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An HPE Machine Learning Development Environment resource pool uses priority scheduling with preemption disabled. Currently Experiment 1 Trial I is using 32 of the pool's 40 total slots; it has priority 42. Users then run two more experiments:
* Experiment 2:1 trial (Trial 2) that needs 24 slots; priority 50
* Experiment 3; l trial (Trial 3) that needs 24 slots; priority I
What happens?
Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots. This is because priority scheduling is used in the HPE Machine Learning Development Environment resource pool, which means higher priority tasks will be given priority over lower priority tasks. As such, Trial 3 with priority 1 will be given priority over Trial 2 with priority 50.
A company has an HPE Machine Learning Development Environment cluster. The ML engineers store training and validation data sets in Google Cloud Storage (GCS). What is an advantage of streaming the data during a trial, as opposed to downloading the data?
Streaming the data during a trial allows the data to be processed more quickly, as it does not need to be downloaded onto the cluster before training can begin. This means that the trial can start up faster and the model can begin training more quickly.
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?
One of the main benefits of HPE Machine Learning Development Environment is its ability to automate the process of hyperparameter optimization (HPO). HPO is a process of automatically tuning the hyperparameters of a model during training, which can greatly improve a model's performance. HPE ML DE provides automated HPO, making the process of tuning and optimizing the model much easier and more efficient.
You want to set up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined all on a local machine. Which OS Is supported?
The OS supported for setting up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined on a local machine is Red Hat 7-based Linux. Red Hat 7-based Linux is an open source operating system that is used extensively in enterprise applications. It provides a stable and secure platform for running applications and is suitable for use in a demo cluster.
The ML engineer wants to run an Adaptive ASHA experiment with hundreds of trials. The engineer knows that several other experiments will be running on the same resource pool, and wants to avoid taking up too large a share of resources. What can the engineer do in the experiment config file to help support this goal?
The ML engineer can set 'maxconcurrenttrials' under 'searcher' in the experiment config file to cap the number of trials run at once by this experiment. This will help ensure that the experiment does not take up too large a share of resources, allowing other experiments to also run concurrently.
