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START TIME: Sat Jul  6 14:01:22 UTC 2024
python3 version = Python 3.10.14
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M	examples/config_tiny_llama.py
M	examples/config_tiny_llama.yaml
M	examples/train_tiny_llama.sh
Your branch is up to date with 'origin/bench_cluster'.
Job status: RUNNING
[2024-07-06 14:01:30,302] torch.distributed.run: [WARNING] 
[2024-07-06 14:01:30,302] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:30,302] torch.distributed.run: [WARNING] 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. 
[2024-07-06 14:01:30,302] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:30,529] torch.distributed.run: [WARNING] 
[2024-07-06 14:01:30,529] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:30,529] torch.distributed.run: [WARNING] 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. 
[2024-07-06 14:01:30,529] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:30,530] torch.distributed.run: [WARNING] 
[2024-07-06 14:01:30,530] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:30,530] torch.distributed.run: [WARNING] 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. 
[2024-07-06 14:01:30,530] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:30,533] torch.distributed.run: [WARNING] 
[2024-07-06 14:01:30,533] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:30,533] torch.distributed.run: [WARNING] 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. 
[2024-07-06 14:01:30,533] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:30,578] torch.distributed.run: [WARNING] 
[2024-07-06 14:01:30,578] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:30,578] torch.distributed.run: [WARNING] 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. 
[2024-07-06 14:01:30,578] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:30,973] torch.distributed.run: [WARNING] 
[2024-07-06 14:01:30,973] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:30,973] torch.distributed.run: [WARNING] 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. 
[2024-07-06 14:01:30,973] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:31,593] torch.distributed.run: [WARNING] 
[2024-07-06 14:01:31,593] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:31,593] torch.distributed.run: [WARNING] 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. 
[2024-07-06 14:01:31,593] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:32,092] torch.distributed.run: [WARNING] 
[2024-07-06 14:01:32,092] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 14:01:32,092] torch.distributed.run: [WARNING] 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. 
[2024-07-06 14:01:32,092] torch.distributed.run: [WARNING] *****************************************
[default0]:07/06/2024 14:01:53 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Vocab Size Padding] Padded vocab (size: 50257) with 7 dummy tokens (new size: 50264)
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config:
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            run='%date_%jobid',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            seed=42,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            step=None,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            consumed_train_samples=None,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            benchmark_csv_path=None,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            ignore_sanity_checks=True),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        parallelism=ParallelismArgs(dp=1,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    pp=8,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    tp=8,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f372a7648b0>,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    tp_linear_async_communication=False,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    expert_parallel_size=1),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 eos_token_id=2,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 hidden_act='silu',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 hidden_size=2048,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 initializer_range=0.02,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 intermediate_size=4096,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 is_llama_config=True,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 max_position_embeddings=4096,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 num_attention_heads=32,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 num_hidden_layers=24,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 num_key_value_heads=32,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 pad_token_id=None,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 pretraining_tp=1,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 rms_norm_eps=1e-05,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 rope_scaling=None,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 rope_theta=10000.0,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 tie_word_embeddings=True,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 use_cache=True,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 vocab_size=50264),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        init_method=RandomInit(std=0.025),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        dtype=torch.bfloat16,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        make_vocab_size_divisible_by=1,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        ddp_bucket_cap_mb=25),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                tokenizer_revision=None,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                tokenizer_max_length=None),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        checkpoints=CheckpointsArgs(checkpoints_path=PosixPath('/dev/null'),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    checkpoint_interval=100000,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    save_initial_state=False,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    resume_checkpoint_path=None,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        logging=LoggingArgs(log_level='info',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            log_level_replica='info',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            iteration_step_info_interval=1),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          train_steps=20,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          micro_batch_size=16,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          batch_accumulation_per_replica=64,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          val_check_interval=-1,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          limit_val_batches=0,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          limit_test_batches=0),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     adam_beta1=0.9,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     adam_beta2=0.95,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     torch_adam_is_fused=True,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     name='adamW'),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                zero_stage=1,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                weight_decay=0.01,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                clip_grad=1.0,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                accumulate_grad_in_fp32=True,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_warmup_steps=1,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_warmup_style='linear',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_decay_style='linear',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_decay_steps=19,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_decay_starting_step=None,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        min_decay_lr=1e-05)),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                      start_training_step=1,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                      data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 hf_dataset_splits='train',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 hf_dataset_config_name=None,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 text_column_name='text'),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                    seed=42,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                    num_loading_workers=0))],
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        profiler=ProfilerArgs(profiler_export_path=PosixPath('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-1_tp-8_pp-8_mbz-16')),
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        lighteval=None)
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Model Config:
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: LlamaConfig(bos_token_id=1,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             eos_token_id=2,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             hidden_act='silu',
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             hidden_size=2048,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             initializer_range=0.02,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             intermediate_size=4096,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             is_llama_config=True,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             max_position_embeddings=4096,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             num_attention_heads=32,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             num_hidden_layers=24,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             num_key_value_heads=32,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             pad_token_id=None,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             pretraining_tp=1,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             rms_norm_eps=1e-05,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             rope_scaling=None,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             rope_theta=10000.0,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             tie_word_embeddings=True,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             use_cache=True,
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             vocab_size=50264)
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Building model..
