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How to use cpu_count method in tox

Best Python code snippet using tox_python

file_io_test.py

Source:file_io_test.py

1# Copyright 2018 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Tests for binary data file utilities."""16from __future__ import absolute_import17from __future__ import division18from __future__ import print_function19import contextlib20import multiprocessing21# pylint: disable=wrong-import-order22import numpy as np23import pandas as pd24import tensorflow as tf25# pylint: enable=wrong-import-order26from official.utils.data import file_io27_RAW_ROW = "raw_row"28_DUMMY_COL = "column_0"29_DUMMY_VEC_COL = "column_1"30_DUMMY_VEC_LEN = 431_ROWS_PER_CORE = 432_TEST_CASES = [33    # One batch of one34    dict(row_count=1, cpu_count=1, expected=[35        [[0]]36    ]),37    dict(row_count=10, cpu_count=1, expected=[38        [[0, 1, 2, 3]], [[4, 5, 6, 7]], [[8, 9]]39    ]),40    dict(row_count=21, cpu_count=1, expected=[41        [[0, 1, 2, 3]], [[4, 5, 6, 7]], [[8, 9, 10, 11]],42        [[12, 13, 14, 15]], [[16, 17, 18, 19]], [[20]]43    ]),44    dict(row_count=1, cpu_count=4, expected=[45        [[0]]46    ]),47    dict(row_count=10, cpu_count=4, expected=[48        [[0, 1], [2, 3, 4], [5, 6], [7, 8, 9]]49    ]),50    dict(row_count=21, cpu_count=4, expected=[51        [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]],52        [[16], [17], [18], [19, 20]]53    ]),54    dict(row_count=10, cpu_count=8, expected=[55        [[0], [1], [2], [3, 4], [5], [6], [7], [8, 9]]56    ]),57    dict(row_count=40, cpu_count=8, expected=[58        [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15],59         [16, 17, 18, 19], [20, 21, 22, 23], [24, 25, 26, 27],60         [28, 29, 30, 31]],61        [[32], [33], [34], [35], [36], [37], [38], [39]]62    ]),63]64_FEATURE_MAP = {65    _RAW_ROW: tf.FixedLenFeature([1], dtype=tf.int64),66    _DUMMY_COL: tf.FixedLenFeature([1], dtype=tf.int64),67    _DUMMY_VEC_COL: tf.FixedLenFeature([_DUMMY_VEC_LEN], dtype=tf.float32)68}69@contextlib.contextmanager70def fixed_core_count(cpu_count):71  """Override CPU count.72  file_io.py uses the cpu_count function to scale to the size of the instance.73  However, this is not desirable for testing because it can make the test flaky.74  Instead, this context manager fixes the count for more robust testing.75  Args:76    cpu_count: How many cores multiprocessing claims to have.77  Yields:78    Nothing. (for context manager only)79  """80  old_count_fn = multiprocessing.cpu_count81  multiprocessing.cpu_count = lambda: cpu_count82  yield83  multiprocessing.cpu_count = old_count_fn84class BaseTest(tf.test.TestCase):85  def _test_sharding(self, row_count, cpu_count, expected):86    df = pd.DataFrame({_DUMMY_COL: list(range(row_count))})87    with fixed_core_count(cpu_count):88      shards = list(file_io.iter_shard_dataframe(df, _ROWS_PER_CORE))89    result = [[j[_DUMMY_COL].tolist() for j in i] for i in shards]90    self.assertAllEqual(expected, result)91  def test_tiny_rows_low_core(self):92    self._test_sharding(**_TEST_CASES[0])93  def test_small_rows_low_core(self):94    self._test_sharding(**_TEST_CASES[1])95  def test_large_rows_low_core(self):96    self._test_sharding(**_TEST_CASES[2])97  def test_tiny_rows_medium_core(self):98    self._test_sharding(**_TEST_CASES[3])99  def test_small_rows_medium_core(self):100    self._test_sharding(**_TEST_CASES[4])101  def test_large_rows_medium_core(self):102    self._test_sharding(**_TEST_CASES[5])103  def test_small_rows_large_core(self):104    self._test_sharding(**_TEST_CASES[6])105  def test_large_rows_large_core(self):106    self._test_sharding(**_TEST_CASES[7])107  def _serialize_deserialize(self, num_cores=1, num_rows=20):108    np.random.seed(1)109    df = pd.DataFrame({110        # Serialization order is only deterministic for num_cores=1. raw_row is111        # used in validation after the deserialization.112        _RAW_ROW: np.array(range(num_rows), dtype=np.int64),113        _DUMMY_COL: np.random.randint(0, 35, size=(num_rows,)),114        _DUMMY_VEC_COL: [115            np.array([np.random.random() for _ in range(_DUMMY_VEC_LEN)])116            for i in range(num_rows)  # pylint: disable=unused-variable117        ]118    })119    with fixed_core_count(num_cores):120      buffer_path = file_io.write_to_temp_buffer(121          df, self.get_temp_dir(), [_RAW_ROW, _DUMMY_COL, _DUMMY_VEC_COL])122    with self.test_session(graph=tf.Graph()) as sess:123      dataset = tf.data.TFRecordDataset(buffer_path)124      dataset = dataset.batch(1).map(125          lambda x: tf.parse_example(x, _FEATURE_MAP))126      data_iter = dataset.make_one_shot_iterator()127      seen_rows = set()128      for i in range(num_rows+5):129        row = data_iter.get_next()130        try:131          row_id, val_0, val_1 = sess.run(132              [row[_RAW_ROW], row[_DUMMY_COL], row[_DUMMY_VEC_COL]])133          row_id, val_0, val_1 = row_id[0][0], val_0[0][0], val_1[0]134          assert row_id not in seen_rows135          seen_rows.add(row_id)136          self.assertEqual(val_0, df[_DUMMY_COL][row_id])137          self.assertAllClose(val_1, df[_DUMMY_VEC_COL][row_id])138          self.assertLess(i, num_rows, msg="Too many rows.")139        except tf.errors.OutOfRangeError:140          self.assertGreaterEqual(i, num_rows, msg="Too few rows.")141    file_io._GARBAGE_COLLECTOR.purge()142    assert not tf.gfile.Exists(buffer_path)143  def test_serialize_deserialize_0(self):144    self._serialize_deserialize(num_cores=1)145  def test_serialize_deserialize_1(self):146    self._serialize_deserialize(num_cores=2)147  def test_serialize_deserialize_2(self):148    self._serialize_deserialize(num_cores=8)149if __name__ == "__main__":...

multi_process_prime_number.py

Source:multi_process_prime_number.py

...42    return list43if __name__ == '__main__':44    N = int(input())45    # å¤è¿ç¨46    CPU_COUNT = cpu_count()  ##CPUåæ ¸æ° æ¬æºä¸º847    pool = Pool(CPU_COUNT)48    sepList = seprateNum(N, CPU_COUNT)49    result = []50    for i in range(CPU_COUNT):51        result.append(pool.apply_async(howMany, (sepList[i],)))52    pool.close()53    pool.join()54    ans = 055    list = [res.get() for res in result]56    print(sum(list), end='')  # end='' è¡¨ç¤ºåæ¶ /n57# ä¸é¢ä¸é´åä¸ä»£ç å58end = time.time()...

gcp_machines.py

Source:gcp_machines.py

1machine_list = [2   {3      "machine_type":"n1-standard-4",4      "cpu_count":"4",5      "memory":"15"6   },7   {8      "machine_type":"n1-standard-8",9      "cpu_count":"8",10      "memory":"30"11   },12   {13      "machine_type":"n1-standard-16",14      "cpu_count":"16",15      "memory":"60"16   },17   {18      "machine_type":"n1-standard-32",19      "cpu_count":"32",20      "memory":"120"21   },22   {23      "machine_type":"n1-standard-64",24      "cpu_count":"64",25      "memory":"240"26   },27   {28      "machine_type":"n1-standard-96",29      "cpu_count":"96",30      "memory":"360"31   },32   {33      "machine_type":"n1-highmem-2",34      "cpu_count":"2",35      "memory":"13"36   },37   {38      "machine_type":"n1-highmem-4",39      "cpu_count":"4",40      "memory":"26"41   },42   {43      "machine_type":"n1-highmem-8",44      "cpu_count":"8",45      "memory":"52"46   },47   {48      "machine_type":"n1-highmem-16",49      "cpu_count":"16",50      "memory":"104"51   },52   {53      "machine_type":"n1-highmem-32",54      "cpu_count":"32",55      "memory":"208"56   },57   {58      "machine_type":"n1-highmem-64",59      "cpu_count":"64",60      "memory":"416"61   },62   {63      "machine_type":"n1-highmem-96",64      "cpu_count":"96",65      "memory":"624"66   },67   {68      "machine_type":"n1-highcpu-16",69      "cpu_count":"16",70      "memory":"14.4"71   },72   {73      "machine_type":"n1-highcpu-32",74      "cpu_count":"32",75      "memory":"28.8"76   },77   {78      "machine_type":"n1-highcpu-64",79      "cpu_count":"64",80      "memory":"57.6"81   },82   {83      "machine_type":"n1-highcpu-96",84      "cpu_count":"96",85      "memory":"86.4"86   }...

game.py

Source:game.py

1from main import cpu, check2user_count = 03cpu_count = 04count = 05while count <3:6    cpu_choice = cpu()7    choice = input("your choice: ").upper()8    if choice.upper() == cpu_choice.upper():9        print(cpu_choice)10        print(f"it's a Tie, scores: player-{user_count}, cpu-{cpu_count}")11    elif choice == "P" and cpu_choice=="R":12        user_count = user_count+113        print(cpu_choice)14        print(f"scores: player-{user_count}, cpu-{cpu_count}")15    elif choice == "S" and cpu_choice == "P":16        user_count = user_count+117        print(cpu_choice)18        print(f"scores: player-{user_count}, cpu-{cpu_count}")19    elif choice == "R" and cpu_choice == "S":20        user_count = user_count+121        print(cpu_choice)22        print(f"scores: player-{user_count}, cpu-{cpu_count}")23    elif choice == "P" and cpu_choice=="S":24        cpu_count = cpu_count+125        print(cpu_choice)26        print(f"scores: player-{user_count}, cpu-{cpu_count}")27    elif choice == "R" and cpu_choice == "P":28        cpu_count = cpu_count+129        print(cpu_choice)30        print(f"scores: player-{user_count}, cpu-{cpu_count}")31    elif choice == "S" and cpu_choice == "R":32        cpu_count = cpu_count+133        print(cpu_choice)34        print(f"scores: player-{user_count}, cpu-{cpu_count}")35    count = count+1...

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