The Thrill of Volleyligaen Women DENMARK: Tomorrow's Matches
The anticipation for tomorrow's matches in the Volleyligaen Women DENMARK is palpable. As fans eagerly await the showdowns, expert betting predictions add an extra layer of excitement. Let's delve into the details of what promises to be a thrilling day of volleyball.
Match Overview
Tomorrow's schedule is packed with intense matchups, each promising to showcase the skill and determination of some of Denmark's top volleyball teams. The league has been a hotbed of talent, with teams battling fiercely for supremacy. Here’s a look at the key matches:
- Team A vs. Team B: This match is expected to be a close contest, with both teams having strong defensive records.
- Team C vs. Team D: Known for their powerful serves, Team C will face off against Team D’s agile defense.
- Team E vs. Team F: A battle between two rising stars in the league, this match could determine future playoff positions.
Each game is not just about winning but also about strategy and adaptability on the court.
Betting Predictions: Insights from Experts
Expert analysts have been closely monitoring team performances and player statistics to provide informed betting predictions for tomorrow’s games. Here are some insights:
- Team A vs. Team B: Analysts predict a narrow victory for Team A, citing their recent form and home-court advantage.
- Team C vs. Team D: The prediction leans towards a high-scoring game, with Team C expected to capitalize on their serving prowess.
- Team E vs. Team F: This match is seen as unpredictable, with potential for either team to emerge victorious depending on in-game adjustments.
Betting enthusiasts should consider these predictions while also keeping an eye on any last-minute changes or player injuries that could influence outcomes.
Tactical Analysis: What to Watch For
The tactical battles between coaches will play a crucial role in determining the outcomes of tomorrow’s matches. Here are some key strategies to watch:
- Serving Strategies: Teams like Team C are known for their aggressive serving tactics, which can disrupt opponents’ rhythm.
- Defensive Formations: Teams with strong defensive setups, such as Team B, often rely on blocking and quick transitions to counter attacks.
- Rhythm and Tempo Control: Controlling the pace of the game can be decisive; teams that manage tempo effectively often gain an edge over their opponents.
Understanding these strategies provides deeper insights into how each team might approach their matches tomorrow.
Fan Engagement: How to Get Involved
Fans have multiple ways to engage with tomorrow’s matches beyond just watching them live:
- Social Media Interaction: Follow official team accounts and hashtags to participate in real-time discussions and polls during the games.
- Betting Communities: Join online forums or groups where enthusiasts share tips and predictions based on expert analysis.
- Volleyball Analysis Shows**: Tune into pre-game shows that offer expert breakdowns of team strengths and weaknesses.
Engaging with fellow fans enhances the viewing experience and adds an interactive dimension to enjoying volleyball matches.
Potential Game-Changers: Key Players to Watch
In every match, certain players have the potential to turn the tide in favor of their teams. Here are some standout athletes:
- Serena from Team A**: Known for her powerful spikes, Serena could be pivotal in securing points against Team B’s defense.
- Lisa from Team C**: With her exceptional serving accuracy, Lisa might disrupt Team D’s play style significantly.
- Maria from Team E**: Her strategic plays and leadership on court make her a crucial factor in any game she participates in.
These players bring unique skills that could influence the outcome of their respective matches significantly.
The Role of Home-Court Advantage: An Analytical Perspective
lukasbalzer/oneflow<|file_sep|>/oneflow/core/control_flow/while_loop_op.cpp
/*
Copyright (c) Facebook, Inc. and its affiliates.
All rights reserved.
This source code is licensed under the BSD-style license found in the
LICENSE file in the root directory of this source tree.
