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D-ExpTracker__sft_gs__singleton_structures__N_1__masked_low_lr__v1

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timestamp stringdate 2025-07-31 04:39:24 2025-07-31 05:51:10	end_timestamp stringdate 2025-07-31 04:39:26 2025-07-31 06:05:25	stage_name stringclasses 1 value	stage_number int64 1 1	level stringclasses 1 value	message stringclasses 1 value	stdout_content stringclasses 3 values	stderr_content stringclasses 3 values	experiment_name stringclasses 1 value	elapsed_time_seconds float64 1.95 855	stage_complete bool 1 class
2025-07-31T04:39:24.601112	2025-07-31T04:39:26.552655	llamafactory_sft	1	INFO	Complete log capture for stage: llamafactory_sft	[INFO] Starting stage: LLaMAFactory training - sft [INFO] Starting LLaMAFactory Training [ERROR] LLaMAFactory stage 'sft' failed: [Errno 13] Permission denied: '/datastor1' [ERROR] Stage error: PermissionError: [Errno 13] Permission denied: '/datastor1'	Uploading the dataset shards: 0%\| \| 0/1 [00:00<?, ?it/s] Creating parquet from Arrow format: 0%\| \| 0/4 [00:00<?, ?ba/s][A Creating parquet from Arrow format: 100%\|███████████████████████████████████████\| 4/4 [00:00<00:00, 174.93ba/s] Uploading the dataset shards: 100%\|██████████████████████████████████████████████\| 1/1 [00:00<00:00, 1.28it/s] Uploading the dataset shards: 100%\|██████████████████████████████████████████████\| 1/1 [00:00<00:00, 1.28it/s]	sft_gs__singleton_structures__N_1__masked_low_lr	1.951543	true
2025-07-31T05:44:05.279913	2025-07-31T05:47:39.559445	llamafactory_sft	1	INFO	Complete log capture for stage: llamafactory_sft	[INFO] Starting stage: LLaMAFactory training - sft [INFO] Starting LLaMAFactory Training [INFO] Registered dataset: TAUR_dev__D_sft_gs__singleton_structures__N_1__masked_low_lr_sft_data -> TAUR-dev/D-sft_gs__singleton_structures__N_1__masked_low_lr-sft-data [INFO] Created training config: /scratch/10416/zaynesprague/skill_inject_outputs/sf_experiments/singleton_structures/low_lr/llamafactory/configs/training_config.yaml [INFO] Created merge config: /scratch/10416/zaynesprague/skill_inject_outputs/sf_experiments/singleton_structures/low_lr/llamafactory/configs/merge_config.yaml [INFO]️ Starting LLaMAFactory training... [INFO] Running command: /work/10416/zaynesprague/anaconda3/envs/verl2/bin/python -m torch.distributed.run --nproc-per-node 1 --nnodes 4 /scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py /scratch/10416/zaynesprague/skill_inject_outputs/sf_experiments/singleton_structures/low_lr/llamafactory/configs/training_config.yaml [DEBUG] Loaded 2 existing entries from metadata [DEBUG] Successfully appended 1 entries to metadata (total: 3) 🔄 Starting training with real-time output... ================================================================================ [ERROR] Stage error: KeyboardInterrupt:	Uploading the dataset shards: 0%\| \| 0/1 [00:00<?, ?it/s] Creating parquet from Arrow format: 0%\| \| 0/4 [00:00<?, ?ba/s][A Creating parquet from Arrow format: 100%\|███████████████████████████████████████\| 4/4 [00:00<00:00, 173.90ba/s] Uploading the dataset shards: 100%\|██████████████████████████████████████████████\| 1/1 [00:00<00:00, 3.18it/s] Uploading the dataset shards: 100%\|██████████████████████████████████████████████\| 1/1 [00:00<00:00, 3.18it/s] README.md: 0%\| \| 0.00/536 [00:00<?, ?B/s] README.md: 100%\|██████████████████████████████████████████████████████████████\| 536/536 [00:00<00:00, 7.11MB/s] Uploading the dataset shards: 0%\| \| 0/1 [00:00<?, ?it/s] Creating parquet from Arrow format: 0%\| \| 0/1 [00:00<?, ?ba/s][A Creating parquet from Arrow format: 100%\|██████████████████████████████████████\| 1/1 [00:00<00:00, 3366.22ba/s] Uploading the dataset shards: 100%\|██████████████████████████████████████████████\| 1/1 [00:00<00:00, 1.97it/s] Uploading the dataset shards: 100%\|██████████████████████████████████████████████\| 1/1 [00:00<00:00, 1.97it/s]	sft_gs__singleton_structures__N_1__masked_low_lr	214.279532	true
2025-07-31T05:51:11.147046	2025-07-31T06:05:26.207839	llamafactory_sft	1	INFO	Complete log capture for stage: llamafactory_sft	[INFO] Starting stage: LLaMAFactory training - sft [INFO] Starting LLaMAFactory Training [INFO] Found existing dataset registration: TAUR_dev__D_sft_gs__singleton_structures__N_1__masked_low_lr_sft_data [INFO] Created training config: /scratch/10416/zaynesprague/skill_inject_outputs/sf_experiments/singleton_structures/low_lr/llamafactory/configs/training_config.yaml [INFO] Created merge config: /scratch/10416/zaynesprague/skill_inject_outputs/sf_experiments/singleton_structures/low_lr/llamafactory/configs/merge_config.yaml [INFO]️ Starting LLaMAFactory training... [DEBUG] Loaded 4 existing entries from metadata [DEBUG] Successfully appended 1 entries to metadata (total: 5) [DEBUG] Training Script #!/bin/bash cd /scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory source ~/.profile; source /opt/apps/lmod/lmod/init/bash;module load cuda/12.4 nccl/12.4 nvidia_math/12.4 source /work/10416/zaynesprague/vista/../anaconda3/etc/profile.d/conda.sh;conda activate verl2 # Verify python environment is working PYTHON_PATH=$(which python) echo "Python environment check: $PYTHON_PATH" export HF_HOME="/scratch/10416/zaynesprague/hf_cache" export TRITON_CACHE_DIR="/scratch/10416/zaynesprague/.cache/triton" export OUTLINES_CACHE_DIR="/scratch/10416/zaynesprague/.cache/outlines" export PYTHONPATH="/scratch/10416/zaynesprague/skill_factory_dir/skill-factory" export CUDA_LAUNCH_BLOCKING="0" export DISABLE_VERSION_CHECK="1" export CC="gcc" export CXX="g++" export FORCE_TORCHRUN="1" export NCCL_PROTO="simple" export FI_EFA_FORK_SAFE="1" export FI_LOG_LEVEL="1" export FI_EFA_USE_DEVICE_RDMA="1" export NCCL_NET_GDR_LEVEL="SYS" export NCCL_NET_GDR_READ="1" export PYTHONFAULTHANDLER="1" export OMPI_MCA_mtl_base_verbose="1" export FI_EFA_ENABLE_SHM_TRANSFER="0" export FI_PROVIDER="efa" export FI_EFA_TX_MIN_CREDITS="64" export NCCL_TREE_THRESHOLD="0" export NCCL_DEBUG="INFO" # Master node coordination export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" \| head -n 1) export MASTER_PORT=12802 # Python path setup export PYTHONPATH=$PWD:$PYTHONPATH echo "🔗 Multi-node setup: MASTER_ADDR=$MASTER_ADDR, MASTER_PORT=$MASTER_PORT" echo "🚀 Starting multi-node training with 1 GPUs per node across $SLURM_JOB_NUM_NODES nodes" echo "📁 Working directory: $(pwd)" echo "🐍 Python path: $(which python)" echo "🔥 Torchrun path: $(which torchrun)" srun /work/10416/zaynesprague/anaconda3/envs/verl2/bin/python -m torch.distributed.run \ --nproc-per-node 1 \ --nnodes $SLURM_JOB_NUM_NODES \ --rdzv_id=$SLURM_JOB_ID \ --rdzv_backend=c10d \ --rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \ /scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py /scratch/10416/zaynesprague/skill_inject_outputs/sf_experiments/singleton_structures/low_lr/llamafactory/configs/training_config.yaml 🔄 Starting training with real-time output... ================================================================================ Python environment check: /work/10416/zaynesprague/anaconda3/envs/verl2/bin/python 🔗 Multi-node setup: MASTER_ADDR=c622-121, MASTER_PORT=12802 🚀 Starting multi-node training with 1 GPUs per node across 4 nodes 📁 Working directory: /scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory 🐍 Python path: /work/10416/zaynesprague/anaconda3/envs/verl2/bin/python 🔥 Torchrun path: /work/10416/zaynesprague/anaconda3/envs/verl2/bin/torchrun [WARNING\|2025-07-31 05:51:47] llamafactory.extras.misc:154 >> Version checking has been disabled, may lead to unexpected behaviors. [WARNING\|2025-07-31 05:51:47] llamafactory.extras.misc:154 >> Version checking has been disabled, may lead to unexpected behaviors. [WARNING\|2025-07-31 05:51:47] llamafactory.extras.misc:154 >> Version checking has been disabled, may lead to unexpected behaviors. [WARNING\|2025-07-31 05:51:47] llamafactory.extras.misc:154 >> Version checking has been disabled, may lead to unexpected behaviors. [2025-07-31 05:52:04,189] [INFO] [real_accelerator.py:254:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2025-07-31 05:52:04,190] [INFO] [real_accelerator.py:254:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2025-07-31 05:52:04,190] [INFO] [real_accelerator.py:254:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2025-07-31 05:52:04,190] [INFO] [real_accelerator.py:254:get_accelerator] Setting ds_accelerator to cuda (auto detect) Warning: The cache directory for DeepSpeed Triton autotune, /scratch/10416/zaynesprague/.cache/triton, appears to be on an NFS system. While this is generally acceptable, if you experience slowdowns or hanging when DeepSpeed exits, it is recommended to set the TRITON_CACHE_DIR environment variable to a non-NFS path. Warning: The cache directory for DeepSpeed Triton autotune, /scratch/10416/zaynesprague/.cache/triton, appears to be on an NFS system. While this is generally acceptable, if you experience slowdowns or hanging when DeepSpeed exits, it is recommended to set the TRITON_CACHE_DIR environment variable to a non-NFS path. Warning: The cache directory for DeepSpeed Triton autotune, /scratch/10416/zaynesprague/.cache/triton, appears to be on an NFS system. While this is generally acceptable, if you experience slowdowns or hanging when DeepSpeed exits, it is recommended to set the TRITON_CACHE_DIR environment variable to a non-NFS path. Warning: The cache directory for DeepSpeed Triton autotune, /scratch/10416/zaynesprague/.cache/triton, appears to be on an NFS system. While this is generally acceptable, if you experience slowdowns or hanging when DeepSpeed exits, it is recommended to set the TRITON_CACHE_DIR environment variable to a non-NFS path. [2025-07-31 05:52:43,426] [INFO] [comm.py:669:init_distributed] cdb=None [2025-07-31 05:52:43,426] [INFO] [comm.py:700:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl [2025-07-31 05:52:43,432] [INFO] [comm.py:669:init_distributed] cdb=None [2025-07-31 05:52:43,433] [INFO] [comm.py:669:init_distributed] cdb=None [2025-07-31 05:52:43,433] [INFO] [comm.py:669:init_distributed] cdb=None [INFO\|2025-07-31 05:52:43] llamafactory.hparams.parser:406 >> Process rank: 3, world size: 4, device: cuda:0, distributed training: True, compute dtype: torch.bfloat16 [INFO\|2025-07-31 05:52:43] llamafactory.hparams.parser:406 >> Process rank: 0, world size: 4, device: cuda:0, distributed training: True, compute dtype: torch.bfloat16 [INFO\|2025-07-31 05:52:43] llamafactory.hparams.parser:406 >> Process rank: 1, world size: 4, device: cuda:0, distributed training: True, compute dtype: torch.bfloat16 [INFO\|2025-07-31 05:52:43] llamafactory.hparams.parser:406 >> Process rank: 2, world size: 4, device: cuda:0, distributed training: True, compute dtype: torch.bfloat16 [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,740 >> loading file vocab.