DPDispatcher’s documentation

DPDispatcher is a Python package used to generate HPC (High Performance Computing) scheduler systems (Slurm/PBS/LSF/dpcloudserver) jobs input scripts and submit these scripts to HPC systems and poke until they finish.

DPDispatcher will monitor (poke) until these jobs finish and download the results files (if these jobs is running on remote systems connected by SSH).

Install DPDispatcher

DPDispatcher can installed by pip:

pip install dpdispatcher

Getting Started

DPDispatcher provides the following classes:

Task class, which represents a command to be run on batch job system, as well as the essential files need by the command.
Submission class, which represents a collection of jobs defined by the HPC system. And there may be common files to be uploaded by them. DPDispatcher will create and submit these jobs when a submission instance execute run_submission method. This method will poke until the jobs finish and return.
Job class, a class used by Submission class, which represents a job on the HPC system. Submission will generate jobs’ submitting scripts used by HPC systems automatically with the Task and Resources
Resources class, which represents the computing resources for each job within a submission.

You can use DPDispatcher in a Python script to submit five tasks:

from dpdispatcher import Machine, Resources, Task, Submission

machine = Machine.load_from_json('machine.json')
resources = Resources.load_from_json('resources.json')

task0 = Task.load_from_json('task.json')

task1 = Task(command='cat example.txt', task_work_path='dir1/', forward_files=['example.txt'], backward_files=['out.txt'], outlog='out.txt')
task2 = Task(command='cat example.txt', task_work_path='dir2/', forward_files=['example.txt'], backward_files=['out.txt'], outlog='out.txt')
task3 = Task(command='cat example.txt', task_work_path='dir3/', forward_files=['example.txt'], backward_files=['out.txt'], outlog='out.txt')
task4 = Task(command='cat example.txt', task_work_path='dir4/', forward_files=['example.txt'], backward_files=['out.txt'], outlog='out.txt')

task_list = [task0, task1, task2, task3, task4]

submission = Submission(work_base='lammps_md_300K_5GPa/',
    machine=machine, 
    resources=reasources,
    task_list=task_list,
    forward_common_files=['graph.pb'], 
    backward_common_files=[]
)

submission.run_submission()

where machine.json is

{
    "batch_type": "Slurm",
    "context_type": "SSHContext",
    "local_root" : "/home/user123/workplace/22_new_project/",
    "remote_root": "/home/user123/dpdispatcher_work_dir/",
    "remote_profile":{
        "hostname": "39.106.xx.xxx",
        "username": "user123",
        "port": 22,
        "timeout": 10
    }
}

resources.json is

{
    "number_node": 1,
    "cpu_per_node": 4,
    "gpu_per_node": 1,
    "queue_name": "GPUV100",
    "group_size": 5
}

and task.json is

{
    "command": "lmp -i input.lammps",
    "task_work_path": "bct-0/",
    "forward_files": [
        "conf.lmp",
        "input.lammps"
    ],
    "backward_files": [
        "log.lammps"
    ],
    "outlog": "log",
    "errlog": "err",
}

You may also submit mutiple GPU jobs: complex resources example

resources = Resources(
    number_node=1,
    cpu_per_node=4,
    gpu_per_node=2,
    queue_name="GPU_2080Ti",
    group_size=4,
    custom_flags=[
        "#SBATCH --nice=100", 
        "#SBATCH --time=24:00:00"
    ],
    strategy={
        # used when you want to add CUDA_VISIBLE_DIVECES automatically
        "if_cuda_multi_devices": True 
    },
    para_deg=1,
    # will unload these modules before running tasks
    module_unload_list=["singularity"],
    # will load these modules before running tasks
    module_list=["singularity/3.0.0"],
    # will source the environment files before running tasks
    source_list=["./slurm_test.env"],
    # the envs option is used to export environment variables
    # And it will generate a line like below.
    # export DP_DISPATCHER_EXPORT=test_foo_bar_baz
    envs={"DP_DISPATCHER_EXPORT": "test_foo_bar_baz"},
)

The details of parameters can be found in Machine Parameters, Resources Parameters, and Task Parameters.