*/
#include "oneflow/core/control_flow/while_loop_op.h"
namespace oneflow {
namespace {
class WhileLoopOp final : public OperatorWithKernel {
public:
};
class WhileLoopOpMaker final : public OperatorMaker {
public:
};
} // namespace
REGISTER_OP("WhileLoop")
.Input("cond_inputs")
.Output("loop_vars")
.Attr("loop_body", const std::string&, "")
.Attr("cond_fn", const std::string&, "")
.Attr("num_parallel_loops", const int64_t&, -1)
.SetIsStateful()
.SetAllowInplace({{"loop_vars", InplaceType::kAllow}});
REGISTER_OP_CPU_KERNEL(WhileLoop,
KernelConf::NotSupportMultiStream(),
WhileLoopOpMaker(),
WhileLoopOp());
} // namespace oneflow<|repo_name|>lukasbalzer/oneflow<|file_sep[env]
CUDA_HOME = /usr/local/cuda-11
[test_env]
python = python
cuda_version = CUDA_11
[build_env]
cuda_version = CUDA_11
[build_env_cmake]
cmake_version = cmake-3-21-1-linux-x86_64.sh
cmake_url = https://github.com/Kitware/CMake/releases/download/v3.21.1/
cmake_hash = sha256:bca4e6e8d9f0b9a0b5c5d5f7e5f0b8d7f5f7e5f0b8d7f5f7e5f0b8d7f5f7e5f0
[test_env_cmake]
cmake_version = cmake-3-21-1-linux-x86_64.sh
cmake_url = https://github.com/Kitware/CMake/releases/download/v3.21.1/
cmake_hash = sha256:bca4e6e8d9f0b9a0b5c5d5f7e5f0b8d7f5f7e5f0b8d7f5f7e5f0b8d7f5f7e5f0
[build_env_boost]
boost_version=boost_1_76_0.tar.gz
boost_url=https://downloads.sourceforge.net/project/boost/boost/1.76.0/
boost_hash=sha256:d252c4c00df33eb4ff2c49cccd40bf36dc2db83a9fdedac15baaddeef3006ed6
[test_env_boost]
boost_version=boost_1_76_0.tar.gz
boost_url=https://downloads.sourceforge.net/project/boost/boost/1.76.0/
boost_hash=sha256:d252c4c00df33eb4ff2c49cccd40bf36dc2db83a9fdedac15baaddeef3006ed6
[build_env_glog]
glog_version=glog-0-12.tar.gz
glog_url=http://google-glog.googlecode.com/files/glog-0-12.tar.gz
glog_hash=sha256:a34ba99ea28cf37067af31a2cecb77a22eb26fb14bc59ae13cebb01152ec028a
[test_env_glog]
glog_version=glog-0-12.tar.gz
glog_url=http://google-glog.googlecode.com/files/glog-0-12.tar.gz
glog_hash=sha256:a34ba99ea28cf37067af31a2cecb77a22eb26fb14bc59ae13cebb01152ec028a
[build_env_gflags]
gflags_version=gflags-master.zip
gflags_url=https://github.com/gflags/gflags/archive/master.zip
gflags_hash=sha256:86607444da98aa46fa24890fc32822dd84cb54cf93eeeb311ba01de18211174c
[test_env_gflags]
gflags_version=gflags-master.zip
gflags_url=https://github.com/gflags/gflags/archive/master.zip
gflags_hash=sha256:86607444da98aa46fa24890fc32822dd84cb54cf93eeeb311ba01de18211174c
[build_env_gtest]
gtest_version=gtest-release-1_10_0.zip # only support gtest >= v1.x.x because we need GTEST_HAS_PTHREAD=ON by default now.
gtest_url=https://github.com/google/googletest/archive/release-1.10.0.zip # only support release version because we don't want non-deterministic behavior caused by git hash.
gtest_hash=sha256:c69487619506470832ab28208ade95bd45806c88bebe17ae546206fe30cdfab9 # only support release version because we don't want non-deterministic behavior caused by git hash.
[test_env_gtest]
gtest_version=gtest-release-1_10_0.zip # only support gtest >= v1.x.x because we need GTEST_HAS_PTHREAD=ON by default now.
gtest_url=https://github.com/google/googletest/archive/release-1.10.0.zip # only support release version because we don't want non-deterministic behavior caused by git hash.
gtest_hash=sha256:c69487619506470832ab28208ade95bd45806c88bebe17ae546206fe30cdfab9 # only support release version because we don't want non-deterministic behavior caused by git hash.
[build_env_hdf5_base]
# hdf version should match torch.hdf file format which requires hdf >= v19 since torch.hdf format was changed since then.
# see more detail here: https://pytorch.org/docs/stable/_modules/torch/hdf.html#HDFWriter.write_tensor#L37-L38
# Note that hdf >= v20 doesn't work well due to its bug when writing string tensor data so currently we stick with hdf19 here.
# See more detail here:
# https://forum.open-ce.io/t/hdf51-and-hdf52-in-open-ce-v20221023-candidate-builds-have-been-released/4126
#
# Also note that open-ce build has built-in libz.so (libz.a) dependency issue which breaks hdf build when linking hdf against libz.a directly,
# so here we use external libz instead when building hdf which requires manually setting ZLIB_ROOT environment variable during build process.
#
# See more detail here:
# https://forum.open-ce.io/t/open-ce-hdf51-and-hdf52-build-issue-with-libz-a-dependency-in-osx-arm-and-linux-aarch64-platforms/4244
#
# Finally note that open-ce build doesn't work well when building hdf without specifying HDF_ROOT environment variable due its custom install prefix issue,
# so here we use external install prefix instead when building hdf which requires manually setting HDF_ROOT environment variable during build process.
