feat(sdk)!: move v2 to main namespace

sdk/python/kfp/v2/compiler/__init__.py → sdk/python/kfp/compiler/__init__.py

@@ -12,4 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from kfp.v2.compiler.compiler import Compiler
+from kfp.compiler.compiler import Compiler

sdk/python/kfp/v2/compiler/compiler.py → sdk/python/kfp/compiler/compiler.py

@@ -11,7 +11,7 @@
 # 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.
-"""KFP DSL v2 compiler.
+"""KFP DSL compiler.
 
 This is an experimental implementation of KFP compiler that compiles KFP
 pipeline into Pipeline IR:
@@ -28,17 +28,17 @@
 import kfp.deprecated as kfp
 from google.protobuf import json_format
 from kfp.pipeline_spec import pipeline_spec_pb2
-from kfp.v2 import dsl
-from kfp.v2.compiler import pipeline_spec_builder as builder
-from kfp.v2.components import utils as component_utils
-from kfp.v2.components import component_factory
-from kfp.v2.components import for_loop
-from kfp.v2.components import pipeline_channel
-from kfp.v2.components import pipeline_context
-from kfp.v2.components import pipeline_task
-from kfp.v2.components import tasks_group
-from kfp.v2.components.types import artifact_types
-from kfp.v2.components.types import type_utils
+from kfp import dsl
+from kfp.compiler import pipeline_spec_builder as builder
+from kfp.components import utils as component_utils
+from kfp.components import component_factory
+from kfp.components import for_loop
+from kfp.components import pipeline_channel
+from kfp.components import pipeline_context
+from kfp.components import pipeline_task
+from kfp.components import tasks_group
+from kfp.components.types import artifact_types
+from kfp.components.types import type_utils
 
 _GroupOrTask = Union[tasks_group.TasksGroup, pipeline_task.PipelineTask]
 
@@ -62,7 +62,7 @@ class Compiler:
         def my_pipeline(a: int = 1, b: str = "default value"):
             ...
 
-        kfp.v2.compiler.Compiler().compile(
+        kfp.compiler.Compiler().compile(
             pipeline_func=my_pipeline,
             package_path='path/to/pipeline.json',
         )
@@ -91,7 +91,7 @@ def compile(
         type_check_old_value = kfp.TYPE_CHECK
         try:
             kfp.TYPE_CHECK = type_check
-            pipeline_spec = self._create_pipeline_v2(
+            pipeline_spec = self._create_pipeline(
                 pipeline_func=pipeline_func,
                 pipeline_name=pipeline_name,
                 pipeline_parameters_override=pipeline_parameters,
@@ -103,7 +103,7 @@ def compile(
         finally:
             kfp.TYPE_CHECK = type_check_old_value
 
-    def _create_pipeline_v2(
+    def _create_pipeline(
         self,
         pipeline_func: Callable[..., Any],
         pipeline_name: Optional[str] = None,

sdk/python/kfp/v2/compiler/compiler_test.py → sdk/python/kfp/compiler/compiler_test.py

@@ -19,11 +19,11 @@
 import unittest
 
 from absl.testing import parameterized
-from kfp.v2 import components
-from kfp.v2 import compiler
-from kfp.v2 import dsl
-from kfp.v2.components.types import type_utils
-from kfp.v2.dsl import PipelineTaskFinalStatus
+from kfp import components
+from kfp import compiler
+from kfp import dsl
+from kfp.components.types import type_utils
+from kfp.dsl import PipelineTaskFinalStatus
 
 VALID_PRODUCER_COMPONENT_SAMPLE = components.load_component_from_text("""
     name: producer
@@ -127,7 +127,7 @@ def test_compile_pipeline_with_dsl_graph_component_should_raise_error(self):
 
         with self.assertRaisesRegex(
                 AttributeError,
-                "module 'kfp.v2.dsl' has no attribute 'graph_component'"):
+                "module 'kfp.dsl' has no attribute 'graph_component'"):
 
