From ab632dff2d22c22ad23eebdeb85d5a527c3c2f6f Mon Sep 17 00:00:00 2001
From: Charlie Wang <chwa@google.com>
Date: Fri, 15 Nov 2024 14:48:08 +0000
Subject: [PATCH 1/7] add uv required project table segment in toml file

---
 pyproject.toml | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 98a6876a..e500633a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,6 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+[project]
+name = "marketing-analytics-jumpstart"
+version = "1.0.0"
+description = "Marketing Analytics Jumpstart"
+authors = ["Marketing Analytics Solutions Architects <ma-se@google.com>"]
+license = "Apache 2.0"
+readme = "README.md"
+requires-python = ">=3.8,<3.11"
+
 [tool.poetry]
 name = "marketing-analytics-jumpstart"
 version = "1.0.0"

From effe0d403bba72400921eec6d08bd2b1f1911b21 Mon Sep 17 00:00:00 2001
From: Charlie Wang <chwa@google.com>
Date: Fri, 15 Nov 2024 15:39:15 +0000
Subject: [PATCH 2/7] switch to uv in terraform deployment

---
 infrastructure/terraform/main.tf              |  49 ++-----
 .../terraform/modules/activation/main.tf      |   7 +-
 .../terraform/modules/activation/variables.tf |   9 +-
 .../terraform/modules/pipelines/pipelines.tf  | 129 +++++++++---------
 .../terraform/modules/pipelines/variables.tf  |   9 +-
 infrastructure/terraform/variables.tf         |   6 +
 6 files changed, 88 insertions(+), 121 deletions(-)

diff --git a/infrastructure/terraform/main.tf b/infrastructure/terraform/main.tf
index a6ede8bc..1136cb91 100644
--- a/infrastructure/terraform/main.tf
+++ b/infrastructure/terraform/main.tf
@@ -69,8 +69,8 @@ locals {
   source_root_dir = "../.."
   # The config_file_name is the name of the config file.
   config_file_name = "config"
-  # The poetry_run_alias is the alias of the poetry command.
-  poetry_run_alias = "${var.poetry_cmd} run"
+  # The uv_run_alias is the alias of the uv run command.
+  uv_run_alias = "${var.uv_cmd} run"
   # The mds_dataset_suffix is the suffix of the marketing data store dataset.
   mds_dataset_suffix = var.create_staging_environment ? "staging" : var.create_dev_environment ? "dev" : "prod"
   # The project_toml_file_path is the path to the project.toml file.
@@ -127,39 +127,22 @@ resource "local_file" "feature_store_configuration" {
   })
 }
 
-# Runs the poetry command to install the dependencies.
-# The command is: poetry install
-resource "null_resource" "poetry_install" {
-  triggers = {
-    create_command       = "${var.poetry_cmd} lock && ${var.poetry_cmd} install"
-    source_contents_hash = local.project_toml_content_hash
-  }
-
-  # Only run the command when `terraform apply` executes and the resource doesn't exist.
-  provisioner "local-exec" {
-    when        = create
-    command     = self.triggers.create_command
-    working_dir = local.source_root_dir
-  }
-}
-
 data "external" "check_ga4_property_type" {
-  program     = ["bash", "-c", "${local.poetry_run_alias} ga4-setup --ga4_resource=check_property_type --ga4_property_id=${var.ga4_property_id} --ga4_stream_id=${var.ga4_stream_id}"]
+  program     = ["bash", "-c", "${local.uv_run_alias} ga4-setup --ga4_resource=check_property_type --ga4_property_id=${var.ga4_property_id} --ga4_stream_id=${var.ga4_stream_id}"]
   working_dir = local.source_root_dir
-  depends_on  = [null_resource.poetry_install]
 }
 
-# Runs the poetry invoke command to generate the sql queries and procedures.
+# Runs the uv invoke command to generate the sql queries and procedures.
 # This command is executed before the feature store is created.
 resource "null_resource" "generate_sql_queries" {
 
   triggers = {
     # The create command generates the sql queries and procedures.
-    # The command is: poetry inv [function_name] --env-name=${local.config_file_name}
+    # The command is: uv inv [function_name] --env-name=${local.config_file_name}
     # The --env-name argument is the name of the configuration file.
     create_command = <<-EOT
-    ${local.poetry_run_alias} inv apply-config-parameters-to-all-queries --env-name=${local.config_file_name}
-    ${local.poetry_run_alias} inv apply-config-parameters-to-all-procedures --env-name=${local.config_file_name}
+    ${local.uv_run_alias} inv apply-config-parameters-to-all-queries --env-name=${local.config_file_name}
+    ${local.uv_run_alias} inv apply-config-parameters-to-all-procedures --env-name=${local.config_file_name}
     EOT
 