#
# See more detail here:
# https://forum.open-ce.io/t/open-ce-custom-install-prefix-issue-in-hdf51-and-hdf52-build-on-osx-arm-and-linux-aarch64-platforms/4246
#
HDF_VERSION=hdf-v19-build-fixes.patched # NOTE(hamaji): Fixing HDF library issue which causes segfault when reading data from hdfs using hdfs dfs -get command (see more detail here: https://github.com/HDFGroup/hdf-forge/pull/124 )
HDF_URL=https://support.hdfgroup.org//ftp/HDF/releases/hdf-${HDF_VERSION%.*}/src/hdf-${HDF_VERSION}.tar.bz2
HDF_HASH=sha256:a85ceff912bf00768dd42cc110da55fe25adfb39bd79473cd93007ca288fc04b
[test_env_hdf_base]
HDF_VERSION=hdf-v19-build-fixes.patched # NOTE(hamaji): Fixing HDF library issue which causes segfault when reading data from hdfs using hdfs dfs -get command (see more detail here: https://github.com/HDFGroup/hdf-forge/pull/124 )
HDF_URL=https://support.hdfgroup.org//ftp/HDF/releases/hdf-${HDF_VERSION%.*}/src/hdf-${HDF_VERSION}.tar.bz2
HDF_HASH=sha256:a85ceff912bf00768dd42cc110da55fe25adfb39bd79473cd93007ca288fc04b
[build_env_zlib_base]
ZLIB_VERSION=zlib126.tar.gz
ZLIB_URL=http://${ZLIB_URL_BASE}/zlib126/${ZLIB_VERSION}
ZLIB_HASH=${ZLIB_HASH}
[test_env_zlib_base]
ZLIB_VERSION=zlib126.tar.gz
ZLIB_URL=http://${ZLIB_URL_BASE}/zlib126/${ZLIB_VERSION}
ZLIB_HASH=${ZLIB_HASH}
[build_test_cuda_arch_flags] # used by test_cudnn.py test script (see more details there).
cuda_arch_flags=-arch sm_${{CUDA_ARCH}}
[solver_configurations]
[solver_configurations.adamw.solver_type] adamw
[solver_configurations.adamw.learning_rate] ${LR}
[solver_configurations.adamw.beta1] ${BETA_ONE}
[solver_configurations.adamw.beta2] ${BETA_TWO}
[solver_configurations.adamw.eps] ${EPS}
[solver_configurations.adamw.weight_decay] ${WEIGHT_DECAY}
[solver_configurations.adagrad.solver_type] adagrad
[solver_configurations.adagrad.learning_rate] ${LR}
[solver_configurations.adagrad.eps] ${EPS}
[solver_configurations.adagrad.initial_accumulator_value] ${INIT_ACCUMULATOR_VALUE}
[solver_configurations.adagrad.weight_decay_mode] ${WEIGHT_DECAY_MODE}
[solver_configurations.rmsprop.solver_type] rmsprop
[solver_configurations.rmsprop.learning_rate] ${LR}
[solver_configurations.rmsprop.decay_rate] ${DECAY_RATE}
[solver_configurations.rmsprop.momentum] ${MOMENTUM}
[solver_configurations.rmsprop.eps] ${EPS}
[solver_configurations.rmsprop.centered_mode] false
##############################################
### Solver configurations used by unittest ###
##############################################
##############################################
#### AdamW #####
[SOLVER_CONFIGURATIONS.unittest_adamw_sgd_solver_type ]
adamw_sgd
[SOLVER_CONFIGURATIONS.unittest_adamw_sgd_learning_rate ]
${LR}
[SOLVER_CONFIGURATIONS.unittest_adamw_sgd_beta_one ]
${BETA_ONE}
[SOLVER_CONFIGURATIONS.unittest_adamw_sgd_beta_two ]
${BETA_TWO}
[SOLVER_CONFIGURATIONS.unittest_adamw_sgd_eps ]
${EPS}
[SOLVER_CONFIGURATIONS.unittest_adamw_sgd_weight_decay ]
${WEIGHT_DECAY}
#### SGD #####
[SOLVER_CONFIGURATIONS.unittest_sgd_solver_type ]
sgd
[SOLVER_CONFIGURATIONS.unittest_sgd_learning_rate ]
${LR}
#### RMSProp #####
[SOLVER_CONFIGURATIONS.unittest_rmsprop_solver_type ]
rmsprop
[SOLVER_CONFIGURATIONS.unittest_rmsprop_learning_rate ]
${LR}
[SOLVER_CONFIGURATIONS.unittest_rmsprop_decay_rate ]
${DECAY_RATE}
[SOLVER_CONFIGURATIONS.unittest_rmsprop_momentum ]
${MOMENTUM}
[SOLVER_CONFIGURATIONS.unittest_rmsprop_eps ]
${EPS}
#### Adagrad #####
[SOLVER_CONFIGURATIONS.unittest_adagrad_solver_type ]
adagrad
[SOLVER_CONFIGURATIONS.unittest_adagrad_learning_rate ]
${LR}
[SOLVER_CONFIGURATIONS.unittest_adagrad_eps ]
${EPS}
[SOLVER_CONFIGURATIONS.unittest_adagrad_initial_accumulator_value ]
${INIT_ACCUMULATOR_VALUE}
[SOLVER_CONFIGURATIONS.unittest_adagrad_weight_decay_mode ]
weight_decay
####################
### Build flags ###
####################
[CXX_FLAGS]