Machine parameters

machine:

type: dict

argument path: machine

batch_type:

type: str

argument path: machine/batch_type

The batch job system type. Option: Slurm, PBS, LSF, Shell, DpCloudServer

context_type:

type: str

argument path: machine/context_type

The connection used to remote machine. Option: LocalContext, LazyLocalContext, SSHContext， DpCloudServerContext

local_root:

type: str

argument path: machine/local_root

The dir where the tasks and relating files locate. Typically the project dir.

remote_root:

type: str, optional

argument path: machine/remote_root

The dir where the tasks are executed on the remote machine. Only needed when context is not lazy-local.

remote_profile:

type: dict

argument path: machine/remote_profile

The information used to maintain the connection with remote machine. Only needed when context is ssh.

hostname:: type: str

argument path: machine/remote_profile/hostname

hostname or ip of ssh connection.
username:: type: str

argument path: machine/remote_profile/username

username of target linux system
password:: type: str, optional

argument path: machine/remote_profile/password

password of linux system
port:: type: int, optional, default: 22

argument path: machine/remote_profile/port

ssh connection port.
key_filename:: type: str | NoneType, optional, default: None

argument path: machine/remote_profile/key_filename

key filename used by ssh connection. If left None, find key in ~/.ssh or use password for login
passphrase:: type: str | NoneType, optional, default: None

argument path: machine/remote_profile/passphrase

passphrase of key used by ssh connection
timeout:: type: int, optional, default: 10

argument path: machine/remote_profile/timeout

timeout of ssh connection
totp_secret:: type: str | NoneType, optional, default: None

argument path: machine/remote_profile/totp_secret

Time-based one time password secret. It should be a base32-encoded string extracted from the 2D code.

clean_asynchronously:

type: bool, optional, default: False

argument path: machine/clean_asynchronously

Clean the remote directory asynchronously after the job finishes.

Resources parameters

resources:

type: dict

argument path: resources

number_node:

type: int

argument path: resources/number_node

The number of node need for each job

cpu_per_node:

type: int

argument path: resources/cpu_per_node

cpu numbers of each node assigned to each job.

gpu_per_node:

type: int

argument path: resources/gpu_per_node

gpu numbers of each node assigned to each job.

queue_name:

type: str

argument path: resources/queue_name

The queue name of batch job scheduler system.

group_size:

type: int

argument path: resources/group_size

The number of tasks in a job.

custom_flags:

type: list, optional

argument path: resources/custom_flags

The extra lines pass to job submitting script header

strategy:

type: dict, optional

argument path: resources/strategy

strategies we use to generation job submitting scripts.

if_cuda_multi_devices:: type: bool, optional, default: True

argument path: resources/strategy/if_cuda_multi_devices

para_deg:

type: int, optional, default: 1

argument path: resources/para_deg

Decide how many tasks will be run in parallel.

source_list:

type: list, optional, default: []

argument path: resources/source_list

The env file to be sourced before the command execution.

module_unload_list:

type: list, optional, default: []

argument path: resources/module_unload_list

The modules to be unloaded on HPC system before submitting jobs

module_list:

type: list, optional, default: []

argument path: resources/module_list

The modules to be loaded on HPC system before submitting jobs

envs:

type: dict, optional, default: {}

argument path: resources/envs

The environment variables to be exported on before submitting jobs

Task parameters

task:

type: dict

argument path: task

command:: type: str

argument path: task/command

A command to be executed of this task. The expected return code is 0.
task_work_path:: type: str

argument path: task/task_work_path

The dir where the command to be executed.
forward_files:: type: list

argument path: task/forward_files

The files to be uploaded in task_work_path before the task exectued.
backward_files:: type: list

argument path: task/backward_files

The files to be download to local_root in task_work_path after the task finished
outlog:: type: str | NoneType

argument path: task/outlog

The out log file name. redirect from stdout
errlog:: type: str | NoneType

argument path: task/errlog

The err log file name. redirect from stderr

DPDispatcher API

dpdispatcher package

dpdispatcher.info()[source]

Subpackages

dpdispatcher.dpcloudserver package

Submodules

dpdispatcher.dpcloudserver.api module

dpdispatcher.dpcloudserver.api.download(oss_file, save_file, endpoint, bucket_name)[source]

dpdispatcher.dpcloudserver.api.get(url, params)[source]

dpdispatcher.dpcloudserver.api.get_jobs(page=1, per_page=10)[source]

dpdispatcher.dpcloudserver.api.get_tasks(job_id, page=1, per_page=10)[source]

dpdispatcher.dpcloudserver.api.get_tasks_v2(job_id, group_id, page=1, per_page=10)[source]

dpdispatcher.dpcloudserver.api.job_create(job_type, oss_path, input_data, program_id=None)[source]

dpdispatcher.dpcloudserver.api.job_create_v2(job_type, oss_path, input_data, program_id=None, group_id=None)[source]

dpdispatcher.dpcloudserver.api.login(password, email=None, username=None)[source]

dpdispatcher.dpcloudserver.api.post(url, params)[source]

dpdispatcher.dpcloudserver.api.upload(oss_task_zip, zip_task_file, endpoint, bucket_name)[source]

dpdispatcher.dpcloudserver.config module

dpdispatcher.dpcloudserver.retcode module

class dpdispatcher.dpcloudserver.retcode.RETCODE[source]

