Key features

Multitenancy

Drives higher utilization and ROI by dynamically sharing server resources among many data scientists running multiple models.

Distributed data ingest, transformation and training

Enables processing jobs in parallel over a cluster of servers—helping to accelerate time to results.

A distributed training fabric

Allows most applications to run in parallel without the need for code changes.

Support for large models

Provides the ability to leverage CPU and GPU memory across a single large model.

Helps avoid interruptions during training

The ability to dynamically increase or decrease the resources allocated to model while training.

Training visualization and tuning

Enables monitoring the accuracy of the model while training is in progress and for making adjustments or stopping if the models is not converging or has low accuracy.

Hyper-parameter search and optimization

Improves accuracy with suggestion-based logic while training is running.

Technical specifications

For a complete list of technical specifications, download the IBM Spectrum Conductor Deep Learning Impact data sheet.

IBM Spectrum Conductor Deep Learning Impact

Read the data sheet

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