Fixes after testing.

kfp/kfp_ray_components/Dockerfile

@@ -2,6 +2,7 @@ FROM docker.io/rayproject/ray:2.9.3-py310
 
 ARG BUILD_DATE
 ARG GIT_COMMIT
+ARG KFP_v2
 
 LABEL build-date=$BUILD_DATE
 LABEL git-commit=$GIT_COMMIT
@@ -22,7 +23,7 @@ RUN cd python_apiserver_client && pip install --no-cache-dir -e .
 
 COPY --chown=ray:users workflow_support_lib workflow_support_lib/
 RUN cd workflow_support_lib && pip install --no-cache-dir -e .
-
+ENV KFP_v2=$KFP_v2
 # remove credentials-containing file
 RUN rm requirements.txt
 # components

kfp/kfp_ray_components/Makefile

@@ -26,14 +26,25 @@ endif
 #DOCKER_IMG=${DOCKER_HOSTNAME}/${DOCKER_NAMESPACE}/${DOCKER_IMAGE_NAME}:${DOCKER_IMAGE_VERSION}
 DOCKER_IMG=$(DOCKER_LOCAL_IMAGE)
 
+.PHONY: .kfp_image
+.kfp_image:: # Must be called with a DOCKER_IMAGE= settings.
+	@# Help: Create the docker image $(DOCKER_LOCAL_IMAGE) and a tag for $(DOCKER_REMOTE_IMAGE)
+	$(DOCKER) build -t $(DOCKER_LOCAL_IMAGE)                \
+		-f $(DOCKER_FILE)                               \
+		--build-arg EXTRA_INDEX_URL=$(EXTRA_INDEX_URL)  \
+		--build-arg BASE_IMAGE=$(BASE_IMAGE)            \
+		--build-arg BUILD_DATE=$(shell date -u +'%Y-%m-%dT%H:%M:%SZ')  	\
+		--build-arg KFP_v2=$(KFPv2) 					\
+		--build-arg GIT_COMMIT=$(shell git log -1 --format=%h) . 	
+	$(DOCKER) tag $(DOCKER_LOCAL_IMAGE)  $(DOCKER_REMOTE_IMAGE)
 
 .PHONY: .lib-src-image
 .lib-src-image:: 
 	$(MAKE) .defaults.copy-lib LIB_PATH=$(DPK_RAY_LIB_DIR) LIB_NAME=data-processing-lib-ray
 	$(MAKE) .defaults.copy-lib LIB_PATH=$(DPK_PYTHON_LIB_DIR) LIB_NAME=data-processing-lib-python
 	$(MAKE) .defaults.copy-lib LIB_PATH=$(REPOROOT)/kfp/kfp_support_lib/python_apiserver_client LIB_NAME=python_apiserver_client
 	$(MAKE) .defaults.copy-lib LIB_PATH=$(REPOROOT)/kfp/kfp_support_lib/$(WORKFLOW_SUPPORT_LIB) LIB_NAME=workflow_support_lib
-	$(MAKE) .defaults.image
+	$(MAKE) .kfp_image
 	rm -rf data-processing-lib-ray
 	rm -rf data-processing-lib-python
 	rm -rf python_apiserver_client

kfp/kfp_ray_components/createRayClusterComponent.yaml

@@ -11,7 +11,7 @@ inputs:
 
 implementation:
     container:
-        image: quay.io/dataprep1/data-prep-kit/kfp-data-processing:0.2.0-v2
+        image: quay.io/dataprep1/data-prep-kit/kfp-data-processing_v2:0.2.0-v2
         # command is a list of strings (command-line arguments).
         # The YAML language has two syntaxes for lists and you can use either of them.
         # Here we use the "flow syntax" - comma-separated strings inside square brackets.

kfp/kfp_ray_components/deleteRayClusterComponent.yaml

@@ -8,7 +8,7 @@ inputs:
 
 implementation:
     container:
-        image: quay.io/dataprep1/data-prep-kit/kfp-data-processing:0.2.0-v2
+        image: quay.io/dataprep1/data-prep-kit/kfp-data-processing_v2:0.2.0-v2
         # command is a list of strings (command-line arguments).
         # The YAML language has two syntaxes for lists and you can use either of them.
         # Here we use the "flow syntax" - comma-separated strings inside square brackets.