Bases: object

DATAERR = '2002'

DBERR = '2000'

IOERR = '2003'

LOGINERR = '2100'

NODATA = '2300'

OK = '0000'

PARAMERR = '2101'

PWDERR = '2104'

REQERR = '2200'

ROLEERR = '2103'

THIRDERR = '2001'

UNDERDEBUG = '2301'

UNKOWNERR = '2400'

USERERR = '2102'

VERIFYERR = '2105'

dpdispatcher.dpcloudserver.temp_test module

dpdispatcher.dpcloudserver.zip_file module

dpdispatcher.dpcloudserver.zip_file.unzip_file(zip_file, out_dir='./')[source]

dpdispatcher.dpcloudserver.zip_file.zip_file_list(root_path, zip_filename, file_list=[])[source]

Submodules

dpdispatcher.JobStatus module

class dpdispatcher.JobStatus.JobStatus(value)[source]

Bases: enum.IntEnum

An enumeration.

completing = 6

finished = 5

running = 3

terminated = 4

unknown = 100

unsubmitted = 1

waiting = 2

dpdispatcher.base_context module

class dpdispatcher.base_context.BaseContext(*args, **kwargs)[source]

Bases: object

Methods

bind_submission
check_finish
clean
download
kill
load_from_dict
read_file
upload
write_file

bind_submission(submission)[source]

check_finish(proc)[source]

clean()[source]

download(submission, check_exists=False, mark_failure=True, back_error=False)[source]

kill(proc)[source]

classmethod load_from_dict(context_dict)[source]

read_file(fname)[source]

subclasses_dict = {'DpCloudServer': <class 'dpdispatcher.dp_cloud_server_context.DpCloudServerContext'>, 'DpCloudServerContext': <class 'dpdispatcher.dp_cloud_server_context.DpCloudServerContext'>, 'LazyLocal': <class 'dpdispatcher.lazy_local_context.LazyLocalContext'>, 'LazyLocalContext': <class 'dpdispatcher.lazy_local_context.LazyLocalContext'>, 'Local': <class 'dpdispatcher.local_context.LocalContext'>, 'LocalContext': <class 'dpdispatcher.local_context.LocalContext'>, 'SSH': <class 'dpdispatcher.ssh_context.SSHContext'>, 'SSHContext': <class 'dpdispatcher.ssh_context.SSHContext'>, 'dpcloudserver': <class 'dpdispatcher.dp_cloud_server_context.DpCloudServerContext'>, 'dpcloudservercontext': <class 'dpdispatcher.dp_cloud_server_context.DpCloudServerContext'>, 'lazylocal': <class 'dpdispatcher.lazy_local_context.LazyLocalContext'>, 'lazylocalcontext': <class 'dpdispatcher.lazy_local_context.LazyLocalContext'>, 'local': <class 'dpdispatcher.local_context.LocalContext'>, 'localcontext': <class 'dpdispatcher.local_context.LocalContext'>, 'ssh': <class 'dpdispatcher.ssh_context.SSHContext'>, 'sshcontext': <class 'dpdispatcher.ssh_context.SSHContext'>}

upload(submission)[source]

write_file(fname, write_str)[source]

dpdispatcher.dp_cloud_server module

class dpdispatcher.dp_cloud_server.DpCloudServer(*args, **kwargs)[source]

Bases: dpdispatcher.machine.Machine

Methods

do_submit(job)

submit a single job, assuming that no job is running there.

arginfo
bind_context
check_finish_tag
check_if_recover
check_status
default_resources
gen_command_env_cuda_devices
gen_local_script
gen_script
gen_script_command
gen_script_custom_flags_lines
gen_script_end
gen_script_env
gen_script_header
gen_script_wait
load_from_dict
load_from_json
map_dp_job_state
sub_script_cmd
sub_script_head

check_finish_tag(job)[source]

check_if_recover(submission)[source]

check_status(job)[source]

do_submit(job)[source]: submit a single job, assuming that no job is running there.

gen_local_script(job)[source]

gen_script(job)[source]

gen_script_header(job)[source]

static map_dp_job_state(status)[source]

dpdispatcher.dp_cloud_server_context module

class dpdispatcher.dp_cloud_server_context.DpCloudServerContext(*args, **kwargs)[source]