             @dsl.graph_component
             def flip_coin_graph_component():

sdk/python/kfp/v2/compiler/main.py → sdk/python/kfp/compiler/main.py

@@ -20,8 +20,8 @@
 from typing import Any, Callable, List, Mapping, Optional
 
 import kfp.deprecated.dsl as dsl
-from kfp.v2 import compiler
-from kfp.v2.components import pipeline_context
+from kfp import compiler
+from kfp.components import pipeline_context
 
 
 def parse_arguments() -> argparse.Namespace:

sdk/python/kfp/v2/compiler/pipeline_spec_builder.py → sdk/python/kfp/compiler/pipeline_spec_builder.py

@@ -18,14 +18,14 @@
 
 from google.protobuf import struct_pb2
 from kfp.pipeline_spec import pipeline_spec_pb2
-from kfp.v2.components import utils as component_utils
-from kfp.v2.components import for_loop
-from kfp.v2.components import pipeline_channel
-from kfp.v2.components import pipeline_task
-from kfp.v2.components import placeholders
-from kfp.v2.components import tasks_group
-from kfp.v2.components.types import artifact_types
-from kfp.v2.components.types import type_utils
+from kfp.components import utils as component_utils
+from kfp.components import for_loop
+from kfp.components import pipeline_channel
+from kfp.components import pipeline_task
+from kfp.components import placeholders
+from kfp.components import tasks_group
+from kfp.components.types import artifact_types
+from kfp.components.types import type_utils
 
 _GroupOrTask = Union[tasks_group.TasksGroup, pipeline_task.PipelineTask]
 

sdk/python/kfp/v2/compiler/pipeline_spec_builder_test.py → sdk/python/kfp/compiler/pipeline_spec_builder_test.py

@@ -11,17 +11,17 @@
 # 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.
-"""Tests for kfp.v2.compiler.pipeline_spec_builder."""
+"""Tests for kfp.compiler.pipeline_spec_builder."""
 
 import unittest
 
 from absl.testing import parameterized
 from google.protobuf import json_format
 from google.protobuf import struct_pb2
 from kfp.pipeline_spec import pipeline_spec_pb2
-from kfp.v2.compiler import pipeline_spec_builder
-from kfp.v2.components import pipeline_channel
-from kfp.v2.components import structures
+from kfp.compiler import pipeline_spec_builder
+from kfp.components import pipeline_channel
+from kfp.components import structures
 
 
 class PipelineSpecBuilderTest(parameterized.TestCase):

sdk/python/kfp/v2/compiler_cli_tests/test_data/lightweight_python_functions_v2_pipeline.json → sdk/python/kfp/compiler_cli_tests/test_data/lightweight_python_functions_v2_pipeline.json