[default0]:07/06/2024 14:01:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Setting PP block ranks...
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=3|ip-26-0-171-62]: Local number of parameters: 12.9M (24.55MiB)
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=3|ip-26-0-171-62]: [After model building] Memory usage: 24.56MiB. Peak allocated: 24.58MiB Peak reserved: 28.00MiB
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=3|ip-26-0-171-62]: No checkpoint path provided.
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=1|ip-26-0-167-9]: Local number of parameters: 15.7M (30.02MiB)
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=1|ip-26-0-167-9]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=1|ip-26-0-167-9]: No checkpoint path provided.
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=0|ip-26-0-167-9]: Local number of parameters: 15.7M (30.02MiB)
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=0|ip-26-0-167-9]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=0|ip-26-0-167-9]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=6|ip-26-0-167-9]: Local number of parameters: 15.7M (30.02MiB)
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=6|ip-26-0-167-9]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=6|ip-26-0-167-9]: No checkpoint path provided.
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: Local number of parameters: 12.9M (24.55MiB)
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: [After model building] Memory usage: 24.56MiB. Peak allocated: 24.58MiB Peak reserved: 28.00MiB
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=5|ip-26-0-171-62]: Local number of parameters: 12.9M (24.55MiB)
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=5|ip-26-0-171-62]: [After model building] Memory usage: 24.56MiB. Peak allocated: 24.58MiB Peak reserved: 28.00MiB
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=5|ip-26-0-171-62]: No checkpoint path provided.
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=6|ip-26-0-171-62]: Local number of parameters: 12.9M (24.55MiB)
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=6|ip-26-0-171-62]: [After model building] Memory usage: 24.56MiB. Peak allocated: 24.58MiB Peak reserved: 28.00MiB
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=6|ip-26-0-171-62]: No checkpoint path provided.
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=1|ip-26-0-171-62]: Local number of parameters: 12.9M (24.55MiB)
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=1|ip-26-0-171-62]: [After model building] Memory usage: 24.56MiB. Peak allocated: 24.58MiB Peak reserved: 28.00MiB
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=1|ip-26-0-171-62]: No checkpoint path provided.
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=4|ip-26-0-171-62]: Local number of parameters: 12.9M (24.55MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=4|ip-26-0-171-62]: [After model building] Memory usage: 24.56MiB. Peak allocated: 24.58MiB Peak reserved: 28.00MiB
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=4|ip-26-0-171-62]: No checkpoint path provided.
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=2|ip-26-0-171-62]: Local number of parameters: 12.9M (24.55MiB)
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=2|ip-26-0-171-62]: [After model building] Memory usage: 24.56MiB. Peak allocated: 24.58MiB Peak reserved: 28.00MiB
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=2|ip-26-0-171-62]: No checkpoint path provided.
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=3|ip-26-0-167-9]: Local number of parameters: 15.7M (30.02MiB)
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=3|ip-26-0-167-9]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=3|ip-26-0-167-9]: No checkpoint path provided.
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=4|ip-26-0-167-9]: Local number of parameters: 15.7M (30.02MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=4|ip-26-0-167-9]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=4|ip-26-0-167-9]: No checkpoint path provided.
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=5|ip-26-0-167-9]: Local number of parameters: 15.7M (30.02MiB)
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=5|ip-26-0-167-9]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=5|ip-26-0-167-9]: No checkpoint path provided.
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=7|ip-26-0-167-9]: Local number of parameters: 15.7M (30.02MiB)
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=7|ip-26-0-167-9]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=7|ip-26-0-167-9]: No checkpoint path provided.