[CXX_FLAGS.common_flags]
[CXX_FLAGS.common_flags.android_armv8l_cpu_only_build_flags]
[CXX_FLAGS.common_flags.android_armv8l_cpu_only_build_flags].cxx_std=c++17 -std=c++17 -stdlib=libc++
[CXX_FLAGS.common_flags.android_armv8l_cpu_only_build_flags].cxx_warnings=-Wall -Wextra -Wno-unused-parameter -Wno-missing-field-initializers -Wno-sign-compare -Wno-deprecated-declarations -Wno-comment -Wno-maybe-uninitialized -Wno-unused-function -pedantic-errors -Werror=format-security --coverage --coverage-paths=all --coverage-dir=/tmp/test_coverage_report_onnxruntime --coverage-filename-prefix=/tmp/test_coverage_report_onnxruntime/
[CXX_FLAGS.common_flags.android_armv8l_cpu_only_build_flags].cxx_linker=-latomic
[CXX_FLAGS.common_flags.android_armv82_cpu_only_build_flags]
[CXX_FLAGS.common_flags.android_armv82_cpu_only_build_flags].cxx_std=c++17 -std=c++17 -stdlib=libc++
[CXX_FLAGS.common_flags.android_armv82_cpu_only_build_flags].cxx_warnings=-Wall -Wextra -Wno-unused-parameter -Wno-missing-field-initializers -Wno-sign-compare -Wno-deprecated-declarations -Wno-comment -Wno-maybe-uninitialized -pedantic-errors --coverage --coverage-paths=all --coverage-dir=/tmp/test_coverage_report_onnxruntime --coverage-filename-prefix=/tmp/test_coverage_report_onnxruntime/
[CXX_FLAGS.common_flags.android_armv82_cpu_only_build_flags].cxx_linker=-latomic
[CXX_FLAGS.common_flags.android_x86_cpu_only_build_flags]
[CXX_FLAGS.common_flags.android_x86_cpu_only_build_flags].cxx_std=c++17 # cxx_std value must be same as cxx_standard value defined above otherwise it'll cause compilation error due missing cxx standard flag "-std=c++17" since android toolchain doesn't set it automatically unlike gcc toolchain does.
[CXX_FLAGS.common_flags.android_x86_cpu_only_build_flags].cxx_warnings=-Wall # cxx_warnings value must be same as cxx_standard value defined above otherwise it'll cause compilation error due missing cxx warnings flag "-Wall" since android toolchain doesn't set it automatically unlike gcc toolchain does.
[CXX_FLAGS.common_flags.android_x86_cpu_only_buildFlags].cxx_linker=-latomic
#[CXX_FLAGS.platform_specific.cpu-only-build-cpp14-android-armv82-cmake-target-flagset.cmake-target-flagset.arm64-v8-a-apple-simulator-host-device-flagset.cmake-target-flagset.build-type-debug-config.cxx-standard.cxx-warnings.cxx-linker]
#[CXX_FLAG_SETS.platform_specific.cpu-only-build-cpp14-android-armv82-cmake-target-flagset.cmake-target-flagset.arm64-v8-a-apple-simulator-host-device-flagset.cmake-target-flagset.build-type-debug-config.cxx-standard.cxx-warnings.cxx-linker]
#[CMAKE_TARGET_FLAGSET.platform_specific.cpu-only-build-cpp14-android-armv82-cmake-target-flagset]
#[HOST_DEVICE_FLAGSET.platform_specific.cpu-only-build-cpp14-android-armv82-host-device-flagset]
#[BUILD_TYPE_DEBUG_CMAKE_FLAGSET.platform_specific.cpu-only-build-cpp14-android-armv82-host-device-flagset.build-type-debug-config]
#[HOST_DEVICE_FLAGSET.platform_specific.cpu-only-build-cpp14-android-armv82-host-device-flagset.build-type-debug-config]
#[HOST_DEVICE_FLAGSET.platform_specific.cpu-only-build-cpp14-android-armv82-host-device-flagset.build-type-release-config]
#[HOST_DEVICE_FLAGSET.platform_specific.cpu-only-build-cpp14-ios-x86-host-device-flagset.build-type-debug-config]
#[HOST_DEVICE_FLAGSET.platform_specific.cpu-only-build-cpp14-ios-x86-host-device-flagset.build-type-release-config]
#[HOST_DEVICE_FLAGSET.platform_specific.cpu-only-build-cpp17-linux-aarch64-gnu-toolchain-system-gcc-toolchain-gnueabi-hardfloat-gnu-system-gnu-host-device-flagsets.gnueabi-hardfloat-gnu-system-gnu-host-device-sysroot-path.host_device_flag_set.gcc_toolchain_host_device_sysroot_path.host_device_flag_set.gnueabi-hardfloat-gnu-system-gnu]
<|repo_name|>lukasbalzer/oneflow<|file_sep#!/usr/bin/env python3 -*- encoding:utf-8 -*-
"""
Copyright (c) Megvii Inc. All rights reserved.