     # The destroy command removes the generated sql queries and procedures.
@@ -171,10 +154,6 @@ resource "null_resource" "generate_sql_queries" {
     # The working directory is the root of the project.
     working_dir = local.source_root_dir
 
-    # The poetry_installed trigger is the ID of the null_resource.poetry_install resource.
-    # This is used to ensure that the poetry command is run before the generate_sql_queries command.
-    poetry_installed = null_resource.poetry_install.id
-
     # The source_contents_hash trigger is the hash of the project.toml file.
     # This is used to ensure that the generate_sql_queries command is run only if the project.toml file has changed.
     # It also ensures that the generate_sql_queries command is run only if the sql queries and procedures have changed.
@@ -415,15 +394,12 @@ module "pipelines" {
   # The source is the path to the pipelines module.
   source           = "./modules/pipelines"
   config_file_path = local_file.feature_store_configuration.id != "" ? local_file.feature_store_configuration.filename : ""
-  poetry_run_alias = local.poetry_run_alias
+  uv_run_alias = local.uv_run_alias
   # The count determines if the pipelines are created or not.
   # If the count is 1, the pipelines are created.
   # If the count is 0, the pipelines are not created.
   # This is done to avoid creating the pipelines if the `deploy_pipelines` variable is set to false in the terraform.tfvars file.
   count = var.deploy_pipelines ? 1 : 0
-  # The poetry_installed trigger is the ID of the null_resource.poetry_install resource.
-  # This is used to ensure that the poetry command is run before the pipelines module is created.
-  poetry_installed = null_resource.poetry_install.id
   # The project_id is the project in which the data is stored.
   # This is set to the data project ID in the terraform.tfvars file.
   mds_project_id = var.data_project_id
@@ -454,9 +430,9 @@ module "activation" {
   # The trigger function is used to trigger the activation function.
   # The trigger function is created in the same region as the activation function.
   trigger_function_location = var.google_default_region
-  # The poetry_cmd is the poetry_cmd variable.
-  # This can be set on the poetry_cmd in the terraform.tfvars file.
-  poetry_cmd = var.poetry_cmd
+  # The uv_run_alias is the uv_run_alias variable.
+  # This can be set on the uv_cmd in the terraform.tfvars file.
+  uv_run_alias = local.uv_run_alias
   # The ga4_measurement_id is the ga4_measurement_id variable.
   # This can be set on the ga4_measurement_id in the terraform.tfvars file.
   ga4_measurement_id = var.ga4_measurement_id
@@ -479,9 +455,6 @@ module "activation" {
   # This is done to avoid creating the activation function if the `deploy_activation` variable is set 
   # to false in the terraform.tfvars file.
   count = var.deploy_activation ? 1 : 0
-  # The poetry_installed is the ID of the null_resource poetry_install
-  # This is used to ensure that the poetry command is run before the activation module is created.
-  poetry_installed   = null_resource.poetry_install.id
   mds_project_id     = var.data_project_id
   mds_dataset_suffix = local.mds_dataset_suffix
 
diff --git a/infrastructure/terraform/modules/activation/main.tf b/infrastructure/terraform/modules/activation/main.tf
index c2c8f8b8..1bf35a9d 100644
--- a/infrastructure/terraform/modules/activation/main.tf
+++ b/infrastructure/terraform/modules/activation/main.tf
@@ -15,7 +15,6 @@
 locals {
   app_prefix                                     = "activation"
   source_root_dir                                = "../.."
-  poetry_run_alias                               = "${var.poetry_cmd} run"
   template_dir                                   = "${local.source_root_dir}/templates"
   pipeline_source_dir                            = "${local.source_root_dir}/python/activation"
   trigger_function_dir                           = "${local.source_root_dir}/python/function"
@@ -373,7 +372,7 @@ resource "null_resource" "create_custom_events" {
   }
   provisioner "local-exec" {
     command     = <<-EOT
-    ${local.poetry_run_alias} ga4-setup --ga4_resource=custom_events --ga4_property_id=${var.ga4_property_id} --ga4_stream_id=${var.ga4_stream_id}
+    ${var.uv_run_alias} ga4-setup --ga4_resource=custom_events --ga4_property_id=${var.ga4_property_id} --ga4_stream_id=${var.ga4_stream_id}
     EOT
     working_dir = local.source_root_dir
   }
@@ -391,7 +390,7 @@ resource "null_resource" "create_custom_dimensions" {
   }
   provisioner "local-exec" {
     command     = <<-EOT
-    ${local.poetry_run_alias} ga4-setup --ga4_resource=custom_dimensions --ga4_property_id=${var.ga4_property_id} --ga4_stream_id=${var.ga4_stream_id}
+    ${var.uv_run_alias} ga4-setup --ga4_resource=custom_dimensions --ga4_property_id=${var.ga4_property_id} --ga4_stream_id=${var.ga4_stream_id}
     EOT
     working_dir = local.source_root_dir
   }
@@ -447,7 +446,7 @@ module "trigger_function_account" {
 # a python command defined in the module ga4_setup.
 # This informatoin can then be used in other parts of the Terraform configuration to access the retrieved information.
 data "external" "ga4_measurement_properties" {
-  program     = ["bash", "-c", "${local.poetry_run_alias} ga4-setup --ga4_resource=measurement_properties --ga4_property_id=${var.ga4_property_id} --ga4_stream_id=${var.ga4_stream_id}"]
+  program     = ["bash", "-c", "${var.uv_run_alias} ga4-setup --ga4_resource=measurement_properties --ga4_property_id=${var.ga4_property_id} --ga4_stream_id=${var.ga4_stream_id}"]
   working_dir = local.source_root_dir
   # The count attribute specifies how many times the external data source should be executed.
   # This means that the external data source will be executed only if either the 
diff --git a/infrastructure/terraform/modules/activation/variables.tf b/infrastructure/terraform/modules/activation/variables.tf
index 6c2d6428..5814361b 100644
--- a/infrastructure/terraform/modules/activation/variables.tf
+++ b/infrastructure/terraform/modules/activation/variables.tf
@@ -43,8 +43,8 @@ variable "trigger_function_location" {
   type        = string
 }
 
