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HF Trainer Comparison⚓︎

📊 Data⚓︎

CONFIG Polaris Aurora
Local Batch Size 1 1
World Size 8 24
Global Batch Size 8 24
Sequence Length 8192 8192
Epoch 1.0 2.24
Perplexity 16.6743 12.7268
Input tokens seen 6,553,600 19,660,800
Total FLOPs 6,691,434 GF 6,691,434 GF
METRICS Polaris Aurora
steps/sec 0.245 0.334
samples/sec 1.958 8.021
tokens/sec 2,004.87 2,737.98
tokens/sample ~ 10243 ~ 3414
tokens/step ~ 81835 ~ 81976
tokens/sec/gpu 250.61 114.08
TRAIN Polaris Aurora
loss 3.0482 2.8478
runtime 0:06:48.60 0:04:59.19
samples 25,000 25,000
EVAL Polaris Aurora
accuracy 0.4328 0.4701
loss 2.8139 2.5437
runtime 0:00:06.26 0:00:09.12
samples 50 50
samples/sec 1.118 0.329
steps/sec 0.16 0.11
W&B run glad-moon-11 cosmic-sunset-52

Model Config⚓︎

AuroraGPT-2B 
{
"architectures": [
    "LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"dtype": "bfloat16",
"eos_token_id": 128001,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": {
    "factor": 32.0,
    "high_freq_factor": 4.0,
    "low_freq_factor": 1.0,
    "original_max_position_embeddings": 8192,
    "rope_type": "llama3"
},
"rope_theta": 500000.0,
"tie_word_embeddings": true,
"transformers_version": "4.57.3",
"use_cache": true,
"vocab_size": 128256
}

🔍 Details⚓︎

Samples vs. Tokens per Step

  • [samples/step] = [samples/sec] / [steps/sec]

    • Polaris: 1.958 / 0.245 ≈ 8.0 samples/step (✅ matches global batch 8)
    • Aurora : 8.021 / 0.334 ≈ 24.0 samples/step (✅ matches global batch 24)

  • [tokens/step] = [tokens/sec]/[steps/sec]

    • Polaris: 2004.87 / 0.245 ≈ 8183 tokens/step
    • Aurora : 2737.98 / 0.334 ≈ 8197 tokens/step

So both runs are doing ~8192 tokens/step (close enough).

Since we know that \(\text{tokens/sec} = (\text{tokens/step}) \times (\text{steps/sec})\), then:

The difference in tokens/sec is almost entirely explained by the difference in steps/sec.

Fair Comparison⚓︎

Since tokens/step is ~ equal, compare steps/sec (or step time):

  • Polaris: 0.245 steps/sec
    • → step time ≈ 4.08 s/step
  • Aurora : 0.334 steps/sec
    • → step time ≈ 2.99 s/step

Aurora is ~1.36× faster per optimizer step (0.334 / 0.245), and therefore ~1.36× faster in tokens/sec, for this exact training configuration.

We can normalize by device count (though this penalizes Aurora with 3× more devices):

  • steps/sec/device:

    • Polaris: 0.245 / 8 = 0.0306
    • Aurora : 0.334 / 24 = 0.0139

    So per device, Polaris is ~2.2× "better" on this metric.

But this is to be somewhat expected since they're operating at different scales, i.e.: 24-way data parallel will usually lose per-device efficiency to comms/overheads vs 8-way, unless you increase work per device (bigger microbatch, longer seq, etc.).

So:

  1. Time-to-train / throughput at chosen scale:
    • Aurora: 1.36× higher steps/sec and tokens/sec; this is what we'd care about, operationally.
  2. Scaling efficiency

    • Per-device efficiency ratio: 

      [0.334 / 24] / [0.245 / 8] ≈ 0.45

      i.e. Aurora with 24 devices is delivering ~45% of the per-device step throughput on Polaris with 8 devices.

      (how much we're paying for using more devices)

🫸 Packing in our Sequences⚓︎

Note that the tokens/sample are different:

  • Polaris: ~ 8183 / 8 ≈ 1024 [tokens/sample]
  • Aurora : ~ 8197 / 24 ≈ 341 [tokens/sample]

This means that we're not feeding the same "sample" definition (sequence length / packing / truncation).

This is OK, as long as we're comparing per-step or per-token, but not per-sample!

