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rapidsai_public_repos/dask-cuda/docs | rapidsai_public_repos/dask-cuda/docs/source/ucx.rst | UCX Integration
===============
Communication can be a major bottleneck in distributed systems.
Dask-CUDA addresses this by supporting integration with `UCX <https://www.openucx.org/>`_, an optimized communication framework that provides high-performance networking and supports a variety of transport methods, includin... | 0 |
rapidsai_public_repos/dask-cuda/docs | rapidsai_public_repos/dask-cuda/docs/source/explicit_comms.rst | Explicit-comms
==============
Communication and scheduling overhead can be a major bottleneck in Dask/Distributed. Dask-CUDA addresses this by introducing an API for explicit communication in Dask tasks.
The idea is that Dask/Distributed spawns workers and distribute data as usually while the user can submit tasks on ... | 0 |
rapidsai_public_repos/dask-cuda/docs | rapidsai_public_repos/dask-cuda/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 0 |
rapidsai_public_repos/dask-cuda/docs | rapidsai_public_repos/dask-cuda/docs/source/api.rst | API
===
Cluster
-------
.. currentmodule:: dask_cuda
.. autoclass:: LocalCUDACluster
:members:
CLI
---
Worker
~~~~~~
.. click:: dask_cuda.cli:worker
:prog: dask cuda
:nested: none
Cluster configuration
~~~~~~~~~~~~~~~~~~~~~
.. click:: dask_cuda.cli:config
:prog: dask cuda
:nested: none
Client initia... | 0 |
rapidsai_public_repos/dask-cuda/docs | rapidsai_public_repos/dask-cuda/docs/source/index.rst | Dask-CUDA
=========
Dask-CUDA is a library extending `Dask.distributed <https://distributed.dask.org/en/latest/>`_'s single-machine `LocalCluster <https://docs.dask.org/en/latest/setup/single-distributed.html#localcluster>`_ and `Worker <https://distributed.dask.org/en/latest/worker.html>`_ for use in distributed GPU ... | 0 |
rapidsai_public_repos/dask-cuda/docs/source | rapidsai_public_repos/dask-cuda/docs/source/examples/ucx.rst | Enabling UCX communication
==========================
A CUDA cluster using UCX communication can be started automatically with LocalCUDACluster or manually with the ``dask cuda worker`` CLI tool.
In either case, a ``dask.distributed.Client`` must be made for the worker cluster using the same Dask UCX configuration; se... | 0 |
rapidsai_public_repos/dask-cuda/docs/source | rapidsai_public_repos/dask-cuda/docs/source/examples/worker_count.rst | .. _controlling-number-of-workers:
Controlling number of workers
=============================
Users can restrict activity to specific GPUs by explicitly setting ``CUDA_VISIBLE_DEVICES``; for a LocalCUDACluster, this can provided as a keyword argument.
For example, to restrict activity to the first two indexed GPUs:
... | 0 |
rapidsai_public_repos/dask-cuda/docs/source | rapidsai_public_repos/dask-cuda/docs/source/examples/best-practices.rst | Best Practices
==============
Multi-GPU Machines
~~~~~~~~~~~~~~~~~~
When choosing between two multi-GPU setups, it is best to pick the one where most GPUs are co-located with one-another. This could be a
`DGX <https://www.nvidia.com/en-us/data-center/dgx-systems/>`_, a cloud instance with `multi-gpu options <https:... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/ci/test_python.sh | #!/bin/bash
# Copyright (c) 2022-2023, NVIDIA CORPORATION.
set -euo pipefail
. /opt/conda/etc/profile.d/conda.sh
rapids-logger "Generate Python testing dependencies"
rapids-dependency-file-generator \
--output conda \
--file_key test_python \
--matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/ci/build_python.sh | #!/bin/bash