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=7|ip-26-0-171-62]: Local number of parameters: 12.9M (24.55MiB)
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=7|ip-26-0-171-62]: [After model building] Memory usage: 24.56MiB. Peak allocated: 24.58MiB Peak reserved: 28.00MiB
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=7|TP=7|ip-26-0-171-62]: No checkpoint path provided.
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Total number of parameters: 1.21G (2314.22MiB)
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Local number of parameters: 33.9M (64.57MiB)
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [After model building] Memory usage: 68.59MiB. Peak allocated: 70.62MiB Peak reserved: 78.00MiB
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Parametrizing model parameters using StandardParametrizator
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=0|ip-26-0-164-18]: Local number of parameters: 15.7M (30.02MiB)
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=0|ip-26-0-164-18]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=0|ip-26-0-164-18]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=6|ip-26-0-163-236]: Local number of parameters: 15.7M (30.02MiB)
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=6|ip-26-0-163-236]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=6|ip-26-0-163-236]: No checkpoint path provided.
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=0|ip-26-0-163-236]: Local number of parameters: 15.7M (30.02MiB)
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=0|ip-26-0-163-236]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=0|ip-26-0-163-236]: No checkpoint path provided.
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=0|ip-26-0-163-58]: Local number of parameters: 21M (40.03MiB)
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=0|ip-26-0-163-58]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=0|ip-26-0-163-58]: No checkpoint path provided.
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: Local number of parameters: 33.9M (64.57MiB)
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: [After model building] Memory usage: 68.59MiB. Peak allocated: 70.62MiB Peak reserved: 78.00MiB
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: No checkpoint path provided.
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: Local number of parameters: 33.9M (64.57MiB)
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: [After model building] Memory usage: 68.59MiB. Peak allocated: 70.62MiB Peak reserved: 78.00MiB
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: No checkpoint path provided.
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=0|ip-26-0-163-147]: Local number of parameters: 15.7M (30.02MiB)
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=0|ip-26-0-163-147]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=0|ip-26-0-163-147]: No checkpoint path provided.
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=3|ip-26-0-163-147]: Local number of parameters: 15.7M (30.02MiB)
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=3|ip-26-0-163-147]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=3|ip-26-0-163-147]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-225]: Local number of parameters: 33.9M (64.57MiB)
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=2|ip-26-0-170-160]: Local number of parameters: 21M (40.03MiB)
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=2|ip-26-0-170-160]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=2|ip-26-0-170-160]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-225]: [After model building] Memory usage: 68.59MiB. Peak allocated: 70.62MiB Peak reserved: 78.00MiB
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=1|ip-26-0-164-18]: Local number of parameters: 15.7M (30.02MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=4|ip-26-0-163-236]: Local number of parameters: 15.7M (30.02MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=4|ip-26-0-163-236]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=4|ip-26-0-163-236]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-225]: No checkpoint path provided.
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=1|ip-26-0-164-18]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-225]: Local number of parameters: 33.9M (64.57MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=4|ip-26-0-164-18]: Local number of parameters: 15.7M (30.02MiB)
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-225]: [After model building] Memory usage: 68.59MiB. Peak allocated: 70.62MiB Peak reserved: 78.00MiB
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-225]: Local number of parameters: 33.9M (64.57MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=4|ip-26-0-164-18]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=1|ip-26-0-164-18]: No checkpoint path provided.
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-225]: [After model building] Memory usage: 68.59MiB. Peak allocated: 70.62MiB Peak reserved: 78.00MiB
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=4|ip-26-0-164-18]: No checkpoint path provided.
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-225]: No checkpoint path provided.
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: Local number of parameters: 33.9M (64.57MiB)
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=7|ip-26-0-163-58]: Local number of parameters: 21M (40.03MiB)
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=7|ip-26-0-163-58]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=7|ip-26-0-163-58]: No checkpoint path provided.
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=2|ip-26-0-163-236]: Local number of parameters: 15.7M (30.02MiB)
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=2|ip-26-0-163-236]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=2|ip-26-0-163-236]: No checkpoint path provided.
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: [After model building] Memory usage: 68.59MiB. Peak allocated: 70.62MiB Peak reserved: 78.00MiB
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-225]: No checkpoint path provided.
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-225]: Local number of parameters: 33.9M (64.57MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-225]: [After model building] Memory usage: 68.59MiB. Peak allocated: 70.62MiB Peak reserved: 78.00MiB
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=7|ip-26-0-163-236]: Local number of parameters: 15.7M (30.02MiB)
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=7|ip-26-0-163-236]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=7|ip-26-0-163-236]: No checkpoint path provided.
