Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
values |
|---|---|---|---|---|---|---|
pipelines | pipelines-master/components/contrib/XGBoost/Predict/from_ApacheParquet/component.py | from kfp.components import InputPath, OutputPath, create_component_from_func
def xgboost_predict(
data_path: InputPath('ApacheParquet'),
model_path: InputPath('XGBoostModel'),
predictions_path: OutputPath('Predictions'),
label_column_name: str = None,
):
'''Make predictions using a trained XGBoost ... | 1,837 | 30.152542 | 166 | py |
pipelines | pipelines-master/components/contrib/XGBoost/Predict/from_ApacheParquet/component.yaml | name: Xgboost predict
description: |-
Make predictions using a trained XGBoost model.
Args:
data_path: Path for the feature data in Apache Parquet format.
model_path: Path for the trained model in binary XGBoost format.
predictions_path: Output path for the predictions.
... | 4,293 | 40.68932 | 547 | yaml |
pipelines | pipelines-master/components/contrib/XGBoost/Train/component.py | from kfp.components import InputPath, OutputPath, create_component_from_func
def xgboost_train(
training_data_path: InputPath('CSV'), # Also supports LibSVM
model_path: OutputPath('XGBoostModel'),
model_config_path: OutputPath('XGBoostModelConfig'),
starting_model_path: InputPath('XGBoostModel') = Non... | 3,707 | 38.031579 | 145 | py |
pipelines | pipelines-master/components/contrib/XGBoost/Train/component.yaml | name: Xgboost train
description: |-
Train an XGBoost model.
Args:
training_data_path: Path for the training data in CSV format.
model_path: Output path for the trained model in binary XGBoost format.
model_config_path: Output path for the internal parameter configuration of Booste... | 10,952 | 51.406699 | 1,577 | yaml |
pipelines | pipelines-master/components/contrib/XGBoost/Train/from_ApacheParquet/component.py | from kfp.components import InputPath, OutputPath, create_component_from_func
def xgboost_train(
training_data_path: InputPath('ApacheParquet'),
model_path: OutputPath('XGBoostModel'),
model_config_path: OutputPath('XGBoostModelConfig'),
label_column_name: str,
starting_model_path: InputPath('XGBoo... | 3,782 | 38.821053 | 164 | py |
pipelines | pipelines-master/components/contrib/XGBoost/Train/from_ApacheParquet/component.yaml | name: Xgboost train
description: |-
Train an XGBoost model.
Args:
training_data_path: Path for the training data in Apache Parquet format.
model_path: Output path for the trained model in binary XGBoost format.
model_config_path: Output path for the internal parameter configuratio... | 11,009 | 52.707317 | 1,605 | yaml |
pipelines | pipelines-master/components/contrib/XGBoost/Train_and_cross-validate_regression/from_CSV/component.py | from collections import OrderedDict
from kfp import components
xgboost_train_regression_and_calculate_metrics_on_csv_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/18e8974288885086b2fd5351f6333210cd237d1b/components/XGBoost/Train_regression_and_calculate_metrics/from_CSV... | 2,633 | 47.777778 | 272 | py |
pipelines | pipelines-master/components/contrib/XGBoost/Train_and_cross-validate_regression/from_CSV/component.yaml | name: Xgboost train and cv regression on csv
inputs:
- {name: data, type: CSV}
- {name: label_column, type: Integer, default: '0', optional: true}
- {name: objective, type: String, default: 'reg:squarederror', optional: true}
- {name: num_iterations, type: Integer, default: '200', optional: true}
outputs:
- {name: mode... | 19,943 | 57.658824 | 187 | yaml |
pipelines | pipelines-master/components/contrib/XGBoost/Train_regression_and_calculate_metrics/from_CSV/component.py | from collections import OrderedDict
from kfp import components
xgboost_train_on_csv_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/567c04c51ff00a1ee525b3458425b17adbe3df61/components/XGBoost/Train/component.yaml')
xgboost_predict_on_csv_op = components.load_component_fro... | 2,937 | 47.163934 | 249 | py |
pipelines | pipelines-master/components/contrib/XGBoost/Train_regression_and_calculate_metrics/from_CSV/component.yaml | name: Xgboost train regression and calculate metrics on csv
inputs:
- {name: training_data, type: CSV}
- {name: testing_data, type: CSV}
- {name: label_column, type: Integer, default: '0', optional: true}
- {name: objective, type: String, default: 'reg:squarederror', optional: true}
- {name: num_iterations, type: Integ... | 4,149 | 50.875 | 186 | yaml |
pipelines | pipelines-master/components/contrib/XGBoost/_samples/recursive_training.py | #!/usr/bin/env python3