-variable "poetry_cmd" {
-  description = "alias for poetry command on the current system"
+variable "uv_run_alias" {
+  description = "alias for uv run command on the current system"
   type        = string
 }
 
@@ -72,11 +72,6 @@ variable "ga4_stream_id" {
   type        = string
 }
 
-variable "poetry_installed" {
-  description = "Construct to specify dependency to poetry installed"
-  type        = string
-}
-
 variable "mds_project_id" {
   type        = string
   description = "MDS Project ID"
diff --git a/infrastructure/terraform/modules/pipelines/pipelines.tf b/infrastructure/terraform/modules/pipelines/pipelines.tf
index bd78fa84..a55e9136 100644
--- a/infrastructure/terraform/modules/pipelines/pipelines.tf
+++ b/infrastructure/terraform/modules/pipelines/pipelines.tf
@@ -309,13 +309,12 @@ resource "null_resource" "build_push_pipelines_components_image" {
     docker_repo_id          = google_artifact_registry_repository.pipelines_docker_repo.id
     docker_repo_create_time = google_artifact_registry_repository.pipelines_docker_repo.create_time
     source_content_hash     = local.component_image_content_hash
-    poetry_installed        = var.poetry_installed
   }
 
   # The provisioner block specifies the command that will be executed to build and push the base component image.
   # This command will execute the build-push function in the base_component_image module, which will build and push the base component image to the specified Docker repository.
   provisioner "local-exec" {
-    command     = "${var.poetry_run_alias} python -m base_component_image.build-push -c ${local.config_file_path_relative_python_run_dir}"
+    command     = "${var.uv_run_alias} python -m base_component_image.build-push -c ${local.config_file_path_relative_python_run_dir}"
     working_dir = self.triggers.working_dir
   }
 }
@@ -367,9 +366,9 @@ resource "null_resource" "compile_feature_engineering_auto_audience_segmentation
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-auto-audience-segmentation.execution -o fe_auto_audience_segmentation.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_auto_audience_segmentation.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-auto-audience-segmentation.execution -i fe_auto_audience_segmentation.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-auto-audience-segmentation.execution -o fe_auto_audience_segmentation.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_auto_audience_segmentation.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-auto-audience-segmentation.execution -i fe_auto_audience_segmentation.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -391,9 +390,9 @@ resource "null_resource" "compile_feature_engineering_aggregated_value_based_bid
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-aggregated-value-based-bidding.execution -o fe_agg_vbb.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_agg_vbb.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-aggregated-value-based-bidding.execution -i fe_agg_vbb.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-aggregated-value-based-bidding.execution -o fe_agg_vbb.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_agg_vbb.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-aggregated-value-based-bidding.execution -i fe_agg_vbb.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -415,9 +414,9 @@ resource "null_resource" "compile_feature_engineering_audience_segmentation_pipe
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-audience-segmentation.execution -o fe_audience_segmentation.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_audience_segmentation.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-audience-segmentation.execution -i fe_audience_segmentation.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-audience-segmentation.execution -o fe_audience_segmentation.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_audience_segmentation.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-audience-segmentation.execution -i fe_audience_segmentation.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -439,9 +438,9 @@ resource "null_resource" "compile_feature_engineering_purchase_propensity_pipeli
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-purchase-propensity.execution -o fe_purchase_propensity.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_purchase_propensity.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-purchase-propensity.execution -i fe_purchase_propensity.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-purchase-propensity.execution -o fe_purchase_propensity.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_purchase_propensity.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-purchase-propensity.execution -i fe_purchase_propensity.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -463,9 +462,9 @@ resource "null_resource" "compile_feature_engineering_churn_propensity_pipeline"
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-churn-propensity.execution -o fe_churn_propensity.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_churn_propensity.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-churn-propensity.execution -i fe_churn_propensity.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-churn-propensity.execution -o fe_churn_propensity.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_churn_propensity.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-churn-propensity.execution -i fe_churn_propensity.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -487,9 +486,9 @@ resource "null_resource" "compile_feature_engineering_customer_lifetime_value_pi
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-customer-ltv.execution -o fe_customer_ltv.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_customer_ltv.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-customer-ltv.execution -i fe_customer_ltv.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-customer-ltv.execution -o fe_customer_ltv.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_customer_ltv.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-customer-ltv.execution -i fe_customer_ltv.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -512,9 +511,9 @@ resource "null_resource" "compile_purchase_propensity_training_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.purchase_propensity.training -o purchase_propensity_training.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f purchase_propensity_training.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.purchase_propensity.training -i purchase_propensity_training.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.purchase_propensity.training -o purchase_propensity_training.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f purchase_propensity_training.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.purchase_propensity.training -i purchase_propensity_training.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -533,9 +532,9 @@ resource "null_resource" "compile_purchase_propensity_prediction_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.purchase_propensity.prediction -o purchase_propensity_prediction.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f purchase_propensity_prediction.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.purchase_propensity.prediction -i purchase_propensity_prediction.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.purchase_propensity.prediction -o purchase_propensity_prediction.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f purchase_propensity_prediction.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.purchase_propensity.prediction -i purchase_propensity_prediction.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -554,9 +553,9 @@ resource "null_resource" "compile_propensity_clv_training_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.propensity_clv.training -o propensity_clv_training.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f propensity_clv_training.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.propensity_clv.training -i propensity_clv_training.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.propensity_clv.training -o propensity_clv_training.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f propensity_clv_training.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.propensity_clv.training -i propensity_clv_training.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -575,9 +574,9 @@ resource "null_resource" "compile_clv_training_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.clv.training -o clv_training.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f clv_training.