HPE2-N69 Real Dumps For Using HPE AI and Machine Learning

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1. Where does TensorFlow fit in the ML/DL Lifecycle?

it helps engineers use a language like Python to code and trail DL models.

it provides pipelines to manage the complete lifecycle.

It is primarily used to transport trained models to a deployment environment.

It adds system and GPU monitoring to the training process.

2. 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 I is allowed to finish. Then Trial 3 is scheduled.

Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.

Trial 1 is allowed to finish. Then Trial 2 is scheduled.

Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.

3. A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment. That GPU fails .

What happens next?

The trial tails, and the ML engineer must restart it manually by re-running the experiment.

The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.

The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.

The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebU

4. You are in a directory on your machine with your experiment config file and your model code.

You enter this command:

det experiment create myfile.yaml

You receive this error:

det experiment create: error: the following arguments are required: model_def

What should you do?

Re-enter the command with "-m" in which is the code filename.

Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.

Re-enter the command with a period (.) at the end.

Make sure that you have already logged into the cluster with the "det login’’ command.

5. What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?

They run validation and checkpoint workloads.

They run training workloads that do not require GPUs.

They host management software such as the conductor and HPC

They run non-distributed training workloads.

6. Your cluster uses Amazon S3 to store checkpoints. You ran an experiment on an HPE Machine Learning Development Environment cluster, you want to find the location tor the best checkpoint created during the experiment .

What can you do?

In the experiment config that you used, look for the "bucket" field under "hyperparameters." This is the UUID for checkpoints.

Use the "det experiment download -top-n I" command, referencing the experiment I

In the Web Ul, go to the Task page and click the checkpoint task that has the experiment I

Look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment I

7. A customer has Men expanding its deep learning (DO prefects and is confronting several challenges .

Which of these challenges does HPE Machine Learning Development Environment specifically address?

Time-consuming data collection

Complex model deployment processes

Complex and time-consuming data cleansing process

Complex and time-consuming hyperparameter optimization (HPO)

8. What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?

It uses a centralized training architecture that is highly efficient.

It helps DL projects complete faster for a faster RO

It helps companies deploy models and generate revenue.

It automatically cleans up data to create better end results.

9. You want to set up a simple demo Ouster tor HPE Machine learning Development Environment for the open source Determined AI) on a local machine. You plan to use "del deploy" to set up the cluster .

What software must be installed on the machine before you run that command?

Kubernetes

PyTorch

Terralorm

Docker

10. ML engineers are defining a convolutional neural network (CNN) model bur they are not sure how many filters to use in each convolutional layer .

What can help them address this concern?

Using hyperparameter optimization (HPO)

Distributing the training across multiple CPUs

Using a variable learning late

Training the model on multiple epochs