# This sample demonstrates continuous training using a train-eval-check recursive loop.
# The main pipeline trains the initial model and then gradually trains the model
# some more until the model evaluation metrics are good enough.
import kfp
from kfp import components
chicago_taxi_dataset_o... | 4,049 | 43.021739 | 253 | py |
pipelines | pipelines-master/components/contrib/XGBoost/_samples/sample_pipeline.py | import kfp
from kfp import components
chicago_taxi_dataset_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e3337b8bdcd63636934954e592d4b32c95b49129/components/datasets/Chicago%20Taxi/component.yaml')
convert_csv_to_apache_parquet_op = components.load_component_from_url('h... | 3,056 | 43.304348 | 227 | py |
pipelines | pipelines-master/components/contrib/XGBoost/_samples/training_with_cross_validation.py | # cross_validation_pipeline compact
import kfp
from kfp import components
chicago_taxi_dataset_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e3337b8bdcd63636934954e592d4b32c95b49129/components/datasets/Chicago%20Taxi/component.yaml')
xgboost_train_and_cv_regression_on_c... | 1,333 | 37.114286 | 254 | py |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/_samples/sample_pipeline.py | import kfp
from kfp import components
component_store = components.ComponentStore(url_search_prefixes=['https://raw.githubusercontent.com/kubeflow/pipelines/af3eaf64e87313795cad1add9bfd9fa1e86af6de/components/'])
chicago_taxi_dataset_op = component_store.load_component(name='datasets/Chicago_Taxi_Trips')
convert_csv_... | 2,487 | 58.238095 | 174 | py |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/from_ApacheArrowFeather/component.py | from kfp.components import InputPath, OutputPath, create_component_from_func
def convert_apache_arrow_feather_to_apache_parquet(
data_path: InputPath('ApacheArrowFeather'),
output_data_path: OutputPath('ApacheParquet'),
):
'''Converts Apache Arrow Feather to Apache Parquet.
[Apache Arrow Feather](http... | 1,169 | 35.5625 | 181 | py |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/from_ApacheArrowFeather/component.yaml | name: Convert apache arrow feather to apache parquet
description: |-
Converts Apache Arrow Feather to Apache Parquet.
[Apache Arrow Feather](https://arrow.apache.org/docs/python/feather.html)
[Apache Parquet](https://parquet.apache.org/)
Annotations:
author: Alexey Volkov <alexey.volkov@... | 3,084 | 38.050633 | 369 | yaml |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/from_CSV/component.py | from kfp.components import InputPath, OutputPath, create_component_from_func
def convert_csv_to_apache_parquet(
data_path: InputPath('CSV'),
output_data_path: OutputPath('ApacheParquet'),
):
'''Converts CSV table to Apache Parquet.
[Apache Parquet](https://parquet.apache.org/)
Annotations:
... | 1,006 | 31.483871 | 166 | py |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/from_CSV/component.yaml | name: Convert csv to apache parquet
description: |-
Converts CSV table to Apache Parquet.
[Apache Parquet](https://parquet.apache.org/)
Annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
inputs:
- {name: data, type: CSV}
outputs:
- {name: output_data, type: ApacheParquet}
metadata:... | 2,700 | 34.077922 | 262 | yaml |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/from_TSV/component.py | from kfp.components import InputPath, OutputPath, create_component_from_func
def convert_tsv_to_apache_parquet(
data_path: InputPath('TSV'),
output_data_path: OutputPath('ApacheParquet'),
):
'''Converts TSV table to Apache Parquet.
[Apache Parquet](https://parquet.apache.org/)
Annotations:
... | 1,054 | 33.032258 | 166 | py |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/from_TSV/component.yaml | name: Convert tsv to apache parquet
description: |-
Converts TSV table to Apache Parquet.
[Apache Parquet](https://parquet.apache.org/)
Annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
inputs:
- {name: data, type: TSV}
outputs:
- {name: output_data, type: ApacheParquet}
metadata:... | 2,748 | 34.701299 | 262 | yaml |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/to_ApacheArrowFeather/component.py | from kfp.components import InputPath, OutputPath, create_component_from_func
def convert_apache_parquet_to_apache_arrow_feather(
data_path: InputPath('ApacheParquet'),
output_data_path: OutputPath('ApacheArrowFeather'),
):
'''Converts Apache Parquet to Apache Arrow Feather.
[Apache Arrow Feather](http... | 1,261 | 38.4375 | 179 | py |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/to_ApacheArrowFeather/component.yaml | name: Convert apache parquet to apache arrow feather
description: |-
Converts Apache Parquet to Apache Arrow Feather.
[Apache Arrow Feather](https://arrow.apache.org/docs/python/feather.html)
[Apache Parquet](https://parquet.apache.org/)
Annotations:
author: Alexey Volkov <alexey.volkov@... | 3,145 | 38.325 | 369 | yaml |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/to_CSV/component.py | from kfp.components import InputPath, OutputPath, create_component_from_func
def convert_apache_parquet_to_csv(
data_path: InputPath('ApacheParquet'),
output_data_path: OutputPath('CSV'),
):
'''Converts Apache Parquet to CSV.