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.clv.training -i clv_training.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.clv.training -o clv_training.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f clv_training.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.clv.training -i clv_training.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -596,9 +595,9 @@ resource "null_resource" "compile_clv_prediction_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.clv.prediction -o clv_prediction.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f clv_prediction.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.clv.prediction -i clv_prediction.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.clv.prediction -o clv_prediction.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f clv_prediction.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.clv.prediction -i clv_prediction.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -617,9 +616,9 @@ resource "null_resource" "compile_segmentation_training_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.segmentation.training -o segmentation_training.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f segmentation_training.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.segmentation.training -i segmentation_training.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.segmentation.training -o segmentation_training.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f segmentation_training.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.segmentation.training -i segmentation_training.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -638,9 +637,9 @@ resource "null_resource" "compile_segmentation_prediction_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.segmentation.prediction -o segmentation_prediction.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f segmentation_prediction.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.segmentation.prediction -i segmentation_prediction.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.segmentation.prediction -o segmentation_prediction.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f segmentation_prediction.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.segmentation.prediction -i segmentation_prediction.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -659,9 +658,9 @@ resource "null_resource" "compile_auto_segmentation_training_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.auto_segmentation.training -o auto_segmentation_training.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f auto_segmentation_training.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.auto_segmentation.training -i auto_segmentation_training.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.auto_segmentation.training -o auto_segmentation_training.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f auto_segmentation_training.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.auto_segmentation.training -i auto_segmentation_training.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -680,9 +679,9 @@ resource "null_resource" "compile_auto_segmentation_prediction_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.auto_segmentation.prediction -o auto_segmentation_prediction.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f auto_segmentation_prediction.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.auto_segmentation.prediction -i auto_segmentation_prediction.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.auto_segmentation.prediction -o auto_segmentation_prediction.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f auto_segmentation_prediction.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.auto_segmentation.prediction -i auto_segmentation_prediction.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -701,9 +700,9 @@ resource "null_resource" "compile_value_based_bidding_training_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.value_based_bidding.training -o vbb_training.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f vbb_training.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.value_based_bidding.training -i vbb_training.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.value_based_bidding.training -o vbb_training.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f vbb_training.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.value_based_bidding.training -i vbb_training.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -722,9 +721,9 @@ resource "null_resource" "compile_value_based_bidding_explanation_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.value_based_bidding.explanation -o vbb_explanation.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f vbb_explanation.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.value_based_bidding.explanation -i vbb_explanation.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.value_based_bidding.explanation -o vbb_explanation.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f vbb_explanation.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.value_based_bidding.explanation -i vbb_explanation.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -743,9 +742,9 @@ resource "null_resource" "compile_churn_propensity_training_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.churn_propensity.training -o churn_propensity_training.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f churn_propensity_training.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.churn_propensity.training -i churn_propensity_training.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.churn_propensity.training -o churn_propensity_training.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f churn_propensity_training.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.churn_propensity.training -i churn_propensity_training.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -764,9 +763,9 @@ resource "null_resource" "compile_churn_propensity_prediction_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.churn_propensity.prediction -o churn_propensity_prediction.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f churn_propensity_prediction.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.churn_propensity.prediction -i churn_propensity_prediction.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.churn_propensity.prediction -o churn_propensity_prediction.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f churn_propensity_prediction.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.churn_propensity.prediction -i churn_propensity_prediction.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -785,9 +784,9 @@ resource "null_resource" "compile_reporting_preparation_aggregate_predictions_pi
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.reporting_preparation.execution -o reporting_preparation.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f reporting_preparation.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.reporting_preparation.execution -i reporting_preparation.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.reporting_preparation.execution -o reporting_preparation.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f reporting_preparation.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.reporting_preparation.execution -i reporting_preparation.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
@@ -806,9 +805,9 @@ resource "null_resource" "compile_gemini_insights_pipelines" {
   # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
   provisioner "local-exec" {
     command     = <<-EOT
-    ${var.poetry_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.gemini_insights.execution -o gemini_insights.yaml
-    ${var.poetry_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f gemini_insights.yaml -t ${self.triggers.tag} -t latest
-    ${var.poetry_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.gemini_insights.execution -i gemini_insights.yaml
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.gemini_insights.execution -o gemini_insights.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f gemini_insights.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.gemini_insights.execution -i gemini_insights.yaml
     EOT
     working_dir = self.triggers.working_dir
   }
diff --git a/infrastructure/terraform/modules/pipelines/variables.tf b/infrastructure/terraform/modules/pipelines/variables.tf
index 3afaaed3..3c618cac 100644
--- a/infrastructure/terraform/modules/pipelines/variables.tf
+++ b/infrastructure/terraform/modules/pipelines/variables.tf
@@ -17,13 +17,8 @@ variable "config_file_path" {
   description = "pipelines config file"
 }
 
