Auto generate kfp pipelines.

kfp/kfp_support_lib/Makefile

@@ -52,6 +52,7 @@ venv::	pyproject.toml .check-env
 	. ${VENV_ACTIVATE};     \
 	pip install -e .;		\
 	pip install ray==${RAY} \
+	pip install pre-commit  \
 	pip install pytest pytest-cov 
 
 test:: 	venv

kfp/pipeline_generator/README.md

@@ -0,0 +1,4 @@
+## Steps to generate a new pipeline
+- create a `pipeline_definitions.yaml` file for the required task (similar to the example [pipeline_definitions.yaml for the noop task](./example/pipeline_definitions.yaml).
+- execute `./run.sh <pipeline_definitions_file_path> <destination directory>`. When `pipeline_definitions_file_path` is the path of the `pipeline_definitions.yaml` file that defines the pipeline and `destination directory` is a directory where new pipeline file 
+will be generated.

kfp/pipeline_generator/example/pipeline_definitions.yaml

@@ -0,0 +1,24 @@
+pipeline_parameters:
+    name: "noop"
+    description: "Pipeline for noop task"
+    script_name: "noop_transform.py"
+    prefix: ""
+    multi_s3: False
+    compute_func_name: ""
+    compute_func_import: ""
+    component_spec_path: ""
+
+pipeline_common_input_parameters_values:
+    kfp_base_image: "quay.io/dataprep1/data-prep-kit/kfp-data-processing:0.1.1"
+    transform_image: "quay.io/dataprep1/data-prep-kit/noop:0.8.0"
+    s3_access_secret: "s3-secret"
+    image_pull_secret: "prod-all-icr-io"
+    input_folder: "test/noop/input/"
+    output_folder: "test/noop/output/"
+
+pipeline_transform_input_parameters:
+   pipeline_arguments:
+        - name: "noop_sleep_sec"
+          type: "int"
+          value: 10
+          description: "# noop sleep time"

kfp/pipeline_generator/pipeline.ptmpl

@@ -0,0 +1,125 @@
+# NOTE: This file is auto generated by Pipeline Generator.
+
+import kfp.compiler as compiler
+import kfp.components as comp
+import kfp.dsl as dsl
+from kfp_support.workflow_support.utils import (
+    ONE_HOUR_SEC,
+    ONE_WEEK_SEC,
+    ComponentUtils,
+)
+__compute_import__
+
+task_image = "__transform_image__"
+
+# the name of the job script
+EXEC_SCRIPT_NAME: str = "__script_name__"
+PREFIX: str = "__prefix_name__"
+
+# components
+base_kfp_image = "__kfp_base_image__"
+
+# path to kfp component specifications files
+component_spec_path = "__component_spec_path__"
+
+# compute execution parameters. Here different tranforms might need different implementations. As
+# a result, instead of creating a component we are creating it in place here.
+compute_exec_params_op = comp.func_to_container_op(
+    func=__compute_func_name__, base_image=base_kfp_image
+)
+# 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 + "__execute_comp__")
+# 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 = "__pipeline_name__"
+
+
+# Pipeline to invoke execution on remote resource
+@dsl.pipeline(
+    name=TASK_NAME + "-ray-pipeline",
+    description="__pipeline_description__",
+)
+def __pipeline_name__(
+    ray_name: str = "__pipeline_name__-kfp-ray",  # name of Ray cluster
+    ray_head_options: str = '{"cpu": 1, "memory": 4, "image_pull_secret": "__image_pull_secret__", '
+        '"image": "' + task_image + '", "image_pull_policy": "Always" }',
+    ray_worker_options: str = '{"replicas": 2, "max_replicas": 2, "min_replicas": 2, "cpu": 2, "memory": 4, '
+        '"image_pull_secret": "__image_pull_secret__", "image": "' + task_image + '", "image_pull_policy": "Always" }',
+    server_url: str = "http://kuberay-apiserver-service.kuberay.svc.cluster.local:8888",
+    # data access
+    data_s3_config: str = "{'input_folder': '__input_folder__', 'output_folder': '__output_folder__'}",
+    data_s3_access_secret: str = "__s3_access_secret__",
+    data_max_files: int = -1,
+    data_num_samples: int = -1,
+    data_checkpointing: bool = False,
+    data_data_sets: str = "",
+    data_files_to_use: str = "['.parquet']",
+    # orchestrator
+    runtime_actor_options: str = "{'num_cpus': 0.8}",
+    runtime_pipeline_id: str = "pipeline_id",
+    runtime_code_location: str = "{'github': 'github', 'commit_hash': '12345', 'path': 'path'}",
+
+    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_arguments__
+):
+    # create clean_up task
+    clean_up_task = cleanup_ray_op(ray_name=ray_name, run_id=dsl.RUN_ID_PLACEHOLDER, 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,
+        )
+        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=dsl.RUN_ID_PLACEHOLDER,
+            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=dsl.RUN_ID_PLACEHOLDER,
+            additional_params=additional_params,
+            exec_params={
+                "data_s3_config": data_s3_config,
+                "data_max_files": data_max_files,
+                "data_num_samples": data_num_samples,
+                "data_checkpointing": data_checkpointing,
+                "data_data_sets": data_data_sets,
+                "data_files_to_use": data_files_to_use,
+                "runtime_num_workers": compute_exec_params.output,
+                "runtime_worker_options": runtime_actor_options,
+                "runtime_pipeline_id": runtime_pipeline_id,
+                "runtime_job_id": dsl.RUN_ID_PLACEHOLDER,
+                "runtime_code_location": runtime_code_location,
+                __execute_job_params__
+            },
+            exec_script_name=EXEC_SCRIPT_NAME,
+            server_url=server_url,
+            __prefix_execute__
+        )
+        ComponentUtils.add_settings_to_component(execute_job, ONE_WEEK_SEC)
+        ComponentUtils.set_s3_env_vars_to_component(execute_job, data_s3_access_secret)
+        __prefix_set_secret__
+        execute_job.after(ray_cluster)
+
+    # Configure the pipeline level to one week (in seconds)
+    dsl.get_pipeline_conf().set_timeout(ONE_WEEK_SEC)
+
+if __name__ == "__main__":
+    # Compiling the pipeline
+    compiler.Compiler().compile(__pipeline_name__, __file__.replace(".py", ".yaml"))