[Apache Parquet](https://parquet.apache.org/)
Annotations:
auth... | 1,096 | 31.264706 | 164 | py |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/to_CSV/component.yaml | name: Convert apache parquet to csv
description: |-
Converts Apache Parquet to CSV.
[Apache Parquet](https://parquet.apache.org/)
Annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
inputs:
- {name: data, type: ApacheParquet}
outputs:
- {name: output_data, type: CSV}
metadata:
ann... | 2,386 | 34.626866 | 256 | yaml |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/to_TSV/component.py | from kfp.components import InputPath, OutputPath, create_component_from_func
def convert_apache_parquet_to_tsv(
data_path: InputPath('ApacheParquet'),
output_data_path: OutputPath('TSV'),
):
'''Converts Apache Parquet to TSV.
[Apache Parquet](https://parquet.apache.org/)
Annotations:
auth... | 1,114 | 30.857143 | 164 | py |
pipelines | pipelines-master/components/contrib/_converters/ApacheParquet/to_TSV/component.yaml | name: Convert apache parquet to tsv
description: |-
Converts Apache Parquet to TSV.
[Apache Parquet](https://parquet.apache.org/)
Annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
inputs:
- {name: data, type: ApacheParquet}
outputs:
- {name: output_data, type: TSV}
metadata:
ann... | 2,410 | 34.455882 | 256 | yaml |
pipelines | pipelines-master/components/contrib/_converters/KerasModelHdf5/to_TensorflowSavedModel/component.py | from kfp.components import create_component_from_func, InputPath, OutputPath
def keras_convert_hdf5_model_to_tf_saved_model(
model_path: InputPath('KerasModelHdf5'),
converted_model_path: OutputPath('TensorflowSavedModel'),
):
'''Converts Keras HDF5 model to Tensorflow SavedModel format.
Args:
... | 1,328 | 38.088235 | 182 | py |
pipelines | pipelines-master/components/contrib/_converters/KerasModelHdf5/to_TensorflowSavedModel/component.yaml | name: Keras convert hdf5 model to tf saved model
description: Converts Keras HDF5 model to Tensorflow SavedModel format.
inputs:
- {name: model, type: KerasModelHdf5, description: Keras model in HDF5 format.}
outputs:
- {name: converted_model, type: TensorflowSavedModel, description: Keras model in Tensorflow SavedMode... | 2,513 | 40.9 | 171 | yaml |
pipelines | pipelines-master/components/contrib/_converters/OnnxModel/from_KerasModelHdf5/component.yaml | name: To ONNX from Keras HDF5 model
inputs:
- {name: Model, type: KerasModelHdf5}
outputs:
- {name: Model, type: OnnxModel}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/_converters/... | 684 | 25.346154 | 162 | yaml |
pipelines | pipelines-master/components/contrib/_converters/OnnxModel/from_TensorflowSavedModel/component.yaml | name: To ONNX from Tensorflow SavedModel
inputs:
- {name: Model, type: TensorflowSavedModel}
outputs:
- {name: Model, type: OnnxModel}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/_... | 707 | 26.230769 | 168 | yaml |
pipelines | pipelines-master/components/contrib/_converters/OnnxModel/to_TensorflowSavedModel/component.py | from kfp.components import create_component_from_func, InputPath, OutputPath
def convert_to_tensorflow_saved_model_from_onnx_model(
model_path: InputPath('OnnxModel'),
converted_model_path: OutputPath('TensorflowSavedModel'),
):
import onnx
import onnx_tf
onnx_model = onnx.load(model_path)
tf... | 1,086 | 39.259259 | 177 | py |
pipelines | pipelines-master/components/contrib/_converters/OnnxModel/to_TensorflowSavedModel/component.yaml | name: Convert to tensorflow saved model from onnx model
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/_converters/OnnxModel/to_TensorflowSavedModel/component.yaml'
inputs:
- {name: ... | 2,077 | 36.781818 | 166 | yaml |
pipelines | pipelines-master/components/contrib/_converters/TensorflowJSGraphModel/from_KerasModelHdf5/component.yaml | name: Convert Keras HDF5 model to Tensorflow JS GraphModel
inputs:
- {name: Model, type: KerasModelHdf5}
outputs:
- {name: Model, type: TensorflowJSGraphModel}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_compo... | 1,061 | 39.846154 | 175 | yaml |
pipelines | pipelines-master/components/contrib/_converters/TensorflowJSGraphModel/from_TensorflowSavedModel/component.yaml | name: Convert Tensorflow SavedModel to Tensorflow JS GraphModel
inputs:
- {name: Model, type: TensorflowSavedModel}
outputs:
- {name: Model, type: TensorflowJSGraphModel}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pip... | 1,087 | 40.846154 | 181 | yaml |
pipelines | pipelines-master/components/contrib/_converters/TensorflowJSLayersModel/from_KerasModelHdf5/component.yaml | name: Convert Keras HDF5 model to Tensorflow JS LayersModel
inputs:
- {name: Model, type: KerasModelHdf5}
outputs:
- {name: Model, type: TensorflowJSLayersModel}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_com... | 1,065 | 40 | 176 | yaml |
pipelines | pipelines-master/components/contrib/_converters/TensorflowJSLayersModel/from_TensorflowSavedModel/component.yaml | name: Convert Keras SavedModel to Tensorflow JS LayersModel
inputs:
- {name: Model, type: TensorflowSavedModel}