-variable "poetry_run_alias" {
-  description = "alias for poetry run command on the current system"
-  type        = string
-}
-
-variable "poetry_installed" {
-  description = "Construct to specify dependency to poetry installed"
+variable "uv_run_alias" {
+  description = "alias for uv run command on the current system"
   type        = string
 }
 
diff --git a/infrastructure/terraform/variables.tf b/infrastructure/terraform/variables.tf
index e479a7d2..0d4e0d09 100644
--- a/infrastructure/terraform/variables.tf
+++ b/infrastructure/terraform/variables.tf
@@ -231,6 +231,12 @@ variable "poetry_cmd" {
   default     = "poetry"
 }
 
+variable "uv_cmd" {
+  description = "alias for uv run command on the current system"
+  type        = string
+  default     = "uv"
+}
+
 variable "feature_store_project_id" {
   type        = string
   description = "Project ID where feature store resources are created"

From fa0c8ac9f6cd0269b6485df2796591336cdf54f3 Mon Sep 17 00:00:00 2001
From: Charlie Wang <chwa@google.com>
Date: Fri, 15 Nov 2024 16:07:44 +0000
Subject: [PATCH 3/7] switch to uv

---
 .../terraform/modules/feature-store/bigquery-procedures.tf  | 2 +-
 infrastructure/terraform/modules/feature-store/main.tf      | 1 -
 infrastructure/terraform/modules/feature-store/variables.tf | 6 +++---
 3 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/infrastructure/terraform/modules/feature-store/bigquery-procedures.tf b/infrastructure/terraform/modules/feature-store/bigquery-procedures.tf
index 96412569..ff043b77 100644
--- a/infrastructure/terraform/modules/feature-store/bigquery-procedures.tf
+++ b/infrastructure/terraform/modules/feature-store/bigquery-procedures.tf
@@ -1471,7 +1471,7 @@ resource "null_resource" "create_gemini_model" {
 
   provisioner "local-exec" {
     command = <<-EOT
-    ${local.poetry_run_alias} bq query --use_legacy_sql=false --max_rows=100 --maximum_bytes_billed=10000000 < ${data.local_file.create_gemini_model_file.filename}
+    ${var.uv_run_alias} bq query --use_legacy_sql=false --max_rows=100 --maximum_bytes_billed=10000000 < ${data.local_file.create_gemini_model_file.filename}
     EOT
   }
 
