pipelines/samples/contrib/pytorch-samples/common/minio/upload_to_minio.py

105 lines
2.5 KiB
Python

#!/usr/bin/env/python3
# Copyright (c) Facebook, Inc. and its affiliates.
# 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.
"""Utility to upload files/folders into minio"""
import os
from argparse import ArgumentParser
from pytorch_kfp_components.components.minio.component import MinIO
from pytorch_kfp_components.components.visualization.component import Visualization
# Argument parser for user defined paths
parser = ArgumentParser()
parser.add_argument(
"--bucket_name",
type=str,
help="Minio bucket name",
)
parser.add_argument(
"--folder_name",
type=str,
help="Path to destination folder",
)
parser.add_argument(
"--input_path",
type=str,
help="Input path of the file or folder to upload",
)
parser.add_argument(
"--filename",
type=str,
help="Name of the file to be uploaded",
)
parser.add_argument(
"--endpoint",
type=str,
default="minio-service.kubeflow:9000",
help="Name of the file to be uploaded",
)
parser.add_argument(
"--mlpipeline_ui_metadata",
type=str,
help="Path to write mlpipeline-ui-metadata.json",
)
args = vars(parser.parse_args())
bucket_name = args["bucket_name"]
input_path = args["input_path"]
folder_name = args["folder_name"]
filename = args["filename"]
if filename:
input_path = os.path.join(input_path, filename)
endpoint = args["endpoint"]
print("File to be uploaded: {}".format(input_path))
print("Uploading file to : {}".format(folder_name))
MinIO(
source=input_path,
bucket_name=bucket_name,
destination=folder_name,
endpoint=endpoint
)
inputs = {}
for key, value in args.items():
inputs[key] = value
outputs = {}
s3_url = f"s3://{bucket_name}/{folder_name}"
if filename:
s3_url += f"/{filename}"
outputs["minio_url"] = s3_url
visualization_arguments = {"inputs": inputs, "outputs": outputs}
markdown_dict = {"storage": "inline", "source": visualization_arguments}
visualization = Visualization(
mlpipeline_ui_metadata=args["mlpipeline_ui_metadata"],
markdown=markdown_dict,
)