Answer-first summary for fast verification
Answer: Create and register an environment that includes the required packages. Use this environment for all experiment jobs.
Option C is the correct answer because it involves creating and registering a custom environment that includes all required packages (SciPy and other non-default packages). This approach ensures consistent package dependencies across both local and remote compute targets with minimal administrative effort. The registered environment can be reused across multiple experiment runs without manual package installation on each compute target. Option A is incorrect because the default environment lacks the required packages, causing the experiment to fail. Option B is insufficient as a config.yaml file alone doesn't create a reusable, registered environment that can be easily deployed across different compute targets. Option D is overly complex and inefficient, as creating and managing a dedicated VM for package dependencies introduces unnecessary administrative overhead and doesn't scale well.
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You need to execute a script as an experiment that requires modules from the SciPy library and other Python packages not found in a default conda environment. The experiment will run on your local workstation for small datasets and be scaled out to more powerful remote compute clusters for larger datasets.
How can you ensure the experiment runs successfully on both local and remote compute with minimal administrative effort?
A
Leave the environment unspecified for the experiment. Run the expenment by using the default environment.
B
Create a config.yaml file that defines the required conda packages and save the file in the experiment folder.
C
Create and register an environment that includes the required packages. Use this environment for all experiment jobs.
D
Create a virtual machine (VM) by using the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.
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