outputs:
- {name: Model, type: TensorflowJSLayersModel}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeli... | 1,089 | 40.923077 | 182 | yaml |
pipelines | pipelines-master/components/contrib/_converters/TensorflowLiteModel/from_KerasModelHdf5/component.yaml | name: Convert Keras HDF5 model to Tensorflow Lite model
inputs:
- {name: Model, type: KerasModelHdf5}
outputs:
- {name: Model, type: TensorflowLiteModel}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/... | 762 | 30.791667 | 172 | yaml |
pipelines | pipelines-master/components/contrib/_converters/TensorflowLiteModel/from_TensorflowSavedModel/component.yaml | name: Convert Tensorflow SavedModel to Tensorflow Lite model
inputs:
- {name: Model, type: TensorflowSavedModel}
outputs:
- {name: Model, type: TensorflowLiteModel}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_... | 778 | 31.458333 | 178 | yaml |
pipelines | pipelines-master/components/contrib/arena/docker/arena_launcher.py | """
Usage:
python arena_launcher.py
--name=tf-test
--tensorboard=true
mpijob
--gpus=1
--workers=2
--image=registry.cn-hangzhou.aliyuncs.com/tensorflow-samples/horovod:0.13.11-tf1.10.0-torch0.4.0-py3.5
--
mpirun python /benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --model res... | 6,650 | 34.951351 | 215 | py |
pipelines | pipelines-master/components/contrib/arena/docker/job_generator.py | import argparse
import datetime
import json
import os
import sys
import logging
import requests
import subprocess
import six
import time
import yaml
from subprocess import Popen,PIPE
from shlex import split
from utils import *
# Generate common options
def generate_options(args):
gpus = args.gpus
cpu = args.c... | 2,817 | 23.08547 | 76 | py |
pipelines | pipelines-master/components/contrib/arena/docker/utils.py | import argparse
import datetime
import json
import os
import sys
import logging
import requests
import subprocess
import six
import time
import yaml
from subprocess import Popen,PIPE
from shlex import split
def setup_custom_logging():
logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s',
... | 4,385 | 32.480916 | 111 | py |
pipelines | pipelines-master/components/contrib/arena/python/build.sh | #!/bin/bash -ex
get_abs_filename() {
# $1 : relative filename
echo "$(cd "$(dirname "$1")" && pwd)/$(basename "$1")"
}
target_archive_file=${1:-kfp-arena-0.6.tar.gz}
target_archive_file=$(get_abs_filename "$target_archive_file")
DIR=$(mktemp -d)
cp -r arena $DIR
cp ./setup.py $DIR
# Build tarball package.
cd ... | 418 | 18.952381 | 62 | sh |
pipelines | pipelines-master/components/contrib/arena/python/setup.py | from setuptools import setup
NAME = 'kfp-arena'
VERSION = '0.6'
REQUIRES = ['kfp >= 0.1']
setup(
name=NAME,
version=VERSION,
description='KubeFlow Pipelines Extended Arena SDK',
author='cheyang',
author_email="cheyang@163.com",
install_requires=REQUIRES,
packages=[
'arena',
],
... | 1,052 | 27.459459 | 69 | py |
pipelines | pipelines-master/components/contrib/arena/python/arena/__init__.py | #
#
# 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 und... | 713 | 38.666667 | 74 | py |
pipelines | pipelines-master/components/contrib/arena/python/arena/_arena_distributed_tf_op.py | #!/usr/bin/env python3
#
#
# 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... | 6,484 | 46.335766 | 137 | py |
pipelines | pipelines-master/components/contrib/arena/python/arena/_arena_mpi_op.py | #!/usr/bin/env python3
#
#
# 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... | 3,061 | 33.404494 | 112 | py |
pipelines | pipelines-master/components/contrib/arena/python/arena/_arena_standalone_op.py | #!/usr/bin/env python3
#
#
# 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... | 2,941 | 32.05618 | 107 | py |
pipelines | pipelines-master/components/contrib/arena/python/arena/_utils.py | # The default Data of training job
default_data = 'None'
def set_defaultData(data):
default_data = data
def get_defaultData():
return default_data | 150 | 17.875 | 34 | py |
pipelines | pipelines-master/components/contrib/basics/Calculate_hash/component.yaml | name: Calculate data hash
inputs:
- {name: Data}
- {name: Hash algorithm, type: String, default: SHA256, description: "Hash algorithm to use. Supported values are MD5, SHA1, SHA256, SHA512, SHA3"}
outputs:
- {name: Hash}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_locati... | 1,443 | 31.088889 | 147 | yaml |
pipelines | pipelines-master/components/contrib/dataset_manipulation/split_data_into_folds/in_CSV/component.py | from kfp.components import InputPath, OutputPath, create_component_from_func
def split_table_into_folds(
table_path: InputPath('CSV'),
train_1_path: OutputPath('CSV'),
train_2_path: OutputPath('CSV'),
train_3_path: OutputPath('CSV'),
train_4_path: OutputPath('CSV'),
train_5_path: OutputPath('C... | 3,017 | 32.164835 | 181 | py |
pipelines | pipelines-master/components/contrib/dataset_manipulation/split_data_into_folds/in_CSV/component.yaml | name: Split table into folds
description: |-
Splits the data table into the specified number of folds.
