diff --git a/examples/llama/pippy_llama.py b/examples/llama/pippy_llama.py
index 168d47045..c82fefa01 100644
--- a/examples/llama/pippy_llama.py
+++ b/examples/llama/pippy_llama.py
@@ -1,65 +1,72 @@
 # $ torchrun --nproc-per-node 4 pippy_llama.py
-import os
 import torch
+import torch.distributed as dist
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from torch.distributed.pipelining import SplitPoint, pipeline, ScheduleGPipe
 
-# Grab the model
-llama = AutoModelForCausalLM.from_pretrained(
-    "meta-llama/Llama-2-7b-chat-hf", low_cpu_mem_usage=True
-)
-print(llama)
+def setup():
+    dist.init_process_group()
+    
+def cleanup():
+    dist.destroy_process_group()
 
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-tokenizer.pad_token = tokenizer.eos_token
-mb_prompts = (
-    "How do you", "I like to",
-)  # microbatch size = 2
+def main():
+    setup()
+    rank = dist.get_rank()
+    world_size = dist.get_world_size()
+    device = torch.device(f"cuda:{rank % torch.cuda.device_count()}")
 
-rank = int(os.environ["RANK"])
-world_size = int(os.environ["WORLD_SIZE"])
-device = torch.device(f"cuda:{rank % torch.cuda.device_count()}")
-torch.distributed.init_process_group(rank=rank, world_size=world_size)
+    # Grab the model and tokenizer
+    llama = AutoModelForCausalLM.from_pretrained(
+        "meta-llama/Llama-2-7b-chat-hf", low_cpu_mem_usage=True
+    )
+    llama.to(device).eval()
+    tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+    tokenizer.pad_token = tokenizer.eos_token
+    
+    # Cut model by equal number of layers per rank
+    layers_per_rank = llama.config.num_hidden_layers // world_size
+    print(f"layers_per_rank = {layers_per_rank}")
+    split_spec = {
+        f"model.layers.{i * layers_per_rank}": SplitPoint.BEGINNING
+        for i in range(1, world_size)
+    }
+    
+    # Create a pipeline representation from the model
+    mb_prompts = ("How do you", "I like to")  # microbatch size = 2
+    mb_inputs = tokenizer(mb_prompts, return_tensors="pt", padding=True).to(device)
+    pipe = pipeline(llama, mb_args=(mb_inputs["input_ids"],), split_spec=split_spec)
 
-llama.to(device).eval()
+    # Create pipeline stage for each rank
+    stage = pipe.build_stage(rank, device=device)
 
-# Cut model by equal number of layers per rank
-layers_per_rank = llama.config.num_hidden_layers // world_size
-print(f"layers_per_rank = {layers_per_rank}")
-split_spec = {
-    f"model.layers.{i * layers_per_rank}": SplitPoint.BEGINNING
-    for i in range(1, world_size)
-}
+    # Run time inputs
+    full_batch_prompts = (
+        "How do you", "I like to", "Can I help", "You need to",
+        "The weather is", "I found a", "What is your", "You are so",
+    )  # full batch size = 8
+    inputs = tokenizer(full_batch_prompts, return_tensors="pt", padding=True).to(device)
 
-# Create a pipeline representation from the model
-mb_inputs = tokenizer(mb_prompts, return_tensors="pt", padding=True).to(device)
-pipe = pipeline(llama, mb_args=(mb_inputs["input_ids"],))
+    # Attach to a schedule
+    # number of microbatches = 8 // 2 = 4
+    num_mbs = 4
+    schedule = ScheduleGPipe(stage, num_mbs)
 
-# Create pipeline stage for each rank
-stage = pipe.build_stage(rank, device=device)
+    # Run
+    if rank == 0:
+        args = inputs["input_ids"]
+    else:
+        args = None
 
-# Run time inputs
-full_batch_prompts = (
-    "How do you", "I like to", "Can I help", "You need to",
-    "The weather is", "I found a", "What is your", "You are so",
-)  # full batch size = 8
-inputs = tokenizer(full_batch_prompts, return_tensors="pt", padding=True).to(device)
+    output = schedule.step(args)
 
-# Attach to a schedule
-# number of microbatches = 8 // 2 = 4
-num_mbs = 4
-schedule = ScheduleGPipe(stage, num_mbs)
-
-# Run
-if rank == 0:
-    args = inputs["input_ids"]
-else:
-    args = None
-
-output = schedule.step(args)
-
-# Decode
-if output is not None:
-    next_token_logits = output[0][:, -1, :]
-    next_token = torch.argmax(next_token_logits, dim=-1)
-    print(tokenizer.batch_decode(next_token))
+    # Decode
+    if output is not None:
+        next_token_logits = output[0][:, -1, :]
+        next_token = torch.argmax(next_token_logits, dim=-1)
+        print(tokenizer.batch_decode(next_token))
+        
+    cleanup()
+        
+if __name__ == "__main__":
+    main()
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