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To reduce the launch time of each step when using the

k8sStepLauncher

, you can consider the following strategies based on the documentation: 1. Optimize Python Module Imports: The time it takes to import your Python module with your definitions is a significant factor during the startup period. You can try to reduce the number of imports or optimize the import statements in your Python code to speed up this process. 2. Split Code into More Locations: If your job is configured to use multiple code locations, the run will only need to load the code location in which its ops or assets are in. This can potentially reduce the startup time as less code needs to be loaded. 3. Use Py-Spy: Py-Spy is a tool that can help diagnose slow imports and other startup time issues. It allows you to profile your Python application and understand what is taking up time during startup. 4. Kubernetes Configuration: Ensure that your Kubernetes cluster has sufficient resources to handle the load. Sometimes, the delay can be due to resource constraints or slow provisioning of the required Kubernetes resources. 5. Executor Configuration: If you are using the

k8s_job_executor

, you can configure the

step_k8s_config

field on the executor to control the Kubernetes configuration for every step pod. This includes setting resource requests and limits to ensure that the pods have the necessary resources allocated to them without delay. 6. Default Configuration: You can apply a default set of Kubernetes configuration to every op launched by an executor without having to individually tag each op. This can be done by setting

run_k8s_config

on the run launcher or defining a default step-level configuration in the Helm chart. 7. Custom Run Launcher: If you have specific requirements or need more control over the launch process, you can create a custom run launcher that can optimize the launch process based on your needs. Please note that these strategies are general suggestions, and the effectiveness of each may vary based on your specific setup and the complexity of your Dagster jobs. It's also important to consider that some startup time may be inherent to the process of provisioning and initializing Kubernetes resources, which may not be entirely eliminable.