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NEW QUESTION 42
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?
- A. Set the "scheduling_unit" to cap the number of resource slots used at once by this experiment.
- B. Under "resources.- set 'priority to I to reduce the share of the resource slots mat the experiment receives.
- C. Under "searcher," set "max_concurrent_trails" to cap the number of trials run at once by this experiment.
- D. Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.
Answer: C
Explanation:
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.
NEW QUESTION 43
A company has recently expanded its ml engineering resources from 5 CPUs 1012 GPUs.
What challenge is likely to continue to stand in the way of accelerating deep learning (DU training?
- A. The requirement that the ML team must wait for the IT team to initiate each new training process
- B. A lack of adequate power and cooling for the GPU-enabled servers
- C. The complexity of adjusting model code to distribute the training process across multiple GPUs
- D. A lack of understanding of the DL model architecture by the NL engineering team
Answer: C
Explanation:
The complexity of adjusting model code to distribute the training process across multiple GPUs. Deep learning (DL) training requires a large amount of computing power and can be accelerated by using multiple GPUs. However, this requires adjusting the model code to distribute the training process across the GPUs, which can be a complex and time-consuming process. Thus, the complexity of adjusting the model code is likely to continue to be a challenge in accelerating DL training.
NEW QUESTION 44
A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment. That GPU fails. What happens next?
- A. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
- B. The trial tails, and the ML engineer must restart it manually by re-running the experiment.
- C. The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
- D. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
Answer: A
Explanation:
If a GPU fails during a trial running on a resource pool on HPE Machine Learning Development Environment, the conductor will reschedule the trial on another available GPU in the pool, and the trial will restart from the latest checkpoint. The trial will not fail, and the ML engineer will not have to manually restart it from the latest checkpoint using the WebUI.
NEW QUESTION 45
You are meeting with a customer how has several DL models deployed. Out wants to expand the projects.
The ML/DL team is growing from 5 members to 7 members. To support the growing team, the customer has assigned 2 dedicated IT start. The customer is trying to put together an on-prem GPU cluster with at least 14 CPUs.
What should you determine about this customer?
- A. The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion.
- B. The customer is not ready for an HPE Machine Learning Development solution, but you could recommend open-source Determined Al.
- C. The customer is a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.
- D. The customer is not ready for an HPE Machine Learning Development solution. Out you could recommend an educational HPE Pointnext ASPS workshop.
Answer: B
NEW QUESTION 46
Where does TensorFlow fit in the ML/DL Lifecycle?
- A. it provides pipelines to manage the complete lifecycle.
- B. It is primarily used to transport trained models to a deployment environment.
- C. It adds system and GPU monitoring to the training process.
- D. it helps engineers use a language like Python to code and trail DL models.
Answer: D
NEW QUESTION 47
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