initial kfp implementation

transforms/universal/fdedup_multi_step/kfp_ray/Makefile_ori

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+REPOROOT=${CURDIR}/../../../../
+WORKFLOW_VENV_ACTIVATE=${REPOROOT}/transforms/venv/bin/activate
+include $(REPOROOT)/transforms/.make.workflows
+
+SRC_DIR=${CURDIR}/../ray/
+
+PYTHON_WF := $(shell find ./ -name '*_wf.py')
+YAML_WF := $(patsubst %.py, %.yaml, ${PYTHON_WF})
+
+workflow-venv: .check_python_version ${WORKFLOW_VENV_ACTIVATE}
+
+.PHONY: clean
+clean:
+	@# Help: Clean up the virtual environment.
+	rm -rf ${REPOROOT}/transforms/venv
+
+venv::
+
+build::
+
+setup::
+
+test::
+
+test-src::
+
+publish::
+
+image::
+
+test-image::
+
+kind-load-image::
+
+docker-load-image::
+
+docker-save-image::
+
+.PHONY: workflow-build
+workflow-build: workflow-venv
+	$(MAKE) $(YAML_WF)
+
+.PHONY: workflow-test
+workflow-test: workflow-build
+	$(MAKE) .workflows.test-pipeline TRANSFORM_SRC=${SRC_DIR} PIPELINE_FILE=fdedup_wf.yaml
+
+.PHONY: workflow-upload
+workflow-upload: workflow-build
+	@for file in $(YAML_WF); do \
+		$(MAKE) .workflows.upload-pipeline PIPELINE_FILE=$$file; \
+	done

transforms/universal/fdedup_multi_step/kfp_ray/README.md

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+# Fuzzy Deduplication Ray-base KubeFlow Pipeline Transformation 
+
+
+## Summary 
+This project allows execution of the [multi step fuzzy dedup](../ray) as a 
+[KubeFlow Pipeline](https://www.kubeflow.org/docs/components/pipelines/overview/)
+
+The detail pipeline is presented in the [Simplest Transform pipeline tutorial](../../../../kfp/doc/simple_transform_pipeline.md) 
+
+## Compilation
+
+In order to compile pipeline definitions run
+```shell
+make workflow-build
+```
+from the directory. It creates a virtual environment (make workflow-venv) and after that compiles the pipeline 
+definitions in the folder. The virtual environment is created once for all transformers. 
+
+Note: the pipelines definitions can be compiled and executed on KFPv1 and KFPv2. Meantime, KFPv1 is our default. If you
+prefer KFPv2, please do the following:
+```shell
+make clean
+export KFPv2=1
+make workflow-build
+```
+
+The next steps are described in [Deploying a pipeline](../../../../kfp/doc/simple_transform_pipeline.md#deploying-a-pipeline-)
+and [Executing pipeline and watching execution results](../../../../kfp/doc/simple_transform_pipeline.md#executing-pipeline-and-watching-execution-results-)