diff --git a/infrastructure/terraform/modules/feature-store/main.tf b/infrastructure/terraform/modules/feature-store/main.tf
index 25b6830e..78c0eb83 100644
--- a/infrastructure/terraform/modules/feature-store/main.tf
+++ b/infrastructure/terraform/modules/feature-store/main.tf
@@ -21,7 +21,6 @@ locals {
   config_bigquery                       = local.config_vars.bigquery
   feature_store_project_id              = local.config_vars.bigquery.dataset.feature_store.project_id
   sql_dir                               = var.sql_dir_input
-  poetry_run_alias                      = "${var.poetry_cmd} run"
   builder_repository_id                 = "marketing-analytics-jumpstart-base-repo"
   purchase_propensity_project_id        = null_resource.check_bigquery_api.id != "" ? local.config_vars.bigquery.dataset.purchase_propensity.project_id : local.feature_store_project_id
   churn_propensity_project_id           = null_resource.check_bigquery_api.id != "" ? local.config_vars.bigquery.dataset.churn_propensity.project_id : local.feature_store_project_id
diff --git a/infrastructure/terraform/modules/feature-store/variables.tf b/infrastructure/terraform/modules/feature-store/variables.tf
index d20b92b7..a9bc07a5 100644
--- a/infrastructure/terraform/modules/feature-store/variables.tf
+++ b/infrastructure/terraform/modules/feature-store/variables.tf
@@ -37,8 +37,8 @@ variable "sql_dir_input" {
   description = "SQL queries directory"
 }
 
-variable "poetry_cmd" {
-  description = "alias for poetry command on the current system"
+variable "uv_run_alias" {
+  description = "alias for uv run command on the current system"
   type        = string
-  default     = "poetry"
+  default     = "uv run"
 }

From 6c3700961c962fe9f1d6f4ed95a730ae0d45a8fb Mon Sep 17 00:00:00 2001
From: Charlie Wang <chwa@google.com>
Date: Fri, 15 Nov 2024 16:18:33 +0000
Subject: [PATCH 4/7] remove poetry usage from terraform

---
 DEVELOPMENT.md                        |  2 +-
 infrastructure/cloudshell/tutorial.md | 40 +++---------------
 infrastructure/terraform/README.md    | 58 ++++++---------------------
 infrastructure/terraform/variables.tf |  6 ---
 4 files changed, 19 insertions(+), 87 deletions(-)

diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md
index 19c0800f..e72dee1b 100644
--- a/DEVELOPMENT.md
+++ b/DEVELOPMENT.md
@@ -3,7 +3,7 @@ Marketing Analytics Jumpstart consists of an easy, extensible and automated impl
 
 ## Developer pre-requisites
 Use Visual Studio Code to develop the solution. Install Gemini Code Assistant, Docker, GitHub, Hashicorp, Terraform, Jinja extensions.
-You should have Python 3, Poetry, Terraform, Git and Docker installed in your developer terminal environment.
+You should have Python 3, uv, Terraform, Git and Docker installed in your developer terminal environment.
 
