_params.train_dataloader_params.batch_size=4 dataset_params.val_dataloader_params.batch_size=8 training_hyperparams.max_epochs=1 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=100 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
 
       - run:
           name: Remove new environment when failed

@@ -22,6 +22,6 @@ __all__ = [
     "setup_device",
 ]
 
-__version__ = "3.0.6"
+__version__ = "3.0.7"
 
 env_sanity_check()

@@ -20,7 +20,7 @@ arch_params:
 experiment_name: mobileNetv3_large_training
 
 architecture: mobilenet_v3_large
-
+ckpt_root_dir:
 
 # THE FOLLOWING PARAMS ARE DIRECTLY USED BY HYDRA
 hydra:

@@ -20,7 +20,7 @@ arch_params:
 experiment_name: mobileNetv3_small_training
 
 architecture: mobilenet_v3_small
-
+ckpt_root_dir:
 
 # THE FOLLOWING PARAMS ARE DIRECTLY USED BY HYDRA
 hydra:

@@ -37,7 +37,7 @@ multi_gpu: DDP
 num_gpus: 4
 
 architecture: repvgg_a0
-
+ckpt_root_dir:
 
 # THE FOLLOWING PARAMS ARE DIRECTLY USED BY HYDRA
 hydra:

@@ -33,7 +33,7 @@ experiment_name: vit_base_imagenet1k
 architecture: vit_base
 multi_gpu: DDP
 num_gpus: 8
-
+ckpt_root_dir:
 
 # THE FOLLOWING PARAMS ARE DIRECTLY USED BY HYDRA
 hydra:

@@ -29,7 +29,7 @@ architecture: vit_large
 experiment_name: vit_large_imagenet1k
 multi_gpu: DDP
 num_gpus: 8
-
+ckpt_root_dir:
 
 # THE FOLLOWING PARAMS ARE DIRECTLY USED BY HYDRA
 hydra:

@@ -1192,19 +1192,19 @@ class Trainer:
 
         self.ckpt_best_name = self.training_params.ckpt_best_name
 
-        if self.training_params.max_train_batches is not None and (
-            self.training_params.max_train_batches > len(self.train_loader) or self.training_params.max_train_batches <= 0
-        ):
-
-            raise ValueError("max_train_batches must be positive and smaller then len(train_loader).")
+        if self.training_params.max_train_batches is not None:
+            if self.training_params.max_train_batches > len(self.train_loader):
+                logger.warning("max_train_batches is greater than len(self.train_loader) and will have no effect.")
+            elif self.training_params.max_train_batches <= 0:
+                raise ValueError("max_train_batches must be positive.")
+
+        if self.training_params.max_valid_batches is not None:
+            if self.training_params.max_valid_batches > len(self.valid_loader):
+                logger.warning("max_valid_batches is greater than len(self.valid_loader) and will have no effect.")
+            elif self.training_params.max_valid_batches <= 0:
+                raise ValueError("max_valid_batches must be positive.")
 
         self.max_train_batches = self.training_params.max_train_batches
-
-        if self.training_params.max_valid_batches is not None and (
-            self.training_params.max_valid_batches > len(self.valid_loader) or self.training_params.max_valid_batches <= 0
-        ):
-
-            raise ValueError("max_valid_batches must be positive and smaller then len(valid_loader).")
         self.max_valid_batches = self.training_params.max_valid_batches
 
         # STATE ATTRIBUTE SET HERE FOR SUBSEQUENT TRAIN() CALLS