# Copyright (c) 2022, NVIDIA CORPORATION.
set -euo pipefail
source rapids-env-update
export CMAKE_GENERATOR=Ninja
rapids-print-env
package_name="dask_cuda"
version=$(rapids-generate-version)
commit=$(git rev-parse HEAD)
echo "${version}" | tr -d '"' > VERSION
sed -i "/^__git_commit__/ s/= .*/= \"${co... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/ci/build_python_pypi.sh | #!/bin/bash
python -m pip install build --user
version=$(rapids-generate-version)
commit=$(git rev-parse HEAD)
# While conda provides these during conda-build, they are also necessary during
# the setup.py build for PyPI
export GIT_DESCRIBE_TAG=$(git describe --abbrev=0 --tags)
export GIT_DESCRIBE_NUMBER=$(git rev-... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/ci/check_style.sh | #!/bin/bash
# Copyright (c) 2020-2022, NVIDIA CORPORATION.
set -euo pipefail
rapids-logger "Create checks conda environment"
. /opt/conda/etc/profile.d/conda.sh
rapids-dependency-file-generator \
--output conda \
--file_key checks \
--matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}"... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/ci/build_docs.sh | #!/bin/bash
set -euo pipefail
rapids-logger "Create test conda environment"
. /opt/conda/etc/profile.d/conda.sh
rapids-dependency-file-generator \
--output conda \
--file_key docs \
--matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
rapids-mamba-retry env creat... | 0 |
rapidsai_public_repos/dask-cuda/ci | rapidsai_public_repos/dask-cuda/ci/release/update-version.sh | #!/bin/bash
# Copyright (c) 2020, NVIDIA CORPORATION.
################################################################################
# dask-cuda version updater
################################################################################
## Usage
# bash update-version.sh <new_version>
# Format is YY.MM.PP - no... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/proxy_object.py | import copy as _copy
import functools
import operator
import os
import pickle
import time
from collections import OrderedDict
from contextlib import nullcontext
from typing import TYPE_CHECKING, Any, Dict, Iterable, Optional, Tuple, Type, Union
import pandas
import dask
import dask.array.core
import dask.dataframe.me... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/cuda_worker.py | from __future__ import absolute_import, division, print_function
import asyncio
import atexit
import logging
import os
import warnings
from toolz import valmap
import dask
from distributed import Nanny
from distributed.core import Server
from distributed.deploy.cluster import Cluster
from distributed.proctitle impor... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/get_device_memory_objects.py | from typing import Set
from dask.sizeof import sizeof
from dask.utils import Dispatch
dispatch = Dispatch(name="get_device_memory_objects")
class DeviceMemoryId:
"""ID and size of device memory objects
Instead of keeping a reference to device memory objects this class
only saves the id and size in orde... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/cli.py | from __future__ import absolute_import, division, print_function
import logging
import click
from tornado.ioloop import IOLoop, TimeoutError
from dask import config as dask_config
from distributed import Client
from distributed.cli.utils import install_signal_handlers
from distributed.preloading import validate_prel... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/device_host_file.py | import itertools
import logging
import os
import time
import numpy
from zict import Buffer, Func
from zict.common import ZictBase
import dask
from distributed.protocol import (
dask_deserialize,
dask_serialize,
deserialize,
deserialize_bytes,
serialize,
serialize_bytelist,
)
from distributed.s... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/local_cuda_cluster.py | import copy
import logging
import os
import warnings
from functools import partial
import dask
from distributed import LocalCluster, Nanny, Worker
from distributed.worker_memory import parse_memory_limit
from .device_host_file import DeviceHostFile
from .initialize import initialize
from .plugins import CPUAffinity, ... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/utils_test.py | from typing import Literal
import distributed
from distributed import Nanny, Worker
class MockWorker(Worker):
"""Mock Worker class preventing NVML from getting used by SystemMonitor.
By preventing the Worker from initializing NVML in the SystemMonitor, we can
mock test multiple devices in `CUDA_VISIBLE_... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/is_device_object.py | from __future__ import absolute_import, division, print_function
from dask.utils import Dispatch
is_device_object = Dispatch(name="is_device_object")
@is_device_object.register(object)
def is_device_object_default(o):
return hasattr(o, "__cuda_array_interface__")
@is_device_object.register(list)
@is_device_ob... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/initialize.py | import logging
import os
import click
import numba.cuda
import dask
from distributed.diagnostics.nvml import get_device_index_and_uuid, has_cuda_context
from .utils import get_ucx_config
logger = logging.getLogger(__name__)
def _create_cuda_context_handler():
if int(os.environ.get("DASK_CUDA_TEST_SINGLE_GPU",... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/_version.py | # Copyright (c) 2023, NVIDIA CORPORATION.
