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Currently, WMLA has multiple ways to use and customers have a steep learning curve. WMLA REST API supports all the different CP4D versions and platforms. To reduce the learning curve and also reduce the workload to support different ways to use WM...
WMLA EDT: Customization, Minimal Example & Documentation
Client asks how to use and customize the elastic distributed training (EDT) feature of WML-A. They explicitly complained about the lack of a detailed documentation (being aware of WMLA/Spectrum Conductor documentation in the knowledge center &...
It would be nice to have the GPU utilization metrics used in WMLA also introduced to the main monitoring dashboard of core CP4D. This is so that we still have a single monitoring dashboard for all services of CP4D.
TensorBoard is TensorFlow’s visualization toolkit enables tracking metrics like loss and accuracy, visualize the model graph, view histograms of weights, biases, or other tensors as they change over time, and much more. A managed TensorBoard suppo...
Interactive Elastic Distributed Training notebook in Watson Studio
Watson Machine Learning Accelerator Elastic Distributed Training (EDT) simplifies the distribution of training workloads for the data scientist. Deep learning experiment in Watson studio provide the UI to submit job in WMLA , but this is not inter...
We have been informed that WMLA 1.2.3 is supported on RH O/S 8.2 & 8.3. Our servers are being installed with RH 8.4 version. We would like to make sure WMLA 1.2.3 is supported for RH 8.4 and we were informed that it may work but need to be tes...
We are seeing some latency on our platform when we submit a HPO job. When we submit a HPO job via WMLA we see that it is taking ~14 seconds to launch a trial in spite of asking number of parallel runs. Just to give you an idea, if we have latency ...
fabric files for both x86 and ppc64le on Same Cluster if HPO Models are running on
https://jazz07.rchland.ibm.com:21443/jazz/web/projects/pc-management#action=com.ibm.team.workitem.viewWorkItem&id=266399 Customer will be purchasing more x86 nodes shortly to add to cluster. Customer needs permanent solution
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