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136 lines
4.4 KiB
136 lines
4.4 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Testing profiling support in DE
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"""
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import json
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import os
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import numpy as np
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import mindspore.dataset as ds
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FILES = ["../data/dataset/testTFTestAllTypes/test.data"]
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DATASET_ROOT = "../data/dataset/testTFTestAllTypes/"
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SCHEMA_FILE = "../data/dataset/testTFTestAllTypes/datasetSchema.json"
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PIPELINE_FILE = "./pipeline_profiling_1.json"
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DATASET_ITERATOR_FILE = "./dataset_iterator_profiling_1.txt"
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def test_profiling_simple_pipeline():
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"""
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Generator -> Shuffle -> Batch
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"""
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os.environ['PROFILING_MODE'] = 'true'
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os.environ['MINDDATA_PROFILING_DIR'] = '.'
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os.environ['DEVICE_ID'] = '1'
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source = [(np.array([x]),) for x in range(1024)]
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data1 = ds.GeneratorDataset(source, ["data"])
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data1 = data1.shuffle(64)
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data1 = data1.batch(32)
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# try output shape type and dataset size and make sure no profiling file is generated
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assert data1.output_shapes() == [[32, 1]]
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assert [str(tp) for tp in data1.output_types()] == ["int64"]
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assert data1.get_dataset_size() == 32
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assert os.path.exists(PIPELINE_FILE) is False
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assert os.path.exists(DATASET_ITERATOR_FILE) is False
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for _ in data1:
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pass
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assert os.path.exists(PIPELINE_FILE) is True
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os.remove(PIPELINE_FILE)
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assert os.path.exists(DATASET_ITERATOR_FILE) is True
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os.remove(DATASET_ITERATOR_FILE)
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del os.environ['PROFILING_MODE']
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del os.environ['MINDDATA_PROFILING_DIR']
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def test_profiling_complex_pipeline():
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"""
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Generator -> Map ->
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-> Zip
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TFReader -> Shuffle ->
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"""
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os.environ['PROFILING_MODE'] = 'true'
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os.environ['MINDDATA_PROFILING_DIR'] = '.'
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os.environ['DEVICE_ID'] = '1'
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source = [(np.array([x]),) for x in range(1024)]
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data1 = ds.GeneratorDataset(source, ["gen"])
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data1 = data1.map(operations=[(lambda x: x + 1)], input_columns=["gen"])
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pattern = DATASET_ROOT + "/test.data"
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data2 = ds.TFRecordDataset(pattern, SCHEMA_FILE, shuffle=ds.Shuffle.FILES)
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data2 = data2.shuffle(4)
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data3 = ds.zip((data1, data2))
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for _ in data3:
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pass
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with open(PIPELINE_FILE) as f:
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data = json.load(f)
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op_info = data["op_info"]
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assert len(op_info) == 5
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for i in range(5):
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assert "size" in op_info[i]["metrics"]["output_queue"]
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assert "length" in op_info[i]["metrics"]["output_queue"]
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assert "throughput" in op_info[i]["metrics"]["output_queue"]
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assert os.path.exists(PIPELINE_FILE) is True
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os.remove(PIPELINE_FILE)
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assert os.path.exists(DATASET_ITERATOR_FILE) is True
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os.remove(DATASET_ITERATOR_FILE)
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del os.environ['PROFILING_MODE']
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del os.environ['MINDDATA_PROFILING_DIR']
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def test_profiling_sampling_interval():
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"""
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Test non-default monitor sampling interval
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"""
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os.environ['PROFILING_MODE'] = 'true'
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os.environ['MINDDATA_PROFILING_DIR'] = '.'
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os.environ['DEVICE_ID'] = '1'
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interval_origin = ds.config.get_monitor_sampling_interval()
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ds.config.set_monitor_sampling_interval(30)
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interval = ds.config.get_monitor_sampling_interval()
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assert interval == 30
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source = [(np.array([x]),) for x in range(1024)]
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data1 = ds.GeneratorDataset(source, ["data"])
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data1 = data1.shuffle(64)
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data1 = data1.batch(32)
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for _ in data1:
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pass
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assert os.path.exists(PIPELINE_FILE) is True
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os.remove(PIPELINE_FILE)
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assert os.path.exists(DATASET_ITERATOR_FILE) is True
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os.remove(DATASET_ITERATOR_FILE)
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ds.config.set_monitor_sampling_interval(interval_origin)
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del os.environ['PROFILING_MODE']
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del os.environ['MINDDATA_PROFILING_DIR']
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if __name__ == "__main__":
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test_profiling_simple_pipeline()
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test_profiling_complex_pipeline()
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test_profiling_sampling_interval()
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