#
# 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... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/plugins.py | import importlib
import os
from distributed import WorkerPlugin
from .utils import get_rmm_log_file_name, parse_device_memory_limit
class CPUAffinity(WorkerPlugin):
def __init__(self, cores):
self.cores = cores
def setup(self, worker=None):
os.sched_setaffinity(0, self.cores)
class RMMSet... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/proxify_host_file.py | import abc
import gc
import io
import logging
import os
import os.path
import pathlib
import threading
import time
import traceback
import warnings
import weakref
from collections import defaultdict
from collections.abc import MutableMapping
from typing import (
Any,
Callable,
DefaultDict,
Dict,
Has... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/__init__.py | import sys
if sys.platform != "linux":
raise ImportError("Only Linux is supported by Dask-CUDA at this time")
import dask
import dask.utils
import dask.dataframe.core
import dask.dataframe.shuffle
import dask.dataframe.multi
import dask.bag.core
from ._version import __git_commit__, __version__
from .cuda_worke... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/is_spillable_object.py | from __future__ import absolute_import, division, print_function
from typing import Optional
from dask.utils import Dispatch
is_spillable_object = Dispatch(name="is_spillable_object")
@is_spillable_object.register(list)
@is_spillable_object.register(tuple)
@is_spillable_object.register(set)
@is_spillable_object.re... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/proxify_device_objects.py | import functools
import pydoc
from collections import defaultdict
from functools import partial
from typing import List, MutableMapping, Optional, Tuple, TypeVar
import dask
from dask.utils import Dispatch
from .proxy_object import ProxyObject, asproxy
dispatch = Dispatch(name="proxify_device_objects")
incompatible_... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/utils.py | import math
import operator
import os
import pickle
import time
import warnings
from contextlib import suppress
from functools import singledispatch
from multiprocessing import cpu_count
from typing import Optional
import numpy as np
import pynvml
import toolz
import dask
import distributed # noqa: required for dask... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/VERSION | 24.02.00
| 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/worker_spec.py | import os
from dask.distributed import Nanny
from distributed.system import MEMORY_LIMIT
from .initialize import initialize
from .local_cuda_cluster import cuda_visible_devices
from .plugins import CPUAffinity
from .utils import get_cpu_affinity, get_gpu_count
def worker_spec(
interface=None,
protocol=None,... | 0 |
rapidsai_public_repos/dask-cuda | rapidsai_public_repos/dask-cuda/dask_cuda/disk_io.py | import itertools
import os
import os.path
import pathlib
import tempfile
import threading
import weakref
from typing import Callable, Iterable, Mapping, Optional, Union
import numpy as np
import dask
from distributed.utils import nbytes
_new_cuda_buffer: Optional[Callable[[int], object]] = None
def get_new_cuda_bu... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/explicit_comms/comms.py | import asyncio
import concurrent.futures
import contextlib
import time
import uuid
from typing import Any, Dict, Hashable, Iterable, List, Optional
import distributed.comm
from distributed import Client, Worker, default_client, get_worker
from distributed.comm.addressing import parse_address, parse_host_port, unparse_... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda/explicit_comms | rapidsai_public_repos/dask-cuda/dask_cuda/explicit_comms/dataframe/shuffle.py | from __future__ import annotations
import asyncio
import functools
import inspect
from collections import defaultdict
from math import ceil
from operator import getitem
from typing import Any, Callable, Dict, List, Optional, Set, TypeVar
import dask
import dask.config
import dask.dataframe
import dask.utils
import di... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_dask_cuda_worker.py | from __future__ import absolute_import, division, print_function
import os
import pkgutil
import subprocess
import sys
from unittest.mock import patch
import pytest
from distributed import Client, wait
from distributed.system import MEMORY_LIMIT
from distributed.utils_test import cleanup, loop, loop_in_thread, popen... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_proxify_host_file.py | from typing import Iterable
from unittest.mock import patch
import numpy as np
import pytest
from pandas.testing import assert_frame_equal
import dask
import dask.dataframe
from dask.dataframe.shuffle import shuffle_group
from dask.sizeof import sizeof
from dask.utils import format_bytes
from distributed import Clien... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_spill.py | import gc
