Improvements for AWS dynamic Kconfig menu generation #52
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This improvement ensures that: - Terraform patterns reflect current AMI naming conventions - Deprecated or outdated AMIs don't influence pattern generation - The generated patterns match actively maintained, security-patched images - The system gracefully handles distributions that release infrequently Generated-by: Claude AI Signed-off-by: Chuck Lever <chuck.lever@oracle.com>
The update removes older AMIs such as RHEL 7 and Fedora 38. Signed-off-by: Chuck Lever <chuck.lever@oracle.com>
Generating the patterns that terraform uses to perform AMI name searches is probably the most challenging part of gen_kconfig_ami. This patch makes some improvements in order to avoid providing the same search pattern for multiple OS release choices. 1. Added OS identifier detection: The function now looks for parenthetical content like "(Ubuntu 20.04)" or "(Amazon Linux 2)" that distinguishes AMI variants 2. Preserves distinguishing characteristics: When OS identifiers are present, they're preserved in the pattern while version numbers and dates are wildcarded 3. Maintains backward compatibility: Distributions without OS identifiers (Amazon Linux, Debian, RedHat, etc.) continue to use simple prefix-based patterns Generated-by: Claude AI Signed-off-by: Chuck Lever <chuck.lever@oracle.com>
Enable selection of AMIs that include the Deep Learning GPU packages. Signed-off-by: Chuck Lever <chuck.lever@oracle.com>
Regenerated with terraform/aws/scripts/gen_kconfig_ami. Signed-off-by: Chuck Lever <chuck.lever@oracle.com>
Following the Lambda Labs pattern, separate AWS Kconfig files into: - Static wrapper files tracked in git (Kconfig.ami, Kconfig.instance, Kconfig.location) - Dynamically generated files ignored by git (*.generated) This prevents users from having to commit generated content after running 'make cloud-config-aws' while still providing working defaults via the static wrapper files. The static files source their .generated counterparts, which are created by running 'make cloud-config-aws' and contain current AWS API data. Generated-by: Claude AI Signed-off-by: Chuck Lever <chuck.lever@oracle.com>
Create aws_common.py to consolidate common functionality used across AWS Kconfig generation scripts (gen_kconfig_location, gen_kconfig_instance, gen_kconfig_ami). The new aws_common.py module provides: - get_default_region(): Read AWS region from ~/.aws/config - get_jinja2_environment(): Standardized Jinja2 template environment - create_ec2_client(): Create boto3 EC2 clients with region override - handle_aws_client_error(): Consistent AWS error handling - handle_aws_credentials_error(): Consistent credentials error handling - get_all_regions(): Retrieve all AWS regions - get_region_availability_zones(): Get AZs for a specific region - get_all_instance_types(): Get all instance types in a region - get_region_kconfig_name(): Convert region names to Kconfig format This refactoring eliminates code duplication among the scripts. Generated-by: Claude AI Signed-off-by: Chuck Lever <chuck.lever@oracle.com>
The gen_kconfig_location script was querying AWS for availability zone information for each region (20-30 regions) sequentially. Each region query required a separate AWS API call to describe_availability_zones, resulting in 5-15 seconds of serial API calls. This change parallelizes the availability zone discovery using ThreadPoolExecutor, allowing all regions to be queried concurrently. This reduces the total execution time from the sum of all requests to approximately the time of the slowest single request, providing a 10-20x speedup for first-time runs. The output order is preserved by collecting results in a dictionary and printing them in the original region order. Generated-by: Claude AI Signed-off-by: Chuck Lever <chuck.lever@oracle.com>
The gen_kconfig_ami script was querying AWS for AMI information for each Linux distribution (Amazon, Ubuntu, RedHat, Debian, etc.) sequentially. With 10 different distributions, this resulted in 20-30 seconds of serial API calls. This change parallelizes the AMI discovery using ThreadPoolExecutor, allowing all 10 distributions to be queried concurrently. This reduces the total execution time from the sum of all requests to approximately the time of the slowest single request, providing an 8-10x speedup for first-time runs. The output order is preserved by collecting results in a dictionary and printing them in the original owner key order. Generated-by: Claude AI Signed-off-by: Chuck Lever <chuck.lever@oracle.com>
Claude was struggling a bit with how to validate the whitespace in .j2 files used to generate Kconfig menus. We stumbled on this method: generate a sample menu file and check that. Generated-by: Claude AI Signed-off-by: Chuck Lever <chuck.lever@oracle.com>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Bring the new AWS dynamic Kconfig menu generation more in line with the Lambda Labs reference implementation: