HPE2-N69 무료 덤프문제 온라인 액세스

Refer to the exhibit.

You are demonstrating HPE Machine Learning Development Environment, and you show details about an experiment, as shown in the exhibits. The customer asks about what "validation loss' means. What should you respond?

• A.Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.
• B.Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.
• C.Validation loss is metadata that indicates how many updates were lost between the conductor and agents.
• D.Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.

A customer is using fair-share scheduling for an HPE Machine Learning Development Environment resource pool. What is one way that users can obtain relatively more resource slots for their important experiments?

• A.Set the weight to a lower than default value.
• B.Set the priority to a higher than default value.
• C.Set the priority to a lower than default value.
• D.Set the weight to a higher than default value.

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?

• A.Make sure that you have already logged into the cluster with the "det login'' command.
• B.Re-enter the command with a period (.) at the end.
• C.Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
• D.Re-enter the command with "-m" in which is the code filename.

What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?

• A.It uploads model checkpoints.
• B.It ensures experiment metadata is stored.
• C.It validates trained models.
• D.it downloads datasets for training.

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 trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
• B.The trial tails, and the ML engineer must restart it manually by re-running the experiment.
• C.The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
• D.The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.