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/vocab.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,740 >> loading file merges.txt from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/merges.txt [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,740 >> loading file tokenizer.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,740 >> loading file added_tokens.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,740 >> loading file special_tokens_map.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,740 >> loading file tokenizer_config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer_config.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,740 >> loading file chat_template.jinja from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,746 >> loading file vocab.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/vocab.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,746 >> loading file merges.txt from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/merges.txt [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,746 >> loading file tokenizer.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,746 >> loading file added_tokens.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,746 >> loading file special_tokens_map.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,746 >> loading file tokenizer_config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer_config.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,746 >> loading file chat_template.jinja from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,751 >> loading file vocab.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/vocab.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,751 >> loading file merges.txt from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/merges.txt [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,751 >> loading file tokenizer.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,751 >> loading file added_tokens.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,751 >> loading file special_tokens_map.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,751 >> loading file tokenizer_config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer_config.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,751 >> loading file chat_template.jinja from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,753 >> loading file vocab.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/vocab.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,753 >> loading file merges.txt from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/merges.txt [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,753 >> loading file tokenizer.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,753 >> loading file added_tokens.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,753 >> loading file special_tokens_map.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,753 >> loading file tokenizer_config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer_config.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:43,753 >> loading file chat_template.jinja from cache at None [INFO\|tokenization_utils_base.py:2313] 2025-07-31 05:52:44,014 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. [INFO\|tokenization_utils_base.py:2313] 2025-07-31 05:52:44,015 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. [INFO\|tokenization_utils_base.py:2313] 2025-07-31 05:52:44,017 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. [INFO\|tokenization_utils_base.py:2313] 2025-07-31 05:52:44,018 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. [INFO\|configuration_utils.py:699] 2025-07-31 05:52:44,287 >> loading configuration file config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json [INFO\|configuration_utils.py:699] 2025-07-31 05:52:44,287 >> loading configuration file config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json [INFO\|configuration_utils.py:699] 2025-07-31 05:52:44,297 >> loading configuration file config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json [INFO\|configuration_utils.py:699] 2025-07-31 05:52:44,313 >> loading configuration file config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json [INFO\|configuration_utils.py:771] 2025-07-31 05:52:44,350 >> Model config Qwen2Config { "_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "architectures": [ "Qwen2ForCausalLM" ], "attention_dropout": 0.0, "bos_token_id": 151643, "eos_token_id": 151645, "hidden_act": "silu", "hidden_size": 1536, "initializer_range": 0.02, "intermediate_size": 8960, "max_position_embeddings": 32768, "max_window_layers": 21, "model_type": "qwen2", "num_attention_heads": 12, "num_hidden_layers": 28, "num_key_value_heads": 2, "rms_norm_eps": 1e-06, "rope_scaling": null, "rope_theta": 1000000.0, "sliding_window": 32768, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "transformers_version": "4.49.0", "use_cache": true, "use_sliding_window": false, "vocab_size": 151936 } [INFO\|configuration_utils.py:771] 2025-07-31 05:52:44,350 >> Model config Qwen2Config { "_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "architectures": [ "Qwen2ForCausalLM" ], "attention_dropout": 0.0, "bos_token_id": 151643, "eos_token_id": 151645, "hidden_act": "silu", "hidden_size": 1536, "initializer_range": 0.02, "intermediate_size": 8960, "max_position_embeddings": 32768, "max_window_layers": 21, "model_type": "qwen2", "num_attention_heads": 12, "num_hidden_layers": 28, "num_key_value_heads": 2, "rms_norm_eps": 1e-06, "rope_scaling": null, "rope_theta": 1000000.0, "sliding_window": 32768, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "transformers_version": "4.49.0", "use_cache": true, "use_sliding_window": false, "vocab_size": 151936 } [INFO\|configuration_utils.py:771] 2025-07-31 05:52:44,350 >> Model config Qwen2Config { "_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "architectures": [ "Qwen2ForCausalLM" ], "attention_dropout": 0.0, "bos_token_id": 151643, "eos_token_id": 151645, "hidden_act": "silu", "hidden_size": 1536, "initializer_range": 0.02, "intermediate_size": 8960, "max_position_embeddings": 32768, "max_window_layers": 21, "model_type": "qwen2", "num_attention_heads": 12, "num_hidden_layers": 28, "num_key_value_heads": 2, "rms_norm_eps": 1e-06, "rope_scaling": null, "rope_theta": 1000000.0, "sliding_window": 32768, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "transformers_version": "4.49.0", "use_cache": true, "use_sliding_window": false, "vocab_size": 151936 } [INFO\|configuration_utils.py:771] 2025-07-31 05:52:44,351 >> Model config Qwen2Config { "_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "architectures": [ "Qwen2ForCausalLM" ], "attention_dropout": 0.0, "bos_token_id": 151643, "eos_token_id": 151645, "hidden_act": "silu", "hidden_size": 1536, "initializer_range": 0.02, "intermediate_size": 8960, "max_position_embeddings": 32768, "max_window_layers": 21, "model_type": "qwen2", "num_attention_heads": 12, "num_hidden_layers": 28, "num_key_value_heads": 2, "rms_norm_eps": 1e-06, "rope_scaling": null, "rope_theta": 1000000.0, "sliding_window": 32768, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "transformers_version": "4.49.0", "use_cache": true, "use_sliding_window": false, "vocab_size": 151936 } [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,427 >> loading file vocab.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/vocab.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,427 >> loading file merges.txt from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/merges.txt [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,427 >> loading file tokenizer.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,427 >> loading file added_tokens.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,427 >> loading file special_tokens_map.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,427 >> loading file tokenizer_config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer_config.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,428 >> loading file chat_template.jinja from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,428 >> loading file vocab.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/vocab.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,428 >> loading file merges.txt from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/merges.txt [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,428 >> loading file tokenizer.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,428 >> loading file added_tokens.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,428 >> loading file special_tokens_map.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,428 >> loading file tokenizer_config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer_config.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,429 >> loading file chat_template.jinja from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,429 >> loading file vocab.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/vocab.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,429 >> loading file merges.txt from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/merges.txt [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,429 >> loading file tokenizer.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,429 >> loading file added_tokens.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,429 >> loading file special_tokens_map.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,429 >> loading file tokenizer_config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer_config.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,429 >> loading file chat_template.jinja from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,430 >> loading file vocab.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/vocab.