t5-3b-samsum-deepspeed

This model was trained using Microsoft's AzureML and DeepSpeed's ZeRO 2 optimization. It was fine-tuned on the SAMSum corpus from t5-3b checkpoint.

More information on the fine-tuning process (includes samples and benchmarks):
(currently still WIP, 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 43m 51.05s
Compute Cost (LowPriority/Dedicated) $3.22/$16.10 (USD)
Average CPU Utilization 46.0%
Average GPU Utilization 56.9%
GPU Memory Usage (Avg/Peak) 26.77/30.49 (GB)
Total GPU Energy Usage 2448.69 (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 2021-07-06T21:57:39
duration 1841.4621863365173
emissions 0.17802492531467784
energy_consumed 0.5982020339874927
country_name USA
region Washington
cloud_provider azure
cloud_region westus2

Hyperparameters

fp16: True
per device batch size: 2
effective batch size: 16
epoch: 3.0
learning rate: 3e-5
weight decay: 0.0
seed: 1

*Same per device batch size for evaluations

DeepSpeed

Optimizer = AdamW, Scheduler = WarmupDecayLR, Offload = none

  "zero_optimization": {
    "stage": 2,
    "allgather_partitions": true,
    "allgather_bucket_size": 1000000000,
    "overlap_comm": true,
    "reduce_scatter": true,
    "reduce_bucket_size": 1000000000,
    "contiguous_gradients": true
  }

Usage

from transformers import pipeline
summarizer = pipeline("summarization", model="henryu-lin/t5-3b-samsum-deepspeed")

conversation = '''Henry: Hey, is Nate coming over to watch the movie tonight?
    Kevin: Yea, 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.
    Kevin: Yea, you should take it out as soon as possible. And also, Nate is bringing his girlfriend too.
    Henry: Nice, I'm really looking forward to seeing them again.
'''
summarizer(conversation)

Results

ROUGE Score
eval_rouge1 54.7875
eval_rouge2 30.565
eval_rougeL 45.7625
eval_rougeLsum 50.3915
predict_rouge1 53.6628
predict_rouge2 29.0196
predict_rougeL 45.1257
predict_rougeLsum 49.171
Metric Value
eval_gen_len 25.3399
predict_gen_len 24.9133
train_loss 1.1206104169494209
eval_loss 1.0732421875
predict_loss 1.087890625
train_runtime 1841.3751
train_samples 14732
train_samples_per_second 24.002
train_steps_per_second 1.501
eval_runtime 163.8357
eval_samples 818
eval_samples_per_second 4.993
eval_steps_per_second 0.317
predict_runtime 168.8245
predict_samples 819
predict_samples_per_second 4.851
predict_steps_per_second 0.308
total_steps 2763
total_flos 1.84452086400811e+17
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Evaluation results

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