import os
from time import sleep
import pytest
import dask
from dask import array as da
from distributed import Client, wait
from distributed.metrics import time
from distributed.sizeof import sizeof
from distributed.utils_test import gen_cluster, gen_test, loop # noqa: F401
from dask_cuda import LocalCUD... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_explicit_comms.py | import asyncio
import multiprocessing as mp
import os
from unittest.mock import patch
import numpy as np
import pandas as pd
import pytest
import dask
from dask import dataframe as dd
from dask.dataframe.shuffle import partitioning_index
from dask.dataframe.utils import assert_eq
from distributed import Client
from d... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_device_host_file.py | from random import randint
import numpy as np
import pytest
import dask.array
from distributed.protocol import (
deserialize,
deserialize_bytes,
serialize,
serialize_bytelist,
)
from dask_cuda.device_host_file import DeviceHostFile, device_to_host, host_to_device
cupy = pytest.importorskip("cupy")
... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_cudf_builtin_spilling.py | import pytest
from distributed.sizeof import safe_sizeof
from dask_cuda.device_host_file import DeviceHostFile
from dask_cuda.is_spillable_object import is_spillable_object
from dask_cuda.proxify_host_file import ProxifyHostFile
cupy = pytest.importorskip("cupy")
pandas = pytest.importorskip("pandas")
pytest.import... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_dgx.py | import multiprocessing as mp
import os
from enum import Enum, auto
import numpy
import pytest
from dask import array as da
from distributed import Client
from dask_cuda import LocalCUDACluster
from dask_cuda.initialize import initialize
mp = mp.get_context("spawn") # type: ignore
psutil = pytest.importorskip("psut... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_worker_spec.py | import pytest
from distributed import Nanny
from dask_cuda.worker_spec import worker_spec
def _check_option(spec, k, v):
assert all([s["options"][k] == v for s in spec.values()])
def _check_env_key(spec, k, enable):
if enable:
assert all([k in s["options"]["env"] for s in spec.values()])
else:... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_initialize.py | import multiprocessing as mp
import numpy
import psutil
import pytest
from dask import array as da
from distributed import Client
from distributed.deploy.local import LocalCluster
from dask_cuda.initialize import initialize
from dask_cuda.utils import get_ucx_config
from dask_cuda.utils_test import IncreasedCloseTim... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_gds.py | import tempfile
import pytest
from distributed.protocol.serialize import deserialize, serialize
from dask_cuda.proxify_host_file import ProxifyHostFile
# Make the "disk" serializer available and use a directory that is
# removed on exit.
if ProxifyHostFile._spill_to_disk is None:
tmpdir = tempfile.TemporaryDire... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_from_array.py | import pytest
import dask.array as da
from distributed import Client
from dask_cuda import LocalCUDACluster
cupy = pytest.importorskip("cupy")
@pytest.mark.parametrize("protocol", ["ucx", "ucxx", "tcp"])
def test_ucx_from_array(protocol):
if protocol == "ucx":
pytest.importorskip("ucp")
elif protoc... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_local_cuda_cluster.py | import asyncio
import os
import pkgutil
import sys
from unittest.mock import patch
import pytest
from dask.distributed import Client
from distributed.system import MEMORY_LIMIT
from distributed.utils_test import gen_test, raises_with_cause
from dask_cuda import CUDAWorker, LocalCUDACluster, utils
from dask_cuda.init... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_utils.py | import os
from unittest.mock import patch
import pytest
from numba import cuda
from dask.config import canonical_name
from dask_cuda.utils import (
cuda_visible_devices,
get_cpu_affinity,
get_device_total_memory,
get_gpu_count,
get_n_gpus,
get_preload_options,
get_ucx_config,
nvml_dev... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/tests/test_proxy.py | import operator
import os
import pickle
import tempfile
from types import SimpleNamespace
import numpy as np
import pandas
import pytest
from packaging import version
from pandas.testing import assert_frame_equal, assert_series_equal
import dask
import dask.array
from dask.dataframe.core import has_parallel_type
from... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/benchmarks/common.py | from argparse import Namespace
from functools import partial
from typing import Any, Callable, List, Mapping, NamedTuple, Optional, Tuple
from warnings import filterwarnings
import numpy as np
import pandas as pd
import dask
from distributed import Client
from dask_cuda.benchmarks.utils import (