The data is split into the specified number of folds k (default: 5).
Each testing subsample has 1/k fraction of samples. The testing subsamples do not overlap.
Each training subsample has (k-1)/k ... | 8,110 | 42.607527 | 828 | yaml |
pipelines | pipelines-master/components/contrib/datasets/Chicago_Taxi_Trips/component.yaml | name: Chicago Taxi Trips dataset
description: |
City of Chicago Taxi Trips dataset: https://data.cityofchicago.org/Transportation/Taxi-Trips/wrvz-psew
The input parameters configure the SQL query to the database.
The dataset is pretty big, so limit the number of results using the `Limit` or `Where` parameters.
... | 2,436 | 54.386364 | 418 | yaml |
pipelines | pipelines-master/components/contrib/datasets/HuggingFace/Load_dataset/component.py | from typing import NamedTuple
from kfp.components import create_component_from_func, OutputPath
def load_dataset_using_huggingface(
dataset_name: str,
dataset_dict_path: OutputPath('HuggingFaceDatasetDict'),
) -> NamedTuple('Outputs', [
('splits', list),
]):
from datasets import load_dataset
dat... | 989 | 30.935484 | 165 | py |
pipelines | pipelines-master/components/contrib/datasets/HuggingFace/Load_dataset/component.yaml | name: Load dataset using huggingface
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/datasets/HuggingFace/Load_dataset/component.yaml'
inputs:
- {name: dataset_name, type: String}
out... | 3,069 | 35.547619 | 154 | yaml |
pipelines | pipelines-master/components/contrib/datasets/HuggingFace/Split_dataset/component.py | from kfp.components import create_component_from_func, InputPath, OutputPath
def split_dataset_huggingface(
dataset_dict_path: InputPath('HuggingFaceDatasetDict'),
dataset_split_path: OutputPath('HuggingFaceDataset'),
dataset_path: OutputPath('HuggingFaceArrowDataset'),
# dataset_indices_path: OutputP... | 1,798 | 47.621622 | 166 | py |
pipelines | pipelines-master/components/contrib/datasets/HuggingFace/Split_dataset/component.yaml | name: Split dataset huggingface
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/datasets/HuggingFace/Split_dataset/component.yaml'
inputs:
- {name: dataset_dict, type: HuggingFaceData... | 3,849 | 45.385542 | 155 | yaml |
pipelines | pipelines-master/components/contrib/datasets/HuggingFace/_samples/sample.pipeline.py | from kfp import components
from kfp import dsl
load_dataset_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/d0e14a1dad4b851ad2a60a0c1a8201493f3d931c/components/datasets/HuggingFace/Load_dataset/component.yaml')
split_dataset_op = components.load_component_from_url('https:... | 984 | 38.4 | 211 | py |
pipelines | pipelines-master/components/contrib/filesystem/get_file/component.yaml | name: Get file
description: Get file from directory.
inputs:
- {name: Directory, type: Directory}
- {name: Subpath, type: String}
outputs:
- {name: File}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/... | 604 | 23.2 | 140 | yaml |
pipelines | pipelines-master/components/contrib/filesystem/get_subdirectory/component.yaml | name: Get subdirectory
description: Get subdirectory from directory.
inputs:
- {name: Directory, type: Directory}
- {name: Subpath, type: String}
outputs:
- {name: Subdir, type: Directory}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubuserconte... | 649 | 25 | 148 | yaml |
pipelines | pipelines-master/components/contrib/filesystem/list_items/component.yaml | name: List items
description: Recursively list directory contents.