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-225]: No checkpoint path provided.
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: No checkpoint path provided.
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=1|ip-26-0-163-58]: Local number of parameters: 21M (40.03MiB)
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=1|ip-26-0-163-58]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=1|ip-26-0-163-58]: No checkpoint path provided.
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=2|ip-26-0-167-9]: Local number of parameters: 15.7M (30.02MiB)
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=2|ip-26-0-167-9]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=5|TP=2|ip-26-0-167-9]: No checkpoint path provided.
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=2|ip-26-0-163-58]: Local number of parameters: 21M (40.03MiB)
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=2|ip-26-0-163-58]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=2|ip-26-0-163-58]: No checkpoint path provided.
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=4|ip-26-0-163-58]: Local number of parameters: 21M (40.03MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=4|ip-26-0-163-58]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=4|ip-26-0-163-58]: No checkpoint path provided.
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=7|ip-26-0-170-160]: Local number of parameters: 21M (40.03MiB)
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=7|ip-26-0-170-160]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=7|ip-26-0-170-160]: No checkpoint path provided.
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=5|ip-26-0-163-147]: Local number of parameters: 15.7M (30.02MiB)
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=1|ip-26-0-170-160]: Local number of parameters: 21M (40.03MiB)
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=1|ip-26-0-170-160]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=1|ip-26-0-170-160]: No checkpoint path provided.
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=5|ip-26-0-170-160]: Local number of parameters: 21M (40.03MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=4|ip-26-0-163-147]: Local number of parameters: 15.7M (30.02MiB)
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=5|ip-26-0-170-160]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=5|ip-26-0-170-160]: No checkpoint path provided.
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=3|ip-26-0-163-58]: Local number of parameters: 21M (40.03MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=4|ip-26-0-163-147]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=6|ip-26-0-170-160]: Local number of parameters: 21M (40.03MiB)
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=6|ip-26-0-170-160]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=3|ip-26-0-163-58]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=3|ip-26-0-163-58]: No checkpoint path provided.
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=4|ip-26-0-163-147]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=6|ip-26-0-170-160]: No checkpoint path provided.
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=5|ip-26-0-163-147]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=4|ip-26-0-170-160]: Local number of parameters: 21M (40.03MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=4|ip-26-0-170-160]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=1|ip-26-0-163-147]: Local number of parameters: 15.7M (30.02MiB)
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=1|ip-26-0-163-147]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=1|ip-26-0-163-147]: No checkpoint path provided.
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=5|ip-26-0-163-147]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=6|ip-26-0-163-147]: Local number of parameters: 15.7M (30.02MiB)
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=6|ip-26-0-163-58]: Local number of parameters: 21M (40.03MiB)
[default4]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=4|ip-26-0-170-160]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=6|ip-26-0-163-147]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=7|ip-26-0-163-147]: Local number of parameters: 15.7M (30.02MiB)
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=7|ip-26-0-163-147]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=7|ip-26-0-163-147]: No checkpoint path provided.
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=0|ip-26-0-170-160]: Local number of parameters: 21M (40.03MiB)
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=0|ip-26-0-170-160]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default0]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=0|ip-26-0-170-160]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=6|ip-26-0-163-147]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=6|ip-26-0-163-58]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=6|ip-26-0-163-58]: No checkpoint path provided.
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=5|ip-26-0-163-58]: Local number of parameters: 21M (40.03MiB)
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=5|ip-26-0-163-58]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=3|TP=5|ip-26-0-163-58]: No checkpoint path provided.
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=5|ip-26-0-163-236]: Local number of parameters: 15.7M (30.02MiB)
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=1|ip-26-0-163-236]: Local number of parameters: 15.7M (30.02MiB)
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=1|ip-26-0-163-236]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=3|ip-26-0-163-236]: Local number of parameters: 15.7M (30.02MiB)
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=3|ip-26-0-163-236]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default1]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=1|ip-26-0-163-236]: No checkpoint path provided.
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=5|ip-26-0-163-236]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=5|ip-26-0-163-236]: No checkpoint path provided.
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=2|TP=3|ip-26-0-163-236]: No checkpoint path provided.
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=2|ip-26-0-163-147]: Local number of parameters: 15.7M (30.02MiB)
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=2|ip-26-0-163-147]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=1|TP=2|ip-26-0-163-147]: No checkpoint path provided.