In order to truly do a fair comparison, we'd need to:

  1. fix seq_len and packing so tokens/sample matches, then
  2. sweep DP size (8 vs 24) on each system and plot step time vs devices

This would tell us whether Aurora is "less efficient per device" because of comms, kernel maturity, input pipeline, or just the scaling point we chose.

🪵 Logs⚓︎

Aurora 
#[aurora_frameworks-2025.2.0](ezpz-distributed-metrics-aurora_frameworks-2025.2.0)
#[12/18/25,13:21:37][x4310c7s4b0n0][/f/A/A/E/A/t/s/ezpz-distributed-metrics][distributed-metrics][$?] [1m11s]
; ckpt=/flare/AuroraGPT/AuroraGPT-v1/Experiments/AuroraGPT-2B/public/sophiag/hf/global_step138650 
; ezpz launch python3 -m ezpz.examples.hf_trainer \
    --streaming \
    --dataset_name=stanfordnlp/imdb \
    --model_name_or_path="${ckpt}" \
    --bf16=true \
    --do_train=true \
    --do_eval=true \
    --report-to=wandb \
    --logging-steps=1 \
    --include-tokens-per-second=true \
    --max-steps=100 \
    --include-num-input-tokens-seen=true \
    --optim=adamw_torch \
    --logging-first-step \
    --include-for-metrics='inputs,loss' \
    --max-eval-samples=50 \
    --per_device_train_batch_size=1 \
    --per_device_eval_batch_size=1 \
    --block_size=8192 \
    --gradient_checkpointing=true \
    --fsdp=auto_wrap \
    --output_dir=$(tstamp)
  • Output 
    # [2025-12-18 13:21:50,091003][I][ezpz/launch:378:launch] ----[🍋 ezpz.launch][started][2025-12-18-132150]----
# [2025-12-18 13:21:51,231621][I][ezpz/launch:396:launch] Job ID: 8219131
# [2025-12-18 13:21:51,232430][I][ezpz/launch:397:launch] nodelist: ['x4310c7s4b0n0', 'x4418c6s1b0n0']
# [2025-12-18 13:21:51,232855][I][ezpz/launch:398:launch] hostfile: /var/spool/pbs/aux/8219131.aurora-pbs-0001.hostmgmt.cm.aurora.alcf.anl.gov
# [2025-12-18 13:21:51,233490][I][ezpz/pbs:329:get_pbs_launch_cmd] ✅ Using [24/24] GPUs [2 hosts] x [12 GPU/host]
# [2025-12-18 13:21:51,234263][I][ezpz/launch:354:build_executable] Building command to execute by piecing together:
# [2025-12-18 13:21:51,234692][I][ezpz/launch:355:build_executable] (1.) launch_cmd: mpiexec --envall --np=24 --ppn=12 --hostfile=/var/spool/pbs/aux/8219131.aurora-pbs-0001.hostmgmt.cm.aurora.alcf.anl.gov --no-vni --cpu-bind=verbose,list:2-4:10-12:18-20:26-28:34-36:42-44:54-56:62-64:70-72:78-80:86-88:94-96
[2025-12-18 13:21:51,235394][I][ezpz/launch:356:build_executable] (2.) cmd_to_launch: python3 -m ezpz.examples.hf_trainer --streaming --dataset_name=stanfordnlp/imdb --model_name_or_path /flare/AuroraGPT/AuroraGPT-v1/Experiments/AuroraGPT-2B/public/sophiag/hf/global_step138650 --bf16=true --do_train=true --do_eval=true --report-to=wandb --logging-steps=1 --include-tokens-per-second=true --max-steps=100 --include-num-input-tokens-seen=true --optim=adamw_torch --logging-first-step --include-for-metrics=inputs,loss --max-eval-samples=50 --per_device_train_batch_size=1 --per_device_eval_batch_size=1 --block_size=8192 --gradient_checkpointing=true --fsdp=auto_wrap --output_dir=2025-12-18-132143
# [2025-12-18 13:21:51,237243][I][ezpz/launch:412:launch] Took: 1.22 seconds to build command.
# [2025-12-18 13:21:51,237645][I][ezpz/launch:413:launch] Executing:
mpiexec
--envall
--np=24
--ppn=12
--hostfile=/var/spool/pbs/aux/8219131.aurora-pbs-0001.hostmgmt.cm.aurora.alcf.anl.gov
--no-vni
--cpu-bind=verbose,list:2-4:10-12:18-20:26-28:34-36:42-44:54-56:62-64:70-72:78-80:86-88:94-96
python3
-m
ezpz.examples.hf_trainer
--streaming