transforms/universal/fdedup_multi_step/kfp_ray/fdedup_bucket_processor_wf.py

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+# (C) Copyright IBM Corp. 2024.
+# Licensed under the Apache License, Version 2.0 (the “License”);
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#  http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an “AS IS” BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+import os
+
+import kfp.compiler as compiler
+import kfp.components as comp
+import kfp.dsl as dsl
+from src.fdedup_bucket_processor_compute_execution_params import fdedup_bucket_processor_compute_execution_params
+from workflow_support.compile_utils import ONE_HOUR_SEC, ONE_WEEK_SEC, ComponentUtils
+
+
+task_image = "quay.io/dataprep1/data-prep-kit/fdedup-ray:latest"
+
+# the name of the job script
+EXEC_SCRIPT_NAME: str = "fdedup_bucket_processor_transform_ray.py"
+
+# components
+base_kfp_image = "quay.io/dataprep1/data-prep-kit/kfp-data-processing:latest"
+
+# path to kfp component specifications files
+component_spec_path = "../../../../kfp/kfp_ray_components/"
+
+# KFPv1 and KFP2 uses different methods to create a component from a function. KFPv1 uses the
+# `create_component_from_func` function, but it is deprecated by KFPv2 and so has a different import path.
+# KFPv2 recommends using the `@dsl.component` decorator, which doesn't exist in KFPv1. Therefore, here we use
+# this if/else statement and explicitly call the decorator.
+if os.getenv("KFPv2", "0") == "1":
+    # In KFPv2 dsl.RUN_ID_PLACEHOLDER is deprecated and cannot be used since SDK 2.5.0. On another hand we cannot create
+    # a unique string in a component (at runtime) and pass it to the `clean_up_task` of `ExitHandler`, due to
+    # https://github.com/kubeflow/pipelines/issues/10187. Therefore, meantime we use a unique string created at
+    # compilation time.
+    import uuid
+
+    compute_exec_params_op = dsl.component_decorator.component(
+        func=fdedup_bucket_processor_compute_execution_params, base_image=base_kfp_image
+    )
+    print(
+        "WARNING: the ray cluster name can be non-unique at runtime, please do not execute simultaneous Runs of the "
+        + "same version of the same pipeline !!!"
+    )
+    run_id = uuid.uuid4().hex
+else:
+    compute_exec_params_op = comp.create_component_from_func(
+        func=fdedup_bucket_processor_compute_execution_params, base_image=base_kfp_image
+    )
+    run_id = dsl.RUN_ID_PLACEHOLDER
+
+# create Ray cluster
+create_ray_op = comp.load_component_from_file(component_spec_path + "createRayClusterComponent.yaml")
+# execute job
+execute_ray_jobs_op = comp.load_component_from_file(component_spec_path + "executeRayJobComponent.yaml")
+# clean up Ray
+cleanup_ray_op = comp.load_component_from_file(component_spec_path + "deleteRayClusterComponent.yaml")
+
+# Task name is part of the pipeline name, the ray cluster name and the job name in DMF.
+TASK_NAME: str = "fdedup_bucket_processor"
+
+
+@dsl.pipeline(
+    name=TASK_NAME + "-ray-pipeline",
+    description="Pipeline for fdedup bucket_processor",
+)
+def fdedup_bucket_processor(
+    # Ray cluster
+    ray_name: str = "fdedup-bucket_processor_kfp-ray",  # name of Ray cluster
+    # Add image_pull_secret and image_pull_policy to ray workers if needed
+    ray_head_options: dict = {"cpu": 1, "memory": 4, "image": task_image},
+    ray_worker_options: dict = {"replicas": 2, "max_replicas": 2, "min_replicas": 2, "cpu": 2, "memory": 4, "image": task_image},
+    server_url: str = "http://kuberay-apiserver-service.kuberay.svc.cluster.local:8888",
+    # data access. checkpointing is not supported by dedup
+    data_s3_config: str = "{'input_folder': 'test/fdedup/input/', 'output_folder': 'test/fdedup/output/'}",
+    data_s3_access_secret: str = "s3-secret",
+    data_max_files: int = -1,
+    data_num_samples: int = -1,
+    # orchestrator
+    runtime_actor_options: dict = {"num_cpus": 0.8},
+    runtime_pipeline_id: str = "pipeline_id",
+    runtime_code_location: dict = {'github': 'github', 'commit_hash': '12345', 'path': 'path'},
+    # infrastructure
+    fdedup_bucket_processor_bucket_cpu: float = 0.5,
+    fdedup_bucket_processor_doc_cpu: float = 0.5,
+    fdedup_bucket_processor_mhash_cpu: float = 0.5,
+    fdedup_bucket_processor_processor_cpu: float = 0.8,
+    fdedup_bucket_processor_num_buckets: int = 1,
+    fdedup_bucket_processor_num_doc_id: int = 1,
+    fdedup_bucket_processor_num_minhashes: int = 1,
+    # fuzzy parameters
+    fdedup_bucket_processor_num_permutations: int = 64,
+    fdedup_bucket_processor_threshold: float = 0.8,
+    # snapshotting
+    fdedup_bucket_processor_minhash_snapshot_directory: str = None,
+    fdedup_bucket_processor_buckets_snapshot_directory: str = None,
+    fdedup_bucket_processor_doc_id_snapshot_directory: str = None,
+    # additional parameters
+    additional_params: str = '{"wait_interval": 2, "wait_cluster_ready_tmout": 400, "wait_cluster_up_tmout": 300, "wait_job_ready_tmout": 400, "wait_print_tmout": 30, "http_retries": 5}',
+):
+    """
+    Pipeline to execute FDEDUP transform
+    :param ray_name: name of the Ray cluster
+    :param ray_head_options: head node options, containing the following:
+        cpu - number of cpus
+        memory - memory
+        image - image to use
+        image_pull_secret - image pull secret
+    :param ray_worker_options: worker node options (we here are using only 1 worker pool), containing the following:
+        replicas - number of replicas to create
+        max_replicas - max number of replicas
+        min_replicas - min number of replicas
+        cpu - number of cpus
+        memory - memory
+        image - image to use
+        image_pull_secret - image pull secret
+    :param server_url - server url
+    :param additional_params: additional (support) parameters, containing the following:
+        wait_interval - wait interval for API server, sec
+        wait_cluster_ready_tmout - time to wait for cluster ready, sec
+        wait_cluster_up_tmout - time to wait for cluster up, sec
+        wait_job_ready_tmout - time to wait for job ready, sec
+        wait_print_tmout - time between prints, sec
+        http_retries - http retries for API server calls
+    :param data_s3_access_secret - s3 access secret
+    :param data_s3_config - s3 configuration
+    :param data_max_files - max files to process
+    :param data_num_samples - num samples to process
+    :param runtime_actor_options - actor options
+    :param runtime_pipeline_id - pipeline id
+    :param runtime_code_location - code location
+    :param fdedup_bucket_processor_bucket_cpu - number of CPUs per bucket hash
+    :param fdedup_bucket_processor_doc_cpu - number of CPUs per doc hash
+    :param fdedup_bucket_processor_mhash_cpu - number of CPUs per minhash hash
+    :param fdedup_bucket_processor_processor_cpu - number of cpus for bucket processor
+    :param fdedup_bucket_processor_num_buckets - number of bucket cache actors (same as preprocessor)
+    :param fdedup_bucket_processor_num_doc_id - number of doc cache actors (same as preprocessor)
+    :param fdedup_bucket_processor_num_minhashes - number of minhash cache actors (same as preprocessor)
+    :param fdedup_bucket_processor_num_permutations - number of permutations
+    :param fdedup_bucket_processor_threshold - threshold
+    :param fdedup_bucket_processor_minhash_snapshot_directory - snapshot directory for minhashes
+    :param fdedup_bucket_processor_buckets_snapshot_directory - snapshot directory for buckets
+    :param fdedup_bucket_processor_doc_id_snapshot_directory - snapshot directory for docs
+    :return: None
+    """
+    # create clean_up task
+    clean_up_task = cleanup_ray_op(ray_name=ray_name, run_id=run_id, server_url=server_url)
+    ComponentUtils.add_settings_to_component(clean_up_task, 60)
+    # pipeline definition
+    with dsl.ExitHandler(clean_up_task):
+        # compute execution params
+        compute_exec_params = compute_exec_params_op(
+            ray_worker_options=ray_worker_options,
+            runtime_actor_options=runtime_actor_options,
+            data_s3_config=data_s3_config,
+            data_max_files=data_max_files,
+            data_num_samples=data_num_samples,
+            runtime_pipeline_id=runtime_pipeline_id,
+            runtime_job_id=run_id,
+            runtime_code_location=runtime_code_location,
+            fdedup_bucket_processor_num_permutations=fdedup_bucket_processor_num_permutations,
+            fdedup_bucket_processor_threshold=fdedup_bucket_processor_threshold,
+            fdedup_bucket_processor_bucket_cpu=fdedup_bucket_processor_bucket_cpu,
+            fdedup_bucket_processor_doc_cpu=fdedup_bucket_processor_doc_cpu,
+            fdedup_bucket_processor_mhash_cpu=fdedup_bucket_processor_mhash_cpu,
+            fdedup_bucket_processor_processor_cpu=fdedup_bucket_processor_processor_cpu,
+            fdedup_bucket_processor_num_buckets=fdedup_bucket_processor_num_buckets,
+            fdedup_bucket_processor_num_doc_id=fdedup_bucket_processor_num_doc_id,
+            fdedup_bucket_processor_num_minhashes=fdedup_bucket_processor_num_minhashes,
+            fdedup_bucket_processor_minhash_snapshot_directory=fdedup_bucket_processor_minhash_snapshot_directory,
+            fdedup_bucket_processor_buckets_snapshot_directory=fdedup_bucket_processor_buckets_snapshot_directory,
+            fdedup_bucket_processor_doc_id_snapshot_directory=fdedup_bucket_processor_doc_id_snapshot_directory,
+        )
+        ComponentUtils.add_settings_to_component(compute_exec_params, ONE_HOUR_SEC * 2)
+        ComponentUtils.set_s3_env_vars_to_component(compute_exec_params, data_s3_access_secret)
+
+        # start Ray cluster
+        ray_cluster = create_ray_op(
+            ray_name=ray_name,
+            run_id=run_id,
+            ray_head_options=ray_head_options,
+            ray_worker_options=ray_worker_options,
+            server_url=server_url,
+            additional_params=additional_params,
+        )
+        ComponentUtils.add_settings_to_component(ray_cluster, ONE_HOUR_SEC * 2)
+        ray_cluster.after(compute_exec_params)
+        # Execute job
+        execute_job = execute_ray_jobs_op(
+            ray_name=ray_name,
+            run_id=run_id,
+            additional_params=additional_params,
+            exec_params=compute_exec_params.output,
+            exec_script_name=EXEC_SCRIPT_NAME,
+            server_url=server_url,
+        )
+        ComponentUtils.add_settings_to_component(execute_job, ONE_WEEK_SEC)
+        ComponentUtils.set_s3_env_vars_to_component(execute_job, data_s3_access_secret)
+        execute_job.after(ray_cluster)
+
+
+if __name__ == "__main__":
+    # Compiling the pipeline
+    compiler.Compiler().compile(fdedup_bucket_processor, __file__.replace(".py", ".yaml"))