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=2|ip-26-0-164-18]: Local number of parameters: 15.7M (30.02MiB)
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=2|ip-26-0-164-18]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default2]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=2|ip-26-0-164-18]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=6|ip-26-0-164-18]: Local number of parameters: 15.7M (30.02MiB)
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=3|ip-26-0-170-160]: Local number of parameters: 21M (40.03MiB)
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=3|ip-26-0-170-160]: [After model building] Memory usage: 44.04MiB. Peak allocated: 46.07MiB Peak reserved: 52.00MiB
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=6|TP=3|ip-26-0-170-160]: No checkpoint path provided.
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=6|ip-26-0-164-18]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default6]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=6|ip-26-0-164-18]: No checkpoint path provided.
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=3|ip-26-0-164-18]: Local number of parameters: 15.7M (30.02MiB)
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=3|ip-26-0-164-18]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default3]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=3|ip-26-0-164-18]: No checkpoint path provided.
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=7|ip-26-0-164-18]: Local number of parameters: 15.7M (30.02MiB)
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=7|ip-26-0-164-18]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default7]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=7|ip-26-0-164-18]: No checkpoint path provided.
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=5|ip-26-0-164-18]: Local number of parameters: 15.7M (30.02MiB)
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=5|ip-26-0-164-18]: [After model building] Memory usage: 33.03MiB. Peak allocated: 35.06MiB Peak reserved: 50.00MiB
[default5]:07/06/2024 14:02:12 [INFO|DP=0|PP=4|TP=5|ip-26-0-164-18]: No checkpoint path provided.
[default0]:07/06/2024 14:02:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/06/2024 14:02:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/06/2024 14:02:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 0 has 33.9M out of 33.9M (100.00%) params' optimizer states
[default0]:07/06/2024 14:02:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/06/2024 14:02:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Using `datasets` library
[default0]:07/06/2024 14:02:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:07/06/2024 14:02:16 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 14:02:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Training Plan] There are 1 training stages 
[default0]:07/06/2024 14:02:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Stage Training Stage] start from step 1 
[default0]:07/06/2024 14:02:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: 
[default0]:07/06/2024 14:02:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Start training] datetime: 2024-07-06 14:02:28.251186 | mbs: 16 | grad_accum: 64 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default2]:07/06/2024 14:02:28 [WARNING|DP=0|PP=0|TP=2|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default2]: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.
[default6]:07/06/2024 14:02:28 [WARNING|DP=0|PP=7|TP=6|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 14:02:28 [WARNING|DP=0|PP=7|TP=2|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 14:02:28 [WARNING|DP=0|PP=7|TP=4|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 14:02:28 [WARNING|DP=0|PP=5|TP=3|ip-26-0-167-9]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 14:02:28 [WARNING|DP=0|PP=5|TP=7|ip-26-0-167-9]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 14:02:28 [WARNING|DP=0|PP=4|TP=0|ip-26-0-164-18]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 14:02:28 [WARNING|DP=0|PP=2|TP=6|ip-26-0-163-236]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 14:02:28 [WARNING|DP=0|PP=5|TP=2|ip-26-0-167-9]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 14:02:28 [WARNING|DP=0|PP=1|TP=4|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 14:02:28 [WARNING|DP=0|PP=2|TP=2|ip-26-0-163-236]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 14:02:28 [WARNING|DP=0|PP=2|TP=7|ip-26-0-163-236]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 14:02:28 [WARNING|DP=0|PP=0|TP=1|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 14:02:28 [WARNING|DP=0|PP=0|TP=6|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 14:02:28 [WARNING|DP=0|PP=3|TP=0|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 14:02:28 [WARNING|DP=0|PP=6|TP=7|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 14:02:28 [WARNING|DP=0|PP=0|TP=5|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 14:02:28 [WARNING|DP=0|PP=0|TP=3|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 14:02:28 [WARNING|DP=0|PP=3|TP=4|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 14:02:28 [WARNING|DP=0|PP=3|TP=2|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 14:02:28 [WARNING|DP=0|PP=3|TP=6|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 14:02:28 [WARNING|DP=0|PP=2|TP=5|ip-26-0-163-236]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 14:02:28 [WARNING|DP=0|PP=2|TP=3|ip-26-0-163-236]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 14:02:28 [WARNING|DP=0|PP=6|TP=6|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 14:02:28 [WARNING|DP=0|PP=4|TP=5|ip-26-0-164-18]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 14:02:28 [WARNING|DP=0|PP=4|TP=7|ip-26-0-164-18]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 14:02:28 [WARNING|DP=0|PP=6|TP=3|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 14:02:28 [WARNING|DP=0|PP=7|TP=3|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 14:02:28 [WARNING|DP=0|PP=5|TP=1|ip-26-0-167-9]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 14:02:28 [WARNING|DP=0|PP=7|TP=5|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 14:02:28 [WARNING|DP=0|PP=5|TP=0|ip-26-0-167-9]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 14:02:28 [WARNING|DP=0|PP=7|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 14:02:28 [WARNING|DP=0|PP=7|TP=1|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 14:02:28 [WARNING|DP=0|PP=5|TP=4|ip-26-0-167-9]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 14:02:28 [WARNING|DP=0|PP=7|TP=7|ip-26-0-171-62]: 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.