 ## Preparing development environment
 
diff --git a/infrastructure/cloudshell/tutorial.md b/infrastructure/cloudshell/tutorial.md
index e0293e30..d4d2e519 100644
--- a/infrastructure/cloudshell/tutorial.md
+++ b/infrastructure/cloudshell/tutorial.md
@@ -12,45 +12,17 @@ export PROJECT_ID="<walkthrough-project-id/>"
 gcloud config set project $PROJECT_ID
 ```
 
-## Install or update Python3
-Install a compatible version of Python 3.8-3.10 and set the CLOUDSDK_PYTHON environment variable to point to it.
-```sh
-sudo apt-get install python3.10
-CLOUDSDK_PYTHON=python3.10
-```
+## Install update uv for running python scripts
+Install [uv](https://docs.astral.sh/uv/) that manages the python version and dependecies for the solution.
 
-## Install Python's Poetry and set Poetry to use Python 3.10 version
-[Poetry](https://python-poetry.org/docs/) is a Python's tool for dependency management and packaging.
-If you are installing on in Cloud Shell use the following commands:
-```sh
-pipx install poetry
-```
-If you don't have pipx installed - follow the [Pipx installation guide](https://pipx.pypa.io/stable/installation/)
-```sh
-sudo apt update
-sudo apt install pipx
-pipx ensurepath
-pipx install poetry
-```
-Verify that `poetry` is on your $PATH variable:
-```sh
-poetry --version
-```
-If it fails - add it to your $PATH variable:
 ```sh
+curl -LsSf https://astral.sh/uv/install.sh | sh
 export PATH="$HOME/.local/bin:$PATH" 
 ```
-Verify poetry is properly installed, run:
-```sh
-poetry --version
-```
-Set poetry to use your latest python3
-```sh
-poetry env use python3
-```
-Install python dependencies, run:
+
+Check uv installation
 ```sh
-poetry install
+uv --version
 ```
 
 ## Authenticate with additional OAuth 2.0 scopes
diff --git a/infrastructure/terraform/README.md b/infrastructure/terraform/README.md
index e0ba4aa9..dd026e8b 100644
--- a/infrastructure/terraform/README.md
+++ b/infrastructure/terraform/README.md
@@ -43,52 +43,18 @@ Also, this method allows you to extend this solution and develop it to satisfy y
     gcloud config set project $PROJECT_ID
     ```
 
-1. Install or update Python3
-    Install a compatible version of Python 3.8-3.10 and set the CLOUDSDK_PYTHON environment variable to point to it.
-
-    ```bash
-    sudo apt-get install python3.10
-    CLOUDSDK_PYTHON=python3.10
-    ```
-    If you are installing on a Mac:
-   ```shell
-   brew install python@3.10
-   CLOUDSDK_PYTHON=python3.10
-   ```
-
-1. Install Python's Poetry and set Poetry to use Python 3.10 version
-
-   [Poetry](https://python-poetry.org/docs/) is a Python's tool for dependency management and packaging.
-
-   If you are installing on in Cloud Shell use the following commands:
-   ```shell
-   pipx install poetry
-   ```
-   If you don't have pipx installed - follow the [Pipx installation guide](https://pipx.pypa.io/stable/installation/)
-   ```shell
-   sudo apt update
-   sudo apt install pipx
-   pipx ensurepath
-   pipx install poetry
-   ```
-   Verify that `poetry` is on your $PATH variable:
-   ```shell
-   poetry --version
-   ```
-   If it fails - add it to your $PATH variable:
-   ```shell
-   export PATH="$HOME/.local/bin:$PATH" 
-   ```
-   If you are installing on a Mac:
-   ```shell
-   brew install poetry
-   ```
-   Set poetry to use your latest python3
-   ```shell
-   SOURCE_ROOT=${HOME}/${REPO}
-   cd ${SOURCE_ROOT}
-   poetry env use python3
-   ```
+1. ## Install update uv for running python scripts
+Install [uv](https://docs.astral.sh/uv/) that manages the python version and dependecies for the solution.
+
+```sh
+curl -LsSf https://astral.sh/uv/install.sh | sh
+export PATH="$HOME/.local/bin:$PATH" 
+```
+
+Check uv installation
+```sh
+uv --version
+```
 
 1. Authenticate with additional OAuth 2.0 scopes needed to use the Google Analytics Admin API:
    ```shell
diff --git a/infrastructure/terraform/variables.tf b/infrastructure/terraform/variables.tf
index 0d4e0d09..cd1e84ae 100644
--- a/infrastructure/terraform/variables.tf
+++ b/infrastructure/terraform/variables.tf
@@ -225,12 +225,6 @@ variable "feature_store_config_env" {
   default     = "config"
 }
 
-variable "poetry_cmd" {
-  description = "alias for poetry run command on the current system"
-  type        = string
-  default     = "poetry"
-}
-
 variable "uv_cmd" {
   description = "alias for uv run command on the current system"
   type        = string

From 8feaf07c76cafd1bcec855a76d88b01550c28b40 Mon Sep 17 00:00:00 2001
From: Charlie Wang <chwa@google.com>
Date: Fri, 15 Nov 2024 16:20:23 +0000
Subject: [PATCH 5/7] format

---
 infrastructure/terraform/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/infrastructure/terraform/README.md b/infrastructure/terraform/README.md
index dd026e8b..bea2a3dd 100644
--- a/infrastructure/terraform/README.md
+++ b/infrastructure/terraform/README.md
@@ -43,7 +43,7 @@ Also, this method allows you to extend this solution and develop it to satisfy y
     gcloud config set project $PROJECT_ID
     ```
 
-1. ## Install update uv for running python scripts
+1. Install update uv for running python scripts
 Install [uv](https://docs.astral.sh/uv/) that manages the python version and dependecies for the solution.
 
