#850

Committed 08 Nov 2023 08:36PM UTC coverage: 0.0% (-77.0%) from 76.982%

Build # #850

Build Type

push

circleci

Committed by

americast

Commit Message

fix metrics logic

Run Details

0 of 1 new or added line in 1 file covered. (0.0%)

9789 existing lines in 252 files now uncovered.

0 of 12428 relevant lines covered (0.0%)

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/evadb/executor/execution_context.py

# coding=utf-8
# Copyright 2018-2023 EvaDB
#
# 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.
import os
import random
from typing import List

from evadb.constants import NO_GPU
from evadb.utils.generic_utils import get_gpu_count, is_gpu_available


class Context:
    """
    Stores the context information of the executor, i.e.,
    if using spark, name of the application, current spark executors,
    if using horovod: current rank etc.
    """

    def __init__(self, user_provided_gpu_conf=[]):
        self._user_provided_gpu_conf = user_provided_gpu_conf
        self._gpus = self._populate_gpu_ids()

    @property
    def gpus(self):
        return self._gpus

    def _populate_gpu_from_config(self) -> List:
        available_gpus = [i for i in range(get_gpu_count())]
        return list(set(available_gpus) & set(self._user_provided_gpu_conf))

    def _populate_gpu_from_env(self) -> List:
        # Populate GPU IDs from env variable.
        gpu_conf = map(
            lambda x: x.strip(),
            os.environ.get("CUDA_VISIBLE_DEVICES", "").strip().split(","),
        )
        gpu_conf = list(filter(lambda x: x, gpu_conf))
        gpu_conf = [int(gpu_id) for gpu_id in gpu_conf]
        available_gpus = [i for i in range(get_gpu_count())]
        return list(set(available_gpus) & set(gpu_conf))

    def _populate_gpu_ids(self) -> List:
        # Populate available GPU IDs from Torch library. Then, select subset of GPUs
        # from available GPUs based on user configuration.
        if not is_gpu_available():
            return []
        gpus = self._populate_gpu_from_config()
        if len(gpus) == 0:
            gpus = self._populate_gpu_from_env()
        return gpus

    def _select_random_gpu(self) -> str:
        """
        A random GPU selection strategy
        Returns:
            (str): GPU device ID
        """
        return random.choice(self.gpus)

    def gpu_device(self) -> str:
        """
        Selects a GPU on which the task can be executed
        Returns:
             (str): GPU device ID
        """
        if self.gpus:
            # TODO: Should allow choosing GPU based on Spark and Horovod
            return self._select_random_gpu()
        return NO_GPU

1	# coding=utf-8
2	# Copyright 2018-2023 EvaDB
3	#
4	# Licensed under the Apache License, Version 2.0 (the "License");
5	# you may not use this file except in compliance with the License.
6	# You may obtain a copy of the License at
7	#
8	# http://www.apache.org/licenses/LICENSE-2.0
9	#
10	# Unless required by applicable law or agreed to in writing, software
11	# distributed under the License is distributed on an "AS IS" BASIS,
12	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13	# See the License for the specific language governing permissions and
14	# limitations under the License.
UNCOV 15	import os	×
UNCOV 16	import random	×
UNCOV 17	from typing import List	×
18
UNCOV 19	from evadb.constants import NO_GPU	×
UNCOV 20	from evadb.utils.generic_utils import get_gpu_count, is_gpu_available	×
21
22
UNCOV 23	class Context:	×
24	"""
25	Stores the context information of the executor, i.e.,
26	if using spark, name of the application, current spark executors,
27	if using horovod: current rank etc.
28	"""
29
UNCOV 30	def __init__(self, user_provided_gpu_conf=[]):	×
UNCOV 31	self._user_provided_gpu_conf = user_provided_gpu_conf	×
UNCOV 32	self._gpus = self._populate_gpu_ids()	×
33
UNCOV 34	@property	×
UNCOV 35	def gpus(self):	×
UNCOV 36	return self._gpus	×
37
UNCOV 38	def _populate_gpu_from_config(self) -> List:	×
UNCOV 39	available_gpus = [i for i in range(get_gpu_count())]	×
UNCOV 40	return list(set(available_gpus) & set(self._user_provided_gpu_conf))	×
41
UNCOV 42	def _populate_gpu_from_env(self) -> List:	×
43	# Populate GPU IDs from env variable.
UNCOV 44	gpu_conf = map(	×
45	lambda x: x.strip(),
46	os.environ.get("CUDA_VISIBLE_DEVICES", "").strip().split(","),
47	)
UNCOV 48	gpu_conf = list(filter(lambda x: x, gpu_conf))	×
UNCOV 49	gpu_conf = [int(gpu_id) for gpu_id in gpu_conf]	×
UNCOV 50	available_gpus = [i for i in range(get_gpu_count())]	×
UNCOV 51	return list(set(available_gpus) & set(gpu_conf))	×
52
UNCOV 53	def _populate_gpu_ids(self) -> List:	×
54	# Populate available GPU IDs from Torch library. Then, select subset of GPUs
55	# from available GPUs based on user configuration.
UNCOV 56	if not is_gpu_available():	×
UNCOV 57	return []	×
UNCOV 58	gpus = self._populate_gpu_from_config()	×
UNCOV 59	if len(gpus) == 0:	×
UNCOV 60	gpus = self._populate_gpu_from_env()	×
UNCOV 61	return gpus	×
62
UNCOV 63	def _select_random_gpu(self) -> str:	×
64	"""
65	A random GPU selection strategy
66	Returns:
67	(str): GPU device ID
68	"""
UNCOV 69	return random.choice(self.gpus)	×
70
UNCOV 71	def gpu_device(self) -> str:	×
72	"""
73	Selects a GPU on which the task can be executed
74	Returns:
75	(str): GPU device ID
76	"""
UNCOV 77	if self.gpus:	×
78	# TODO: Should allow choosing GPU based on Spark and Horovod
UNCOV 79	return self._select_random_gpu()	×
UNCOV 80	return NO_GPU	×

georgia-tech-db / eva / #850

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