address_to_index... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/benchmarks/local_cupy.py | import contextlib
from collections import ChainMap
from time import perf_counter as clock
import numpy as np
import pandas as pd
from nvtx import end_range, start_range
from dask import array as da
from dask.distributed import performance_report, wait
from dask.utils import format_bytes, parse_bytes
from dask_cuda.b... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/benchmarks/local_cudf_merge.py | import contextlib
import math
from collections import ChainMap
from time import perf_counter
import numpy as np
import pandas as pd
import dask
from dask.base import tokenize
from dask.dataframe.core import new_dd_object
from dask.distributed import performance_report, wait
from dask.utils import format_bytes, parse_... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/benchmarks/local_cudf_groupby.py | import contextlib
from collections import ChainMap
from time import perf_counter as clock
import pandas as pd
import dask
import dask.dataframe as dd
from dask.distributed import performance_report, wait
from dask.utils import format_bytes, parse_bytes
from dask_cuda.benchmarks.common import Config, execute_benchmar... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/benchmarks/local_cudf_shuffle.py | import contextlib
from collections import ChainMap
from time import perf_counter
from typing import Tuple
import numpy as np
import pandas as pd
import dask
import dask.dataframe
from dask.dataframe.core import new_dd_object
from dask.dataframe.shuffle import shuffle
from dask.distributed import Client, performance_r... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/benchmarks/local_cupy_map_overlap.py | import contextlib
from collections import ChainMap
from time import perf_counter as clock
import cupy as cp
import numpy as np
import pandas as pd
from cupyx.scipy.ndimage.filters import convolve as cp_convolve
from scipy.ndimage import convolve as sp_convolve
from dask import array as da
from dask.distributed import... | 0 |
rapidsai_public_repos/dask-cuda/dask_cuda | rapidsai_public_repos/dask-cuda/dask_cuda/benchmarks/utils.py | import argparse
import itertools
import json
import os
import time
from collections import defaultdict
from datetime import datetime
from operator import itemgetter
from typing import Any, Callable, Mapping, NamedTuple, Optional, Tuple
import numpy as np
import pandas as pd
from dask.distributed import Client, SSHClu... | 0 |
rapidsai_public_repos/dask-cuda/examples | rapidsai_public_repos/dask-cuda/examples/ucx/dask_cuda_worker.sh | #!/bin/bash
usage() {
echo "usage: $0 [-a <scheduler_address>] [-i <interface>] [-r <rmm_pool_size>] [-t <transports>]" >&2
exit 1
}
# parse arguments
rmm_pool_size=1GB
while getopts ":a:i:r:t:" flag; do
case "${flag}" in
i) interface=${OPTARG};;
r) rmm_pool_size=${OPTARG};;
t... | 0 |
rapidsai_public_repos/dask-cuda/examples | rapidsai_public_repos/dask-cuda/examples/ucx/local_cuda_cluster.py | import click
import cupy
from dask import array as da
from dask.distributed import Client
from dask.utils import parse_bytes
from dask_cuda import LocalCUDACluster
@click.command(context_settings=dict(ignore_unknown_options=True))
@click.option(
"--enable-nvlink/--disable-nvlink",
default=False,
help="E... | 0 |
rapidsai_public_repos/dask-cuda/examples | rapidsai_public_repos/dask-cuda/examples/ucx/client_initialize.py | import click
import cupy
from dask import array as da
from dask.distributed import Client
from dask_cuda.initialize import initialize
@click.command(context_settings=dict(ignore_unknown_options=True))
@click.argument(
"address",
required=True,
type=str,
)
@click.option(
"--enable-nvlink/--disable-nv... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/.pre-commit-config.yaml | ---
# Copyright (c) 2023, NVIDIA CORPORATION.
repos:
- repo: https://github.com/psf/black
rev: 22.10.0
hooks:
- id: black
files: python/.*
args: [--config, python/pyproject.toml]
- repo: https://github.com/PyCQA/flake8
rev: 5.0.4
hooks:
- id: ... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/pyproject.toml | [tool.codespell]
# note: pre-commit passes explicit lists of files here, which this skip file list doesn't override -
# this is only to allow you to run codespell interactively
skip = "./.git,./.github,./cpp/build,.*egg-info.*,./.mypy_cache,.*_skbuild,CHANGELOG.md,_stop_words.py,,*stemmer.*"
# ignore short words, and t... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/fetch_rapids.cmake | # =============================================================================
# Copyright (c) 2022, NVIDIA CORPORATION.
#
# 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.o... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/README.md | # <div align="left"><img src="img/rapids_logo.png" width="90px"/> cuML - GPU Machine Learning Algorithms</div>
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other [RAPIDS](https://rapids.ai/) projects.