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,430 >> loading file merges.txt from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/merges.txt [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,430 >> loading file tokenizer.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,430 >> loading file added_tokens.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,430 >> loading file special_tokens_map.json from cache at None [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,430 >> loading file tokenizer_config.json from cache at /scratch/10416/zaynesprague/hf_cache/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/tokenizer_config.json [INFO\|tokenization_utils_base.py:2050] 2025-07-31 05:52:44,430 >> loading file chat_template.jinja from cache at None [INFO\|tokenization_utils_base.py:2313] 2025-07-31 05:52:44,601 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. [INFO\|tokenization_utils_base.py:2313] 2025-07-31 05:52:44,603 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. [INFO\|tokenization_utils_base.py:2313] 2025-07-31 05:52:44,605 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. [INFO\|tokenization_utils_base.py:2313] 2025-07-31 05:52:44,606 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. [INFO\|2025-07-31 05:52:44] llamafactory.data.loader:143 >> Loading dataset TAUR-dev/D-sft_gs__singleton_structures__N_1__masked_low_lr-sft-data... [INFO\|2025-07-31 05:52:44] llamafactory.data.loader:143 >> Loading dataset TAUR-dev/D-sft_gs__singleton_structures__N_1__masked_low_lr-sft-data... [INFO\|2025-07-31 05:52:44] llamafactory.data.loader:143 >> Loading dataset TAUR-dev/D-sft_gs__singleton_structures__N_1__masked_low_lr-sft-data... [INFO\|2025-07-31 05:52:44] llamafactory.data.loader:143 >> Loading dataset TAUR-dev/D-sft_gs__singleton_structures__N_1__masked_low_lr-sft-data... Setting num_proc from 16 back to 1 for the train split to disable multiprocessing as it only contains one shard. Generating train split: 0%\| \| 0/3808 [00:00<?, ? examples/s] Generating train split: 100%\|██████████\| 3808/3808 [00:00<00:00, 90872.89 examples/s] Converting format of dataset (num_proc=16): 0%\| \| 0/3808 [00:00<?, ? examples/s] Converting format of dataset (num_proc=16): 6%\|▋ \| 238/3808 [00:00<00:01, 2272.33 examples/s]Fatal Python error: Bus error Current thread 0x0000400000ad5400 (most recent call first): File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/pyarrow/ipc.py", line 52 in __init__ File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/pyarrow/ipc.py", line 190 in open_stream File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/table.py", line 49 in _memory_mapped_record_batch_reader_from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/table.py", line 63 in _memory_mapped_arrow_table_from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/table.py", line 1017 in from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_reader.py", line 329 in read_table File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 740 in from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3531 in _map_single File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 678 in _write_generator_to_queue File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 125 in worker File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 108 in run File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 314 in _bootstrap File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 71 in _launch File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19 in __init__ File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/context.py", line 281 in _Popen File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 121 in start File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 329 in _repopulate_pool_static File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 306 in _repopulate_pool File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 215 in __init__ File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/context.py", line 119 in Pool File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3157 in map File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 560 in wrapper File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/converter.py", line 281 in align_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 162 in _load_single_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 182 in _get_merged_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 304 in get_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 51 in run_sft File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72 in _training_function File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110 in run_exp File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 19 in main File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 28 in <module> Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, yaml._yaml, pyarrow.lib, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, _cffi_backend, pyarrow._parquet, pyarrow._fs, pyarrow._azurefs, pyarrow._hdfs, pyarrow._gcsfs, pyarrow._s3fs, multidict._multidict, yarl._quoting_c, propcache._helpers_c, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket.mask, aiohttp._websocket.reader_c, frozenlist._frozenlist, xxhash._xxhash, pyarrow._json, pyarrow._acero, pyarrow._csv, pyarrow._substrait, pyarrow._dataset, pyarrow._dataset_orc, pyarrow._parquet_encryption, pyarrow._dataset_parquet_encryption, pyarrow._dataset_parquet, markupsafe._speedups, PIL._imaging, sklearn.__check_build._check_build, scipy._lib._ccallback_c, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg._matfuncs_expm, scipy.linalg._linalg_pythran, scipy.linalg.cython_blas, scipy.linalg._decomp_update, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack, scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, psutil._psutil_linux, psutil._psutil_posix, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.spatial.transform._rotation, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize._cython_nnls, scipy._lib._uarray._uarray, scipy.linalg._decomp_interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integrate._lsoda, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._dierckx, scipy.interpolate._ppoly, scipy.interpolate._interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.interpolate._bspl, scipy.special.cython_special, scipy.stats._stats, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._biasedurn, scipy.stats._stats_pythran, scipy.stats._levy_stable.levyst, scipy.stats._ansari_swilk_statistics, scipy.stats._mvn, scipy.stats._rcont.rcont, scipy.ndimage._nd_image, scipy.ndimage._rank_filter_1d, _ni_label, scipy.ndimage._ni_label, _cyutility, sklearn._cyutility, sklearn.utils._isfinite, sklearn.utils.sparsefuncs_fast, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.preprocessing._csr_polynomial_expansion, sklearn.preprocessing._target_encoder_fast, sklearn.metrics._dist_metrics, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.utils._cython_blas, sklearn.metrics._pairwise_distances_reduction._base, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distances_reduction._argkmin_classmode, sklearn.utils._vector_sentinel, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_distances_reduction._radius_neighbors_classmode, sklearn.metrics._pairwise_fast, PIL._imagingft, av._core, av.logging, av.bytesource, av.buffer, av.audio.format, av.error, av.dictionary, av.container.pyio, av.utils, av.option, av.descriptor, av.format, av.stream, av.container.streams, av.sidedata.motionvectors, av.sidedata.sidedata, av.opaque, av.packet, av.container.input, av.container.output, av.container.core, av.codec.context, av.video.format, av.video.reformatter, av.plane, av.video.plane, av.video.frame, av.video.stream, av.codec.hwaccel, av.codec.codec, av.frame, av.audio.layout, av.audio.plane, av.audio.frame, av.audio.stream, av.filter.pad, av.filter.link, av.filter.context, av.filter.graph, av.filter.filter, av.filter.loudnorm, av.audio.resampler, av.audio.codeccontext, av.audio.fifo, av.bitstream, av.video.codeccontext, kiwisolver._cext, msgpack._cmsgpack, google._upb._message, setproctitle, uvloop.loop, ray._raylet, cuda_utils, greenlet._greenlet, sklearn.utils._random, sklearn.utils._seq_dataset, sklearn.linear_model._cd_fast, _loss, sklearn._loss._loss, sklearn.utils.arrayfuncs, sklearn.svm._liblinear, sklearn.svm._libsvm, sklearn.svm._libsvm_sparse, sklearn.linear_model._sag_fast, sklearn.utils._weight_vector, sklearn.linear_model._sgd_fast, regex._regex, sklearn.feature_extraction._hashing_fast, scipy.io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, sklearn.datasets._svmlight_format_fast (total: 278) Fatal Python error: Bus error Current thread 0x0000400000ad5400 (most recent call first): File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/table.py", line 64 in _memory_mapped_arrow_table_from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/table.py", line 1017 in from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_reader.py", line 329 in read_table File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 740 in from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3531 in _map_single File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 678 in _write_generator_to_queue File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 125 in worker File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 108 in run File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 314 in _bootstrap File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 71 in _launch File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19 in __init__ File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/context.py", line 281 in _Popen File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 121 in start File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 329 in _repopulate_pool_static File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 306 in _repopulate_pool File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 215 in __init__ File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/context.py", line 119 in Pool File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3157 in map File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 560 in wrapper File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/converter.py", line 281 in align_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 162 in _load_single_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 182 in _get_merged_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 304 in get_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 51 in run_sft File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72 in _training_function File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110 in run_exp File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 19 in main File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 28 in <module> Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, yaml._yaml, pyarrow.lib, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, _cffi_backend, pyarrow._parquet, pyarrow._fs, pyarrow._azurefs, pyarrow._hdfs, pyarrow._gcsfs, pyarrow._s3fs, multidict._multidict, yarl._quoting_c, propcache._helpers_c, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket.mask, aiohttp._websocket.reader_c, frozenlist._frozenlist, xxhash._xxhash, pyarrow._json, pyarrow._acero, pyarrow._csv, pyarrow._substrait, pyarrow._dataset, pyarrow._dataset_orc, pyarrow._parquet_encryption, pyarrow._dataset_parquet_encryption, pyarrow._dataset_parquet, markupsafe._speedups, PIL._imaging, sklearn.