 ```sh

From 9ace77482f77b5cb40e005a336f6e36528d1eac1 Mon Sep 17 00:00:00 2001
From: Charlie Wang <chwa@google.com>
Date: Fri, 15 Nov 2024 16:25:16 +0000
Subject: [PATCH 6/7] remove poetry

---
 infrastructure/terraform/README.md | 18 +++++++++---------
 scripts/generate-tf-backend.sh     | 10 +++-------
 2 files changed, 12 insertions(+), 16 deletions(-)

diff --git a/infrastructure/terraform/README.md b/infrastructure/terraform/README.md
index bea2a3dd..5d49e845 100644
--- a/infrastructure/terraform/README.md
+++ b/infrastructure/terraform/README.md
@@ -44,17 +44,17 @@ Also, this method allows you to extend this solution and develop it to satisfy y
     ```
 
 1. Install update uv for running python scripts
-Install [uv](https://docs.astral.sh/uv/) that manages the python version and dependecies for the solution.
+    Install [uv](https://docs.astral.sh/uv/) that manages the python version and dependecies for the solution.
 
-```sh
-curl -LsSf https://astral.sh/uv/install.sh | sh
-export PATH="$HOME/.local/bin:$PATH" 
-```
+    ```sh
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+    export PATH="$HOME/.local/bin:$PATH"
+    ```
 
-Check uv installation
-```sh
-uv --version
-```
+    Check uv installation:
+    ```sh
+    uv --version
+    ```
 
 1. Authenticate with additional OAuth 2.0 scopes needed to use the Google Analytics Admin API:
    ```shell
diff --git a/scripts/generate-tf-backend.sh b/scripts/generate-tf-backend.sh
index 5a1178fc..2611dc0e 100755
--- a/scripts/generate-tf-backend.sh
+++ b/scripts/generate-tf-backend.sh
@@ -19,15 +19,15 @@ set -o nounset
 
 . scripts/common.sh
 
-section_open "Check if the necessary dependencies are available: gcloud, gsutil, terraform, poetry"
+section_open "Check if the necessary dependencies are available: gcloud, gsutil, terraform, uv"
     check_exec_dependency "gcloud"
     check_exec_version "gcloud"
     check_exec_dependency "gsutil"
     check_exec_version "gsutil"
     check_exec_dependency "terraform"
     check_exec_version "terraform"
-    check_exec_dependency "poetry"
-    check_exec_version "poetry"
+    check_exec_dependency "uv"
+    check_exec_version "uv"
 section_close
 
 section_open "Check if the necessary variables are set: PROJECT_ID"
@@ -51,10 +51,6 @@ section_open "Enable all the required APIs"
     enable_all_apis
 section_close
 
-section_open "Install poetry libraries in the virtual environment for Terraform"
-    poetry install
-section_close
-
 section_open "Creating a new Google Cloud Storage bucket to store the Terraform state in ${TF_STATE_PROJECT} project, bucket: ${TF_STATE_BUCKET}"
     if gsutil ls -b gs://"${TF_STATE_BUCKET}" >/dev/null 2>&1; then
         printf "The ${TF_STATE_BUCKET} Google Cloud Storage bucket already exists. \n"

From cdde58c5ccb53c7dd7c1ff4bcd8335ed8f0b4b17 Mon Sep 17 00:00:00 2001
From: Charlie Wang <2144018+kingman@users.noreply.github.com>
Date: Fri, 15 Nov 2024 18:54:25 +0100
Subject: [PATCH 7/7] Add files via upload

---
 notebooks/quick_installation.ipynb | 18 ++++--------------
 1 file changed, 4 insertions(+), 14 deletions(-)

diff --git a/notebooks/quick_installation.ipynb b/notebooks/quick_installation.ipynb
index d7c31085..a2d1be9f 100644
--- a/notebooks/quick_installation.ipynb
+++ b/notebooks/quick_installation.ipynb
@@ -6,8 +6,7 @@
       "provenance": [],
       "collapsed_sections": [
         "DDGHqJNhq5Oi",
-        "mOISt4ShqIbc",
-        "US36yJ8lmqnP"
+        "mOISt4ShqIbc"
       ]
     },
     "kernelspec": {
@@ -305,19 +304,10 @@
         "# @title\n",
         "%%capture\n",
         "%%bash\n",
-        "# prompt: install packages\n",
-        "apt-get install python3.10\n",
-        "CLOUDSDK_PYTHON=python3.10\n",
-        "\n",
-        "#pip3 install poetry\n",
-        "sudo apt update\n",
-        "sudo apt install pipx\n",
-        "pipx ensurepath\n",
-        "pipx install poetry\n",
-        "\n",
+        "# prompt: install uv\n",
+        "curl -LsSf https://astral.sh/uv/install.sh | sh\n",
         "export PATH=\"/root/.local/bin:$PATH\"\n",
-        "poetry env use python3.10\n",
-        "poetry --version\n",
+        "uv --version\n",
         "\n",
         "git clone --depth=1 https://github.com/tfutils/tfenv.git ~/.tfenv\n",
         "echo 'export PATH=\"~/.tfenv/bin:$PATH\"' >> ~/.bash_profile\n",