--dataset_name=stanfordnlp/imdb
--model_name_or_path
/flare/AuroraGPT/AuroraGPT-v1/Experiments/AuroraGPT-2B/public/sophiag/hf/global_step138650
--bf16=true
--do_train=true
--do_eval=true
--report-to=wandb
--logging-steps=1
--include-tokens-per-second=true
--max-steps=100
--include-num-input-tokens-seen=true
--optim=adamw_torch
--logging-first-step
--include-for-metrics=inputs,loss
--max-eval-samples=50
--per_device_train_batch_size=1
--per_device_eval_batch_size=1
--block_size=8192
--gradient_checkpointing=true
--fsdp=auto_wrap
--output_dir=2025-12-18-132143
[2025-12-18 13:21:51,240032][I][ezpz/launch:213:get_aurora_filters] Filtering for Aurora-specific messages. To view list of filters, run with EZPZ_LOG_LEVEL=DEBUG
[2025-12-18 13:21:51,240580][I][ezpz/launch:420:launch] Execution started @ 2025-12-18-132151...
[2025-12-18 13:21:51,241009][I][ezpz/launch:421:launch] ----[🍋 ezpz.launch][stop][2025-12-18-132151]----
[2025-12-18 13:21:51,241473][I][ezpz/launch:132:run_command] Caught 24 filters
[2025-12-18 13:21:51,241853][I][ezpz/launch:133:run_command] Running command:
mpiexec --envall --np=24 --ppn=12 --hostfile=/var/spool/pbs/aux/8219131.aurora-pbs-0001.hostmgmt.cm.aurora.alcf.anl.gov --no-vni --cpu-bind=verbose,list:2-4:10-12:18-20:26-28:34-36:42-44:54-56:62-64:70-72:78-80:86-88:94-96 python3 -m ezpz.examples.hf_trainer --streaming --dataset_name=stanfordnlp/imdb --model_name_or_path /flare/AuroraGPT/AuroraGPT-v1/Experiments/AuroraGPT-2B/public/sophiag/hf/global_step138650 --bf16=true --do_train=true --do_eval=true --report-to=wandb --logging-steps=1 --include-tokens-per-second=true --max-steps=100 --include-num-input-tokens-seen=true --optim=adamw_torch --logging-first-step --include-for-metrics=inputs,loss --max-eval-samples=50 --per_device_train_batch_size=1 --per_device_eval_batch_size=1 --block_size=8192 --gradient_checkpointing=true --fsdp=auto_wrap --output_dir=2025-12-18-132143
[2025-12-18 13:22:09,760765][I][ezpz/dist:1926:setup_wandb] Using WB_PROJECT=ezpz-hf_trainer--flare-AuroraGPT-AuroraGPT-v1-Experiments-AuroraGPT-2B-public-sophiag-hf-global_step138650
[2025-12-18 13:22:11,079430][I][ezpz/dist:1955:setup_wandb] wandb.run=[cosmic-sunset-5](https://wandb.ai/aurora_gpt/ezpz-hf_trainer--flare-AuroraGPT-AuroraGPT-v1-Experiments-AuroraGPT-2B-public-sophiag-hf-global_step138650/runs/pqytcarn)
[WARNING|trainer.py:982] 2025-12-18 13:23:46,860 >> The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly,being updated with the tokenizers values. Updated tokens: {'eos_token_id': 1, 'bos_token_id': 2, 'pad_token_id': 0}.
[INFO|trainer.py:2519] 2025-12-18 13:23:51,164 >> ***** Running training *****
[INFO|trainer.py:2520] 2025-12-18 13:23:51,164 >>   Num examples = 2,400
[INFO|trainer.py:2521] 2025-12-18 13:23:51,164 >>   Num Epochs = 9,223,372,036,854,775,807
[INFO|trainer.py:2522] 2025-12-18 13:23:51,164 >>   Instantaneous batch size per device = 1
[INFO|trainer.py:2525] 2025-12-18 13:23:51,164 >>   Total train batch size (w. parallel, distributed & accumulation) = 24
[INFO|trainer.py:2526] 2025-12-18 13:23:51,165 >>   Gradient Accumulation steps = 1
[INFO|trainer.py:2527] 2025-12-18 13:23:51,165 >>   Total optimization steps = 100
[INFO|trainer.py:2528] 2025-12-18 13:23:51,165 >>   Number of trainable parameters = 82,752,264
[INFO|integration_utils.py:867] 2025-12-18 13:23:51,171 >> Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"
0%|          | 0/100 [00:00<?, ?it/s]

