The following example shows how to get training with MosaicML composer. 
name: composer-train

num_nodes: 4

resources:
  cpus: 124
  memory: 1400
  accelerators: H200:8
  image_id: nvcr.io/nvidia/pytorch:24.10-py3 
  labels:
    kueue.x-k8s.io/queue-name: user-queue
    maxRunDurationSeconds: "3200"

run: |
	git clone https://github.com/mosaicml/llm-foundry.git
	cd llm-foundry/scripts

	# Convert C4 dataset to StreamingDataset format
	python data_prep/convert_dataset_hf.py \
	  --dataset allenai/c4 --data_subset en \
	  --out_root my-copy-c4 --splits train_small val_small \
	  --concat_tokens 2048 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>'	

	composer -n ${NUM_GPUS_PER_NODE} \
    	--world_size $(($NUM_NODES * $NUM_GPUS_PER_NODE)) \
    	--node_rank $RANK \
    	--master_addr $MASTER_ADDR \
    	--master_port 8080 \
	    train/train.py \
	    train/yamls/pretrain/mpt-125m.yaml \
  		variables.data_local=my-copy-c4 \
		train_loader.dataset.split=train_small \
	    eval_loader.dataset.split=val_small \
	    max_duration=10ba \
	    eval_interval=0 \
	    save_folder=mpt-125m