| ======================== | |
| START TIME: Tue Jul 2 19:24:09 UTC 2024 | |
| python3 version = Python 3.10.14 | |
| ======================== | |
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| Already on 'bench_cluster' | |
| M examples/config_tiny_llama.py | |
| M examples/config_tiny_llama.yaml | |
| M examples/train_tiny_llama.sh | |
| M src/nanotron/models/llama.py | |
| M src/nanotron/trainer.py | |
| Your branch is up to date with 'origin/bench_cluster'. | |
| Job status: RUNNING | |
| W0702 19:24:13.139000 139752326530880 torch/distributed/run.py:757] | |
| W0702 19:24:13.139000 139752326530880 torch/distributed/run.py:757] ***************************************** | |
| W0702 19:24:13.139000 139752326530880 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. | |
| W0702 19:24:13.139000 139752326530880 torch/distributed/run.py:757] ***************************************** | |
| W0702 19:24:17.930000 139948174030656 torch/distributed/run.py:757] | |
| W0702 19:24:17.930000 139948174030656 torch/distributed/run.py:757] ***************************************** | |
| W0702 19:24:17.930000 139948174030656 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. | |
| W0702 19:24:17.930000 139948174030656 torch/distributed/run.py:757] ***************************************** | |
| [default0]:07/02/2024 19:24:40 [WARNING|DP=0|PP=0|TP=0|ip-26-0-163-226]: [Vocab Size Padding] Padded vocab (size: 50257) with 7 dummy tokens (new size: 50264) | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Config: | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Config(general=GeneralArgs(project='bench_cluster', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: run='%date_%jobid', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: seed=42, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: step=None, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: consumed_train_samples=None, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: benchmark_csv_path=None, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: ignore_sanity_checks=True), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: parallelism=ParallelismArgs(dp=1, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: pp=2, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: tp=8, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f4b548b8730>, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: tp_linear_async_communication=False, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: expert_parallel_size=1), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: eos_token_id=2, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: hidden_act='silu', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: hidden_size=2048, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: initializer_range=0.02, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: intermediate_size=4096, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: is_llama_config=True, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: max_position_embeddings=4096, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: num_attention_heads=32, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: num_hidden_layers=24, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: num_key_value_heads=32, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: pad_token_id=None, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: pretraining_tp=1, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: rms_norm_eps=1e-05, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: rope_scaling=None, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: rope_theta=10000.0, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: tie_word_embeddings=True, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: use_cache=True, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: vocab_size=50264), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: init_method=RandomInit(std=0.025), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: dtype=torch.bfloat16, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: make_vocab_size_divisible_by=1, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: ddp_bucket_cap_mb=25), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: tokenizer_revision=None, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: tokenizer_max_length=None), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: checkpoint_interval=100000, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: save_initial_state=False, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: resume_checkpoint_path=None, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: checkpoints_path_is_shared_file_system=False), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: logging=LoggingArgs(log_level='info', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: log_level_replica='info', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: iteration_step_info_interval=1), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: tokens=TokensArgs(sequence_length=4096, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: train_steps=20, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: micro_batch_size=32, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: batch_accumulation_per_replica=32, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: val_check_interval=-1, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: limit_val_batches=0, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: limit_test_batches=0), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: adam_beta1=0.9, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: adam_beta2=0.95, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: torch_adam_is_fused=True, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: name='adamW'), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: zero_stage=1, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: weight_decay=0.01, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: clip_grad=1.0, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: accumulate_grad_in_fp32=True, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: lr_warmup_steps=1, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: lr_warmup_style='linear', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: lr_decay_style='linear', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: lr_decay_steps=19, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: lr_decay_starting_step=None, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: min_decay_lr=1e-05)), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: data_stages=[DatasetStageArgs(name='Training Stage', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: start_training_step=1, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: hf_dataset_splits='train', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: hf_dataset_config_name=None, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: dataset_processing_num_proc_per_process=64, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: dataset_overwrite_cache=False, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: text_column_name='text'), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: seed=42, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: num_loading_workers=32))], | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-8_pp-2_mbz-32')), | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: lighteval=None) | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Model Config: | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: LlamaConfig(bos_token_id=1, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: eos_token_id=2, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: hidden_act='silu', | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: hidden_size=2048, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: initializer_range=0.02, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: intermediate_size=4096, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: is_llama_config=True, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: max_position_embeddings=4096, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: num_attention_heads=32, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: num_hidden_layers=24, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: num_key_value_heads=32, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: pad_token_id=None, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: pretraining_tp=1, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: rms_norm_eps=1e-05, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: rope_scaling=None, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: rope_theta=10000.0, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: tie_word_embeddings=True, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: use_cache=True, | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: vocab_size=50264) | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Building model.. | |
| [default0]:07/02/2024 19:24:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Setting PP block ranks... | |
| [default2]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=2|ip-26-0-163-226]: Local number of parameters: 86.3M (164.65MiB) | |
| [default2]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=2|ip-26-0-163-226]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB | |
| [default2]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=2|ip-26-0-163-226]: No checkpoint path provided. | |
| [default3]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=3|ip-26-0-163-226]: Local number of parameters: 86.3M (164.65MiB) | |
| [default3]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=3|ip-26-0-163-226]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB | |
| [default3]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=3|ip-26-0-163-226]: No checkpoint path provided. | |
| [default6]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=6|ip-26-0-169-139]: Local number of parameters: 65.3M (124.62MiB) | |
| [default6]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=6|ip-26-0-169-139]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB | |
| [default6]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=6|ip-26-0-169-139]: No checkpoint path provided. | |
| [default0]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: Local number of parameters: 65.3M (124.62MiB) | |
| [default0]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB | |
| [default0]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: No checkpoint path provided. | |
| [default5]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=5|ip-26-0-169-139]: Local number of parameters: 65.3M (124.62MiB) | |
| [default5]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=5|ip-26-0-169-139]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB | |
| [default5]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=5|ip-26-0-169-139]: No checkpoint path provided. | |
| [default5]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=5|ip-26-0-163-226]: Local number of parameters: 86.3M (164.65MiB) | |
| [default5]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=5|ip-26-0-163-226]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB | |
| [default5]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=5|ip-26-0-163-226]: No checkpoint path provided. | |
| [default0]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Total number of parameters: 1.21G (2314.22MiB) | |
| [default0]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Local number of parameters: 86.3M (164.65MiB) | |
| [default0]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB | |
| [default0]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: No checkpoint path provided. | |
| [default0]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Parametrizing model parameters using StandardParametrizator | |
| [default6]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=6|ip-26-0-163-226]: Local number of parameters: 86.3M (164.65MiB) | |
| [default6]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=6|ip-26-0-163-226]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB | |
| [default6]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=6|ip-26-0-163-226]: No checkpoint path provided. | |
| [default1]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=1|ip-26-0-163-226]: Local number of parameters: 86.3M (164.65MiB) | |
| [default1]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=1|ip-26-0-163-226]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB | |
| [default1]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=1|ip-26-0-163-226]: No checkpoint path provided. | |
| [default7]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=7|ip-26-0-163-226]: Local number of parameters: 86.3M (164.65MiB) | |
| [default7]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=7|ip-26-0-163-226]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB | |
| [default7]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=7|ip-26-0-163-226]: No checkpoint path provided. | |
| [default4]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=4|ip-26-0-163-226]: Local number of parameters: 86.3M (164.65MiB) | |
| [default4]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=4|ip-26-0-163-226]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB | |
| [default4]:07/02/2024 19:24:57 [INFO|DP=0|PP=0|TP=4|ip-26-0-163-226]: No checkpoint path provided. | |
| [default4]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=4|ip-26-0-169-139]: Local number of parameters: 65.3M (124.62MiB) | |
| [default4]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=4|ip-26-0-169-139]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB | |
| [default4]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=4|ip-26-0-169-139]: No checkpoint path provided. | |
| [default1]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-139]: Local number of parameters: 65.3M (124.62MiB) | |
| [default1]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-139]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB | |
| [default2]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-139]: Local number of parameters: 65.3M (124.62MiB) | |
| [default2]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-139]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB | |
| [default2]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-139]: No checkpoint path provided. | |
| [default1]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-139]: No checkpoint path provided. | |
| [default3]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-139]: Local number of parameters: 65.3M (124.62MiB) | |
| [default3]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-139]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB | |
| [default3]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-139]: No checkpoint path provided. | |
| [default7]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=7|ip-26-0-169-139]: Local number of parameters: 65.3M (124.62MiB) | |
| [default7]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=7|ip-26-0-169-139]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB | |
| [default7]:07/02/2024 19:24:57 [INFO|DP=0|PP=1|TP=7|ip-26-0-169-139]: No checkpoint path provided. | |
| [default0]:07/02/2024 19:24:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: [Optimizer Building] Using LearningRateForSP as learning rate | |
| [default0]:07/02/2024 19:24:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: [ZeRO sharding] Size of optimizer params per rank: | |
| [default0]:07/02/2024 19:24:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: [ZeRO sharding] DP Rank 0 has 86.3M out of 86.3M (100.00%) params' optimizer states | |
| [default0]:07/02/2024 19:24:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples | |
| [default0]:07/02/2024 19:24:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Using `datasets` library | |
| [default0]:07/02/2024 19:24:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4') | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/02/2024 19:24:59 [WARNING|DP=0|PP=0|TP=0|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/02/2024 19:25:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: [Training Plan] There are 1 training stages | |
| [default0]:07/02/2024 19:25:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: [Stage Training Stage] start from step 1 | |
| [default0]:07/02/2024 19:25:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: | |
| [default0]:07/02/2024 19:25:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: [Start training] datetime: 2024-07-02 19:25:00.994370 | mbs: 32 | grad_accum: 32 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 | |
| [default0]:07/02/2024 19:25:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps | |
| [default0]:07/02/2024 19:25:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 839.67MiB. Peak allocated 839.67MiB. Peak reserved: 858.00MiB | |