kfp/kfp_ray_components/executeRayJobComponent.yaml

@@ -12,7 +12,7 @@ inputs:
 
 implementation:
     container:
-        image: quay.io/dataprep1/data-prep-kit/kfp-data-processing:0.2.0-v2
+        image: quay.io/dataprep1/data-prep-kit/kfp-data-processing_v2:0.2.0-v2
         # command is a list of strings (command-line arguments).
         # The YAML language has two syntaxes for lists and you can use either of them.
         # Here we use the "flow syntax" - comma-separated strings inside square brackets.

kfp/kfp_ray_components/executeRayJobComponent_multi_s3.yaml

@@ -13,7 +13,7 @@ inputs:
 
 implementation:
     container:
-        image: quay.io/dataprep1/data-prep-kit/kfp-data-processing:0.2.0-v2
+        image: quay.io/dataprep1/data-prep-kit/kfp-data-processing_v2:0.2.0-v2
         # command is a list of strings (command-line arguments).
         # The YAML language has two syntaxes for lists and you can use either of them.
         # Here we use the "flow syntax" - comma-separated strings inside square brackets.

kfp/kfp_ray_components/executeSubWorkflowComponent.yaml

@@ -27,7 +27,7 @@ outputs:
 
 implementation:
     container:
-        image: quay.io/dataprep1/data-prep-kit/kfp-data-processing:0.2.0-v2
+        image: quay.io/dataprep1/data-prep-kit/kfp-data-processing_v2:0.2.0-v2
         # command is a list of strings (command-line arguments).
         # The YAML language has two syntaxes for lists, and you can use either of them.
         # Here we use the "flow syntax" - comma-separated strings inside square brackets.

kfp/kfp_ray_components/src/create_ray_cluster.py

@@ -13,7 +13,8 @@
 import sys
 
 kfp_v2 = os.getenv("KFP_v2", 0)
-if kfp_v2 == 1:
+print(kfp_v2)
+if kfp_v2 == "1":
     from workflow_support.runtime_utils import KFPUtils,  RayRemoteJobs
     print(f"Load KFPv2 libs")
 else:

kfp/kfp_ray_components/src/delete_ray_cluster.py

@@ -14,7 +14,7 @@
 import sys
 
 kfp_v2 = os.getenv("KFP_v2", 0)
-if kfp_v2 == 1:
+if kfp_v2 == "1":
     from workflow_support.runtime_utils import KFPUtils, RayRemoteJobs
     print(f"Load KFPv2 libs")
 else:

kfp/kfp_ray_components/src/execute_ray_job.py

@@ -13,7 +13,7 @@
 import os
 
 kfp_v2 = os.getenv("KFP_v2", 0)
-if kfp_v2 == 1:
+if kfp_v2 == "1":
     from workflow_support.runtime_utils import KFPUtils, execute_ray_jobs
     print(f"Load KFPv2 libs")
 else:

kfp/kfp_ray_components/src/execute_ray_job_multi_s3.py

@@ -13,7 +13,7 @@
 import os
 
 kfp_v2 = os.getenv("KFP_v2", 0)
-if kfp_v2 == 1:
+if kfp_v2 == "1":
     from workflow_support.runtime_utils import KFPUtils, execute_ray_jobs
     print(f"Load KFPv2 libs")
 else:

kfp/kfp_support_lib/kfp_v2_workflow_support/pyproject.toml

@@ -13,6 +13,7 @@ authors = [
 ]
 dependencies = [
     "kfp==2.7.0",
+    "kfp-kubernetes==1.2.0",
     "ray==2.9.3",
     "requests",
     "data-prep-toolkit==0.2.0",

kfp/kfp_support_lib/kfp_v2_workflow_support/src/workflow_support/compile_utils/__init__.py

@@ -1,3 +1,6 @@
-from kfp_support.workflow_support.compile_utils.component import (
+from workflow_support.compile_utils.component import (
+    ONE_HOUR_SEC, 
+    ONE_DAY_SEC, 
+    ONE_WEEK_SEC,
     ComponentUtils
 )

kfp/kfp_support_lib/kfp_v2_workflow_support/src/workflow_support/compile_utils/component.py

@@ -4,6 +4,10 @@
 
 RUN_NAME = "KFP_RUN_NAME"
 