@@ -107,8 +107,8 @@
             "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet     --no-warn-script-location 'kfp==2.0.0b0' && \"$0\" \"$@\"\n",
             "sh",
             "-ec",
-            "program_path=$(mktemp -d)\nprintf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\npython3 -m kfp.v2.components.executor_main                         --component_module_path                         \"$program_path/ephemeral_component.py\"                         \"$@\"\n",
-            "\nimport kfp\nfrom kfp.v2 import dsl\nfrom kfp.v2.dsl import *\nfrom typing import *\n\ndef preprocess(\n    # An input parameter of type string.\n    message: str,\n    # An input parameter of type dict.\n    input_dict_parameter: Dict[str, int],\n    # An input parameter of type list.\n    input_list_parameter: List[str],\n    # Use Output[T] to get a metadata-rich handle to the output artifact\n    # of type `Dataset`.\n    output_dataset_one: Output[Dataset],\n    # A locally accessible filepath for another output artifact of type\n    # `Dataset`.\n    output_dataset_two_path: OutputPath('Dataset'),\n    # A locally accessible filepath for an output parameter of type string.\n    output_parameter_path: OutputPath(str),\n    # A locally accessible filepath for an output parameter of type bool.\n    output_bool_parameter_path: OutputPath(bool),\n    # A locally accessible filepath for an output parameter of type dict.\n    output_dict_parameter_path: OutputPath(Dict[str, int]),\n    # A locally accessible filepath for an output parameter of type list.\n    output_list_parameter_path: OutputPath(List[str]),\n):\n    \"\"\"Dummy preprocessing step.\"\"\"\n\n    # Use Dataset.path to access a local file path for writing.\n    # One can also use Dataset.uri to access the actual URI file path.\n    with open(output_dataset_one.path, 'w') as f:\n        f.write(message)\n\n    # OutputPath is used to just pass the local file path of the output artifact\n    # to the function.\n    with open(output_dataset_two_path, 'w') as f:\n        f.write(message)\n\n    with open(output_parameter_path, 'w') as f:\n        f.write(message)\n\n    with open(output_bool_parameter_path, 'w') as f:\n        f.write(\n            str(True))  # use either `str()` or `json.dumps()` for bool values.\n\n    import json\n    with open(output_dict_parameter_path, 'w') as f:\n        f.write(json.dumps(input_dict_parameter))\n\n    with open(output_list_parameter_path, 'w') as f:\n        f.write(json.dumps(input_list_parameter))\n\n"
+            "program_path=$(mktemp -d)\nprintf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\npython3 -m kfp.components.executor_main                         --component_module_path                         \"$program_path/ephemeral_component.py\"                         \"$@\"\n",
+            "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import *\n\ndef preprocess(\n    # An input parameter of type string.\n    message: str,\n    # An input parameter of type dict.\n    input_dict_parameter: Dict[str, int],\n    # An input parameter of type list.\n    input_list_parameter: List[str],\n    # Use Output[T] to get a metadata-rich handle to the output artifact\n    # of type `Dataset`.\n    output_dataset_one: Output[Dataset],\n    # A locally accessible filepath for another output artifact of type\n    # `Dataset`.\n    output_dataset_two_path: OutputPath('Dataset'),\n    # A locally accessible filepath for an output parameter of type string.\n    output_parameter_path: OutputPath(str),\n    # A locally accessible filepath for an output parameter of type bool.\n    output_bool_parameter_path: OutputPath(bool),\n    # A locally accessible filepath for an output parameter of type dict.\n    output_dict_parameter_path: OutputPath(Dict[str, int]),\n    # A locally accessible filepath for an output parameter of type list.\n    output_list_parameter_path: OutputPath(List[str]),\n):\n    \"\"\"Dummy preprocessing step.\"\"\"\n\n    # Use Dataset.path to access a local file path for writing.\n    # One can also use Dataset.uri to access the actual URI file path.\n    with open(output_dataset_one.path, 'w') as f:\n        f.write(message)\n\n    # OutputPath is used to just pass the local file path of the output artifact\n    # to the function.\n    with open(output_dataset_two_path, 'w') as f:\n        f.write(message)\n\n    with open(output_parameter_path, 'w') as f:\n        f.write(message)\n\n    with open(output_bool_parameter_path, 'w') as f:\n        f.write(\n            str(True))  # use either `str()` or `json.dumps()` for bool values.\n\n    import json\n    with open(output_dict_parameter_path, 'w') as f:\n        f.write(json.dumps(input_dict_parameter))\n\n    with open(output_list_parameter_path, 'w') as f:\n        f.write(json.dumps(input_list_parameter))\n\n"
           ],
           "image": "python:3.7"
         }
@@ -127,8 +127,8 @@
             "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet     --no-warn-script-location 'kfp==2.0.0b0' && \"$0\" \"$@\"\n",
             "sh",
             "-ec",
-            "program_path=$(mktemp -d)\nprintf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\npython3 -m kfp.v2.components.executor_main                         --component_module_path                         \"$program_path/ephemeral_component.py\"                         \"$@\"\n",
-            "\nimport kfp\nfrom kfp.v2 import dsl\nfrom kfp.v2.dsl import *\nfrom typing import *\n\ndef train(\n    # Use InputPath to get a locally accessible path for the input artifact\n    # of type `Dataset`.\n    dataset_one_path: InputPath('Dataset'),\n    # Use Input[T] to get a metadata-rich handle to the input artifact\n    # of type `Dataset`.\n    dataset_two: Input[Dataset],\n    # An input parameter of type string.\n    message: str,\n    # Use Output[T] to get a metadata-rich handle to the output artifact\n    # of type `Dataset`.\n    model: Output[Model],\n    # An input parameter of type bool.\n    input_bool: bool,\n    # An input parameter of type dict.\n    input_dict: Dict[str, int],\n    # An input parameter of type List[str].\n    input_list: List[str],\n    # An input parameter of type int with a default value.\n    num_steps: int = 100,\n):\n    \"\"\"Dummy Training step.\"\"\"\n    with open(dataset_one_path, 'r') as input_file:\n        dataset_one_contents = input_file.read()\n\n    with open(dataset_two.path, 'r') as input_file:\n        dataset_two_contents = input_file.read()\n\n    line = (f'dataset_one_contents: {dataset_one_contents} || '\n            f'dataset_two_contents: {dataset_two_contents} || '\n            f'message: {message} || '\n            f'input_bool: {input_bool}, type {type(input_bool)} || '\n            f'input_dict: {input_dict}, type {type(input_dict)} || '\n            f'input_list: {input_list}, type {type(input_list)} \\n')\n\n    with open(model.path, 'w') as output_file:\n        for i in range(num_steps):\n            output_file.write('Step {}\\n{}\\n=====\\n'.format(i, line))\n\n    # model is an instance of Model artifact, which has a .metadata dictionary\n    # to store arbitrary metadata for the output artifact.\n    model.metadata['accuracy'] = 0.9\n\n"
+            "program_path=$(mktemp -d)\nprintf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\npython3 -m kfp.components.executor_main                         --component_module_path                         \"$program_path/ephemeral_component.py\"                         \"$@\"\n",
+            "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import *\n\ndef train(\n    # Use InputPath to get a locally accessible path for the input artifact\n    # of type `Dataset`.\n    dataset_one_path: InputPath('Dataset'),\n    # Use Input[T] to get a metadata-rich handle to the input artifact\n    # of type `Dataset`.\n    dataset_two: Input[Dataset],\n    # An input parameter of type string.\n    message: str,\n    # Use Output[T] to get a metadata-rich handle to the output artifact\n    # of type `Dataset`.\n    model: Output[Model],\n    # An input parameter of type bool.\n    input_bool: bool,\n    # An input parameter of type dict.\n    input_dict: Dict[str, int],\n    # An input parameter of type List[str].\n    input_list: List[str],\n    # An input parameter of type int with a default value.\n    num_steps: int = 100,\n):\n    \"\"\"Dummy Training step.\"\"\"\n    with open(dataset_one_path, 'r') as input_file:\n        dataset_one_contents = input_file.read()\n\n    with open(dataset_two.path, 'r') as input_file:\n        dataset_two_contents = input_file.read()\n\n    line = (f'dataset_one_contents: {dataset_one_contents} || '\n            f'dataset_two_contents: {dataset_two_contents} || '\n            f'message: {message} || '\n            f'input_bool: {input_bool}, type {type(input_bool)} || '\n            f'input_dict: {input_dict}, type {type(input_dict)} || '\n            f'input_list: {input_list}, type {type(input_list)} \\n')\n\n    with open(model.path, 'w') as output_file:\n        for i in range(num_steps):\n            output_file.write('Step {}\\n{}\\n=====\\n'.format(i, line))\n\n    # model is an instance of Model artifact, which has a .metadata dictionary\n    # to store arbitrary metadata for the output artifact.\n    model.metadata['accuracy'] = 0.9\n\n"
           ],
           "image": "python:3.7"
         }
@@ -245,5 +245,5 @@
     }
   },
   "schemaVersion": "2.1.0",
-  "sdkVersion": "kfp-1.8.9"
+  "sdkVersion": "kfp-1.8.11"
 }

sdk/python/kfp/v2/compiler_cli_tests/test_data/lightweight_python_functions_v2_pipeline.py → sdk/python/kfp/compiler_cli_tests/test_data/lightweight_python_functions_v2_pipeline.py

@@ -11,12 +11,12 @@
 # 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.
-"""Sample pipeline for passing data in KFP v2."""
+"""Sample pipeline for passing data in KFP."""
 from typing import Dict, List
 
-from kfp.v2 import compiler
-from kfp.v2 import dsl
-from kfp.v2.dsl import (Dataset, Input, InputPath, Model, Output, OutputPath,
+from kfp import compiler
+from kfp import dsl
+from kfp.dsl import (Dataset, Input, InputPath, Model, Output, OutputPath,
                         component)