inputs:
- {name: Directory, type: Directory}
outputs:
- {name: Items}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components... | 613 | 24.583333 | 142 | yaml |
pipelines | pipelines-master/components/contrib/git/clone/component.yaml | name: Git clone
description: Creates a shallow clone of the specified repo branch
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/git/clone/component.yaml'
volatile_component: "tru... | 650 | 26.125 | 130 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/Optimizer/Add_measurement_for_trial/component.py | from typing import NamedTuple
from kfp.components import create_component_from_func
def add_measurement_for_trial_in_gcp_ai_platform_optimizer(
trial_name: str,
metric_value: float,
complete_trial: bool = True,
step_count: float = None,
gcp_project_id: str = None,
gcp_region: str = "us-central... | 4,838 | 38.341463 | 180 | py |
pipelines | pipelines-master/components/contrib/google-cloud/Optimizer/Add_measurement_for_trial/component.yaml | name: Add measurement for trial in gcp ai platform optimizer
description: Add measurement for a trial and check whether to continue.
inputs:
- {name: trial_name, type: String, description: Full trial resource name.}
- {name: metric_value, type: Float, description: Result of the trial evaluation.}
- name: complete_trial... | 9,457 | 41.79638 | 176 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/Optimizer/Create_study/component.py | from typing import NamedTuple
from kfp.components import create_component_from_func
def create_study_in_gcp_ai_platform_optimizer(
study_id: str,
parameter_specs: list,
optimization_goal: str = 'MAXIMIZE',
metric_specs: list = None,
gcp_project_id: str = None,
gcp_region: str = "us-central1",
... | 3,415 | 39.188235 | 167 | py |
pipelines | pipelines-master/components/contrib/google-cloud/Optimizer/Create_study/component.yaml | name: Create study in gcp ai platform optimizer
description: Creates a Google Cloud AI Plaform Optimizer study.
inputs:
- {name: study_id, type: String, description: Name of the study.}
- {name: parameter_specs, type: JsonArray, description: 'List of parameter specs.
See https://cloud.google.com/ai-platform/optimiz... | 7,207 | 43.770186 | 189 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/Optimizer/Suggest_parameter_sets_based_on_measurements/component.py | from typing import NamedTuple
from kfp.components import create_component_from_func
def suggest_parameter_sets_from_measurements_using_gcp_ai_platform_optimizer(
parameter_specs: list,
metrics_for_parameter_sets: list,
suggestion_count: int,
maximize: bool = False,
metric_specs: list = None,
g... | 8,331 | 43.084656 | 247 | py |
pipelines | pipelines-master/components/contrib/google-cloud/Optimizer/Suggest_parameter_sets_based_on_measurements/component.yaml | name: Suggest parameter sets from measurements using gcp ai platform optimizer
description: Suggests trials (parameter sets) to evaluate.
inputs:
- {name: parameter_specs, type: JsonArray, description: 'List of parameter specs.
See https://cloud.google.com/ai-platform/optimizer/docs/reference/rest/v1/projects.locat... | 13,606 | 46.74386 | 253 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/Optimizer/Suggest_trials/component.py | from typing import NamedTuple
from kfp.components import create_component_from_func
def suggest_trials_in_gcp_ai_platform_optimizer(
study_name: str,
suggestion_count: int,
gcp_project_id: str = None,
gcp_region: str = "us-central1",
) -> NamedTuple('Outputs', [
("suggested_trials", list),
]):
... | 3,904 | 37.663366 | 169 | py |
pipelines | pipelines-master/components/contrib/google-cloud/Optimizer/Suggest_trials/component.yaml | name: Suggest trials in gcp ai platform optimizer
description: Suggests trials (parameter sets) to evaluate.
inputs:
- {name: study_name, type: String, description: Full resource name of the study.}
- {name: suggestion_count, type: Integer, description: Number of suggestions to request.}
- {name: gcp_project_id, type: ... | 6,952 | 41.396341 | 174 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/Optimizer/_samples/multi_stage_optimization_atomic_pipeline.py | # This pipeline demonstrates hyper-parameter optimization.
# The goal is to find a set of hyper-parameter values that helps train the best model.
# We launch several optimization stages sequentially.
# At each stage the optimizer suggests several parameter sets to explore based on the available measurements.
# For each... | 7,126 | 45.279221 | 271 | py |
pipelines | pipelines-master/components/contrib/google-cloud/Optimizer/_samples/multi_stage_optimization_pipeline.py | kfp_endpoint = None
import kfp
from kfp import components
optimizer_create_study_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/40e117cca61fd923a57a1e84cbd08c22dce4bf00/components/google-cloud/Optimizer/Create_study/component.yaml')
optimizer_suggest_trials_op = compone... | 5,756 | 41.962687 | 237 | py |
pipelines | pipelines-master/components/contrib/google-cloud/automl/Tables/Create_dataset/from_CSV/component.py | from typing import NamedTuple
from kfp.components import create_component_from_func, InputPath, OutputPath
def automl_create_tables_dataset_from_csv(
data_path: InputPath('CSV'),
target_column_name: str = None,
column_nullability: dict = {},
column_types: dict = {},
gcs_staging_uri: str = None, #... | 8,116 | 43.355191 | 183 | py |
pipelines | pipelines-master/components/contrib/google-cloud/automl/Tables/Create_dataset/from_CSV/component.yaml | name: Automl create tables dataset from csv
description: Creates Google Cloud AutoML Tables Dataset from CSV data.
