Simple Command Line Job Manager
TQ -- Simple Command Line Job Manager
Description in English:
TQ (Task Queue) is a simple Command Line Job Manager. In principle TQ is a very flexible and smart atd(8), which could arrange a series of jobs in an efficient way.
(1) By default TQ will run the jobs one by one in the FIFO order.
(2) A job with high priority will be processed earlier.
(3) Given the estimated occupancy coefficient, jobs can be executed in parallel as long as possible.
The management of job queue is based on SQLite3 database, in which information about every job, including the start and end time, is stored.
中文描述(Description in Chinese):
在一位大佬的怂恿下，我把自己炼丹用的队列管理器tq传到了pypi上。 tq本质上就是一个异常灵活的atd，可以用来安排执行一系列耗时的命令。 默认情况下tq会串行（FIFO）执行给定的任务队列。如果指定了优先级 或者预计资源占用比，tq还能根据情况优先执行高优先任务，或者并行 执行若干非独占的任务。队列的管理依赖于SQLite3数据库，每一个任务 的各类信息，包括起止时间，都会被记录在其中。
TQ keeps your command in a queue and execute them one by one.
Priority attribute is supported. The scheduler will execute high priority command lines first.
Resource occupancy coefficient is supported. The scheduler will execute command lines in parallel as long as possible.
The program output will be redirected to
tq.errunder the directory where the task was added.
The queue is saved in SQLite3. Won't lose any data even if a powerloss had happend.
Dependency free and light weight. This tool only depends on python3 itself. (with SQLite3 support)
This tools is available on Pypi. Just issue the following command:
pip3 install tq1
python3 >= 3.6 is required due to new language features used
in the code. There is no plan to support lower version of python.
TQ can be used to deal with some commands in an async manner. e.g.
$ tq -- git push # Doesn't block. Have it done in async. $ vim mycode.py
TQ can be used to manage a series of computation experiments, such as deep learning experiments, e.g.
$ tq r5 -- caffe train -solver net1forfun.prototxt $ tq r5 -- caffe train -solver net2forfun.prototxt $ tq 1 5 -- python3 train.py --lr 1e-2 $ tq 1 5 -- python3 train.py --lr 1e-3 $ tq 1 5 -- python3 train.py --lr 1e-4 $ tq 1 5 -- python3 train.py --lr 1e-5 $ tq p10 -- python3 important_train.py
One can just put many computation tasks in the queue, and TQ will smartly schedule these experiments according to the given priority and resource occupancy parameters.
Usage: tq ACTION [COMMAND_ARGS] tq [P R] -- TASK Available Actions: start start TQ's daemon stop stop TQ's daemon log dump log to screen ls fancy print of task queue db print database content to screen rm <ID> remove task with specified id, see ID with tq ls clean remove finished tasks from queue purge remove log file and sqlite3 db file Apending Task: -- TASK append TASK to the queue p<P> -- TASK append TASK with priority P to the queue r<R> -- TASK append TASK with resource occupancy R to the queue P R -- TASK append TASK with priority P and estimated occupancy R int P default 0 range [INT_MIN, INT_MAX], large=important int R detault 10 range [1, 10], large=consuming
0. Daemon: to start or stop the daemon tq start tq stop 1. Serial: the two given tasks should be executed one by one tq -- sleep 100 tq -- sleep 100 2. Parallel: each task occupies 40% of resource. In this example two tasks will be active at the same time. tq r4 -- sleep 100 tq r4 -- sleep 100 tq r4 -- sleep 100 3. Priority: break the FIFO order of tasks. 1 > default Priority. tq p1 -- sleep 100 4. Special Case: run the given task right away ignoring Pri and Rsc tq 1 0 -- sleep 100
atd(8), HTCondor, Torque, Slurm, PBS.
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