cuML enables da... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/CHANGELOG.md | # cuML 23.10.00 (11 Oct 2023)
## 🚨 Breaking Changes
- add sample_weight parameter to dbscan.fit ([#5574](https://github.com/rapidsai/cuml/pull/5574)) [@mfoerste4](https://github.com/mfoerste4)
- Update to Cython 3.0.0 ([#5506](https://github.com/rapidsai/cuml/pull/5506)) [@vyasr](https://github.com/vyasr)
## 🐛 Bug... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/build.sh | #!/bin/bash
# Copyright (c) 2019-2023, NVIDIA CORPORATION.
# cuml build script
# This script is used to build the component(s) in this repo from
# source, and can be called with various options to customize the
# build as needed (see the help output for details)
# Abort script on first error
set -e
NUMARGS=$#
ARGS... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/codecov.yml | #Configuration File for CodeCov
coverage:
status:
project: off
patch: off
comment:
behavior: new
# Suggested workaround to fix "missing base report" issue when using Squash and
# Merge Strategy in GitHub. See this comment from CodeCov support about this
# undocumented option:
# https://community.codecov.io... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/dependencies.yaml | # Dependency list for https://github.com/rapidsai/dependency-file-generator
files:
all:
output: conda
matrix:
cuda: ["11.8", "12.0"]
arch: [x86_64]
includes:
- common_build
- cudatoolkit
- docs
- py_build
- py_run
- py_version
- test_python
cpp_all:
... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/CONTRIBUTING.md | # Contributing to cuML
If you are interested in contributing to cuML, your contributions will fall
into three categories:
1. You want to report a bug, feature request, or documentation issue
- File an [issue](https://github.com/rapidsai/cuml/issues/new/choose)
describing what you encountered or what you want t... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/LICENSE | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/VERSION | 23.12.00
| 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/print_env.sh | #!/usr/bin/env bash
# Reports relevant environment information useful for diagnosing and
# debugging cuML issues.
# Usage:
# "./print_env.sh" - prints to stdout
# "./print_env.sh > env.txt" - prints to file "env.txt"
print_env() {
echo "**git***"
if [ "$(git rev-parse --is-inside-work-tree 2>/dev/null)" == "true" ]; ... | 0 |
rapidsai_public_repos | rapidsai_public_repos/cuml/BUILD.md | # cuML Build From Source Guide
## Setting Up Your Build Environment
To install cuML from source, ensure the following dependencies are met:
1. [cuDF](https://github.com/rapidsai/cudf) (Same as cuML Version)
2. zlib
3. cmake (>= 3.26.4)
4. CUDA (>= 11+)
5. Cython (>= 0.29)
6. gcc (>= 9.0)
7. BLAS - Any BLAS compatibl... | 0 |
rapidsai_public_repos/cuml | rapidsai_public_repos/cuml/wiki/README.md | # cuML Wiki Documentation
This wiki is provided as an extension to cuML's public documentation, geared toward developers on the project.
If you are interested in contributing to cuML, read through our [contributing guide](../CONTRIBUTING.md). You are
also encouraged to read through our Python [developer guide](python... | 0 |
rapidsai_public_repos/cuml | rapidsai_public_repos/cuml/wiki/DEFINITION_OF_DONE_CRITERIA.md | # Defining cuML's Definition of Done Criteria
## Algorithm Completion Checklist
Below is a quick and simple checklist for developers to determine whether an algorithm is complete and ready for release. Most of these items contain more detailed descriptions in their corresponding developer guide. The checklist is bro... | 0 |
rapidsai_public_repos/cuml/wiki | rapidsai_public_repos/cuml/wiki/python/ESTIMATOR_GUIDE.md | # cuML Python Estimators Developer Guide
This guide is meant to help developers follow the correct patterns when creating/modifying any cuML Estimator object and ensure a uniform cuML API.
