Afaque Ahmad
02/20/2024, 10:53 AMkedro-azureml
plugin and I see that there are performance differences when I run the pipeline the pipeline locally vs on the azureml cluster. I understand that each node spins up a container and this may contribute to additional overhead.
Is there any way to make the pipelines run faster (without throwing more resources). Any optimizations that could be possibly make?marrrcin
02/20/2024, 2:18 PMCody Peterson
02/20/2024, 3:12 PMArtur Dobrogowski
02/26/2024, 12:31 PMAfaque Ahmad
02/28/2024, 6:22 AMCody Peterson
02/28/2024, 1:28 PMAfaque Ahmad
02/29/2024, 2:21 AMcpu
and memory
of the containers if using the azureml
sdk as below:
# Install azureml-core package first: pip install azureml-core
from azureml.core import RunConfiguration, Experiment, Workspace, ScriptRunConfig, Environment
from azureml.core.runconfig import DockerConfiguration
workspace = Workspace("<SUBSCRIPTION_ID>", "<RESOURCE_GROUP_NAME>", "<AZURE_ML_WORKSPACE_NAME>")
...
cluster = workspace.compute_targets['<COMPUTE_CLUSTER_NAME>']
run_config = RunConfiguration()
# Define the number of CPU cores and the amount of memory to be used by the Docker container instance.
run_config.docker = DockerConfiguration(use_docker=True, arguments=["--cpus=16", "--memory=128g"], shm_size="64M")
Given, I'm using the plugin, I cannot find a way to be able to do / change something like the above: DockerConfiguration(use_docker=True, arguments=["--cpus=16", "--memory=128g"]
Is there any way the docker configs can be changed?Cody Peterson
02/29/2024, 2:53 AMAfaque Ahmad
02/29/2024, 3:39 AM