如何将Azure机器学习批量评分结果写入Data Lake? [英] How to write Azure machine learning batch scoring results to data lake?
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问题描述
我正在尝试将批评分的输出写入datalake:
I'm trying to write the output of batch scoring into datalake:
parallel_step_name = "batchscoring-" + datetime.now().strftime("%Y%m%d%H%M")
output_dir = PipelineData(name="scores",
datastore=def_ADL_store,
output_mode="upload",
output_path_on_compute="path in data lake")
parallel_run_config = ParallelRunConfig(
environment=curated_environment,
entry_script="use_model.py",
source_directory="./",
output_action="append_row",
mini_batch_size="20",
error_threshold=1,
compute_target=compute_target,
process_count_per_node=2,
node_count=2
)
batch_score_step = ParallelRunStep(
name=parallel_step_name,
inputs=[test_data.as_named_input("test_data")],
output=output_dir,
parallel_run_config=parallel_run_config,
allow_reuse=False
)
但是我遇到错误:"code":"UserError",:用户程序因异常而失败:缺少参数--output或它的值为空."
However I meet the error: "code": "UserError", "message": "User program failed with Exception: Missing argument --output or its value is empty."
如何将批处理分数的结果写入数据湖?
How can I write results of batch score to data lake?
推荐答案
我认为 PipelineData
不支持ADLS.我的建议是将工作区的默认Blob存储用于 PipelineData
,然后在 ParallelRunStep
完成后使用 DataTransferStep
.
I don’t think ADLS is supported for PipelineData
. My suggestion is to use the workspace’s default blob store for the PipelineData
, then use a DataTransferStep
for after the ParallelRunStep
is completed.
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