| [default2]:07/02/2024 19:25:01 [WARNING|DP=0|PP=0|TP=2|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/02/2024 19:25:01 [WARNING|DP=0|PP=0|TP=3|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/02/2024 19:25:01 [WARNING|DP=0|PP=1|TP=6|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/02/2024 19:25:01 [WARNING|DP=0|PP=1|TP=5|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/02/2024 19:25:01 [WARNING|DP=0|PP=0|TP=7|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/02/2024 19:25:01 [WARNING|DP=0|PP=0|TP=6|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/02/2024 19:25:01 [WARNING|DP=0|PP=0|TP=4|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/02/2024 19:25:01 [WARNING|DP=0|PP=1|TP=4|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/02/2024 19:25:01 [WARNING|DP=0|PP=1|TP=2|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/02/2024 19:25:01 [WARNING|DP=0|PP=1|TP=1|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/02/2024 19:25:01 [WARNING|DP=0|PP=1|TP=7|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/02/2024 19:25:01 [WARNING|DP=0|PP=1|TP=0|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/02/2024 19:25:01 [WARNING|DP=0|PP=0|TP=5|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/02/2024 19:25:01 [WARNING|DP=0|PP=0|TP=1|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/02/2024 19:25:01 [WARNING|DP=0|PP=1|TP=3|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default6]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default6]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default0]: warnings.warn( | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default5]: warnings.warn( | |
| [default0]:07/02/2024 19:25:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 911.79MiB. Peak allocated 60816.71MiB. Peak reserved: 61496.00MiB | |
| [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default6]: warnings.warn( | |
| [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default1]: warnings.warn( | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default7]: warnings.warn( | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default4]: warnings.warn( | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default2]: warnings.warn( | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default3]: warnings.warn( | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default7]: warnings.warn( | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default3]: warnings.warn( | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default5]: warnings.warn( | |
| [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default6]: warnings.warn( | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default2]: warnings.warn( | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default0]: warnings.warn( | |
| [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default1]: warnings.warn( | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default4]: warnings.warn( | |
| [default0]:07/02/2024 19:25:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 1573.27MiB. Peak reserved: 61496.00MiB | |
| [default0]:07/02/2024 19:25:42 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 35.3K | tokens_per_sec: 119K | tokens_per_sec_per_gpu: 7.43K | global_batch_size: 1.02K | lm_loss: 11.2 | lr: 0.0001 | model_tflops_per_gpu: 67.4 | hardware_tflops_per_gpu: 67.4 | grad_norm: 12.1 | cuda_memory_allocated: 1.26G | cuda_max_memory_reserved: 31.9G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G | |
| [default0]:07/02/2024 19:25:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:25:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 1573.31MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:25:59 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 16.8K | tokens_per_sec: 250K | tokens_per_sec_per_gpu: 15.6K | global_batch_size: 1.02K | lm_loss: 11.2 | lr: 9.53e-05 | model_tflops_per_gpu: 142 | hardware_tflops_per_gpu: 142 | grad_norm: 12.2 | cuda_memory_allocated: 1.26G | cuda_max_memory_reserved: 31.9G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G | |
| [default0]:07/02/2024 19:26:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:STAGE:2024-07-02 19:26:16 3030049:3030049 ActivityProfilerController.cpp:314] Completed Stage: Warm Up | |
| [default0]:07/02/2024 19:26:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 1573.31MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:26:16 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 17K | tokens_per_sec: 246K | tokens_per_sec_per_gpu: 15.4K | global_batch_size: 1.02K | lm_loss: 10 | lr: 9.05e-05 | model_tflops_per_gpu: 140 | hardware_tflops_per_gpu: 140 | grad_norm: 51.6 | cuda_memory_allocated: 1.26G | cuda_max_memory_reserved: 31.9G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G | |
| [default0]:07/02/2024 19:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:26:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 1573.31MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:26:33 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 16.8K | tokens_per_sec: 249K | tokens_per_sec_per_gpu: 15.6K | global_batch_size: 1.02K | lm_loss: 11.7 | lr: 8.58e-05 | model_tflops_per_gpu: 141 | hardware_tflops_per_gpu: 141 | grad_norm: 18.3 | cuda_memory_allocated: 1.26G | cuda_max_memory_reserved: 31.9G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G | |
| [default0]:07/02/2024 19:26:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:26:49 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 16.8K | tokens_per_sec: 250K | tokens_per_sec_per_gpu: 15.6K | global_batch_size: 1.02K | lm_loss: 10.4 | lr: 8.11e-05 | model_tflops_per_gpu: 142 | hardware_tflops_per_gpu: 142 | grad_norm: 16 | |
| [default0]:07/02/2024 19:27:06 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 16.4K | tokens_per_sec: 255K | tokens_per_sec_per_gpu: 16K | global_batch_size: 1.02K | lm_loss: 9.9 | lr: 7.63e-05 | model_tflops_per_gpu: 145 | hardware_tflops_per_gpu: 145 | grad_norm: 9.07 | |
| [default0]:STAGE:2024-07-02 19:27:18 3030049:3030049 ActivityProfilerController.cpp:320] Completed Stage: Collection | |
| [default0]:STAGE:2024-07-02 19:27:19 3030049:3030049 ActivityProfilerController.cpp:324] Completed Stage: Post Processing | |