+ONE_HOUR_SEC = 60 * 60
+ONE_DAY_SEC = ONE_HOUR_SEC * 24
+ONE_WEEK_SEC = ONE_DAY_SEC * 7
+
 class ComponentUtils:
     """
     Class containing methods supporting building pipelines
@@ -52,50 +56,3 @@ def set_s3_env_vars_to_component(
             for env_name, _ in env2key.items():
                 env2key[prefix + "_" + env_name] = env2key.pop(env_name)
         kubernetes.use_secret_as_env(task=task, secret_name='s3-secret', secret_key_to_env=env2key)
-
-    @staticmethod
-    def default_compute_execution_params(
-            worker_options: str,  # ray worker configuration
-            actor_options: str,  # cpus per actor
-    ) -> str:
-        """
-        This is the most simplistic transform execution parameters computation
-        :param worker_options: configuration of ray workers
-        :param actor_options: actor request requirements
-        :return: number of actors
-        """
-        import sys
-
-        from data_processing.utils import GB, get_logger
-        from kfp_support.workflow_support.runtime_utils import KFPUtils
-
-        logger = get_logger(__name__)
-
-        # convert input
-        w_options = KFPUtils.load_from_json(worker_options.replace("'", '"'))
-        a_options = KFPUtils.load_from_json(actor_options.replace("'", '"'))
-        # Compute available cluster resources
-        cluster_cpu = w_options["replicas"] * w_options["cpu"]
-        cluster_mem = w_options["replicas"] * w_options["memory"]
-        cluster_gpu = w_options["replicas"] * w_options.get("gpu", 0.0)
-        logger.info(f"Cluster available CPUs {cluster_cpu}, Memory {cluster_mem}, GPUs {cluster_gpu}")
-        # compute number of actors
-        n_actors_cpu = int(cluster_cpu * 0.85 / a_options.get("num_cpus", 0.5))
-        n_actors_memory = int(cluster_mem * 0.85 / (a_options.get("memory", GB) / GB))
-        n_actors = min(n_actors_cpu, n_actors_memory)
-        # Check if we need gpu calculations as well
-        actor_gpu = a_options.get("num_gpus", 0)
-        if actor_gpu > 0:
-            n_actors_gpu = int(cluster_gpu / actor_gpu)
-            n_actors = min(n_actors, n_actors_gpu)
-        logger.info(f"Number of actors - {n_actors}")
-        if n_actors < 1:
-            logger.warning(
-                f"Not enough cpu/gpu/memory to run transform, "
-                f"required cpu {a_options.get('num_cpus', .5)}, available {cluster_cpu}, "
-                f"required memory {a_options.get('memory', 1)}, available {cluster_mem}, "
-                f"required cpu {actor_gpu}, available {cluster_gpu}"
-            )
-            sys.exit(1)
-
-        return str(n_actors)

kfp/kfp_support_lib/kfp_v2_workflow_support/src/workflow_support/runtime_utils/__init__.py

@@ -1,2 +1,2 @@
-from kfp_support.workflow_support.runtime_utils.kfp_utils import KFPUtils
-from kfp_support.workflow_support.runtime_utils.remote_jobs_utils import RayRemoteJobs, execute_ray_jobs
+from workflow_support.runtime_utils.kfp_utils import KFPUtils
+from workflow_support.runtime_utils.remote_jobs_utils import RayRemoteJobs, execute_ray_jobs

kfp/kfp_support_lib/kfp_v2_workflow_support/src/workflow_support/runtime_utils/kfp_utils.py

