torch.__version__ =1.9.0+cu111
/tmp/pip-req-build-6xbwecb4/setup.py:67: UserWarning: Option --pyprof not specified. Not installing PyProf dependencies!
warnings.warn("Option --pyprof not specified. Not installing PyProf dependencies!")
Compiling cuda extensions with
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c)2005-2021 NVIDIA Corporation
Built on Sun_Feb_14_21:12:58_PST_2021
Cuda compilation tools, release 11.2, V11.2.152
Build cuda_11.2.r11.2/compiler.29618528_0
from/usr/local/cuda/bin
Traceback (most recent call last):
File "", line 1,in<module>
File "/tmp/pip-req-build-6xbwecb4/setup.py", line 171,in<module>
check_cuda_torch_binary_vs_bare_metal(torch.utils.cpp_extension.CUDA_HOME)
File "/tmp/pip-req-build-6xbwecb4/setup.py", line 102,in check_cuda_torch_binary_vs_bare_metal
raise RuntimeError("Cuda extensions are being compiled with a version of Cuda that does "+
RuntimeError: Cuda extensions are being compiled with a version of Cuda that does not match the version used to compile Pytorch binaries. Pytorch binaries were compiled with Cuda 11.1.
In some cases, a minor-version mismatch will not cause later errors: https://github.com/NVIDIA/apex/pull/323#discussion_r287021798. You can try commenting out this check (at your own risk).
Running setup.py install for apex ... error
ERROR: Command errored out with exit status 1:/home/liuyuan/anaconda3/envs/yolox/bin/python3.8-u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-6xbwecb4/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-6xbwecb4/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))'--cpp_ext --cuda_ext install --record /tmp/pip-record-l1tq4rlf/install-record.txt --single-version-externally-managed --compile--install-headers /home/liuyuan/anaconda3/envs/yolox/include/python3.8/apex Check the logs for full command output.
defcheck_cuda_torch_binary_vs_bare_metal(cuda_dir):return
raw_output, bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir)
torch_binary_major = torch.version.cuda.split(".")[0]
torch_binary_minor = torch.version.cuda.split(".")[1]print("\nCompiling cuda extensions with")print(raw_output +"from "+ cuda_dir +"/bin\n")if(bare_metal_major != torch_binary_major)or(bare_metal_minor != torch_binary_minor):raise RuntimeError("Cuda extensions are being compiled with a version of Cuda that does "+"not match the version used to compile Pytorch binaries. "+"Pytorch binaries were compiled with Cuda {}.\n".format(torch.version.cuda)+"In some cases, a minor-version mismatch will not cause later errors: "+"https://github.com/NVIDIA/apex/pull/323#discussion_r287021798. ""You can try commenting out this check (at your own risk).")
项目需要当某事件触发时，执行http请求任务，失败时需要有重试机制，并根据失败次数的增加，重试间隔也相应增加，任务可能并发。
由于是耗时任务，首先考虑的就是用线程来实现，并且为了节约资源，因而选择线程池。
为了解决不定间隔的重试，选择Timer和TimerTask来完成
package threadpool;
public class ThreadPoolTest {
首先要说的是，不同版本数据库提供的系统表会有不同，你可以根据数据字典查看该版本数据库所提供的表。
select * from dict where table_name like '%SESSION%';
就可以查出一些表，然后根据这些表就可以获得会话信息
select sid,serial#,status,username,schemaname,osuser,terminal,ma
Admin类的主要方法注释：
1. 创建表
/**
* Creates a new table. Synchronous operation.
*
* @param desc table descriptor for table
* @throws IllegalArgumentException if the table name is res
public class LinkListTest {
/**
* we deal with two main missions:
*
* A.
* 1.we create two joined-List(both have no loop)
* 2.whether list1 and list2 join
* 3.print the join
事件回顾：由于需要修改同一个模板，里面包含2个不同的内容，第一个里面使用的时间差和第二个里面名称不一样，其他过滤器，内容都大同小异。希望杜绝If这样比较傻的来判断if-show or not，继续追究其源码。
var b = "{{",
a = "}}";
this.startSymbol = function(a) {