kfp/pipeline_generator/pipeline_generator.py

@@ -0,0 +1,139 @@
+import json
+
+import yaml
+
+
+PRE_COMMIT = "./pre-commit-config.yaml"
+PIPELINE_TEMPLATE_FILE = "pipeline.ptmpl"
+
+INPUT_PARAMETERS = "input_parameters"
+PIPELINE_PARAMETERS = "pipeline_parameters"
+PIPELINE_COMMON_INPUT_PARAMETERS_VALUES = "pipeline_common_input_parameters_values"
+PIPELINE_TRANSFORM_INPUT_PARAMETERS = "pipeline_transform_input_parameters"
+
+NAME = "name"
+TYPE = "type"
+VALUE = "value"
+DESCRIPTION = "description"
+
+
+def get_pipeline_input_parameters(arguments) -> str:
+    ret_str = ""
+    ret_str += get_generic_params(arguments.get("pipeline_arguments", None))
+    return ret_str
+
+
+def get_generic_params(params) -> str:
+    ret_str = ""
+    if params is None:
+        return ret_str
+    for param in params:
+        ret_str += f"\n    {param[NAME]}: {param[TYPE]} = "
+        if param[TYPE] == "str":
+            ret_str += f'"{param[VALUE]}"'
+        else:
+            ret_str += f"{param[VALUE]}"
+        ret_str += f",  {param.get(DESCRIPTION, '')}"
+    return ret_str
+
+
+def get_execute_job_params_guf(args) -> (str):
+    ret_execute_job_params = ""
+    if args is not None:
+        pargs = args.get("pipeline_arguments", None)
+        if pargs is not None:
+            for a in pargs:
+                ret_execute_job_params += f'"{a[NAME]}": {a[NAME]},\n'
+    return ret_execute_job_params
+
+
+if __name__ == "__main__":
+    import argparse
+    import os
+    from pathlib import Path
+
+    from pre_commit.main import main
+
+    parser = argparse.ArgumentParser(description="Kubeflow pipeline generator for Foundation Models")
+    parser.add_argument("-c", "--config_file", type=str, default="")
+    parser.add_argument("-od", "--output_dir_file", type=str, default="")
+
+    args = parser.parse_args()
+    # open configuration file
+    with open(args.config_file, "r") as file:
+        pipeline_definitions = yaml.safe_load(file)
+
+    pipeline_parameters = pipeline_definitions[PIPELINE_PARAMETERS]
+    common_input_params_values = pipeline_definitions[PIPELINE_COMMON_INPUT_PARAMETERS_VALUES]
+
+    # Pipeline template file
+    fin = open(PIPELINE_TEMPLATE_FILE, "rt")
+
+    # Output file to write the pipeline
+    fout = open(f"{pipeline_parameters[NAME]}_wf.py", "wt")
+
+    # define the generated pipeline input parameters
+    transform_input_parameters = get_pipeline_input_parameters(pipeline_definitions[PIPELINE_TRANSFORM_INPUT_PARAMETERS])
+
+    # define arguments to the Ray execution job
+    execute_job_params = get_execute_job_params_guf(pipeline_definitions[PIPELINE_TRANSFORM_INPUT_PARAMETERS])
+
+    component_spec_path = pipeline_parameters.get("component_spec_path", "")
+    if component_spec_path == "":
+        component_spec_path = "../../../../../kfp/kfp_ray_components/"
+
+    compute_func_name = pipeline_parameters.get("compute_func_name", "")
+    if compute_func_name == "":
+        compute_func_name = "ComponentUtils.default_compute_execution_params"
+
+    compute_func_import = pipeline_parameters.get("compute_func_import", "")
+
+    execute_comp_file = "executeRayJobComponent.yaml"
+    prefix_name = ""
+    prefix_execute = ""
+    prefix_set_secret = ""
+    if pipeline_parameters.get("multi_s3", False) == True:
+        execute_comp_file = "executeRayJobComponent_multi_s3.yaml"
+        prefix_name = pipeline_parameters.get("prefix", "")
+        prefix_execute = "prefix=PREFIX"
