It should be noted that the maximum capacity for each queue in weight mode in Capacity Scheduler is still defined as a percentage value. Each time a new queue is added, any existing sibling queues’ capacities will automatically change accordingly. Since weights determine the resources relative to the sibling queues under a parent, any number of extra queues can be added freely under a parent without having to adjust any capacities. This new mode of resource allocation in Capacity Scheduler is very similar to the weighted queues in CDH Fair Scheduler. In this mode, the capacity value for each queue would be specified in fractions of total resources available within a parent queue, called weights. So when adding a new queue under a parent, capacities of all or many child queues might have to be adjusted so as to not go above the total capacity of the parent.ĬDP Private Cloud Base 7.1.6 added a new weight mode for resource allocation to queues. For example, for each parent queue the sum of all child queue capacities should add up to 100% (in relative mode) or the exact resource value defined in parent (in absolute mode). Both these modes are very rigid and have strict rules on the resource allocation while creating queues. Prior to CDP Private Cloud Base 7.1.6, Capacity Scheduler had two modes of defining queue resource allocation: using percentage values (relative mode), or using absolute resource vectors (absolute mode). In this blog we will discuss the fine-tuning of Capacity Scheduler in weight mode to mimic some of the Fair Scheduler behavior from prior to the CDP upgrade. So some manual fine-tuning is required to ensure that the resulting scheduling configuration fits your organization’s internal resource allocation goals and workload SLAs. And with CDP Private Cloud Base 7.1.6, the default mode of conversion from Fair Scheduler to Capacity Scheduler when using the fs2cs utility is now switched to the new “weight mode.” Even with the addition of this new mode in Capacity Scheduler, the fs2cs conversion utility cannot convert every Fair Scheduler configuration into a corresponding Capacity Scheduler configuration. This mode will be most familiar to CDH users, and was created to help ease their transition to CDP.Īs mentioned previously Cloudera provides the fs2cs conversion utility, which makes the transition from Fair Scheduler to Capacity Scheduler much easier. In this part, we will discuss the fine-tuning of Capacity Scheduler in the new “weight mode” that was introduced in CDP Private Cloud Base 7.1.6. In the first part of this blog series, we described the fine-tuning of Capacity Scheduler deployed in “relative mode” in CDP Private Cloud Base to mimic some of the Fair Scheduler behavior from before the upgrade. Enabling this combined functionality allows customers to minimize expensive testing and manual conversion operations in the migration, and reduces the overall risk that can occur when switching from one methodology to another. In merging this scheduler functionality, Cloudera significantly reduced the time and effort to migrate from CDH and HDP. As part of that unification process, Cloudera merged the YARN Scheduler functionality from the legacy platforms, creating a Capacity Scheduler that better services all customers. Lower bound on the learning rate of all param groupsĮps ( float) – Minimal decay applied to lr.Cloudera Data Platform (CDP) unifies the technologies from Cloudera Enterprise Data Hub (CDH) and Hortonworks Data Platform (HDP). Min_lr ( float or list) – A scalar or a list of scalars. Normal operation after lr has been reduced. Default: ‘rel’.Ĭooldown ( int) – Number of epochs to wait before resuming Max mode or best - threshold in min mode. In abs mode, dynamic_threshold = best + threshold in Mode or best * ( 1 - threshold ) in min mode. In rel mode,ĭynamic_threshold = best * ( 1 + threshold ) in ‘max’ Threshold ( float) – Threshold for measuring the new optimum, With no improvement, and will only decrease the LR after theģrd epoch if the loss still hasn’t improved then. Patience = 2, then we will ignore the first 2 epochs Patience ( int) – Number of epochs with no improvement after Default: ‘min’.įactor ( float) – Factor by which the learning rate will be Quantity monitored has stopped increasing. In min mode, lr willīe reduced when the quantity monitored has stoppedĭecreasing in max mode it will be reduced when the Optimizer ( Optimizer) – Wrapped optimizer. Quantity and if no improvement is seen for a ‘patience’ number Models often benefit from reducing the learning rate by a factor Reduce learning rate when a metric has stopped improving. ReduceLROnPlateau ( optimizer, mode = 'min', factor = 0.1, patience = 10, threshold = 0.0001, threshold_mode = 'rel', cooldown = 0, min_lr = 0, eps = 1e-08, verbose = False ) ¶
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