[default4]: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.
[default0]:07/06/2024 14:02:28 [WARNING|DP=0|PP=2|TP=0|ip-26-0-163-236]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 14:02:28 [WARNING|DP=0|PP=6|TP=2|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 14:02:28 [WARNING|DP=0|PP=1|TP=5|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 14:02:28 [WARNING|DP=0|PP=1|TP=0|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 14:02:28 [WARNING|DP=0|PP=1|TP=3|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 14:02:28 [WARNING|DP=0|PP=3|TP=7|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 14:02:28 [WARNING|DP=0|PP=6|TP=1|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 14:02:28 [WARNING|DP=0|PP=6|TP=5|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 14:02:28 [WARNING|DP=0|PP=0|TP=7|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 14:02:28 [WARNING|DP=0|PP=0|TP=4|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 14:02:28 [WARNING|DP=0|PP=3|TP=1|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 14:02:28 [WARNING|DP=0|PP=6|TP=4|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 14:02:28 [WARNING|DP=0|PP=6|TP=0|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 14:02:28 [WARNING|DP=0|PP=4|TP=2|ip-26-0-164-18]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 14:02:28 [WARNING|DP=0|PP=4|TP=6|ip-26-0-164-18]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 14:02:28 [WARNING|DP=0|PP=4|TP=3|ip-26-0-164-18]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 14:02:28 [WARNING|DP=0|PP=5|TP=6|ip-26-0-167-9]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 14:02:28 [WARNING|DP=0|PP=1|TP=6|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 14:02:28 [WARNING|DP=0|PP=1|TP=7|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 14:02:28 [WARNING|DP=0|PP=1|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 14:02:28 [WARNING|DP=0|PP=2|TP=4|ip-26-0-163-236]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 14:02:28 [WARNING|DP=0|PP=4|TP=4|ip-26-0-164-18]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 14:02:28 [WARNING|DP=0|PP=4|TP=1|ip-26-0-164-18]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 14:02:28 [WARNING|DP=0|PP=2|TP=1|ip-26-0-163-236]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 14:02:28 [WARNING|DP=0|PP=3|TP=3|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 14:02:28 [WARNING|DP=0|PP=3|TP=5|ip-26-0-163-58]: 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.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 14:02:28 [WARNING|DP=0|PP=5|TP=5|ip-26-0-167-9]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 14:02:28 [WARNING|DP=0|PP=1|TP=2|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 14:03:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/06/2024 14:03:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 328.58MiB. Peak allocated 328.59MiB. Peak reserved: 338.00MiB
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/__init__.py:266: 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]:  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/__init__.py:266: 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]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/__init__.py:266: 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]:  Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:  Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  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/__init__.py:266: 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]:  Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:07/06/2024 14:07:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 395.15MiB. Peak allocated 34579.76MiB. Peak reserved: 34684.00MiB
[default0]:07/06/2024 14:07:46 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 260K | tokens_per_sec: 16.1K | tokens_per_sec_per_gpu: 252 | global_batch_size: 1.02K | lm_loss: 11.1 | lr: 0.0001 | model_tflops_per_gpu: 2.29 | hardware_tflops_per_gpu: 2.29 | grad_norm: 13.4 | cuda_memory_allocated: 301M | cuda_max_memory_reserved: 4.85G | hd_total_memory_tb: 312G | hd_used_memory_tb: 69.5G | hd_free_memory_tb: 243G
[default0]:07/06/2024 14:07:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 655.29MiB. Peak reserved: 34684.00MiB