inputs:
- {name: data, type: CSV, description: Data in CSV format that will be imported to
the dataset.}
- {name: target_column_name, type: String, description: Name of the target column
for traini... | 13,048 | 46.450909 | 189 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/automl/create_dataset_for_tables/component.py | # Copyright 2019 The Kubeflow Authors
#
# 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... | 2,711 | 37.742857 | 168 | py |
pipelines | pipelines-master/components/contrib/google-cloud/automl/create_dataset_for_tables/component.yaml | name: Automl create dataset for tables
description: automl_create_dataset_for_tables creates an empty Dataset for AutoML
tables
inputs:
- {name: gcp_project_id, type: String}
- {name: gcp_region, type: String}
- {name: display_name, type: String}
- {name: description, type: String, optional: true}
- {name: tables_dat... | 5,900 | 38.604027 | 171 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/automl/create_model_for_tables/component.py | # Copyright 2019 The Kubeflow Authors
#
# 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... | 2,933 | 39.75 | 170 | py |
pipelines | pipelines-master/components/contrib/google-cloud/automl/create_model_for_tables/component.yaml | name: Automl create model for tables
inputs:
- {name: gcp_project_id, type: String}
- {name: gcp_region, type: String}
- {name: display_name, type: String}
- {name: dataset_id, type: String}
- {name: target_column_path, type: String, optional: true}
- {name: input_feature_column_paths, type: JsonArray, optional: true}
... | 6,126 | 41.846154 | 176 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/automl/deploy_model/component.py | from typing import NamedTuple
from kfp.components import create_component_from_func
def automl_deploy_model(
model_path: str,
) -> NamedTuple('Outputs', [
('model_path', str),
]):
"""Deploys a trained model.
Args:
model_path: The resource name of the model to export. Format: 'projects/<projec... | 1,328 | 28.533333 | 155 | py |
pipelines | pipelines-master/components/contrib/google-cloud/automl/deploy_model/component.yaml | name: Automl deploy model
description: |-
Deploys a trained model.
Args:
model_path: The resource name of the model to export. Format: 'projects/<project>/locations/<location>/models/<model>'
Annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
inputs:
- {name: model_path, ... | 3,210 | 35.488636 | 327 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/automl/export_data_to_gcs/component.py | # Copyright 2019 The Kubeflow Authors
#
# 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... | 2,374 | 37.306452 | 186 | py |
pipelines | pipelines-master/components/contrib/google-cloud/automl/export_data_to_gcs/component.yaml | name: Automl export data to gcs
description: |
Exports dataset data to GCS.
inputs:
- name: dataset_path
type: String
- name: gcs_output_uri_prefix
optional: true
type: String
- name: timeout
optional: true
type: Float
- default: '{}'
name: metadata
optional: true
type: JsonObject
outputs:
- name: gcs... | 4,012 | 33.008475 | 192 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/automl/export_model_to_gcs/component.py | from typing import NamedTuple
from kfp.components import create_component_from_func
def automl_export_model_to_gcs(
model_path: str,
gcs_output_uri_prefix: str,
model_format: str = 'tf_saved_model',
) -> NamedTuple('Outputs', [
('model_directory', 'Uri'),
]):
"""Exports a trained model to a user s... | 2,284 | 39.087719 | 339 | py |
pipelines | pipelines-master/components/contrib/google-cloud/automl/export_model_to_gcs/component.yaml | name: Automl export model to gcs
description: |-
Exports a trained model to a user specified Google Cloud Storage location.
