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Add dispatchable workflow for deleting test model runs #60
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# Workflow that can be manually dispatched to delete test model runs that | ||
# do not need to be persisted indefinitely. | ||
# | ||
# Gated such that it's impossible to delete runs older than the current | ||
# assessment cycle, where each assessment cycle starts in April. | ||
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name: delete-model-runs | ||
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on: | ||
workflow_dispatch: | ||
inputs: | ||
run-ids: | ||
description: > | ||
Run IDs: Space-delimited list of IDs of model runs to delete. Note | ||
that the workflow assumes these IDs correspond to model runs for the | ||
current assessment cycle, and if that's not the case the deletion | ||
script will raise an error. | ||
required: true | ||
type: string | ||
default: 2024-01-01-foo-bar 2024-01-02-bar-baz | ||
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jobs: | ||
delete-model-runs: | ||
runs-on: ubuntu-latest | ||
permissions: | ||
# Needed to interact with GitHub's OIDC Token endpoint so we can auth AWS | ||
contents: read | ||
id-token: write | ||
steps: | ||
- name: Checkout repo code | ||
uses: actions/checkout@v4 | ||
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- name: Setup R | ||
uses: r-lib/actions/setup-r@v2 | ||
with: | ||
use-public-rspm: true | ||
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- name: Install system dependencies | ||
run: sudo apt-get install libgit2-dev | ||
shell: bash | ||
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- name: Disable renv sandbox to speed up install time | ||
run: echo "RENV_CONFIG_SANDBOX_ENABLED=FALSE" >> .Renviron | ||
shell: bash | ||
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- name: Setup renv | ||
uses: r-lib/actions/setup-renv@v2 | ||
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- name: Configure AWS credentials | ||
uses: aws-actions/configure-aws-credentials@v4 | ||
with: | ||
role-to-assume: ${{ secrets.AWS_IAM_ROLE_MODEL_DELETION_ARN }} | ||
aws-region: us-east-1 | ||
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- name: Delete model runs | ||
run: Rscript ./R/delete_current_year_model_runs.R "${RUN_IDS// /,}" | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. praise: Good thinking! |
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shell: bash | ||
env: | ||
RUN_IDS: ${{ inputs.run-ids }} |
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# Script to delete a list of model runs by ID from AWS. | ||
# | ||
# Accepts one argument, a comma-delimited list of run IDs for model runs | ||
# whose artifacts should be deleted. | ||
# | ||
# Assumes that model runs are restricted to the current assessment cycle, where | ||
# each assessment cycle starts in May. Raises an error if no objects matching | ||
# a given ID for the current year could be located in S3. This error will get | ||
# raised before any deletion occurs, so if one or more IDs are invalid then | ||
# no objects will be deleted. | ||
# | ||
# Example usage (delete the runs 123, 456, and 789 in the current year): | ||
# | ||
# delete_current_year_model_runs.R 123,456,789 | ||
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library(glue) | ||
library(here) | ||
library(magrittr) | ||
source(here("R", "helpers.R")) | ||
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current_date <- as.POSIXct(Sys.Date()) | ||
current_month <- current_date %>% format("%m") | ||
current_year <- current_date %>% format("%Y") | ||
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# The following heuristic determines the current upcoming assessment cycle year: | ||
# | ||
# * From May to December (post assessment), `year` = next year | ||
# * From January to April (during assessment), `year` = current year | ||
year <- if (current_month < "05") { | ||
current_year | ||
} else { | ||
as.character(as.numeric(current_year) + 1) | ||
} | ||
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# Convert the comma-delimited input to a vector of run IDs. Accepting one or | ||
# more positional arguments would be a cleaner UX, but since this script is | ||
# intended to be called from a dispatched GitHub workflow, it's easier to parse | ||
# one comma-delimited string than convert a space-separated string passed as a | ||
# workflow input to an array of function arguments | ||
raw_run_ids <- commandArgs(trailingOnly = TRUE) | ||
run_ids <- raw_run_ids %>% | ||
strsplit(split = ",", fixed = TRUE) %>% | ||
unlist() | ||
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"Confirming artifacts exist for run IDs in year {year}: {raw_run_ids}" %>% | ||
glue::glue() %>% | ||
print() | ||
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# We consider a run ID to be valid if it has any matching data in S3 for | ||
# the current year | ||
run_id_is_valid <- function(run_id, year) { | ||
return( | ||
model_get_s3_artifacts_for_run(run_id, year) %>% | ||
sapply(aws.s3::object_exists) %>% | ||
any() | ||
) | ||
} | ||
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# We check for validity separate from the deletion operation for two reasons: | ||
# | ||
# 1. The aws.s3::delete_object API does not raise an error if an object does | ||
# not exist, so a delete operation alone won't alert us for an incorrect | ||
# ID | ||
# 2. Even if aws.s3::delete_object could raise an error for missing objects, | ||
# we want to alert the caller that one or more of the IDs were incorrect | ||
# before deleting any objects so that this script is nondestructive | ||
# in the case of a malformed ID | ||
valid_run_ids <- run_ids %>% sapply(run_id_is_valid, year = year) | ||
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if (!all(valid_run_ids)) { | ||
invalid_run_ids <- run_ids[which(valid_run_ids == FALSE)] %>% | ||
paste(collapse = ", ") | ||
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"Some run IDs are missing all S3 artifacts for {year}: {invalid_run_ids}" %>% | ||
glue::glue() %>% | ||
stop() | ||
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} | ||
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"Deleting S3 artifacts run IDs in year {year}: {run_ids}" %>% | ||
glue::glue() %>% | ||
print() | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. To test deletion, I copied over run artifacts for two different model runs from In order to delete existing test model runs from There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. That works for me! Let's also delete other test runs created during the model infra construction. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Sounds good, will do! |
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run_ids %>% sapply(model_delete_run, year = year) |
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suggestion: IMO it'd be really helpful here to have a default input showing an example (nonexistent) run ID.
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True, done in a4895c2! Note that the trade-off here is that default arguments are populated as actual values in the workflow input form, rather than placeholder text, so there's a chance of a caller misinterpreting the form and submitting it with the (fake) default values. This should just raise an error and not actually delete any data, however, so the risk is worth it in my view.