Skip to content

Commit

Permalink
feat(sdk)!: move v2 to main namespace (kubeflow#7376)
Browse files Browse the repository at this point in the history 
* feat(sdk)!: move v2 to main namespace

* edit samples
  • Loading branch information
@ji-yaqi @abaland
ji-yaqi authored and abaland committed May 29, 2022
1 parent a444e9c commit 5d04af8
Show file tree
Hide file tree
Showing 167 changed files with 470 additions and 977 deletions.
4 changes: 2 additions & 2 deletions backend/src/v2/compiler/testdata/component_used_twice.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -12,8 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.

from kfp.v2 import dsl
from kfp.v2 import components
from kfp import dsl
from kfp import components


@components.create_component_from_func
Expand Down
4 changes: 2 additions & 2 deletions frontend/mock-backend/data/v2/pipeline/mock_lightweight_python_functions_v2_pipeline.json
View file
Original file line number Diff line number Diff line change
Expand Up @@ -104,8 +104,8 @@
"(python3 -m ensurepip || python3 -m ensurepip --user) && (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'kfp==1.8.0' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'kfp==1.8.0' --user) && \"$0\" \"$@\"",
"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",
"\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",
"\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"
}
Expand Down
8 changes: 4 additions & 4 deletions ...tend/mock-backend/data/v2/pipeline/mock_lightweight_python_functions_v2_pipeline_rev.json
View file
Original file line number Diff line number Diff line change
Expand Up @@ -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==1.8.9' && \"$0\" \"$@\"\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"
}
Expand All @@ -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==1.8.9' && \"$0\" \"$@\"\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"
}
Expand Down
Loading

0 comments on commit 5d04af8

Please sign in to comment.