[default0]:07/06/2024 14:08:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:STAGE:2024-07-06 14:08:33 985827:985827 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[default0]:07/06/2024 14:08:33 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 47.5K | tokens_per_sec: 88.3K | tokens_per_sec_per_gpu: 1.38K | global_batch_size: 1.02K | lm_loss: 11.1 | lr: 9.53e-05 | model_tflops_per_gpu: 12.5 | hardware_tflops_per_gpu: 12.5 | grad_norm: 13.5 | cuda_memory_allocated: 301M | cuda_max_memory_reserved: 4.85G | hd_total_memory_tb: 312G | hd_used_memory_tb: 69.5G | hd_free_memory_tb: 243G
[default0]:07/06/2024 14:08:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 655.30MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:08:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:08:58 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 25.2K | tokens_per_sec: 166K | tokens_per_sec_per_gpu: 2.6K | global_batch_size: 1.02K | lm_loss: 9.89 | lr: 9.05e-05 | model_tflops_per_gpu: 23.6 | hardware_tflops_per_gpu: 23.6 | grad_norm: 17.3 | cuda_memory_allocated: 301M | cuda_max_memory_reserved: 4.85G | hd_total_memory_tb: 312G | hd_used_memory_tb: 69.5G | hd_free_memory_tb: 243G
[default0]:07/06/2024 14:08:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 655.30MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:09:22 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:09:22 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 23.8K | tokens_per_sec: 176K | tokens_per_sec_per_gpu: 2.76K | global_batch_size: 1.02K | lm_loss: 10.5 | lr: 8.58e-05 | model_tflops_per_gpu: 25 | hardware_tflops_per_gpu: 25 | grad_norm: 11.9 | cuda_memory_allocated: 301M | cuda_max_memory_reserved: 4.85G | hd_total_memory_tb: 312G | hd_used_memory_tb: 69.5G | hd_free_memory_tb: 243G
[default0]:07/06/2024 14:09:22 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 655.30MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:09:43 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 20.5K | tokens_per_sec: 204K | tokens_per_sec_per_gpu: 3.19K | global_batch_size: 1.02K | lm_loss: 9.72 | lr: 8.11e-05 | model_tflops_per_gpu: 29 | hardware_tflops_per_gpu: 29 | grad_norm: 9.81
[default0]:STAGE:2024-07-06 14:09:49 985827:985827 ActivityProfilerController.cpp:320] Completed Stage: Collection
[default0]:STAGE:2024-07-06 14:09:50 985827:985827 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[default0]:07/06/2024 14:10:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:11:05 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 21.5K | tokens_per_sec: 195K | tokens_per_sec_per_gpu: 3.04K | global_batch_size: 1.02K | lm_loss: 9.12 | lr: 7.63e-05 | model_tflops_per_gpu: 27.6 | hardware_tflops_per_gpu: 27.6 | grad_norm: 5.97
[default0]:07/06/2024 14:11:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:11:26 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 20.7K | tokens_per_sec: 202K | tokens_per_sec_per_gpu: 3.16K | global_batch_size: 1.02K | lm_loss: 8.76 | lr: 7.16e-05 | model_tflops_per_gpu: 28.7 | hardware_tflops_per_gpu: 28.7 | grad_norm: 5.56
[default0]:07/06/2024 14:11:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:11:46 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 20.4K | tokens_per_sec: 206K | tokens_per_sec_per_gpu: 3.22K | global_batch_size: 1.02K | lm_loss: 8.54 | lr: 6.68e-05 | model_tflops_per_gpu: 29.2 | hardware_tflops_per_gpu: 29.2 | grad_norm: 6.82
[default0]:07/06/2024 14:11:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:12:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:12:05 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 18.7K | tokens_per_sec: 224K | tokens_per_sec_per_gpu: 3.5K | global_batch_size: 1.02K | lm_loss: 8.22 | lr: 6.21e-05 | model_tflops_per_gpu: 31.7 | hardware_tflops_per_gpu: 31.7 | grad_norm: 4.82
[default0]:07/06/2024 14:12:24 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 18.9K | tokens_per_sec: 222K | tokens_per_sec_per_gpu: 3.47K | global_batch_size: 1.02K | lm_loss: 8.03 | lr: 5.74e-05 | model_tflops_per_gpu: 31.5 | hardware_tflops_per_gpu: 31.5 | grad_norm: 4.81
[default0]:07/06/2024 14:12:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:12:43 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 18.8K | tokens_per_sec: 223K | tokens_per_sec_per_gpu: 3.48K | global_batch_size: 1.02K | lm_loss: 7.84 | lr: 5.26e-05 | model_tflops_per_gpu: 31.6 | hardware_tflops_per_gpu: 31.6 | grad_norm: 4.56