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.
This script creates wheel package for pybind11 extension module using cmake build system.
For example usage see README.md document located at root directory level.
NOTE(hjning): This script uses cmake python bindings API so it's required that you install cmake python package first before running this script:
sudo apt-get update && sudo apt-get install python-pip && pip install cmake==version_number_here where version_number_here should be equal or greater than v3.XX.X according latest release date at time being writing this comment.
"""
import os.path as osp;
import os;
import sys;
import argparse;
from typing import List;
sys.path.insert(1,'./scripts');
import utils;
def parse_args():
args_parser = argparse.ArgumentParser(description="create wheel package using cmake");
args_parser.add_argument("--source_dir",type=str,default=None);
args_parser.add_argument("--output_dir",type=str,default=None);
args_parser.add_argument("--target_platform",type=str,default=None);
args_parser.add_argument("--wheel_name_prefix",type=str,default=None);
args_parser.add_argument("--wheel_name_suffix",type=str,default=None);
args_parser.add_argument("--wheel_file_extension",type=str,default=".whl");
return args_parser.parse_args();
def main():
"""Create wheel package."""
print('create_wheel_package.py');
if not osp.exists(osp.abspath(__file__)):
raise ValueError('script file {} does not exist'.format(osp.abspath(__file__)));
source_dir,output_dir,target_platform,wheel_name_prefix,wheel_name_suffix,wheel_file_extension = parse_args().source_dir,parsed_args.output_dir,parsed_args.target_platform,parsed_args.wheel_name_prefix,parsed_args.wheel_name_suffix,parsed_args.wheel_file_extension;
if source_dir == None:
raise ValueError('--source_dir argument must be specified!');
if output_dir == None:
raise ValueError('--output_dir argument must be specified!');
if target_platform == None:
raise ValueError('--target_platform argument must be specified!');
print('Creating wheel package ...');
utils.run_command(['python','setup.py','bdist_wheel','-q','--plat-name={}'.format(target_platform),'--dist-dir={}'.format(output_dir)]);
print('Done creating wheel package.');
if __name__ == '__main__':
main();
<|file_sep./scripts/create_wheel_package.py
--source_dir=./python
--output_dir=./dist
--target_platform='linux-x86_64'
--wheel_file_extension='.whl'
./scripts/create_wheel_package.py
--source_dir=./python
--output_dir=./dist
--target_platform='manylinux2014_x86_64'
--wheel_file_extension='.whl'
<|repo_name|>lukasbalzer/oneflow<|file_sepacc_nbit_fp16=false acc_nbit_fp32=false acc_nbit_int32=false acc_nbit_int16=false acc_nbit_int8=false cudnn_benchmark=false cudnn_deterministic=true cudnn_exhaustive_search=false cudnn_fast_math=true cuda_graph_capture_stream_priority_level=max cuda_graph_max_replay_count=max cuda_graph_max_replay_time_sec=max cuda_graph_max_replay_count_per_stream=max cuda_graph_max_replay_time_sec_per_stream=max cuda_graph_capture_buffer_size_bytes=max cuda_graph_launch_buffer_size_bytes=max cuda_graph_populate_streams_for_multi_stream_launches=true cuda_graph_allow_concurrent_executes=true cuda_graph_allow_execution_merge=true cuDNN fused_kernels_enabled=true graph_optimization_level=default fused_kernel_optimization_level=default kernel_optimization_level=default cpu_fallback_threshold_us=default fusion_pass_group_id_to_run_by_default=default fusion_pass_group_id_to_skip_by_default=default fusion_pass_group_id_to_debug_by_default=default fusion_pass_group_id_to_log_by_default=default static_fusion_pass_group_id_to_run_by_default=default static_fusion_pass_group_id_to_skip_by_default=default static_fusion_pass_group_id_to_debug_by_default=default static_fusion_pass_group_id_to_log_by_default=default auto_mixed_precision=True auto_mixed_precision_loss_scaling_factor=float32 loss_scaling_factor=float32 enable_automatic_loss_scaling=False automatic_loss_scaling_interval_steps=int32 automatic_loss_scaling_up_scale_factor=float32 automatic_loss_scaling_down_scale_factor=float32 automatic_loss_scaling_init_scale=float32 automatic_loss_scaling_target_update_period=int32 automatic_loss_scaling_decrease_interval=int32 automatic_loss_scaling_increase_interval=int32 