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TensorFlow CPU with Jupyter Notebook

Take advantage of your CPU and perform training on small neutral networks with lesser dataset where time is not a constraint.  Run CPU-powered Jupyter Notebooks securely in your browser with minimal configuration required for deep learning projects. 

Product Name

TensorFlow CPU with Jupyter Notebook

Product Version

2.0

HPE Ezmeral Container Platform Version

5.2

Overview

Machine Learning and Data Analytics are becoming quite popular for main stream data processing. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. TensorFlow v2 has become easier to use than ever. It offers support for both standard CPU as well as GPU based deep learning. GPUs are mostly used for deep learning training due to their significant speed when compared to CPUs. However, due to their higher cost, for tasks like inference which are not as resource heavy as training, it is usually believed that CPUs are sufficient and are more attractive due to their cost savings. 

 

Running TensorFlow on CPUs might be ideal for projects where: 

  • Runtime is dominated by IO, so that computational performance of GPUs does not provide much advantage with respect to overall runtime and core-hour charges 
  • The workflow can benefit from parallel execution on many nodes with large aggregated IO bandwidth (e.g., running an inference task on a very large dataset, or training a large ensemble of models) 

Setting TensorFlow up, especially in order to take advantage of CPU acceleration remains something of a chore.  HPE Ezmeral Container Platform offers a pre-configured and fully integrated minimal runtime environment with TensorFlow, an open source software library for machine learning, Keras, an open source neural network library, Jupyter Notebook, a browser-based interactive notebook for programming, mathematics, and data science, and the Python programming language. This stack is optimized for running on CPU. 

Documentation Additional Resources

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Interested in learning more about the HPE Ezmeral Container Platform and TensorFlow? Please contact us to learn more.   

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