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Titel:

Dataset of "Network Traffic Characteristics of Machine Learning Frameworks Under the Microscope"

Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
27.10.2021
Verantwortlich:
Zerwas, Johannes
Autorinnen / Autoren:
Zerwas, Johannes; Aykurt, Kaan; Schmid, Stefan; Blenk, Andreas
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Identifikator:
doi:10.14459/2021mp1632489
Enddatum der Datenerzeugung:
15.06.2021
Fachgebiet:
DAT Datenverarbeitung, Informatik
Quellen der Daten:
Experimente und Beobachtungen / experiments and observations
Datentyp:
Texte / texts; Datenbanken / data bases
Anderer Datentyp:
Network traffic traces
Methode der Datenerhebung:
The traffic was collected in a four worker testbed setup. The workers were interconnected with a 10G Ethernet network via a single packet switch. Each worker was equipped with an Nvidia Tesla T4 GPU. Traffic traces were directly taken on the worker nodes. The models were trained for 20 epochs on the CIFAR-10 image dataset.
Beschreibung:
Network traffic collection (PCAP) of three widely-used state-of-the-art Distributed Machine Learning (DML) frameworks (Tensorflow, Horovod, KungFu). The collection contains distributed training runs of four models (MobileNetV2, ResNet50, Resnet101, DenseNet201) with varying configurations of the frameworks. Varied parameters are the communication topology and backend, the distributed optimizer, the batch size and the packet loss in the network.
Links:
This dataset relates to the publication: https://doi.org/10.23919/CNSM52442.2021.9615524
Schlagworte:
Distributed Machine Learning; Network Traffic Measurement
Technische Hinweise:
View and download (151 GB total, 90 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1632489):
rsync rsync://m1632489@dataserv.ub.tum.de/m1632489/
Sprache:
en
Rechte:
by-sa, http://creativecommons.org/licenses/by-sa/4.0
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