__check_build._check_build, scipy._lib._ccallback_c, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg._matfuncs_expm, scipy.linalg._linalg_pythran, scipy.linalg.cython_blas, scipy.linalg._decomp_update, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack, scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, psutil._psutil_linux, psutil._psutil_posix, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.spatial.transform._rotation, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize._cython_nnls, scipy._lib._uarray._uarray, scipy.linalg._decomp_interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integrate._lsoda, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._dierckx, scipy.interpolate._ppoly, scipy.interpolate._interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.interpolate._bspl, scipy.special.cython_special, scipy.stats._stats, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._biasedurn, scipy.stats._stats_pythran, scipy.stats._levy_stable.levyst, scipy.stats._ansari_swilk_statistics, scipy.stats._mvn, scipy.stats._rcont.rcont, scipy.ndimage._nd_image, scipy.ndimage._rank_filter_1d, _ni_label, scipy.ndimage._ni_label, _cyutility, sklearn._cyutility, sklearn.utils._isfinite, sklearn.utils.sparsefuncs_fast, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.preprocessing._csr_polynomial_expansion, sklearn.preprocessing._target_encoder_fast, sklearn.metrics._dist_metrics, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.utils._cython_blas, sklearn.metrics._pairwise_distances_reduction._base, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distances_reduction._argkmin_classmode, sklearn.utils._vector_sentinel, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_distances_reduction._radius_neighbors_classmode, sklearn.metrics._pairwise_fast, PIL._imagingft, av._core, av.logging, av.bytesource, av.buffer, av.audio.format, av.error, av.dictionary, av.container.pyio, av.utils, av.option, av.descriptor, av.format, av.stream, av.container.streams, av.sidedata.motionvectors, av.sidedata.sidedata, av.opaque, av.packet, av.container.input, av.container.output, av.container.core, av.codec.context, av.video.format, av.video.reformatter, av.plane, av.video.plane, av.video.frame, av.video.stream, av.codec.hwaccel, av.codec.codec, av.frame, av.audio.layout, av.audio.plane, av.audio.frame, av.audio.stream, av.filter.pad, av.filter.link, av.filter.context, av.filter.graph, av.filter.filter, av.filter.loudnorm, av.audio.resampler, av.audio.codeccontext, av.audio.fifo, av.bitstream, av.video.codeccontext, kiwisolver._cext, msgpack._cmsgpack, google._upb._message, setproctitle, uvloop.loop, ray._raylet, cuda_utils, greenlet._greenlet, sklearn.utils._random, sklearn.utils._seq_dataset, sklearn.linear_model._cd_fast, _loss, sklearn._loss._loss, sklearn.utils.arrayfuncs, sklearn.svm._liblinear, sklearn.svm._libsvm, sklearn.svm._libsvm_sparse, sklearn.linear_model._sag_fast, sklearn.utils._weight_vector, sklearn.linear_model._sgd_fast, regex._regex, sklearn.feature_extraction._hashing_fast, scipy.io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, sklearn.datasets._svmlight_format_fast (total: 278) Fatal Python error: Bus error Current thread 0x0000400000ad5400 (most recent call first): File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/table.py", line 64 in _memory_mapped_arrow_table_from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/table.py", line 1017 in from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_reader.py", line 329 in read_table File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 740 in from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3531 in _map_single File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 678 in _write_generator_to_queue File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 125 in worker File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 108 in run File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 314 in _bootstrap File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 71 in _launch File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19 in __init__ File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/context.py", line 281 in _Popen File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 121 in start File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 329 in _repopulate_pool_static File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 306 in _repopulate_pool File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 215 in __init__ File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/context.py", line 119 in Pool File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3157 in map File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 560 in wrapper File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/converter.py", line 281 in align_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 162 in _load_single_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 182 in _get_merged_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 304 in get_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 51 in run_sft File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72 in _training_function File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110 in run_exp File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 19 in main File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 28 in <module> Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, yaml._yaml, pyarrow.lib, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, _cffi_backend, pyarrow._parquet, pyarrow._fs, pyarrow._azurefs, pyarrow._hdfs, pyarrow._gcsfs, pyarrow._s3fs, multidict._multidict, yarl._quoting_c, propcache._helpers_c, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket.mask, aiohttp._websocket.reader_c, frozenlist._frozenlist, xxhash._xxhash, pyarrow._json, pyarrow._acero, pyarrow._csv, pyarrow._substrait, pyarrow._dataset, pyarrow._dataset_orc, pyarrow._parquet_encryption, pyarrow._dataset_parquet_encryption, pyarrow._dataset_parquet, markupsafe._speedups, PIL._imaging, sklearn.__check_build._check_build, scipy._lib._ccallback_c, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg._matfuncs_expm, scipy.linalg._linalg_pythran, scipy.linalg.cython_blas, scipy.linalg._decomp_update, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack, scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, psutil._psutil_linux, psutil._psutil_posix, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.spatial.transform._rotation, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize._cython_nnls, scipy._lib._uarray._uarray, scipy.linalg._decomp_interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integrate._lsoda, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._dierckx, scipy.interpolate._ppoly, scipy.interpolate._interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.interpolate._bspl, scipy.special.cython_special, scipy.stats._stats, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._biasedurn, scipy.stats._stats_pythran, scipy.stats._levy_stable.levyst, scipy.stats._ansari_swilk_statistics, 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in Manager File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 696 in iflatmap_unordered File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3165 in map File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 560 in wrapper File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/converter.py", line 281 in align_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 162 in _load_single_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 182 in _get_merged_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 304 in get_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 51 in run_sft File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72 in _training_function File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110 in run_exp File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 19 in main File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 28 in <module> Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, yaml._yaml, pyarrow.lib, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, _cffi_backend, pyarrow._parquet, pyarrow._fs, pyarrow._azurefs, pyarrow._hdfs, pyarrow._gcsfs, pyarrow._s3fs, multidict._multidict, yarl._quoting_c, propcache._helpers_c, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket.mask, aiohttp._websocket.reader_c, frozenlist._frozenlist, xxhash._xxhash, pyarrow._json, pyarrow._acero, pyarrow._csv, pyarrow._substrait, pyarrow._dataset, pyarrow._dataset_orc, pyarrow._parquet_encryption, pyarrow._dataset_parquet_encryption, pyarrow._dataset_parquet, markupsafe._speedups, PIL._imaging, sklearn.