diff --git a/3.test_cases/10.FSDP/1.distributed-training.sbatch b/3.test_cases/10.FSDP/1.distributed-training.sbatch
index 0e20b147..e76d5129 100755
--- a/3.test_cases/10.FSDP/1.distributed-training.sbatch
+++ b/3.test_cases/10.FSDP/1.distributed-training.sbatch
@@ -39,11 +39,11 @@ export FI_EFA_SET_CUDA_SYNC_MEMOPS=0
 ###########################
 
 declare -a TORCHRUN_ARGS=(
-    --nproc_per_node=$GPUS_PER_NODE \
-    --nnodes=$SLURM_JOB_NUM_NODES \
-    --rdzv_id=$SLURM_JOB_ID \
-    --rdzv_backend=c10d \
-    --rdzv_endpoint=$(hostname) \
+    --nproc_per_node=$GPUS_PER_NODE
+    --nnodes=$SLURM_JOB_NUM_NODES
+    --rdzv_id=$SLURM_JOB_ID
+    --rdzv_backend=c10d
+    --rdzv_endpoint=$(hostname)
 )
 
 export TORCHRUN=./pt_fsdp/bin/torchrun
@@ -54,25 +54,31 @@ export TRAIN_SCRIPT=./train.py
 ############################
 
 declare -a TRAINING_ARGS=(
-    --max_context_width=4096 \
-    --num_key_value_heads=32 \ # 7b: 32 13b: 40 70b: 8
-    --llama_intermediate_size=11008 \ # 7b: 11008 13b: 13824 70b: 28672
-    --hidden_width=4096 \ # 7b: 4096 13b: 5120 70b: 8192
-    --num_layers=32 \ # 7b: 32 13b: 40 70b: 80
-    --num_heads=32 \ # 7b: 32 13b: 40 70b: 64
-    --model_type=llama_v2 \
-    --tokenizer="hf-internal-testing/llama-tokenizer" \
-    --checkpoint_freq=5000 \
-    --validation_freq=500 \
-    --max_steps=5000 \
-    --checkpoint_dir=./checkpoints \
-    --dataset='c4' \
-    --dataset_config_name='en' \
-    --resume_from_checkpoint=./checkpoints \
-    --train_batch_size=1 \
-    --val_batch_size=1 \
-    --sharding_strategy="full" \ # https://pytorch.org/docs/stable/fsdp.html
+    --max_context_width=4096
+    --num_key_value_heads=32 # 7b: 32 13b: 40 70b: 8
+    --llama_intermediate_size=11008 # 7b: 11008 13b: 13824 70b: 28672
+    --hidden_width=4096 # 7b: 4096 13b: 5120 70b: 8192
+    --num_layers=32 # 7b: 32 13b: 40 70b: 80
+    --num_heads=32 # 7b: 32 13b: 40 70b: 64
+    --model_type=llama_v2
+    --tokenizer="hf-internal-testing/llama-tokenizer"
+    --checkpoint_freq=5000
+    --validation_freq=500
+    --max_steps=5000
+    --checkpoint_dir=./checkpoints
+    --dataset='c4'
+    --dataset_config_name='en'
+    --resume_from_checkpoint=./checkpoints
+    --train_batch_size=1
+    --val_batch_size=1
+    --sharding_strategy="full" # https://pytorch.org/docs/stable/fsdp.html
     --offload_activations=1
 )
 
-srun -l ${TORCHRUN} "${TORCHRUN_ARGS[@]}" $TRAIN_SCRIPT "${TRAINING_ARGS[@]}"
+AUTO_RESUME=""
+if [ -d "/opt/sagemaker_cluster" ]; then
+    echo "Detected Hyperpod cluster.. enabling --auto-resume=1"
+    AUTO_RESUME="--auto-resume=1"
+fi
+
+srun ${AUTO_RESUME} -l ${TORCHRUN} "${TORCHRUN_ARGS[@]}" $TRAIN_SCRIPT "${TRAINING_ARGS[@]}"