add license select transform

Signed-off-by: Shivdeep Singh <[email protected]>

.make.versions

@@ -103,6 +103,8 @@ PII_REDACTOR_PYTHON_VERSION=$(DPK_VERSION)
 
 HTML2PARQUET_PYTHON_VERSION=$(DPK_VERSION)
 
+LICENSE_SELECT_PYTHON_VERSION=$(DPK_VERSION)
+LICENSE_SELECT_RAY_VERSION=$(DPK_VERSION)
 ################## ################## ################## ################## ################## ##################
 # Begin versions that the repo depends on. 
 

README.md

@@ -92,6 +92,7 @@ Matrix below shows the the combination of modules and supported runtimes. All th
 | [Malware annotation](transforms/code/malware/python/README.md)               | :white_check_mark: |:white_check_mark:|                  |:white_check_mark:      |
 | [Header cleanser](transforms/code/header_cleanser/python/README.md)                  | :white_check_mark: |:white_check_mark:|                  |:white_check_mark:      |
 | [Semantic file ordering](transforms/code/repo_level_ordering/ray/README.md)              |                    |:white_check_mark:|                  |                        |
+| [License Select Annotation](transforms/code/license_select/python/README.md)                  | :white_check_mark: |:white_check_mark:|                  |:white_check_mark:      |
 
 
 Contributors are welcome to add new modules as well as add runtime support for existing modules!

kfp/README.md

@@ -9,6 +9,7 @@
 | code/code2parquet                   |                  [code2parquet_wf.py](../transforms/code/code2parquet/kfp_ray/code2parquet_wf.py)                   |
 | code/code_quality                   |            [code_quality_wf.py](../transforms/code/code_quality/kfp_ray/code_quality_wf.py)            |
 | code/proglang_select                | [proglang_select_wf.py](../transforms/code/proglang_select/kfp_ray/proglang_select_wf.py)              |
+| code/license_select                  | [license_select_wf.py](../transforms/code/license_select/kfp_ray/license_select_wf.py)
 | universal/doc_id                    |                  [doc_id_wf.py](../transforms/universal/doc_id/kfp_ray/doc_id_wf.py)                   |
 | universal/ededup                    |                  [ededup_wf.py](../transforms/universal/ededup/kfp_ray/ededup_wf.py)                   |
 | universal/fdedup                    |                  [fdedup_wf.py](../transforms/universal/fdedup/kfp_ray/fdedup_wf.py)                   |

transforms/code/license_select/Makefile

@@ -0,0 +1,62 @@
+REPOROOT=../../..
+# Use make help, to see the available rules
+include $(REPOROOT)/.make.defaults
+
+setup::
+	@# Help: Recursively make $@ all subdirs 
+	$(MAKE) RULE=$@ .recurse
+
+clean::
+	@# Help: Recursively make $@ all subdirs 
+	$(MAKE) RULE=$@ .recurse
+
+build::
+	@# Help: Recursively make $@ in subdirs 
+	$(MAKE) RULE=$@ .recurse
+venv::
+	@# Help: Recursively make $@ in subdirs 
+	$(MAKE) RULE=$@ .recurse
+
+image:: 
+	@# Help: Recursively make $@ in all subdirs 
+	@$(MAKE) RULE=$@ .recurse
+
+set-versions:  
+	@# Help: Recursively $@ in all subdirs 
+	@$(MAKE) RULE=$@ .recurse
+
+publish:: 
+	@# Help: Recursively make $@ in all subdirs 
+	@$(MAKE) RULE=$@ .recurse
+
+test-image:: 
+	@# Help: Recursively make $@ in all subdirs 
+	@$(MAKE) RULE=$@ .recurse
+
+test:: 
+	@# Help: Recursively make $@ in all subdirs 
+	@$(MAKE) RULE=$@ .recurse
+
+test-src::
+	@# Help: Recursively make $@ in all subdirs 
+	$(MAKE) RULE=$@ .recurse
+
+load-image::
+	@# Help: Recursively make $@ in all subdirs 
+	$(MAKE) RULE=$@ .recurse
+
+.PHONY: workflow-venv
+workflow-venv:
+	$(MAKE) -C  kfp_ray workflow-venv
+
+.PHONY: workflow-test
+workflow-test:
+	$(MAKE) -C  kfp_ray workflow-test
+
+.PHONY: workflow-upload
+workflow-upload:
+	$(MAKE) -C  kfp_ray workflow-upload
+
+.PHONY: workflow-build
+workflow-build:
+	$(MAKE) -C  kfp_ray workflow-build

