End-to-end support for the deep learning workflow
Accelerates training times
Optimized software reduces the time spent importing, transforming and preparing data. Distributed training on multiple servers and GPUs speeds time to results.
Improves model accuracy
Hyper-parameter search and optimization, and training visualization and tuning enable greater model accuracy.
Shared resources among many data scientists running different models drives higher utilization.
Leverages IBM Spectrum Conductor
This highly available multitenant framework is designed to build a shared, enterprise-class Apache Spark environment.
Runs on IBM Power Systems Servers
When optimized to take full advantage of IBM Power Systems™ Servers with NVLink CPUs and NVIDIA GPUs, IBM benchmarks have seen 50x improvements, cutting training times from days to hours.
- Addresses the most challenging deep learning issues
- Meet needs of high performance deep learning applications
- Simplifies and optimizes an end-to-end workflow
- Takes advantage of a distributed server architecture