| [default0]:07/02/2024 19:28:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:29:02 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 117K | tokens_per_sec: 36K | tokens_per_sec_per_gpu: 2.25K | global_batch_size: 1.02K | lm_loss: 9.37 | lr: 7.16e-05 | model_tflops_per_gpu: 20.4 | hardware_tflops_per_gpu: 20.4 | grad_norm: 6.23 | |
| [default0]:07/02/2024 19:29:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:29:19 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 16.6K | tokens_per_sec: 253K | tokens_per_sec_per_gpu: 15.8K | global_batch_size: 1.02K | lm_loss: 8.89 | lr: 6.68e-05 | model_tflops_per_gpu: 144 | hardware_tflops_per_gpu: 144 | grad_norm: 5.76 | |
| [default0]:07/02/2024 19:29:19 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:29:36 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 17.3K | tokens_per_sec: 243K | tokens_per_sec_per_gpu: 15.2K | global_batch_size: 1.02K | lm_loss: 8.8 | lr: 6.21e-05 | model_tflops_per_gpu: 138 | hardware_tflops_per_gpu: 138 | grad_norm: 11.2 | |
| [default0]:07/02/2024 19:29:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:29:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:29:53 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 16.5K | tokens_per_sec: 255K | tokens_per_sec_per_gpu: 15.9K | global_batch_size: 1.02K | lm_loss: 8.33 | lr: 5.74e-05 | model_tflops_per_gpu: 144 | hardware_tflops_per_gpu: 144 | grad_norm: 5.72 | |
| [default0]:07/02/2024 19:30:09 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:30:09 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 16.7K | tokens_per_sec: 252K | tokens_per_sec_per_gpu: 15.7K | global_batch_size: 1.02K | lm_loss: 8.06 | lr: 5.26e-05 | model_tflops_per_gpu: 143 | hardware_tflops_per_gpu: 143 | grad_norm: 4.91 | |
| [default0]:07/02/2024 19:30:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:30:26 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 16.9K | tokens_per_sec: 249K | tokens_per_sec_per_gpu: 15.5K | global_batch_size: 1.02K | lm_loss: 7.9 | lr: 4.79e-05 | model_tflops_per_gpu: 141 | hardware_tflops_per_gpu: 141 | grad_norm: 4.86 | |
| [default0]:07/02/2024 19:30:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:30:43 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 17K | tokens_per_sec: 247K | tokens_per_sec_per_gpu: 15.4K | global_batch_size: 1.02K | lm_loss: 7.75 | lr: 4.32e-05 | model_tflops_per_gpu: 140 | hardware_tflops_per_gpu: 140 | grad_norm: 4.69 | |
| [default0]:07/02/2024 19:31:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:31:00 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 16.5K | tokens_per_sec: 254K | tokens_per_sec_per_gpu: 15.9K | global_batch_size: 1.02K | lm_loss: 7.62 | lr: 3.84e-05 | model_tflops_per_gpu: 144 | hardware_tflops_per_gpu: 144 | grad_norm: 4.69 | |
| [default0]:07/02/2024 19:31:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:31:16 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 16.7K | tokens_per_sec: 251K | tokens_per_sec_per_gpu: 15.7K | global_batch_size: 1.02K | lm_loss: 7.48 | lr: 3.37e-05 | model_tflops_per_gpu: 143 | hardware_tflops_per_gpu: 143 | grad_norm: 4.49 | |
| [default0]:07/02/2024 19:31:33 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 16.9K | tokens_per_sec: 248K | tokens_per_sec_per_gpu: 15.5K | global_batch_size: 1.02K | lm_loss: 7.34 | lr: 2.89e-05 | model_tflops_per_gpu: 141 | hardware_tflops_per_gpu: 141 | grad_norm: 3.99 | |
| [default0]:07/02/2024 19:31:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:31:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:31:51 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 17.5K | tokens_per_sec: 240K | tokens_per_sec_per_gpu: 15K | global_batch_size: 1.02K | lm_loss: 7.23 | lr: 2.42e-05 | model_tflops_per_gpu: 136 | hardware_tflops_per_gpu: 136 | grad_norm: 3.54 | |
| [default0]:07/02/2024 19:32:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:32:08 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 17.1K | tokens_per_sec: 245K | tokens_per_sec_per_gpu: 15.3K | global_batch_size: 1.02K | lm_loss: 7.16 | lr: 1.95e-05 | model_tflops_per_gpu: 139 | hardware_tflops_per_gpu: 139 | grad_norm: 3.28 | |
| [default0]:07/02/2024 19:32:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-226]: Memory usage: 1573.27MiB. Peak allocated 61477.19MiB. Peak reserved: 62200.00MiB | |
| [default0]:07/02/2024 19:32:25 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 16.9K | tokens_per_sec: 247K | tokens_per_sec_per_gpu: 15.5K | global_batch_size: 1.02K | lm_loss: 7.09 | lr: 1.47e-05 | model_tflops_per_gpu: 140 | hardware_tflops_per_gpu: 140 | grad_norm: 3.2 | |
| [default0]:07/02/2024 19:32:42 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 16.6K | tokens_per_sec: 252K | tokens_per_sec_per_gpu: 15.7K | global_batch_size: 1.02K | lm_loss: 7.03 | lr: 1e-05 | model_tflops_per_gpu: 143 | hardware_tflops_per_gpu: 143 | grad_norm: 3.1 | |
| W0702 19:33:03.841000 139942507210496 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-169-139.ec2.internal_2495797_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousTimeoutError. | |
| Traceback (most recent call last): | |
| File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/main.py", line 4, in <module> | |
| from bench_cluster.submit_jobs import submit_jobs, check_status | |
| ImportError: cannot import name 'check_status' from 'bench_cluster.submit_jobs' (/fsx/ferdinandmom/ferdinand-hf/bench_cluster/bench_cluster/submit_jobs.py) | |
| Traceback (most recent call last): | |
| File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/main.py", line 4, in <module> | |
| from bench_cluster.submit_jobs import submit_jobs, check_status | |
| ImportError: cannot import name 'check_status' from 'bench_cluster.submit_jobs' (/fsx/ferdinandmom/ferdinand-hf/bench_cluster/bench_cluster/submit_jobs.py) | |
| Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details. | |
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