__check_build._check_build, scipy._lib._ccallback_c, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg._matfuncs_expm, scipy.linalg._linalg_pythran, scipy.linalg.cython_blas, scipy.linalg._decomp_update, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack, scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, psutil._psutil_linux, psutil._psutil_posix, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.spatial.transform._rotation, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize._cython_nnls, scipy._lib._uarray._uarray, scipy.linalg._decomp_interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integrate._lsoda, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._dierckx, scipy.interpolate._ppoly, scipy.interpolate._interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.interpolate._bspl, scipy.special.cython_special, scipy.stats._stats, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._biasedurn, scipy.stats._stats_pythran, scipy.stats._levy_stable.levyst, scipy.stats._ansari_swilk_statistics, scipy.stats._mvn, scipy.stats._rcont.rcont, scipy.ndimage._nd_image, scipy.ndimage._rank_filter_1d, _ni_label, scipy.ndimage._ni_label, _cyutility, sklearn._cyutility, sklearn.utils._isfinite, sklearn.utils.sparsefuncs_fast, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.preprocessing._csr_polynomial_expansion, sklearn.preprocessing._target_encoder_fast, sklearn.metrics._dist_metrics, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.utils._cython_blas, sklearn.metrics._pairwise_distances_reduction._base, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distances_reduction._argkmin_classmode, sklearn.utils._vector_sentinel, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_distances_reduction._radius_neighbors_classmode, sklearn.metrics._pairwise_fast, PIL._imagingft, av._core, av.logging, av.bytesource, av.buffer, av.audio.format, av.error, av.dictionary, av.container.pyio, av.utils, av.option, av.descriptor, av.format, av.stream, av.container.streams, av.sidedata.motionvectors, av.sidedata.sidedata, av.opaque, av.packet, av.container.input, av.container.output, av.container.core, av.codec.context, av.video.format, av.video.reformatter, av.plane, av.video.plane, av.video.frame, av.video.stream, av.codec.hwaccel, av.codec.codec, av.frame, av.audio.layout, av.audio.plane, av.audio.frame, av.audio.stream, av.filter.pad, av.filter.link, av.filter.context, av.filter.graph, av.filter.filter, av.filter.loudnorm, av.audio.resampler, av.audio.codeccontext, av.audio.fifo, av.bitstream, av.video.codeccontext, kiwisolver._cext, msgpack._cmsgpack, google._upb._message, setproctitle, uvloop.loop, ray._raylet, cuda_utils, greenlet._greenlet, sklearn.utils._random, sklearn.utils._seq_dataset, sklearn.linear_model._cd_fast, _loss, sklearn._loss._loss, sklearn.utils.arrayfuncs, sklearn.svm._liblinear, sklearn.svm._libsvm, sklearn.svm._libsvm_sparse, sklearn.linear_model._sag_fast, sklearn.utils._weight_vector, sklearn.linear_model._sgd_fast, regex._regex, sklearn.feature_extraction._hashing_fast, scipy.io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, sklearn.datasets._svmlight_format_fast (total: 278) Converting format of dataset (num_proc=16): 0%\| \| 0/3808 [00:00<?, ? examples/s] Converting format of dataset (num_proc=16): 11%\|█ \| 416/3808 [00:00<00:00, 4123.97 examples/s]Fatal Python error: Bus error Current thread 0x0000400031d55400 (most recent call first): File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/table.py", line 64 in _memory_mapped_arrow_table_from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/table.py", line 1017 in from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_reader.py", line 329 in read_table File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 740 in from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3531 in _map_single File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 678 in _write_generator_to_queue File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 125 in worker File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 108 in run File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 314 in _bootstrap File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 71 in _launch File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19 in __init__ File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/context.py", line 281 in _Popen File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 121 in start File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 329 in _repopulate_pool_static File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 306 in _repopulate_pool File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 215 in __init__ File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/context.py", line 119 in Pool File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3157 in map File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 560 in wrapper File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/converter.py", line 281 in align_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 162 in _load_single_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 182 in _get_merged_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 304 in get_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 51 in run_sft File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72 in _training_function File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110 in run_exp File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 19 in main File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 28 in <module> Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, yaml._yaml, pyarrow.lib, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, _cffi_backend, pyarrow._parquet, pyarrow._fs, pyarrow._azurefs, pyarrow._hdfs, pyarrow._gcsfs, pyarrow._s3fs, multidict._multidict, yarl._quoting_c, propcache._helpers_c, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket.mask, aiohttp._websocket.reader_c, frozenlist._frozenlist, xxhash._xxhash, pyarrow._json, pyarrow._acero, pyarrow._csv, pyarrow._substrait, pyarrow._dataset, pyarrow._dataset_orc, pyarrow._parquet_encryption, pyarrow._dataset_parquet_encryption, pyarrow._dataset_parquet, markupsafe._speedups, PIL._imaging, sklearn.__check_build._check_build, scipy._lib._ccallback_c, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg._matfuncs_expm, scipy.linalg._linalg_pythran, scipy.linalg.cython_blas, scipy.linalg._decomp_update, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack, scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, psutil._psutil_linux, psutil._psutil_posix, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.spatial.transform._rotation, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize._cython_nnls, scipy._lib._uarray._uarray, scipy.linalg._decomp_interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integrate._lsoda, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._dierckx, scipy.interpolate._ppoly, scipy.interpolate._interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.interpolate._bspl, scipy.special.cython_special, scipy.stats._stats, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._biasedurn, scipy.stats._stats_pythran, scipy.stats._levy_stable.levyst, scipy.stats._ansari_swilk_statistics, scipy.stats._mvn, scipy.stats._rcont.rcont, scipy.ndimage._nd_image, scipy.ndimage._rank_filter_1d, _ni_label, scipy.ndimage._ni_label, _cyutility, sklearn._cyutility, sklearn.utils._isfinite, sklearn.utils.sparsefuncs_fast, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.metrics.cluster._expected_mutual_info_fast, 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av.container.input, av.container.output, av.container.core, av.codec.context, av.video.format, av.video.reformatter, av.plane, av.video.plane, av.video.frame, av.video.stream, av.codec.hwaccel, av.codec.codec, av.frame, av.audio.layout, av.audio.plane, av.audio.frame, av.audio.stream, av.filter.pad, av.filter.link, av.filter.context, av.filter.graph, av.filter.filter, av.filter.loudnorm, av.audio.resampler, av.audio.codeccontext, av.audio.fifo, av.bitstream, av.video.codeccontext, kiwisolver._cext, msgpack._cmsgpack, google._upb._message, setproctitle, uvloop.loop, ray._raylet, cuda_utils, greenlet._greenlet, sklearn.utils._random, sklearn.utils._seq_dataset, sklearn.linear_model._cd_fast, _loss, sklearn._loss._loss, sklearn.utils.arrayfuncs, sklearn.svm._liblinear, sklearn.svm._libsvm, sklearn.svm._libsvm_sparse, sklearn.linear_model._sag_fast, sklearn.utils._weight_vector, sklearn.linear_model._sgd_fast, regex._regex, sklearn.feature_extraction._hashing_fast, scipy.io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, sklearn.datasets._svmlight_format_fast (total: 278) Converting format of dataset (num_proc=16): 100%\|██████████\| 3808/3808 [00:00<00:00, 19036.70 examples/s] Converting format of dataset (num_proc=16): 100%\|██████████\| 3808/3808 [00:00<00:00, 14971.61 examples/s] Converting format of dataset (num_proc=16): 0%\| \| 0/3808 [00:00<?, ? examples/s] Converting format of dataset (num_proc=16): 6%\|▋ \| 238/3808 [00:00<00:01, 2271.08 examples/s] Converting format of dataset (num_proc=16): 100%\|██████████\| 3808/3808 [00:00<00:00, 14538.98 examples/s] Converting format of dataset (num_proc=16): 100%\|██████████\| 3808/3808 [00:00<00:00, 9713.91 examples/s] Converting format of dataset (num_proc=16): 0%\| \| 0/3808 [00:00<?, ? examples/s] Converting format of dataset (num_proc=16): 51%\|█████ \| 1924/3808 [00:00<00:00, 17581.81 examples/s] Converting format of dataset (num_proc=16): 100%\|██████████\| 3808/3808 [00:00<00:00, 11821.26 examples/s] [rank3]:[W731 05:52:47.456438800 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 3] using GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id. [rank0]:[W731 05:52:47.439378833 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 0] using GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id. [rank2]:[W731 05:52:47.260647412 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 2] using GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id. [rank1]:[W731 05:52:47.597086551 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 1] using GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. 