@@ -111,3 +111,50 @@ def load_from_json(js: str) -> dict[str, Any]:
         except Exception as e:
             logger.warning(f"Failed to load parameters {js} with error {e}")
             sys.exit(1)
+
+    @staticmethod
+    def default_compute_execution_params(
+            worker_options: str,  # ray worker configuration
+            actor_options: str,  # cpus per actor
+    ) -> str:
+        """
+        This is the most simplistic transform execution parameters computation
+        :param worker_options: configuration of ray workers
+        :param actor_options: actor request requirements
+        :return: number of actors
+        """
+        import sys
+
+        from data_processing.utils import GB, get_logger
+        from workflow_support.runtime_utils import KFPUtils
+
+        logger = get_logger(__name__)
+
+        # convert input
+        w_options = KFPUtils.load_from_json(worker_options.replace("'", '"'))
+        a_options = KFPUtils.load_from_json(actor_options.replace("'", '"'))
+        # Compute available cluster resources
+        cluster_cpu = w_options["replicas"] * w_options["cpu"]
+        cluster_mem = w_options["replicas"] * w_options["memory"]
+        cluster_gpu = w_options["replicas"] * w_options.get("gpu", 0.0)
+        logger.info(f"Cluster available CPUs {cluster_cpu}, Memory {cluster_mem}, GPUs {cluster_gpu}")
+        # compute number of actors
+        n_actors_cpu = int(cluster_cpu * 0.85 / a_options.get("num_cpus", 0.5))
+        n_actors_memory = int(cluster_mem * 0.85 / (a_options.get("memory", GB) / GB))
+        n_actors = min(n_actors_cpu, n_actors_memory)
+        # Check if we need gpu calculations as well
+        actor_gpu = a_options.get("num_gpus", 0)
+        if actor_gpu > 0:
+            n_actors_gpu = int(cluster_gpu / actor_gpu)
+            n_actors = min(n_actors, n_actors_gpu)
+        logger.info(f"Number of actors - {n_actors}")
+        if n_actors < 1:
+            logger.warning(
+                f"Not enough cpu/gpu/memory to run transform, "
+                f"required cpu {a_options.get('num_cpus', .5)}, available {cluster_cpu}, "
+                f"required memory {a_options.get('memory', 1)}, available {cluster_mem}, "
+                f"required cpu {actor_gpu}, available {cluster_gpu}"
+            )
+            sys.exit(1)
+
+        return str(n_actors)

kfp/kfp_support_lib/kfp_v2_workflow_support/src/workflow_support/runtime_utils/remote_jobs_utils.py

@@ -17,8 +17,8 @@
 
 from data_processing.data_access import DataAccess, DataAccessFactory
 from data_processing.utils import ParamsUtils, get_logger
-from kfp_support.api_server_client import KubeRayAPIs
-from kfp.kfp_support_lib.python_apiserver_client.src.python_apiserver_client.params import (
+from python_apiserver_client import KubeRayAPIs
+from python_apiserver_client.params import (
     DEFAULT_HEAD_START_PARAMS,
     DEFAULT_WORKER_START_PARAMS,
     Cluster,
@@ -30,7 +30,7 @@
     environment_variables_decoder,
     volume_decoder,
 )
-from kfp_support.workflow_support.runtime_utils import KFPUtils
+from workflow_support.runtime_utils import KFPUtils
 from ray.job_submission import JobStatus
 
 

kfp/requirements.env

@@ -3,9 +3,9 @@ KFP_v2=2.7.0
 KFP_v1=1.8.22
 
 ifeq ($(KFPv2), 1)
-KFP=$(KFP_v2)
-WORKFLOW_SUPPORT_LIB=kfp_v2_workflow_support
+	KFP=$(KFP_v2)
+	WORKFLOW_SUPPORT_LIB=kfp_v2_workflow_support
 else
-KFP=$(KFP_v1)
-WORKFLOW_SUPPORT_LIB=kfp_v1_workflow_support
-endif
+	KFP=$(KFP_v1)
+	WORKFLOW_SUPPORT_LIB=kfp_v1_workflow_support
+endif

transforms/universal/noop/Makefile

@@ -47,15 +47,29 @@ workflow-venv:
 
 .PHONY: workflow-build
 workflow-build:
+ifeq ($(KFPv2), 0)
 	$(MAKE) -C kfp_ray/v1 workflow-build
+else
+	$(MAKE) -C kfp_ray/v2 workflow-build
+endif
+
 
 .PHONY: workflow-test
 workflow-test:
-	$(MAKE) -C $(PIPELINE_PATH) workflow-test
+ifeq ($(KFPv2), 0)
+	$(MAKE) -C kfp_ray/v2 workflow-test
+else
+	$(MAKE) -C kfp_ray/v2 workflow-test
+endif
 
 .PHONY: workflow-upload
 workflow-upload:
-	$(MAKE) -C $(PIPELINE_PATH) workflow-upload
+ifeq ($(KFPv2), 0)
+	$(MAKE) -C kfp_ray/v1 workflow-upload
+else
+	$(MAKE) -C kfp_ray/v2 workflow-upload
+endif
+
 
 .PHONY: workflow-reconcile-requirements
 workflow-reconcile-requirements: