Ramdoot Kumar P
TECHNICAL LEAD
Cisco Systems
location_on India
Member since 3 years
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Ramdoot Kumar P
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Have an industry experience of 13+ years working on systems pertaining to domains of networking - that includes software stack development, asics, device drivers, field deployments. Have exposure to open-source big-data software stacks - Apache Mnemonic, Apache spark, Ceph, Elasticsearch. Have good hands-on experience with Kubernetes.
Currently, involved with open-source Kubeflow, a ML platform for Kubernetes, for on-premise deployments at Cisco.
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keyboard_arrow_downA Scalable Hyperparameter Optimization framework for ML workloads
Johnu GeorgeTechnical LeadCisco Systems
Ramdoot Kumar PTECHNICAL LEADCisco Systemsschedule 3 years ago
20 Mins
Demonstration
Intermediate
In machine learning, hyperparameters are parameters that governs the training process itself. For example, learning rate, number of hidden layers, number of nodes per layer are typical hyperparameters for neural networks. Hyperparameter Tuning is the process of searching the best hyper parameters to initialize the learning algorithm, thus improving training performance.
We present Katib, a scalable and general hyper parameter tuning framework based on Kubernetes which is ML framework agnostic (Tensorflow, Pytorch, MXNet, XGboost etc). You will learn about Katib in Kubeflow, an open source ML toolkit for Kubernetes, as we demonstrate the advantages of hyperparameter optimization by running a sample classification problem. In addition, as we dive into the implementation details, you will learn how to contribute as we expand this platform to include autoML tools.
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