Args:
model_path: The resource name of the model to export. Format: 'projects/<project>/locations/<location>/models/<model>'
gcs_output_uri_prefix: The Google Cloud St... | 5,686 | 51.657407 | 902 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/automl/import_data_from_bigquery/component.py | # Copyright 2019 The Kubeflow Authors
#
# 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... | 2,215 | 34.741935 | 186 | py |
pipelines | pipelines-master/components/contrib/google-cloud/automl/import_data_from_bigquery/component.yaml | name: Automl import data from bigquery
inputs:
- name: dataset_path
- name: input_uri
type: String
- name: retry
optional: true
- name: timeout
optional: true
- name: metadata
type: JsonObject
optional: true
outputs:
- name: dataset_path
type: String
metadata:
annotations:
author: Alexey Volkov <alexe... | 3,779 | 32.451327 | 192 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/automl/import_data_from_gcs/component.py | # Copyright 2019 The Kubeflow Authors
#
# 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... | 2,200 | 33.936508 | 186 | py |
pipelines | pipelines-master/components/contrib/google-cloud/automl/import_data_from_gcs/component.yaml | name: Automl import data from gcs
inputs:
- name: dataset_path
type: String
- name: input_uris
type: JsonArray
- name: retry
optional: true
- name: timeout
optional: true
- name: metadata
type: JsonObject
optional: true
outputs:
- name: dataset_path
type: String
metadata:
annotations:
author: Alexey... | 3,788 | 32.236842 | 192 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/automl/prediction_service_batch_predict/component.py | # Copyright 2019 The Kubeflow Authors
#
# 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... | 3,046 | 37.56962 | 186 | py |
pipelines | pipelines-master/components/contrib/google-cloud/automl/prediction_service_batch_predict/component.yaml | name: Automl prediction service batch predict
inputs:
- name: model_path
- name: gcs_input_uris
type: JsonArray
optional: true
- name: gcs_output_uri_prefix
type: String
optional: true
- name: bq_input_uri
type: String
optional: true
- name: bq_output_uri
type: String
optional: true
- name: params
opt... | 6,134 | 33.857955 | 192 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/automl/split_dataset_table_column_names/component.py | # Copyright 2019 The Kubeflow Authors
#
# 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... | 2,461 | 40.033333 | 186 | py |
pipelines | pipelines-master/components/contrib/google-cloud/automl/split_dataset_table_column_names/component.yaml | name: Automl split dataset table column names
inputs:
- name: dataset_path
type: String
- name: target_column_name
type: String
- name: table_index
type: Integer
default: '0'
optional: true
outputs:
- name: target_column_path
type: String
- name: feature_column_paths
type: JsonArray
metadata:
annotation... | 3,662 | 37.15625 | 192 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/dataproc/create_cluster/README.md |
# Name
Component: Data processing by creating a cluster in Cloud Dataproc
# Label
Cloud Dataproc, Kubeflow
# Facets
<!--Make sure the asset has data for the following facets:
Use case
Technique
Input data type
ML workflow
The data must map to the acceptable values for these facets, as documented on the “taxonomy” ... | 6,573 | 36.141243 | 301 | md |
pipelines | pipelines-master/components/contrib/google-cloud/dataproc/create_cluster/component.yaml | # Copyright 2019 The Kubeflow Authors
#
# 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... | 3,274 | 34.989011 | 112 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/dataproc/delete_cluster/README.md |
# Name
Component: Data preparation by deleting a cluster in Cloud Dataproc
# Label
Cloud Dataproc, Kubeflow
# Summary
A Kubeflow pipeline component to delete a cluster in Cloud Dataproc.
## Intended use
Use this component at the start of a Kubeflow pipeline to delete a temporary Cloud Dataproc cluster when runnin... | 5,149 | 33.563758 | 338 | md |
pipelines | pipelines-master/components/contrib/google-cloud/dataproc/delete_cluster/component.yaml | # Copyright 2019 The Kubeflow Authors
#
# 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... | 1,671 | 32.44 | 92 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/dataproc/submit_hadoop_job/README.md |
# Name
Component: Data preparation using Hadoop MapReduce on YARN with Cloud Dataproc
# Labels
Cloud Dataproc, Hadoop, YARN, Apache, MapReduce
# Summary
A Kubeflow pipeline component to prepare data by submitting an Apache Hadoop MapReduce job on Apache Hadoop YARN to Cloud Dataproc.
# Facets
<!--Make sure the ass... | 8,224 | 38.927184 | 399 | md |
pipelines | pipelines-master/components/contrib/google-cloud/dataproc/submit_hadoop_job/component.yaml | # Copyright 2019 The Kubeflow Authors
#
# 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... | 3,550 | 34.158416 | 101 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/dataproc/submit_hive_job/README.md |
# Name
Component: Data preparation using Apache Hive on YARN with Cloud Dataproc
# Label
Cloud Dataproc, YARN, Apache Hive
# Summary
A Kubeflow pipeline component to prepare data by submitting an Apache Hive job on YARN to Cloud Dataproc.
# Facets
<!--Make sure the asset has data for the following facets:
Use case
... | 7,491 | 38.431579 | 301 | md |
pipelines | pipelines-master/components/contrib/google-cloud/dataproc/submit_hive_job/component.yaml | # Copyright 2019 The Kubeflow Authors
#
# 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... | 3,239 | 32.75 | 101 | yaml |
pipelines | pipelines-master/components/contrib/google-cloud/dataproc/submit_pig_job/README.md |
# Name
Component: Data preparation using Apache Pig on YARN with Cloud Dataproc
# Labels
Cloud Dataproc, YARN, Apache Pig, Kubeflow
# Summary
A Kubeflow pipeline component to prepare data by submitting an Apache Pig job on YARN to Cloud Dataproc.
# Facets
<!--Make sure the asset has data for the following facets:
... | 6,618 | 34.586022 | 301 | md |