**Note:** This guide is long, because it includes internal details on how cuML manages input and output types for advanced use ca... | 0 |
rapidsai_public_repos/cuml/wiki | rapidsai_public_repos/cuml/wiki/python/DEVELOPER_GUIDE.md | # cuML Python Developer Guide
This document summarizes guidelines and best practices for contributions to the python component of the library cuML, the machine learning component of the RAPIDS ecosystem. This is an evolving document so contributions, clarifications and issue reports are highly welcome.
## General
Plea... | 0 |
rapidsai_public_repos/cuml/wiki | rapidsai_public_repos/cuml/wiki/mnmg/Using_Infiniband_for_MNMG.md | # Using Infiniband for Multi-Node Multi-GPU cuML
These instructions outline how to run multi-node multi-GPU cuML on devices with Infiniband. These instructions assume the necessary Infiniband hardware has already been installed and the relevant software has already been configured to enable communication over the Infi... | 0 |
rapidsai_public_repos/cuml/wiki | rapidsai_public_repos/cuml/wiki/cpp/DEVELOPER_GUIDE.md | # cuML developer guide
This document summarizes rules and best practices for contributions to the cuML C++ component of rapidsai/cuml. This is a living document and contributions for clarifications or fixes and issue reports are highly welcome.
## General
Please start by reading [CONTRIBUTING.md](../../CONTRIBUTING.md... | 0 |
rapidsai_public_repos/cuml | rapidsai_public_repos/cuml/python/pyproject.toml | # Copyright (c) 2022, NVIDIA CORPORATION.
#
# 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... | 0 |
rapidsai_public_repos/cuml | rapidsai_public_repos/cuml/python/.flake8 | # Copyright (c) 2018-2023, NVIDIA CORPORATION.
[flake8]
filename = *.py, *.pyx, *.pxd
exclude =
*.egg,
.git,
__pycache__,
_thirdparty,
build/,
cpp,
docs,
thirdparty,
versioneer.py
# Cython Rules ignored:
# E999: invalid syntax (works for Python, not Cython)
# E225: Missing whitespace around... | 0 |
rapidsai_public_repos/cuml | rapidsai_public_repos/cuml/python/CMakeLists.txt | # =============================================================================
# Copyright (c) 2022-2023 NVIDIA CORPORATION.
#
# 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.apac... | 0 |
rapidsai_public_repos/cuml | rapidsai_public_repos/cuml/python/README.md | # cuML Python Package
This folder contains the Python and Cython code of the algorithms and ML primitives of cuML, that are distributed in the Python cuML package.
Contents:
- [cuML Python Package](#cuml-python-package)
- [Build Configuration](#build-configuration)
- [RAFT Integration in cuml.raft](#raft-int... | 0 |
rapidsai_public_repos/cuml | rapidsai_public_repos/cuml/python/setup.py | #
# Copyright (c) 2018-2023, NVIDIA CORPORATION.
#
# 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 ag... | 0 |
rapidsai_public_repos/cuml | rapidsai_public_repos/cuml/python/pytest.ini | [pytest]
markers =
unit: Quickest tests focused on accuracy and correctness
quality: More intense tests than unit with increased runtimes
stress: Longest running tests focused on stressing hardware compute resources
mg: Multi-GPU tests
memleak: Test that checks for memory leaks
no_bad_cuml_array_check: Test... | 0 |
rapidsai_public_repos/cuml | rapidsai_public_repos/cuml/python/LICENSE | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | 0 |
rapidsai_public_repos/cuml | rapidsai_public_repos/cuml/python/.coveragerc | # Configuration file for Python coverage tests
[run]
omit = cuml/test/*
plugins = Cython.Coverage
parallel = true
source = cuml
[report]
# Regexes for lines to exclude from consideration
exclude_lines =
# Re-specify the `pragma: no cover` since it will be overridden by this
# option. See the docs:
# https:... | 0 |
rapidsai_public_repos/cuml/python | rapidsai_public_repos/cuml/python/cuml/_version.py | # Copyright (c) 2023, NVIDIA CORPORATION.