transforms/code/license_select/README.md

@@ -0,0 +1,11 @@
+# License Select 
+
+The License Select transform checks if the `license` of input data is in approved/denied list. It is implemented as per the set of [transform project conventions](../../README.md#transform-project-conventions) the following runtimes are available:
+
+* [python](python/README.md) - provides the base python-based transformation 
+implementation.
+* [ray](ray/README.md) - enables the running of the base python transformation
+in a Ray runtime
+* [kfp](kfp_ray/README.md) - enables running the ray docker image 
+in a kubernetes cluster using a generated `yaml` file.
+

transforms/code/license_select/kfp_ray/Makefile

@@ -0,0 +1,51 @@
+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::
+
+test-image::
+
+publish::
+
+image::
+
+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=license_select_wf.yaml
+
+.PHONY: workflow-upload
+workflow-upload: workflow-build
+	@for file in $(YAML_WF); do \
+		$(MAKE) .workflows.upload-pipeline PIPELINE_FILE=$$file; \
+	done

transforms/code/license_select/kfp_ray/license_select_wf.py

@@ -0,0 +1,210 @@
+# (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 workflow_support.compile_utils import ONE_HOUR_SEC, ONE_WEEK_SEC, ComponentUtils
+
+
+# the name of the job script
+EXEC_SCRIPT_NAME: str = "license_select_transform_ray.py"
+
+task_image = "quay.io/dataprep1/data-prep-kit/license_select-ray:latest"
+
+
+# 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/"
+
+# compute execution parameters. Here different transforms might need different implementations. As
+# a result, instead of creating a component we are creating it in place here.
+def compute_exec_params_func(
+    worker_options: dict,
+    actor_options: dict,
+    data_s3_config: str,
+    data_max_files: int,
+    data_num_samples: int,
+    runtime_pipeline_id: str,
+    runtime_job_id: str,
+    runtime_code_location: str,
+    lc_license_column_name: str,
+    lc_licenses_file: str,
+) -> dict:
+    from runtime_utils import KFPUtils
+
+    return {
+        "data_s3_config": data_s3_config,
+        "data_max_files": data_max_files,
+        "data_num_samples": data_num_samples,
+        "runtime_num_workers": KFPUtils.default_compute_execution_params(str(worker_options), str(actor_options)),
+        "runtime_worker_options": str(actor_options),
+        "runtime_pipeline_id": runtime_pipeline_id,
+        "runtime_job_id": runtime_job_id,
+        "runtime_code_location": str(runtime_code_location),
+        "lc_license_column_name": lc_license_column_name,
+        "lc_licenses_file": lc_licenses_file,
+    }
+
+
+# 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=compute_exec_params_func, 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=compute_exec_params_func, 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 = "license_select"
+PREFIX: str = "license_select"
+
+
+@dsl.pipeline(
+    name=TASK_NAME + "-ray-pipeline",
+    description="Pipeline for license select task",
+)
+def license_select(
+    ray_name: str = "license_select-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
+    data_s3_config: str = "{'input_folder': 'test/license_select/input/', 'output_folder': 'test/license_select/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 = "runtime_pipeline_id",
+    runtime_code_location: str = "{'github': 'github', 'commit_hash': '12345', 'path': 'path'}",
+    # license select parameters
+    lc_license_column_name: str = "license",
+    lc_licenses_file: str = "test/license_select/sample_approved_licenses.json",
+    # 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 License Select 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 - httpt retries for API server calls
+    :param data_s3_config - s3 configuration
+    :param data_s3_access_secret - s3 access secret
+    :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 lc_license_column_name  - Name of the column holds the license to process
+    :param lc_licenses_file - path to license list json file
+    :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(
+            worker_options=ray_worker_options,
+            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,
+            lc_license_column_name=lc_license_column_name,
+            lc_licenses_file=lc_licenses_file,
+        )
+        ComponentUtils.add_settings_to_component(compute_exec_params, ONE_HOUR_SEC * 2)
+        # 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,
+            # note that the parameters below are specific for this transform
+            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(license_select, __file__.replace(".py", ".yaml"))

transforms/code/license_select/python/.dockerignore

@@ -0,0 +1 @@
+venv/