This model was trained using Microsoft's AzureML and DeepSpeed's ZeRO 2 optimization. It was fine-tuned on the SAMSum corpus from t5-large checkpoint.

More information on the fine-tuning process (includes samples and benchmarks):
(currently still WIP, major updates coming soon: 7/6/21~7/9/21)

Resource Usage

These results are retrieved from AzureML Studio's resource monitoring module. All experiments were ran on AzureML's low priority clusters.

key value
AzureML SKU ND40rs_v2 (8 X V100 32GB)
Region US West 2
Run Duration 12m 47.13s
Compute Cost (LowPriority/Dedicated) $0.94/$4.69 (USD)
Average CPU Utilization 51.2%
Average GPU Utilization 42.0%
GPU Memory Usage (Avg/Peak) 24.85/28.79 (GB)
Total GPU Energy Usage 670.38 (kJ)

*Compute cost is calculated from run duration and SKU's price per hour. Updated SKU pricing could be found here: https://azure.microsoft.com/en-us/pricing/details/machine-learning/
*Peak memory usage is calculated from average peak across all utilized GPUs.

Carbon Emissions

These results are obtained using codecarbon. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime).
CodeCarbon: https://github.com/mlco2/codecarbon

key value
timestamp 2021-07-08T06:29:27
duration 515.5018835067749
emissions 0.043562840982919106
energy_consumed 0.14638051405550773
country_name USA
region Washington
cloud_provider azure
cloud_region westus2


fp16: True
per device batch size: 8
effective batch size: 64
epoch: 3.0
learning rate: 1e-4
weight decay: 0.1
seed: 1

*Same per device batch size for evaluations


Optimizer = AdamW, Scheduler = WarmupDecayLR, Offload = none

  "zero_optimization": {
    "stage": 2,
    "allgather_partitions": true,
    "allgather_bucket_size": 1300000000,
    "overlap_comm": true,
    "reduce_scatter": true,
    "reduce_bucket_size": 1300000000,
    "contiguous_gradients": true


from transformers import pipeline
summarizer = pipeline("summarization", model="henryu-lin/t5-large-samsum-deepspeed")

conversation = '''Kevin: Hey man, are you excited to watch Finding Nemo tonight?
    Henry: Yea, I can't wait to watch that same movie for the 89th time. Is Nate coming over to watch it with us tonight?
    Kevin: Yep, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet? It's starting to make the kitchen really smell.
    Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class. I didn't get to start on it until an hour ago, and it's due in 30 minutes.
    Kevin: Okay dude, you should take it out as soon as possible. By the way, Nate is bringing his girlfriend and their cat too.
    Henry: Nice, I'm really looking forward to seeing them again.


eval_rouge1 53.0823
eval_rouge2 28.7097
eval_rougeL 43.939
eval_rougeLsum 49.067
predict_rouge1 51.6716
predict_rouge2 26.5372
predict_rougeL 42.9681
predict_rougeLsum 47.4084
Metric Value
eval_gen_len 26.4071
predict_gen_len 25.9451
train_loss 1.3212629926497115
eval_loss 1.23828125
predict_loss 1.2333984375
train_runtime 515.2198
train_samples 14732
train_samples_per_second 85.781
train_steps_per_second 1.345
eval_runtime 61.275
eval_samples 818
eval_samples_per_second 13.35
eval_steps_per_second 0.212
predict_runtime 63.3732
predict_samples 819
predict_samples_per_second 12.923
predict_steps_per_second 0.205
total_steps 693
total_flos 7.20140924616704e+16

Select AutoNLP in the “Train” menu to fine-tune this model automatically.

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