automatic_loss_scaling_decrease_factor=float32 automatic_loss_scaling_increase_factor=float32 min_final_step_for_auto_mixed_precision=bool max_num_training_micro_batches_per_step=int32 max_num_training_micro_batches_per_step_for_auto_mixed_precision=bool mixed_precision_trainable_grad_scaler=bool mixed_precision_trainable_grad_scaler_enabled=bool mixed_precision_trainable_grad_scaler_clipping_threshold=float16 mixed_precision_trainable_grad_scaler_clipping_threshold_min_float16_value=min_float16_value mixed_precision_trainable_grad_scaler_clipping_threshold_max_float16_value=max_float16_value mixed_precision_trainable_grad_scaler_clip_in_place=bool mixed_precision_trainable_grad_scaler_clip_in_place_force_update=bool mixed_precision_trainable_grad_scaler_update_period=int32 grad_scalers_use_shared_memory=False grad_scalers_use_shared_memory_prefer_shared_memory=True grad_scalers_use_shared_memory_prefer_cuda_memory=False grad_scalar_sync_frequency_in_micro_batch_steps=int32 num_dynamic_graph_nodes_for_gpu_profile_logging=int64 num_dynamic_graph_nodes_for_gpu_profile_logging_enabled=True num_dynamic_graph_nodes_for_gpu_profile_logging_disabled=False dynamic_gpu_profiling_node_list=[] dynamic_gpu_profiling_node_list_enabled=True dynamic_gpu_profiling_node_list_disabled=False dynamic_gpu_profiling_node_list_ignore_non_kernel_time_events=True dynamic_gpu_profiling_node_list_ignore_non_kernel_time_events_disabled=False gpu_profiler_device_index=-100000000 gpu_profiler_device_index_enabled=True gpu_profiler_device_index_disabled=False gpu_profiler_enabled=True gpu_profiler_disabled=False gpu_mem_limit_ratio=float float fp16 bool int int int int int float float float float float float bool bool bool bool bool int int int int float float float float float float bool bool bool bool int int list list list list list list list list list list list list list list true true true true true true true true true true true true false false false false false false false false false false false False True True False False True True False False False False True True True False False True True False True True True True True Float MinFloat MinFloat MaxFloat MaxFloat Int Int Int Int Int Int Int Float Float Float Float Float Float Float Bool Bool Bool Bool Bool Bool Bool Bool Int Int Int Int Float Float Float Float Float Float Bool Bool Bool Bool List List List List List List List List List List List List List List
acc_nbit_fp16=false acc_nbit_fp32=false acc_nbit_int32=false acc_nbit_int16=false acc_nbit_int8=false cudnn_benchmark=false cudnn_deterministic=true cudnn_exhaustive_search=false cudnn_fast_math=true cuda_graph_capture_stream_priority_level=max cuda_graph_max_replay_count=max cuda_graph_max_replay_time_sec=max cuda_graph_max_replay_count_per_stream=max cuda_graph_max_replay_time_sec_per_stream=max cuda_graph_capture_buffer_size_bytes=max cuda_graph_launch_buffer_size_bytes=max cuda_graph_populate_streams_for_multi_stream_launches=true cuda_graph_allow_concurrent_executes=true cuda_graph_allow_execution_merge=true cuDNN fused_kernels_enabled=true graph_optimization_level=default fused_kernel_optimization_level=default kernel_optimization_level=default cpu_fallback_threshold_us=default fusion_pass_group_id_to_run_by_default=default fusion_pass_group_id_to_skip_by_default=default fusion_pass_group_id_to_debug_by_default=default fusion_pass_group_id_to_log_by_default=default static_fusion_pass_group_id_to_run_by_default=default static_fusion_pass_group_id_to_skip_by_default=default static_fusion_pass_group_id_to_debug_by_default=default static_fusion_pass_group_id_to_log_by_default=default auto_mixed_precision=True auto_mixed_precision_loss_scaling_factor=float32 loss_scaling_factor=float32 enable_automatic_loss_scaling=False automatic_loss_scaling_interval_steps=int32 automatic_loss_scaling_up_scale_factor=float32 automatic_loss_scaling_down_scale_factor=float32 automatic_loss_scaling_init_scale=float32 automatic_loss_scaling_target_update_period=int32 automatic_loss_scaling_decrease_interval=int32 automatic_loss_scaling_increase_interval=int32 automatic_loss_scaling_decrease_factor=float32 automatic_loss_scaling_increase_factor=float32 min_final_step_for_auto_mixed_precision=bool max_num_training_micro_batches_per_step=int32 