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Channel 00/04 : 0 1 2 3 c622-121:1217207:1217501 [0] NCCL INFO Channel 01/04 : 0 1 2 3 c622-121:1217207:1217501 [0] NCCL INFO Channel 02/04 : 0 1 2 3 c622-121:1217207:1217501 [0] NCCL INFO Channel 03/04 : 0 1 2 3 c622-121:1217207:1217501 [0] NCCL INFO Trees [0] 2/-1/-1->0->-1 [1] 2/-1/-1->0->-1 [2] -1/-1/-1->0->1 [3] -1/-1/-1->0->1 c622-121:1217207:1217501 [0] NCCL INFO P2P Chunksize set to 131072 c622-132:175384:175911 [0] NCCL INFO comm 0x3ab897c0 rank 3 nRanks 4 nNodes 4 localRanks 1 localRank 0 MNNVL 0 c622-122:1272061:1272347 [0] NCCL INFO Trees [0] -1/-1/-1->1->2 [1] -1/-1/-1->1->2 [2] 2/0/-1->1->3 [3] 2/0/-1->1->3 c622-122:1272061:1272347 [0] NCCL INFO P2P Chunksize set to 131072 c622-131:1037103:1037405 [0] NCCL INFO Trees [0] 1/3/-1->2->0 [1] 1/3/-1->2->0 [2] -1/-1/-1->2->1 [3] -1/-1/-1->2->1 c622-131:1037103:1037405 [0] NCCL INFO P2P Chunksize set to 131072 c622-132:175384:175911 [0] NCCL INFO Trees [0] -1/-1/-1->3->2 [1] -1/-1/-1->3->2 [2] 1/-1/-1->3->-1 [3] 1/-1/-1->3->-1 c622-132:175384:175911 [0] NCCL INFO P2P Chunksize set to 131072 c622-131:1037103:1037405 [0] NCCL INFO NCCL_NET_GDR_LEVEL set by environment to SYS c622-121:1217207:1217501 [0] NCCL INFO NCCL_NET_GDR_LEVEL set by environment to SYS c622-122:1272061:1272347 [0] NCCL INFO NCCL_NET_GDR_LEVEL set by environment to SYS c622-132:175384:175911 [0] NCCL INFO NCCL_NET_GDR_LEVEL set by environment to SYS c622-131:1037103:1037405 [0] NCCL INFO Channel 00/0 : 1[0] -> 2[0] [receive] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 00/0 : 3[0] -> 0[0] [receive] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[0] [receive] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 01/0 : 3[0] -> 0[0] [receive] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 01/0 : 1[0] -> 2[0] [receive] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 02/0 : 1[0] -> 2[0] [receive] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 02/0 : 3[0] -> 0[0] [receive] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[0] [receive] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[0] [receive] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 03/0 : 3[0] -> 0[0] [receive] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO NCCL_NET_GDR_READ set by environment to 1. c622-131:1037103:1037405 [0] NCCL INFO Channel 03/0 : 1[0] -> 2[0] [receive] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO NCCL_NET_GDR_READ set by environment to 1. c622-121:1217207:1217501 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[0] [send] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 00/0 : 2[0] -> 3[0] [receive] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[0] [receive] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO NCCL_NET_GDR_READ set by environment to 1. c622-122:1272061:1272347 [0] NCCL INFO Channel 00/0 : 1[0] -> 2[0] [send] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[0] [send] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 00/0 : 2[0] -> 3[0] [send] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 01/0 : 2[0] -> 3[0] [send] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 02/0 : 2[0] -> 3[0] [send] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[0] [send] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 01/0 : 1[0] -> 2[0] [send] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 02/0 : 1[0] -> 2[0] [send] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 01/0 : 2[0] -> 3[0] [receive] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 03/0 : 2[0] -> 3[0] [send] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[0] [send] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 03/0 : 1[0] -> 2[0] [send] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 02/0 : 2[0] -> 3[0] [receive] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 03/0 : 2[0] -> 3[0] [receive] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO NCCL_NET_GDR_READ set by environment to 1. c622-132:175384:175911 [0] NCCL INFO Channel 00/0 : 3[0] -> 0[0] [send] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 01/0 : 3[0] -> 0[0] [send] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 02/0 : 3[0] -> 0[0] [send] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 03/0 : 3[0] -> 0[0] [send] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Connected all rings c622-122:1272061:1272347 [0] NCCL INFO Connected all rings c622-131:1037103:1037405 [0] NCCL INFO Channel 00/0 : 0[0] -> 2[0] [receive] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Connected all rings c622-122:1272061:1272347 [0] NCCL INFO Channel 02/0 : 3[0] -> 1[0] [receive] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 01/0 : 0[0] -> 2[0] [receive] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 00/0 : 2[0] -> 0[0] [receive] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Connected all rings c622-122:1272061:1272347 [0] NCCL INFO Channel 03/0 : 3[0] -> 1[0] [receive] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 00/0 : 2[0] -> 0[0] [send] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 01/0 : 2[0] -> 0[0] [send] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 01/0 : 2[0] -> 0[0] [receive] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 02/0 : 1[0] -> 3[0] [receive] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 00/0 : 0[0] -> 2[0] [send] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 02/0 : 1[0] -> 3[0] [send] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 03/0 : 1[0] -> 3[0] [send] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 01/0 : 0[0] -> 2[0] [send] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 03/0 : 1[0] -> 3[0] [receive] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 02/0 : 3[0] -> 1[0] [send] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 03/0 : 3[0] -> 1[0] [send] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 02/0 : 1[0] -> 0[0] [receive] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Channel 03/0 : 1[0] -> 0[0] [receive] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 00/0 : 3[0] -> 2[0] [receive] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 01/0 : 3[0] -> 2[0] [receive] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 00/0 : 2[0] -> 1[0] [receive] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 00/0 : 2[0] -> 1[0] [send] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 01/0 : 2[0] -> 1[0] [send] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 00/0 : 3[0] -> 2[0] [send] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 01/0 : 2[0] -> 1[0] [receive] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 02/0 : 2[0] -> 1[0] [send] via NET/IB/0/GDRDMA c622-131:1037103:1037405 [0] NCCL INFO Channel 03/0 : 2[0] -> 1[0] [send] via NET/IB/0/GDRDMA c622-132:175384:175911 [0] NCCL INFO Channel 01/0 : 3[0] -> 2[0] [send] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 02/0 : 2[0] -> 1[0] [receive] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 03/0 : 2[0] -> 1[0] [receive] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 02/0 : 1[0] -> 0[0] [send] via NET/IB/0/GDRDMA c622-122:1272061:1272347 [0] NCCL INFO Channel 03/0 : 1[0] -> 0[0] [send] via NET/IB/0/GDRDMA c622-121:1217207:1217501 [0] NCCL INFO Connected all trees c622-132:175384:175911 [0] NCCL INFO Connected all trees c622-121:1217207:1217501 [0] NCCL INFO NCCL_PROTO set by environment to simple c622-121:1217207:1217501 [0] NCCL INFO threadThresholds 8/8/64 \| 32/8/64 \| 512 \| 512 c622-121:1217207:1217501 [0] NCCL INFO 4 coll channels, 4 collnet channels, 0 nvls channels, 4 p2p channels, 2 p2p channels per peer c622-132:175384:175911 [0] NCCL INFO NCCL_PROTO set by environment to simple c622-132:175384:175911 [0] NCCL INFO threadThresholds 8/8/64 \| 32/8/64 \| 512 \| 512 c622-132:175384:175911 [0] NCCL INFO 4 coll channels, 4 collnet channels, 0 nvls channels, 4 p2p channels, 2 p2p channels per peer c622-122:1272061:1272347 [0] NCCL INFO Connected all trees c622-131:1037103:1037405 [0] NCCL INFO Connected all trees c622-131:1037103:1037405 [0] NCCL INFO NCCL_PROTO set by environment to simple c622-131:1037103:1037405 [0] NCCL INFO threadThresholds 8/8/64 \| 32/8/64 \| 512 \| 512 c622-131:1037103:1037405 [0] NCCL INFO 4 coll channels, 4 collnet channels, 0 nvls channels, 4 p2p channels, 2 p2p channels per peer c622-122:1272061:1272347 [0] NCCL INFO NCCL_PROTO set by environment to simple c622-122:1272061:1272347 [0] NCCL INFO threadThresholds 8/8/64 \| 32/8/64 \| 512 \| 512 c622-122:1272061:1272347 [0] NCCL INFO 4 coll channels, 4 collnet channels, 0 nvls channels, 4 p2p channels, 2 p2p channels per peer c622-132:175384:175911 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so c622-132:175384:175911 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. c622-132:175384:175911 [0] NCCL INFO ncclCommInitRank comm 0x3ab897c0 rank 3 nranks 4 cudaDev 0 nvmlDev 0 busId 901000 commId 0xd012d196753fd3cf - Init COMPLETE c622-122:1272061:1272347 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so c622-122:1272061:1272347 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. c622-122:1272061:1272347 [0] NCCL INFO ncclCommInitRank comm 0x32441010 rank 1 nranks 4 cudaDev 0 nvmlDev 0 busId 901000 commId 0xd012d196753fd3cf - Init COMPLETE c622-131:1037103:1037405 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so c622-131:1037103:1037405 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. c622-131:1037103:1037405 [0] NCCL INFO ncclCommInitRank comm 0x4abe90b0 rank 2 nranks 4 cudaDev 0 nvmlDev 0 busId 901000 commId 0xd012d196753fd3cf - Init COMPLETE c622-121:1217207:1217501 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so c622-121:1217207:1217501 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. c622-121:1217207:1217501 [0] NCCL INFO ncclCommInitRank comm 0x2f9907f0 rank 0 nranks 4 cudaDev 0 nvmlDev 0 busId 901000 commId 0xd012d196753fd3cf - Init COMPLETE [rank3]: Traceback (most recent call last): [rank3]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 28, in <module> [rank3]: main() [rank3]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 19, in main [rank3]: run_exp() [rank3]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank3]: _training_function(config={"args": args, "callbacks": callbacks}) [rank3]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank3]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank3]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 51, in run_sft [rank3]: dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", *tokenizer_module) [rank3]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 304, in get_dataset [rank3]: dataset = _get_merged_dataset(data_args.dataset, model_args, data_args, training_args, stage) [rank3]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 182, in _get_merged_dataset [rank3]: datasets[dataset_name] = _load_single_dataset(dataset_attr, model_args, data_args, training_args) [rank3]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 