| ### | |
| # Service account that will be the identity of our GCF | |
| # | |
| resource "google_service_account" "sa_gcf" { | |
| account_id = "gcf-golden-image-scanner" | |
| display_name = "Google Cloud Function that runs the golden image scanner" | |
| } | |
| resource "google_organization_iam_member" "org_scc_findings" { | |
| org_id = var.org_id |
| import datetime | |
| import uuid | |
| import functions_framework | |
| import google.cloud.logging | |
| from google.cloud import securitycenter | |
| from google.cloud.securitycenter_v1 import Finding | |
| logging_client = google.cloud.logging.Client() | |
| logging_client.setup_logging() |
| ### | |
| # Cloud Scheduler | |
| # | |
| data "google_compute_default_service_account" "default" { | |
| } | |
| resource "google_cloud_scheduler_job" "job" { | |
| name = "Golden-Image-Scanner" | |
| description = "A job that scans the image family ${var.image_family}" | |
| region = var.region |
| ### | |
| # Google Cloud Function | |
| # | |
| resource "google_cloudfunctions_function" "scanner" { | |
| name = "Golden-Image-Scanner" | |
| description = "GCF to run daily that scans for disks using out of date golden images" | |
| runtime = "python39" | |
| region = var.region | |
| available_memory_mb = 128 | |
| trigger_http = true |
| ### | |
| # Service account that will be the identity of our GCF | |
| # | |
| resource "google_service_account" "sa_gcf" { | |
| account_id = "gcf-golden-image-scanner" | |
| display_name = "Google Cloud Function that runs the golden image scanner" | |
| } | |
| resource "google_organization_iam_member" "org_scc_findings" { | |
| org_id = var.org_id | |
| role = "roles/securitycenter.findingsEditor" |
| resource "google_scc_source" "custom_source" { | |
| display_name = "Golden Image Scanner" | |
| organization = var.org_id | |
| description = "Creates findings when disks are not using the latest golden image within a family" | |
| } |
| variable "org_id" { | |
| description = "The GCP organization that the SA of the Golden Image Scanner will have access to it's Security Command Center" | |
| type = string | |
| } | |
| variable "project_id" { | |
| description = "The GCP project that your Golden Image Scanner will run in" | |
| type = string | |
| } | |
| variable "region" { | |
| description = "The GCP region where resources will be provisioned" |
| #!/bin/bash | |
| HELP="Usage: bootstrap-dask [OPTIONS] | |
| Example AWS EMR Bootstrap Action to install and configure Dask and Jupyter | |
| By default it does the following things: | |
| - Installs miniconda | |
| - Installs dask, distributed, dask-yarn, pyarrow, and s3fs. This list can be | |
| extended using the --conda-packages flag below. | |
| - Packages this environment for distribution to the workers. |
| import unreal | |
| ##### | |
| # For Texture2D, Material and TextureCube | |
| # BEFORE - 72.8 GB | |
| # AFTER - 49.0 GB, deleted 6869 duplicate files | |
| # 22GB savings of data in /Game/Content | |
| # | |
| # Regarding MaterialFunctions, there are over 3200 of them, unfortunately the editor crashes when | |
| # processing these, and the crash results in your character no longer having a material |
Demonstrates a solution for the simple multi-vehicle routing problem (VRP) using a combination of Google libraries and services. Sample depot and shipment locations randomly chosen in the San Antonio, TX metro area. Distances and times are based on Google's road network.
You will need a Google Maps Platform API Key.