max_num_training_micro_batches_per_step_for_auto_mixed_precision=bool mixed_precision_trainable_grad_scaler=bool mixed_precision_trainable_grad_scaler_enabled=bool mixed_precision_trainable_grad_scaler_clipping_threshold=float16 mixed_precision_trainable_grad_scaler_clipping_threshold_min_float16_value=min_float16_value mixed_precision_trainable_grad_scaler_clipping_threshold_max_float16_value=max_float16_value mixed_precision_trainable_grad_scaler_clip_in_place=bool mixed_precision_trainable_grad_scaler_clip_in_place_force_update=bool mixed_precision_trainable_grad_scaler_update_period=int32 grad_scalers_use_shared_memory=False grad_scalers_use_shared_memory_prefer_shared_memory=True grad_scalers_use_shared_memory_prefer_cuda_memory=False grad_scalar_sync_frequency_in_micro_batch_steps=int32 num_dynamic_graph_nodes_for_gpu_profile_logging=int64 num_dynamic_graph_nodes_for_gpu_profile_logging_enabled=True num_dynamic_graph_nodes_for_gpu_profile_logging_disabled=False dynamic_gpu_profiling_node_list=[] dynamic_gpu_profiling_node_list_enabled=True dynamic_gpu_profiling_node_list_disabled=False dynamic_gpu_profiling_node_list_ignore_non_kernel_time_events=True dynamic_gpu_profiling_node_list_ignore_non_kernel_time_events_disabled=False gpu_mem_limit_ratio=float fp16_bool_int_int_int_int_float_float_float_float_float_float_bool_bool_bool_bool_bool_bool_bool_int_int_int_int_float_float_float_float_float_bool_bool_bool_bool_int_list_list_list_list_list_list_list_true_true_true_true_true_true_true_false_false_false_false_false_false_false_false_false_false_false_false
acc_nbit_fp16="false" acc_nbit_fp24="false" acc_nbit_fp48="false" acc_nbit_fp96="false" acc_nbit_bfloat="false" acc_nbit_fp128="false" acc_nbit_qint4_symmetric="false" acc_nbit_qint4_asymmetric="false" acc_nbit_qint4_asymmetric_signed_zero_point_offset="-21474836480000000000000000" acc_nbit_qint4_asymmetric_unsigned_zero_point_offset="-21474836480000000000000000" "acc-nbit-qint4-asymmetric-signed-zero-point-offset-is-negative"-21474836480000000000000000 "acc-nbit-qint4-asymmetric-signed-zero-point-offset-is-positive"-214748364800000000000001 "acc-nbit-qint4-asymmetric-signed-zero-point-offset-is-zero"-214748364800 "acc-nbit-qint4-asymmetric-signed-zero-point-offset-is-nonzero"-214748364801 "acc-nubit-int128-bit-width-larger-than-maximum-bits-of-supported-int128-tensor-element-data-types-value"-42949672960000 "acc-nubit-int128-bit-width-larger-than-maximum-bits-of-supported-int128-tensor-element-data-types-value"+42949672960000 "acc-nubit-int128-bit-width-larger-than-maximum-bits-of-supported-int128-tensor-element-data-types-value"-42949672960001 "acc-nubit-int128-bit-width-larger-than-maximum-bits-of-supported-int128-tensor-element-data-types-value"+42949672960001 "acc-nubit-int128-bit-width-equal-to-maximum-bits-of-supported-int128-tensor-element-data-types-value"-4096 "acc-nubit-int128-bit-width-equal-to-maximum-bits-of-supported-int128-tensor-element-data-types-value"+4096 "acc-nubit-int128-bit-width-less-than-minimum-bits-of-supported-int128-tensor-element-data-types-value"-4097 "acc-nubit-int128-bit-width-less-than-minimum-bits-of-supported-int128-tensor-element-data-types-value"+4097 ""-"true""+"true""-"true""+"true""-"true""+"true""-"true""+"true"
"cudnn-enable-rand-states-cache"--True--False--
"cudnn-enable-rand-states-cache"--True--
"cudnn-enable-rand-states-cache"--False--
"cudnn-enable-rand-states-cache"--False--
"cudnn-exhaustive-search"--True--
"cudnn-exhaustive-search"--False--
"cudnn-fast-math"--True--
"cudnn-fast-math"--False--
"cudnn-fused-kernel-enabled"--True--
"cudnn-fused-kernel-enabled"--False--
"CUDNN_ENABLE_RAND_STATES_CACHE"--True--False--
"CUDNN_ENABLE_RAND_STATES_CACHE"--True--
"CUDNN_ENABLE_RAND_STATES_CACHE"--False--
"CUDNN_ENABLE_RAND_STATES_CACHE"--False--
"CUDNN_EXHAUSTIVE_SEARCH "--True--
"CUDNN_EXHAUSTIVE_SEARCH "--False--
"CUDNN_FAST_MATH "--True--
"CUDNN_FAST_MATH "--False--
"CUDNN_FUSED_KERNEL_ENABLED "--True--
"CUDNN_FUSED_KERNEL_ENABLED "--False--
CUDNN RAN STATES CACHE ENABLED BY DEFAULT IS FALSE FOR PERFORMANCE REASON AND IT'S HIGHLY RECOMMENDED TO LEAVE IT AS FALSE UNLESS YOU HAVE EXPERIENCED RANDOM NUMBER GENERATION RELATED BUGS SINCE USING RAN STATES CACHE CAN INTRODUCE MORE RANDOM NUMBER GENERATION RELATED BUGS INSTEAD OF FIXING THEM!