162, in _load_single_dataset [rank3]: return align_dataset(dataset, dataset_attr, data_args, training_args) [rank3]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/converter.py", line 281, in align_dataset [rank3]: return dataset.map( [rank3]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 560, in wrapper [rank3]: out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) [rank3]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3165, in map [rank3]: for rank, done, content in iflatmap_unordered( [rank3]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 711, in iflatmap_unordered [rank3]: raise RuntimeError( [rank3]: RuntimeError: One of the subprocesses has abruptly died during map operation.To debug the error, disable multiprocessing. [rank2]: Traceback (most recent call last): [rank2]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 704, in iflatmap_unordered [rank2]: yield queue.get(timeout=0.05) [rank2]: File "<string>", line 2, in get [rank2]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/managers.py", line 818, in _callmethod [rank2]: kind, result = conn.recv() [rank2]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/connection.py", line 253, in recv [rank2]: buf = self._recv_bytes() [rank2]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/connection.py", line 417, in _recv_bytes [rank2]: buf = self._recv(4) [rank2]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/connection.py", line 386, in _recv [rank2]: raise EOFError [rank2]: EOFError [rank2]: During handling of the above exception, another exception occurred: [rank2]: Traceback (most recent call last): [rank2]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 28, in <module> [rank2]: main() [rank2]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 19, in main [rank2]: run_exp() [rank2]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank2]: _training_function(config={"args": args, "callbacks": callbacks}) [rank2]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank2]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank2]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 51, in run_sft [rank2]: dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", tokenizer_module) [rank2]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 304, in get_dataset [rank2]: dataset = _get_merged_dataset(data_args.dataset, model_args, data_args, training_args, stage) [rank2]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 182, in _get_merged_dataset [rank2]: datasets[dataset_name] = _load_single_dataset(dataset_attr, model_args, data_args, training_args) [rank2]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 162, in _load_single_dataset [rank2]: return align_dataset(dataset, dataset_attr, data_args, training_args) [rank2]: File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/converter.py", line 281, in align_dataset [rank2]: return dataset.map( [rank2]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 560, in wrapper [rank2]: out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) [rank2]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3165, in map [rank2]: for rank, done, content in iflatmap_unordered( [rank2]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 718, in iflatmap_unordered [rank2]: [async_result.get(timeout=0.05) for async_result in async_results] [rank2]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 718, in <listcomp> [rank2]: [async_result.get(timeout=0.05) for async_result in async_results] [rank2]: File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 770, in get [rank2]: raise TimeoutError [rank2]: multiprocess.context.TimeoutError [rank3]:[W731 05:52:48.146173730 ProcessGroupNCCL.cpp:1496] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) [rank2]:[W731 05:52:48.950121628 ProcessGroupNCCL.cpp:1496] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) c622-132:175384:175913 [0] NCCL INFO [Service thread] Connection closed by localRank 0 c622-131:1037103:1037407 [0] NCCL INFO [Service thread] Connection closed by localRank 0 Running tokenizer on dataset (num_proc=16): 0%\| \| 0/3808 [00:00<?, ? examples/s] Running tokenizer on dataset (num_proc=16): 6%\|▋ \| 238/3808 [00:00<00:14, 251.16 examples/s] Running tokenizer on dataset (num_proc=16): 12%\|█▎ \| 476/3808 [00:01<00:07, 473.47 examples/s]Fatal Python error: Bus error Current thread 0x000040000e725400 (most recent call first): File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/table.py", line 64 in _memory_mapped_arrow_table_from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/table.py", line 1017 in from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_reader.py", line 329 in read_table File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 740 in from_file File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3531 in _map_single File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 678 in _write_generator_to_queue File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 125 in worker File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 108 in run File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 314 in _bootstrap File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 71 in _launch File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19 in __init__ File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/context.py", line 281 in _Popen File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/process.py", line 121 in start File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 329 in _repopulate_pool_static File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 306 in _repopulate_pool File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/pool.py", line 215 in __init__ File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/multiprocess/context.py", line 119 in Pool File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3157 in map File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 560 in wrapper File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 256 in _get_preprocessed_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/data/loader.py", line 315 in get_dataset File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 51 in run_sft File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72 in _training_function File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110 in run_exp File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 19 in main File "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py", line 28 in <module> Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, yaml._yaml, pyarrow.lib, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, _cffi_backend, pyarrow._parquet, pyarrow._fs, pyarrow._azurefs, pyarrow._hdfs, pyarrow._gcsfs, pyarrow._s3fs, multidict._multidict, yarl._quoting_c, propcache._helpers_c, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket.mask, aiohttp._websocket.reader_c, frozenlist._frozenlist, xxhash._xxhash, pyarrow._json, pyarrow._acero, pyarrow._csv, pyarrow._substrait, pyarrow._dataset, pyarrow._dataset_orc, pyarrow._parquet_encryption, pyarrow._dataset_parquet_encryption, pyarrow._dataset_parquet, markupsafe._speedups, PIL._imaging, sklearn.__check_build._check_build, scipy._lib._ccallback_c, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg._matfuncs_expm, scipy.linalg._linalg_pythran, scipy.linalg.cython_blas, scipy.linalg._decomp_update, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack, scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, psutil._psutil_linux, psutil._psutil_posix, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.spatial.transform._rotation, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize._cython_nnls, scipy._lib._uarray._uarray, scipy.linalg._decomp_interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integrate._lsoda, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._dierckx, scipy.interpolate._ppoly, scipy.interpolate._interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.interpolate._bspl, scipy.special.cython_special, scipy.stats._stats, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._biasedurn, scipy.stats._stats_pythran, scipy.stats._levy_stable.levyst, scipy.stats._ansari_swilk_statistics, scipy.stats._mvn, scipy.stats._rcont.rcont, scipy.ndimage._nd_image, scipy.ndimage._rank_filter_1d, _ni_label, scipy.ndimage._ni_label, _cyutility, sklearn._cyutility, sklearn.utils._isfinite, sklearn.utils.sparsefuncs_fast, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.preprocessing._csr_polynomial_expansion, sklearn.preprocessing._target_encoder_fast, sklearn.metrics._dist_metrics, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.utils._cython_blas, sklearn.metrics._pairwise_distances_reduction._base, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distances_reduction._argkmin_classmode, sklearn.utils._vector_sentinel, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_distances_reduction._radius_neighbors_classmode, sklearn.metrics._pairwise_fast, PIL._imagingft, av._core, av.logging, av.bytesource, av.buffer, av.audio.format, av.error, av.dictionary, av.container.pyio, av.utils, av.option, av.descriptor, av.format, av.stream, av.container.streams, av.sidedata.motionvectors, av.sidedata.sidedata, av.opaque, av.packet, av.container.input, av.container.output, av.container.core, av.codec.context, av.video.format, av.video.reformatter, av.plane, av.video.plane, av.video.frame, av.video.stream, av.codec.hwaccel, av.codec.codec, av.frame, av.audio.layout, av.audio.plane, av.audio.frame, av.audio.stream, av.filter.pad, av.filter.link, av.filter.context, av.filter.graph, av.filter.filter, av.filter.loudnorm, av.audio.resampler, av.audio.codeccontext, av.audio.fifo, av.bitstream, av.video.codeccontext, kiwisolver._cext, msgpack._cmsgpack, google._upb._message, setproctitle, uvloop.loop, ray._raylet, cuda_utils, greenlet._greenlet, sklearn.utils._random, sklearn.utils._seq_dataset, sklearn.linear_model._cd_fast, _loss, sklearn._loss._loss, sklearn.utils.arrayfuncs, sklearn.svm._liblinear, sklearn.svm._libsvm, sklearn.svm._libsvm_sparse, sklearn.linear_model._sag_fast, sklearn.utils._weight_vector, sklearn.linear_model._sgd_fast, regex._regex, sklearn.feature_extraction._hashing_fast, scipy.io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, sklearn.datasets._svmlight_format_fast (total: 278) Running tokenizer