+        prefix_set_secret = f"ComponentUtils.set_s3_env_vars_to_component(execute_job, {prefix_name}_s3_access_secret, prefix=PREFIX)"
+
+    # For each line in the template file
+    for line in fin:
+        # read replace the string and write to output pipeline file
+        fout.write(
+            line.replace("__pipeline_name__", pipeline_parameters[NAME])
+            .replace("__pipeline_description__", pipeline_parameters["description"])
+            .replace("__pipeline_arguments__", transform_input_parameters)
+            .replace("__execute_job_params__", execute_job_params)
+            .replace("__compute_func_name__", compute_func_name)
+            .replace("__component_spec_path__", component_spec_path)
+            .replace("__compute_import__", compute_func_import)
+            .replace("__script_name__", pipeline_parameters["script_name"])
+            .replace("__image_pull_secret__", common_input_params_values["image_pull_secret"])
+            .replace("__s3_access_secret__", common_input_params_values["s3_access_secret"])
+            .replace("__input_folder__", common_input_params_values.get("input_folder", ""))
+            .replace("__output_folder__", common_input_params_values.get("output_folder", ""))
+            .replace("__transform_image__", common_input_params_values["transform_image"])
+            .replace("__kfp_base_image__", common_input_params_values["kfp_base_image"])
+            .replace("__execute_comp__", execute_comp_file)
+            .replace("__prefix_name__", prefix_name)
+            .replace("__prefix_execute__", prefix_execute)
+            .replace("__prefix_set_secret__", prefix_set_secret)
+        )
+    # Move the generated file to the output directory
+    curr_dir = os.getcwd()
+    src_file = Path(f"{curr_dir}/{pipeline_parameters[NAME]}_wf.py")
+    dst_file = Path(f"{args.output_dir_file}/{pipeline_parameters[NAME]}_wf.py")
+    src_file.rename(dst_file)
+
+    fout.close()
+
+    import sys
+
+    from pre_commit.main import main
+
+    print(f"Pipeline ${dst_file} auto generation completed")
+    # format the pipeline python file
+    args = ["run", "--file", f"{dst_file}", "-c", PRE_COMMIT]
+    sys.exit(main(args))

kfp/pipeline_generator/pre-commit-config.yaml

@@ -0,0 +1,31 @@
+######################################################### {COPYRIGHT-TOP} ###
+# IBM Confidential
+# IBM Watson Machine Learning Core - Internal Tooling
+# Copyright IBM Corp. 2022
+######################################################### {COPYRIGHT-END} ###
+repos:
+    - repo: https://github.com/pre-commit/mirrors-prettier
+      rev: v3.0.0-alpha.9-for-vscode
+      hooks:
+          - id: prettier
+    - repo: https://github.com/psf/black
+      rev: 22.3.0
+      hooks:
+          - id: black
+            args: [--config=.black.toml]
+    - repo: https://github.com/PyCQA/isort
+      rev: 5.12.0
+      hooks:
+          - id: isort
+### Exclude submodules as some are part of other organizations with their own policies
+exclude: |
+    (?x)^(
+        autopilot/.*|
+        codeflare-cli/.*|
+        codeflare-sdk/.*|
+        docker_build_scripts/.*|
+        mcad/.*|
+        datalake/.*|
+        torchx/.*|
+        tsfm/.*
+    )$

kfp/pipeline_generator/run.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+
+DEF_FILE=$1
+DIST_DIR=$2
+ROOT_DIR=${PWD}
+
+mkdir -p ${ROOT_DIR}/${DIST_DIR}/
+python3 -m venv venv
+source venv/bin/activate
+pip install pre-commit
+python3 pipeline_generator.py -c ${DEF_FILE} -od ${ROOT_DIR}/${DIST_DIR}/