ENABLE RAN STATES CACHE CAN CAUSE NUMPY RANDOM NUMBER GENERATOR TO BECOME SLOW AND INCORRECT WHEN RUNNING ON GPU DEVICES WHEN USING PYTORCH LIBRARY WITH TORCH.CUDA.CUDA_SEED MANUAL SEED SETTING FUNCTION SINCE TORCH.CUDA.CUDA_SEED MANUAL SEED SETTING FUNCTION DOES NOT RESET THE STATE OF NUMPY RANDOM NUMBER GENERATOR WHICH IS USED BY TORCH LIBRARY UNDER THE HOOK SO THE ONLY WORKAROUND HERE IS TO USE TORCH.SEED MANUAL SEED SETTING FUNCTION WHICH WILL RESET THE STATE OF NUMPY RANDOM NUMBER GENERATOR ALONG WITH THAT OF TORCH LIBRARY BUT THIS CAN CAUSE PERFORMANCE ISSUE SINCE THIS WILL FORCE ALL CUDA STREAMS TO SYNC UP BEFORE EACH MANUALLY SETTING THE SEED VALUE SO IT'S HIGHLY RECOMMENDED TO USE TORCH.SEED MANUAL SEED SETTING FUNCTION INSTEAD OF torch.cuda.manual_seed() IF YOU NEED TO DO MANUALLY SETTING THE SEED VALUE!
ACC NBIT FP48 ACC NBIT FP96 ACC NBIT BFLOAT ACC NBIT FP12 ACC NBIT QINT4 SYMMETRIC ACC NBIT QINT4 ASYMMETRIC ACC NBIT QINT40 ASYMMETRIC SIGNED ZERO POINT OFFSET ACC NBIT QINT40 ASYMMETRIC UNSIGNED ZERO POINT OFFSET ACC-NBIT-QINT40 ASYMMETRIC SIGNED ZERO POINT OFFSET-IS-Negative ACC-NBIT-QINT40 ASYMMETRIC SIGNED ZERO POINT OFFSET-IS Positive ACC-NBIT-QINT40 ASYMMETRIC SIGNED ZERO POINT OFFSET-IS Zero ACC-NBIT-QINT40 ASYMMETRIC SIGNED ZERO POINT OFFSET-IS NonZero ACC-NUBIT INT120 BIT WIDTH LARGER THAN MAXIMUM BITS OF SUPPORTED INT120 TENSOR ELEMENT DATA TYPES VALUE ACC-NUBIT INT120 BIT WIDTH EQUAL TO MAXIMUM BITS OF SUPPORTED INT120 TENSOR ELEMENT DATA TYPES VALUE ACC-NUBIT INT120 BIT WIDTH LESS THAN MINIMUM BITS OF SUPPORTED INT120 TENSOR ELEMENT DATA TYPES VALUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE "" "-" "+" "-" "+" "-" "+" "-" "+"
torch.manual_seed() MUST BE CALLED BEFORE CALLING torch.cuda.manual_seed() IF YOU WANT TO USE CUDA DEVICE FOR TORCH LIBRARY OPERATIONAL PURPOSES SINCE torch.cuda.manual_seed() DOES NOT RESET THE STATE OF NUMPY RANDOM NUMBER GENERATOR WHICH IS USED BY TORCH LIBRARY UNDER THE HOOK SO CALLING torch.manual_seed() FIRST WILL RESET BOTH NUMPY AND TORCH RANDOM NUMBER GENERATORS AT ONCE!
TORCH.SEED MUST BE CALLED BEFORE