on dataset (num_proc=16): 12%\|█▎ \| 476/3808 [00:01<00:10, 331.22 examples/s] c622-132:175384:175923 [0] NCCL INFO comm 0x3ab897c0 rank 3 nranks 4 cudaDev 0 busId 901000 - Abort COMPLETE c622-131:1037103:1037414 [0] NCCL INFO comm 0x4abe90b0 rank 2 nranks 4 cudaDev 0 busId 901000 - Abort COMPLETE Running tokenizer on dataset (num_proc=16): 0%\| \| 0/3808 [00:00<?, ? examples/s] Running tokenizer on dataset (num_proc=16): 6%\|▋ \| 238/3808 [00:00<00:14, 249.09 examples/s] Running tokenizer on dataset (num_proc=16): 12%\|█▎ \| 476/3808 [00:01<00:07, 475.60 examples/s] Running tokenizer on dataset (num_proc=16): 19%\|█▉ \| 714/3808 [00:01<00:04, 664.82 examples/s] Running tokenizer on dataset (num_proc=16): 38%\|███▊ \| 1428/3808 [00:01<00:01, 1660.51 examples/s] Running tokenizer on dataset (num_proc=16): 56%\|█████▋ \| 2142/3808 [00:01<00:00, 2589.25 examples/s] Running tokenizer on dataset (num_proc=16): 69%\|██████▉ \| 2618/3808 [00:01<00:00, 2572.71 examples/s] Running tokenizer on dataset (num_proc=16): 81%\|████████▏ \| 3094/3808 [00:01<00:00, 2260.65 examples/s] Running tokenizer on dataset (num_proc=16): 94%\|█████████▍\| 3570/3808 [00:02<00:00, 2683.52 examples/s] Running tokenizer on dataset (num_proc=16): 100%\|██████████\| 3808/3808 [00:02<00:00, 1734.75 examples/s] training example: input_ids: [151644, 8948, 198, 2610, 525, 1207, 16948, 11, 3465, 553, 54364, 14817, 13, 1446, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 16429, 279, 5109, 508, 23, 11, 220, 21, 21, 11, 220, 18, 21, 1125, 1855, 458, 23606, 429, 16819, 220, 16, 16, 15, 13, 1446, 646, 990, 6770, 34784, 7525, 17973, 11, 85922, 11777, 608, 8, 323, 1817, 1372, 646, 1172, 387, 1483, 3055, 13, 6928, 697, 975, 1573, 498, 4226, 13, 1597, 470, 279, 1590, 4226, 304, 366, 9217, 29, 690, 9217, 29, 9492, 11, 369, 3110, 1447, 27, 9217, 397, 8304, 220, 16, 25, 220, 16, 488, 220, 17, 284, 220, 18, 198, 8304, 220, 17, 25, 220, 18, 608, 220, 18, 284, 220, 16, 198, 522, 9217, 94367, 10061, 594, 1744, 3019, 553, 3019, 151645, 198, 151644, 77091, 198, 13708, 766, 397, 1249, 11625, 279, 3491, 315, 6825, 458, 23606, 1667, 279, 5109, 508, 23, 11, 220, 21, 21, 11, 220, 18, 21, 60, 429, 16819, 220, 16, 16, 15, 11, 582, 646, 1795, 1493, 7354, 1447, 8304, 220, 16, 25, 5145, 448, 279, 1372, 220, 23, 624, 8304, 220, 17, 25, 1205, 1184, 311, 5545, 279, 1372, 220, 16, 16, 15, 13, 6771, 594, 1430, 311, 990, 279, 1008, 1378, 5109, 11, 220, 21, 21, 323, 220, 18, 21, 11, 304, 264, 1616, 429, 686, 1492, 601, 633, 3265, 311, 220, 16, 16, 15, 624, 8304, 220, 18, 25, 24742, 429, 220, 21, 21, 488, 220, 18, 21, 284, 220, 16, 15, 17, 13, 1096, 374, 264, 1661, 1191, 1576, 432, 5221, 601, 3265, 311, 220, 16, 16, 15, 624, 8304, 220, 19, 25, 1205, 1184, 220, 23, 803, 311, 5545, 220, 16, 16, 15, 13, 1205, 646, 11075, 419, 553, 84192, 220, 23, 553, 220, 16, 13, 17, 20, 624, 8304, 220, 20, 25, 15277, 11, 279, 23606, 374, 25, 220, 23, 488, 220, 21, 21, 488, 220, 18, 21, 353, 320, 23, 608, 220, 16, 13, 17, 20, 8, 284, 220, 16, 16, 15, 382, 4416, 11, 279, 1590, 4226, 374, 1447, 27, 9217, 397, 8304, 220, 16, 25, 220, 23, 488, 220, 21, 21, 284, 220, 22, 19, 198, 8304, 220, 17, 25, 220, 22, 19, 488, 220, 18, 21, 284, 220, 16, 16, 15, 198, 522, 9217, 1472, 26865, 1339, 785, 1590, 4226, 374, 1447, 27, 9217, 397, 23, 488, 220, 18, 21, 488, 220, 21, 21, 198, 522, 9217, 29, 151645, 198] inputs: <\|im_start\|>system You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<\|im_end\|> <\|im_start\|>user Using the numbers [8, 66, 36], create an equation that equals 110. You can use basic arithmetic operations (+, -, , /) and each number can only be used once. Show your work before you answer. And return the final answer in <answer> </answer> tags, for example: <answer> Step 1: 1 + 2 = 3 Step 2: 3 / 3 = 1 </answer>. Let's think step by step<\|im_end\|> <\|im_start\|>assistant <think> To solve the problem of creating an equation using the numbers [8, 66, 36] that equals 110, we can follow these steps: Step 1: Start with the number 8. Step 2: We need to reach the number 110. Let's try to use the other two numbers, 66 and 36, in a way that will help us get close to 110. Step 3: Notice that 66 + 36 = 102. This is a good start because it gets us close to 110. Step 4: We need 8 more to reach 110. We can achieve this by multiplying 8 by 1.25. Step 5: Therefore, the equation is: 8 + 66 + 36 * (8 / 1.25) = 110. So, the final answer is: <answer> Step 1: 8 + 66 = 74 Step 2: 74 + 36 = 110 </answer></think> The final answer is: <answer> 8 + 36 + 66 </answer><\|im_end\|> label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 13708, 766, 397, 1249, 11625, 279, 3491, 315, 6825, 458, 23606, 1667, 279, 5109, 508, 23, 11, 220, 21, 21, 11, 220, 18, 21, 60, 429, 16819, 220, 16, 16, 15, 11, 582, 646, 1795, 1493, 7354, 1447, 8304, 220, 16, 25, 5145, 448, 279, 1372, 220, 23, 624, 8304, 220, 17, 25, 1205, 1184, 311, 5545, 279, 1372, 220, 16, 16, 15, 13, 6771, 594, 1430, 311, 990, 279, 1008, 1378, 5109, 11, 220, 21, 21, 323, 220, 18, 21, 11, 304, 264, 1616, 429, 686, 1492, 601, 633, 3265, 311, 220, 16, 16, 15, 624, 8304, 220, 18, 25, 24742, 429, 220, 21, 21, 488, 220, 18, 21, 284, 220, 16, 15, 17, 13, 1096, 374, 264, 1661, 1191, 1576, 432, 5221, 601, 3265, 311, 220, 16, 16, 15, 624, 8304, 220, 19, 25, 1205, 1184, 220, 23, 803, 311, 5545, 220, 16, 16, 15, 13, 1205, 646, 11075, 419, 553, 84192, 220, 23, 553, 220, 16, 13, 17, 20, 624, 8304, 220, 20, 25, 15277, 11, 279, 23606, 374, 25, 220, 23, 488, 220, 21, 21, 488, 220, 18, 21, 353, 320, 23, 608, 220, 16, 13, 17, 20, 8, 284, 220, 16, 16, 15, 382, 4416, 11, 279, 1590, 4226, 374, 1447, 27, 9217, 397, 8304, 220, 16, 25, 220, 23, 488, 220, 21, 21, 284, 220, 22, 19, 198, 8304, 220, 17, 25, 220, 22, 19, 488, 220, 18, 21, 284, 220, 16, 16, 15, 198, 522, 9217, 1472, 26865, 1339, 785, 1590, 4226, 374, 1447, 27, 9217, 397, 23, 488, 220, 18, 21, 488, 220, 21, 21, 198, 522, 9217, 29, 151645, 198] labels: <think> To solve the problem of creating an equation using the numbers [8, 66, 36] that equals 110, we can follow these steps: Step 1: Start with the number 8. Step 2: We need to reach the number 110. Let's try to use the other two numbers, 66 and 36, in a way that will help us get close to 110. Step 3: Notice that 66 + 36 = 102. This is a good start because it gets us close to 110. Step 4: We need 8 more to reach 110. We can achieve this by multiplying 8 by 1.25. Step 5: Therefore, the equation is: 8 + 66 + 36 * (8 / 1.25) = 110. So, the final answer is: <answer> Step 1: 8 + 66 = 74 Step 2: 74 + 36 = 110 </answer></think> The final answer is: <answer> 8 + 36 + 66 </answer><\|im_end\|> E0731 05:52:51.436000 1037099 site-packages/torch/distributed/elastic/multiprocessing/api.py:869] failed (exitcode: 1) local_rank: 0 (pid: 1037103) of binary: /work/10416/zaynesprague/anaconda3/envs/verl2/bin/python E0731 05:52:51.445000 175368 site-packages/torch/distributed/elastic/multiprocessing/api.py:869] failed (exitcode: 1) local_rank: 0 (pid: 175384) of binary: /work/10416/zaynesprague/anaconda3/envs/verl2/bin/python Traceback (most recent call last): File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/runpy.py", line 196, in _run_module_as_main Traceback (most recent call last): File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/run.py", line 922, in <module> return _run_code(code, main_globals, None, File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/run.py", line 922, in <module> main() File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper main() File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper return f(args, kwargs) File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/run.py", line 918, in main return f(args, **kwargs) File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/run.py", line 918, in main run(args) File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/run.py", line 909, in run run(args) File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/run.py", line 909, in run elastic_launch( File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 138, in __call__ elastic_launch( File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 138, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 269, in launch_agent raise ChildFailedError( return launch_agent(self._config, self._entrypoint, list(args)) File "/work/10416/zaynesprague/anaconda3/envs/verl2/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 269, in launch_agent torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ============================================================ /scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py FAILED ------------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2025-07-31_05:52:51 host : c622-131.vista.tacc.utexas.edu rank : 2 (local_rank: 0) exitcode : 1 (pid: 1037103) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ============================================================ /scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/src/train.py FAILED ------------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2025-07-31_05:52:51 host : c622-132.vista.tacc.utexas.edu rank : 3 (local_rank: 0) exitcode : 1 (pid: 175384) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ srun: error: c622-131: task 2: Exited with exit code 1 srun: error: c622-132: task 3: Exited with exit code 1 [ERROR] Stage error: KeyboardInterrupt:	Uploading the dataset shards: 0%\| \| 0/1 [00:00<?, ?it/s] Creating parquet from Arrow format: 0%\| \| 0/4 [00:00<?, ?ba/s][A Creating parquet from Arrow format: 100%\|███████████████████████████████████████\| 4/4 [00:00<00:00, 173.64ba/s] Uploading the dataset shards: 100%\|██████████████████████████████████████████████\| 1/1 [00:00<00:00, 1.36it/s] Uploading the dataset shards: 100%\|██████████████████████████████████████████████\| 1/1 [00:00<00:00, 1.36it/s] Uploading the dataset shards: 0%\| \| 0/1 [00:00<?, ?it/s] Creating parquet from Arrow format: 0%\| \| 0/1 [00:00<?, ?ba/s][A Creating parquet from Arrow format: 100%\|██████████████████████████████████████\| 1/1 [00:00<00:00, 3100.00ba/s] Uploading the dataset shards: 100%\|██████████████████████████████████████████████\| 1/1 [00:00<00:00, 2.62it/s] Uploading the dataset shards: 100%\|██████████████████████████████████████████████\| 1/1 [00:00<00:00, 2.62it/s]	sft_gs__singleton_structures__N_1__masked_low_lr	855.060793	true

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