Deep Learning With TensorFlow
TensorFlow is an open source software library for high-performance numerical computation that is used mostly for deep learning and other computationally intensive machine learning tasks. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs).
TensorFlow and Apache Ignite can be used together to provide a full toolset needed to work with operational and historical data, perform data analysis and build complex mathematical models based on neural networks.
Technically, TensorFlow uses Ignite as a data source for neural network training, inference and all other computations supported getting the following advantages:
- Unlimited Capacity - Ignite is used as a distributed database with unlimited capacity which is capable of holding petabytes of data needed for deep learning tasks of TensorFlow.
- Faster Performance - There will be minimal or zero data movement over the network if TensorFlow workers are deployed on the same machines with Ignite nodes. Each TensorFlow worker will work with an Ignite node local to it.
- Fault Tolerance - In case of a failure during calculation, Ignite will be capable of restarting the process from the point of failure.