#
# 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... | 0 |
rapidsai_public_repos/cuml/python | rapidsai_public_repos/cuml/python/cuml/__init__.py | #
# Copyright (c) 2022-2023, NVIDIA CORPORATION.
#
# 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 ag... | 0 |
rapidsai_public_repos/cuml/python | rapidsai_public_repos/cuml/python/cuml/VERSION | 23.12.00
| 0 |
rapidsai_public_repos/cuml/python/cuml | rapidsai_public_repos/cuml/python/cuml/_thirdparty/__init__.py | # Third party code, respective licenses apply
from . import sklearn
| 0 |
rapidsai_public_repos/cuml/python/cuml/_thirdparty | rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn/README.md | # GPU accelerated Scikit-Learn preprocessing
This directory contains code originating from the Scikit-Learn library. The Scikit-Learn license applies accordingly (see `/thirdparty/LICENSES/LICENSE.scikit_learn`). Original authors mentioned in the code do not endorse or promote this production.
This work is dedicated ... | 0 |
rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn | rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn/preprocessing/_imputation.py | # Original authors from Sckit-Learn:
# Nicolas Tresegnie <nicolas.tresegnie@gmail.com>
# Sergey Feldman <sergeyfeldman@gmail.com>
# License: BSD 3 clause
# This code originates from the Scikit-Learn library,
# it was since modified to allow GPU acceleration.
# This code is under BSD 3 clause license... | 0 |
rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn | rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn/preprocessing/_discretization.py | # Original authors from Sckit-Learn:
# Henry Lin <hlin117@gmail.com>
# Tom Dupré la Tour
# License: BSD
# This code originates from the Scikit-Learn library,
# it was since modified to allow GPU acceleration.
# This code is under BSD 3 clause license.
# Authors mentioned above do not endorse or promo... | 0 |
rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn | rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn/preprocessing/_column_transformer.py | # Original authors from Sckit-Learn:
# Andreas Mueller
# Joris Van den Bossche
# License: BSD
# This code originates from the Scikit-Learn library,
# it was since modified to allow GPU acceleration.
# This code is under BSD 3 clause license.
# Authors mentioned above do not endorse or promote this pr... | 0 |
rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn | rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn/preprocessing/_function_transformer.py | # This code originates from the Scikit-Learn library,
# it was since modified to allow GPU acceleration.
# This code is under BSD 3 clause license.
# Authors mentioned above do not endorse or promote this production.
import warnings
import cuml
from ....internals.array_sparse import SparseCumlArray
from ..utils.skl_... | 0 |
rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn | rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn/preprocessing/_data.py | # Original authors from Sckit-Learn:
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Eric Martin <eric@ericmart.in>
# Giorgio Patri... | 0 |
rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn | rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn/preprocessing/__init__.py | # This code originates from the Scikit-Learn library,
# it was since modified to allow GPU acceleration.
# This code is under BSD 3 clause license.
from ._data import Binarizer
from ._data import KernelCenterer
from ._data import MinMaxScaler
from ._data import MaxAbsScaler
from ._data import Normalizer
from ._data i... | 0 |
rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn | rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn/utils/validation.py | # Original authors from Sckit-Learn:
# Olivier Grisel
# Gael Varoquaux
# Andreas Mueller
# Lars Buitinck
# Alexandre Gramfort
# Nicolas Tresegnie
# Sylvain Marie
# License: BSD 3 clause
# This code originates from the Scikit-Learn library,
# it was since ... | 0 |
rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn | rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn/utils/sparsefuncs.py | # Original authors from Sckit-Learn:
# Manoj Kumar
# Thomas Unterthiner
# Giorgio Patrini
#
# License: BSD 3 clause
# This code originates from the Scikit-Learn library,
# it was since modified to allow GPU acceleration.
# This code is under BSD 3 clause license.
# Authors mentioned above d... | 0 |
rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn | rapidsai_public_repos/cuml/python/cuml/_thirdparty/sklearn/utils/skl_dependencies.py | # Original authors from Sckit-Learn:
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
# This code originates from the Scikit-Learn library,
# it was since modified to allow GPU acceleration.
# This code is under BSD 3 clause license.
# Authors mentioned above do not endorse or promote ... | 0 |
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