# [...clipped...]

{'loss': 2.6031, 'grad_norm': 1.8801486492156982, 'learning_rate': 5.000000000000001e-07, 'epoch': 2.24, 'num_input_tokens_seen': 19660800, 'train_runtime': 252.007, 'train_tokens_per_second': 78016.894}
100%|##########| 100/100 [04:11<00:00,  2.27s/it]

# [...clipped...]

[INFO|trainer.py:4309] 2025-12-18 13:28:05,079 >> Saving model checkpoint to 2025-12-18-132143/checkpoint-100
{'train_runtime': 299.1983, 'train_samples_per_second': 8.021, 'train_steps_per_second': 0.334, 'train_tokens_per_second': 2737.984, 'train_loss': 2.8478338932991027, 'epoch': 2.24, 'num_input_tokens_seen': 19660800}
100%|##########| 100/100 [04:59<00:00,  2.27s/it]
[INFO|trainer.py:4309] 2025-12-18 13:28:52,199 >> Saving model checkpoint to 2025-12-18-132143
***** train metrics *****
epoch                    =       2.24
num_input_tokens_seen    =   19660800
total_flos               =  6691434GF
train_loss               =     2.8478
train_runtime            = 0:04:59.19
train_samples            =      25000
train_samples_per_second =      8.021
train_steps_per_second   =      0.334
train_tokens_per_second  =   2737.984
[INFO|trainer.py:4643] 2025-12-18 13:29:00,076 >>
***** Running Evaluation *****
[INFO|trainer.py:4647] 2025-12-18 13:29:00,077 >>   Num examples: Unknown
[INFO|trainer.py:4648] 2025-12-18 13:29:00,077 >>   Batch size = 1
***** eval metrics *****
epoch                   =       2.24
eval_accuracy           =     0.4701
eval_loss               =     2.5437
eval_runtime            = 0:00:09.12
eval_samples            =         50
eval_samples_per_second =      0.329
eval_steps_per_second   =       0.11
num_input_tokens_seen   =   19660800
perplexity              =    12.7268
wandb:
wandb: 🚀 View run cosmic-sunset-5 at:
wandb: Find logs at: ../../../../../../../../lus/flare/projects/AuroraGPT/AuroraGPT-v1/Experiments/AuroraGPT-2B/tt/saforem2/ezpz-distributed-metrics/wandb/run-20251218_132210-pqytcarn/logs
Polaris 
# (2025-09-25/base) (ezpz-distributed-metrics-mconda3)
#[/e/A/f/p/s/ezpz-distributed-metrics][🌱 distributed-metrics][🤷✓] [⏱️ 1m28s]
#[12/18/25 @ 13:32:34][x3006c0s1b0n0]
; ckpt=/eagle/AuroraGPT/foremans/Downloads/global_step138650
; ezpz launch python3 -m ezpz.examples.hf_trainer \
    --streaming \
    --dataset_name=stanfordnlp/imdb \
    --model_name_or_path "${ckpt}" \
    --bf16=true \
    --do_train=true \
    --do_eval=true \
    --report-to=wandb \
    --logging-steps=1 \
    --include-tokens-per-second=true \
    --max-steps=100 \
    --include-num-input-tokens-seen=true \
    --optim=adamw_torch \
    --logging-first-step \
    --include-for-metrics='inputs,loss' \
    --max-eval-samples=50 \
    --per_device_train_batch_size=1 \
    --per_device_eval_batch_size=1 \
    --block_size=8192 \
    --gradient_checkpointing=true \
    --fsdp=auto_wrap \
    --output_dir=$(tstamp)
  • Output 
    [2025-12-18 13:32:45,480638][i][ezpz/launch:378:launch] ----[🍋 ezpz.launch][started][2025-12-18-133245]----
[2025-12-18 13:32:46,548182][i][ezpz/launch:396:launch] job id: 6815207
[2025-12-18 13:32:46,549238][i][ezpz/launch:397:launch] nodelist: ['x3006c0s1b0n0', 'x3006c0s25b0n0']
[2025-12-18 13:32:46,549674][i][ezpz/launch:398:launch] hostfile: /var/spool/pbs/aux/6815207.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov
[2025-12-18 13:32:46,550353][i][ezpz/pbs:329:get_pbs_launch_cmd] ✅ using [8/8] gpus [2 hosts] x [4 gpu/host]
[2025-12-18 13:32:46,551713][i][ezpz/launch:354:build_executable] building command to execute by piecing together:
[2025-12-18 13:32:46,552123][i][ezpz/launch:355:build_executable] (1.) launch_cmd: mpiexec --envall --np=8 --ppn=4 --hostfile=/var/spool/pbs/aux/6815207.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov --cpu-bind=depth --depth=8
[2025-12-18 13:32:46,552678][i][ezpz/launch:356:build_executable] (2.) cmd_to_launch: python3 -m ezpz.examples.hf_trainer --streaming --dataset_name=stanfordnlp/imdb --model_name_or_path /eagle/auroragpt/foremans/downloads/global_step138650 --bf16=true --do_train=true --do_eval=true --report-to=wandb --logging-steps=1--include-tokens-per-second=true --max-steps=100 --include-num-input-tokens-seen=true --optim=adamw_torch --logging-first-step --include-for-metrics=inputs,loss --max-eval-samples=50 --per_device_train_batch_size=1 --per_device_eval_batch_size=1 --block_size=8192 --gradient_checkpointing=true --fsdp=auto_wrap --output_dir=2025-12-18-133234
[2025-12-18 13:32:46,554200][i][ezpz/launch:412:launch] took: 1.43 seconds to build command.
[2025-12-18 13:32:46,554592][i][ezpz/launch:413:launch] executing:
mpiexec
--envall
--np=8
--ppn=4
--hostfile=/var/spool/pbs/aux/6815207.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov
--cpu-bind=depth
--depth=8
python3
-m
ezpz.examples.hf_trainer
--streaming
--dataset_name=stanfordnlp/imdb
--model_name_or_path
/eagle/auroragpt/foremans/downloads/global_step138650
--bf16=true
--do_train=true
--do_eval=true
--report-to=wandb
--logging-steps=1
--include-tokens-per-second=true
--max-steps=100
--include-num-input-tokens-seen=true
--optim=adamw_torch
--logging-first-step
--include-for-metrics=inputs,loss
--max-eval-samples=50
--per_device_train_batch_size=1
--per_device_eval_batch_size=1
--block_size=8192
--gradient_checkpointing=true
--fsdp=auto_wrap
--output_dir=2025-12-18-133234
[2025-12-18 13:32:46,557424][i][ezpz/launch:420:launch] execution started @ 2025-12-18-133246...
[2025-12-18 13:32:46,557890][i][ezpz/launch:421:launch] ----[🍋 ezpz.launch][stop][2025-12-18-133246]----
[2025-12-18 13:32:46,558395][i][ezpz/launch:133:run_command] running command:
mpiexec --envall --np=8 --ppn=4 --hostfile=/var/spool/pbs/aux/6815207.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov --cpu-bind=depth --depth=8 python3 -m ezpz.examples.hf_trainer --streaming --dataset_name=stanfordnlp/imdb --model_name_or_path /eagle/auroragpt/foremans/downloads/global_step138650 --bf16=true --do_train=true --do_eval=true --report-to=wandb --logging-steps=1 --include-tokens-per-second=true --max-steps=100 --include-num-input-tokens-seen=true --optim=adamw_torch --logging-first-step --include-for-metrics=inputs,loss --max-eval-samples=50 --per_device_train_batch_size=1 --per_device_eval_batch_size=1 --block_size=8192 --gradient_checkpointing=true --fsdp=auto_wrap --output_dir=2025-12-18-133234
[2025-12-18 13:33:46,605589][i][ezpz/dist:1926:setup_wandb] using wb_project=ezpz-hf_trainer--eagle-auroragpt-foremans-downloads-global_step138650
[2025-12-18 13:33:54,462981][i][ezpz/dist:1955:setup_wandb] wandb.run=[glad-moon-1](https://wandb.ai/aurora_gpt/ezpz-hf_trainer--eagle-auroragpt-foremans-downloads-global_step138650/runs/k1rvbdmc)
[info|trainer.py:2519] 2025-12-18 13:34:42,086 >> ***** running training *****
[info|trainer.py:2520] 2025-12-18 13:34:42,087 >>   num examples = 800
[info|trainer.py:2521] 2025-12-18 13:34:42,087 >>   num epochs = 9,223,372,036,854,775,807
[info|trainer.py:2522] 2025-12-18 13:34:42,087 >>   instantaneous batch size per device = 1
[info|trainer.py:2525] 2025-12-18 13:34:42,087 >>   total train batch size (w. parallel, distributed & accumulation) = 8
[info|trainer.py:2526] 2025-12-18 13:34:42,087 >>   gradient accumulation steps = 1
[info|trainer.py:2527] 2025-12-18 13:34:42,087 >>   total optimization steps = 100
[info|trainer.py:2528] 2025-12-18 13:34:42,087 >>   number of trainable parameters = 248,256,768
[info|integration_utils.py:867] 2025-12-18 13:34:43,444 >> automatic weights & biases logging enabled, to disable set os.environ["wandb_disabled"] = "true"
0%|          | 0/100 [00:00<?, ?it/s]
# [...clipped...]
{'loss': 2.9705, 'grad_norm': 1.2514474391937256, 'learning_rate': 5.000000000000001e-07, 'epoch': 1.0, 'num_input_tokens_seen': 6553600, 'train_runtime': 356.0234, 'train_tokens_per_second': 18407.78}
100%|##########| 100/100 [05:54<00:00,  3.45s/it]
# [...clipped...]
[info|trainer.py:4309] 2025-12-18 13:40:42,445 >> saving model checkpoint to 2025-12-18-133234/checkpoint-100
{'train_runtime': 408.6051, 'train_samples_per_second': 1.958, 'train_steps_per_second': 0.245, 'train_tokens_per_second': 2004.87, 'train_loss': 3.048191022872925, 'epoch': 1.0, 'num_input_tokens_seen': 6553600}
100%|##########| 100/100 [06:47<00:00,  3.45s/it]
100%|##########| 100/100 [06:47<00:00,  4.07s/it]
[info|trainer.py:4309] 2025-12-18 13:41:34,895 >> saving model checkpoint to 2025-12-18-133234
***** train metrics *****
epoch                    =        1.0
num_input_tokens_seen    =    6553600
total_flos               =  6691434gf
train_loss               =     3.0482
train_runtime            = 0:06:48.60
train_samples            =      25000
train_samples_per_second =      1.958
train_steps_per_second   =      0.245
train_tokens_per_second  =    2004.87
[info|trainer.py:4643] 2025-12-18 13:41:43,747 >>
***** running evaluation *****
[info|trainer.py:4647] 2025-12-18 13:41:43,747 >>   num examples: unknown
[info|trainer.py:4648] 2025-12-18 13:41:43,747 >>   batch size = 1
***** eval metrics *****
epoch                   =        1.0
eval_accuracy           =     0.4328
eval_loss               =     2.8139
eval_runtime            = 0:00:06.26
eval_samples            =         50
eval_samples_per_second =      1.118
eval_steps_per_second   =       0.16
num_input_tokens_seen   =    6553600
perplexity              =    16.6743
wandb:
wandb: 🚀 view run glad-moon-1 at:
wandb: find logs at: ../../../../../../lus/eagle/projects/auroragpt/foremans/projects/saforem2/ezpz-distributed-metrics/wandb/run-20251218_133347-k1rvbdmc/logs

  1. W&B Run: glad-moon-1 ↩

  2. W&B Run: cosmic-sunset-5 ↩

  3. Tokens per sample on Polaris: ↩

  4. Tokens per sample on Aurora: ↩

  5. Tokens per optimizer step on Polaris: - ~ 8183 = (2004.87 / 0.245) [tokens_per_second / steps_per_second] ↩

  6. Tokens per optimizer step on Aurora: - ~ 8197 = (2737.984 / 0.334) [tokens_per_second / steps_per_second] ↩