[default0]:07/06/2024 14:12:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:13:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:13:02 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 19K | tokens_per_sec: 221K | tokens_per_sec_per_gpu: 3.45K | global_batch_size: 1.02K | lm_loss: 7.69 | lr: 4.79e-05 | model_tflops_per_gpu: 31.3 | hardware_tflops_per_gpu: 31.3 | grad_norm: 4.35
[default0]:07/06/2024 14:13:20 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 18.3K | tokens_per_sec: 230K | tokens_per_sec_per_gpu: 3.59K | global_batch_size: 1.02K | lm_loss: 7.54 | lr: 4.32e-05 | model_tflops_per_gpu: 32.6 | hardware_tflops_per_gpu: 32.6 | grad_norm: 3.75
[default0]:07/06/2024 14:13:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:13:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:13:39 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 19K | tokens_per_sec: 221K | tokens_per_sec_per_gpu: 3.45K | global_batch_size: 1.02K | lm_loss: 7.42 | lr: 3.84e-05 | model_tflops_per_gpu: 31.3 | hardware_tflops_per_gpu: 31.3 | grad_norm: 3.53
[default0]:07/06/2024 14:13:58 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 18.9K | tokens_per_sec: 221K | tokens_per_sec_per_gpu: 3.46K | global_batch_size: 1.02K | lm_loss: 7.29 | lr: 3.37e-05 | model_tflops_per_gpu: 31.4 | hardware_tflops_per_gpu: 31.4 | grad_norm: 3.36
[default0]:07/06/2024 14:13:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:14:16 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 17.9K | tokens_per_sec: 234K | tokens_per_sec_per_gpu: 3.66K | global_batch_size: 1.02K | lm_loss: 7.2 | lr: 2.89e-05 | model_tflops_per_gpu: 33.2 | hardware_tflops_per_gpu: 33.2 | grad_norm: 3.45
[default0]:07/06/2024 14:14:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:14:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:14:34 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 18K | tokens_per_sec: 232K | tokens_per_sec_per_gpu: 3.63K | global_batch_size: 1.02K | lm_loss: 7.12 | lr: 2.42e-05 | model_tflops_per_gpu: 33 | hardware_tflops_per_gpu: 33 | grad_norm: 3.57
[default0]:07/06/2024 14:14:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:14:51 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 17.5K | tokens_per_sec: 240K | tokens_per_sec_per_gpu: 3.74K | global_batch_size: 1.02K | lm_loss: 7.04 | lr: 1.95e-05 | model_tflops_per_gpu: 34 | hardware_tflops_per_gpu: 34 | grad_norm: 3.52
[default0]:07/06/2024 14:15:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 655.29MiB. Peak allocated 34839.89MiB. Peak reserved: 35196.00MiB
[default0]:07/06/2024 14:15:10 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 18.6K | tokens_per_sec: 226K | tokens_per_sec_per_gpu: 3.53K | global_batch_size: 1.02K | lm_loss: 6.98 | lr: 1.47e-05 | model_tflops_per_gpu: 32 | hardware_tflops_per_gpu: 32 | grad_norm: 3.33
[default0]:07/06/2024 14:15:28 [INFO|DP=0|PP=7|TP=0|ip-26-0-171-62]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 18.4K | tokens_per_sec: 228K | tokens_per_sec_per_gpu: 3.57K | global_batch_size: 1.02K | lm_loss: 6.93 | lr: 1e-05 | model_tflops_per_gpu: 32.3 | hardware_tflops_per_gpu: 32.3 | grad_norm: 3.08
[2024-07-06 14:15:39,220] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-163-147.ec2.internal_2066903_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 14:15:39,218] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-167-9.ec2.internal_2898288_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 14:15:39,236] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-164-18.ec2.internal_1031595_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 14:15:39,239] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-170-160.ec2.internal_2272474_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
Saved 1 csv files over 1 completed logs
Processing file: /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-1_tp-8_pp-8_mbz-16/profiler/ip-26-0-160-225_985827.1720275032620289115.pt.trace.json
Results written to /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-1_tp-8_pp-8_mbz-16/profiler.csv
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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