Bases: dpdispatcher.base_context.BaseContext

Methods

bind_submission
check_file_exists
check_finish
check_home_file_exits
clean
download
kill
load_from_dict
read_file
read_home_file
upload
write_file
write_home_file
write_local_file

bind_submission(submission)[source]

check_file_exists(fname)[source]

check_home_file_exits(fname)[source]

clean()[source]

download(submission)[source]

kill(cmd_pipes)[source]

classmethod load_from_dict(context_dict)[source]

read_file(fname)[source]

read_home_file(fname)[source]

upload(submission)[source]

write_file(fname, write_str)[source]

write_home_file(fname, write_str)[source]

write_local_file(fname, write_str)[source]

dpdispatcher.dpdisp module

dpdispatcher.dpdisp.main()[source]

dpdispatcher.lazy_local_context module

class dpdispatcher.lazy_local_context.LazyLocalContext(*args, **kwargs)[source]

Bases: dpdispatcher.base_context.BaseContext

Methods

bind_submission
block_call
block_checkcall
call
check_file_exists
check_finish
clean
download
get_job_root
get_return
kill
load_from_dict
read_file
upload
write_file

bind_submission(submission)[source]

block_call(cmd)[source]

block_checkcall(cmd)[source]

call(cmd)[source]

check_file_exists(fname)[source]

check_finish(proc)[source]

clean()[source]

download(jobs, check_exists=False, mark_failure=True, back_error=False)[source]

get_job_root()[source]

get_return(proc)[source]

kill(proc)[source]

classmethod load_from_dict(context_dict)[source]

read_file(fname)[source]

upload(jobs, dereference=True)[source]

write_file(fname, write_str)[source]

class dpdispatcher.lazy_local_context.SPRetObj(ret)[source]

Bases: object

Methods

read
readlines

read()[source]

readlines()[source]

dpdispatcher.local_context module

class dpdispatcher.local_context.LocalContext(*args, **kwargs)[source]

Bases: dpdispatcher.base_context.BaseContext

Methods

bind_submission
block_call
block_checkcall
call
check_file_exists
check_finish
clean
download
download_
get_job_root
get_return
kill
load_from_dict
read_file
upload
upload_
write_file

bind_submission(submission)[source]

block_call(cmd)[source]

block_checkcall(cmd)[source]

call(cmd)[source]

check_file_exists(fname)[source]

check_finish(proc)[source]

clean()[source]

download(submission, check_exists=False, mark_failure=True, back_error=False)[source]

download_(job_dirs, remote_down_files, check_exists=False, mark_failure=True, back_error=False)[source]

get_job_root()[source]

get_return(proc)[source]

kill(proc)[source]

classmethod load_from_dict(context_dict)[source]

read_file(fname)[source]

upload(submission)[source]

upload_(job_dirs, local_up_files, dereference=True)[source]

write_file(fname, write_str)[source]

class dpdispatcher.local_context.SPRetObj(ret)[source]

Bases: object

Methods

read
readlines

read()[source]

readlines()[source]

dpdispatcher.lsf module

class dpdispatcher.lsf.LSF(*args, **kwargs)[source]

Bases: dpdispatcher.machine.Machine

LSF batch

Methods

`default_resources`(resources)
`do_submit`(job)	submit a single job, assuming that no job is running there.

arginfo
bind_context
check_finish_tag
check_if_recover
check_status
gen_command_env_cuda_devices
gen_script
gen_script_command
gen_script_custom_flags_lines
gen_script_end
gen_script_env
gen_script_header
gen_script_wait
load_from_dict
load_from_json
sub_script_cmd
sub_script_head

check_finish_tag(job)[source]

check_status(job)[source]

default_resources(resources)[source]

do_submit(job)[source]: submit a single job, assuming that no job is running there.

gen_script(job)[source]

gen_script_header(job)[source]

sub_script_cmd(res)[source]

sub_script_head(res)[source]

dpdispatcher.machine module

class dpdispatcher.machine.Machine(*args, **kwargs)[source]

Bases: object

A machine is used to handle the connection with remote machines.

Parameters

contextSubClass derived from BaseContext: The context is used to mainatin the connection with remote machine.

Methods

do_submit(job)

submit a single job, assuming that no job is running there.

arginfo
bind_context
check_finish_tag
check_if_recover
check_status
default_resources
gen_command_env_cuda_devices
gen_script
gen_script_command
gen_script_custom_flags_lines
gen_script_end
gen_script_env
gen_script_header
gen_script_wait
load_from_dict
load_from_json
sub_script_cmd
sub_script_head

static arginfo()[source]

bind_context(context)[source]

check_finish_tag(**kwargs)[source]

check_if_recover(submission)[source]

check_status(job)[source]

default_resources(res)[source]

do_submit(job)[source]: submit a single job, assuming that no job is running there.

gen_command_env_cuda_devices(resources)[source]

gen_script(job)[source]

gen_script_command(job)[source]

gen_script_custom_flags_lines(job)[source]

gen_script_end(job)[source]

gen_script_env(job)[source]

gen_script_header(job)[source]

gen_script_wait(resources)[source]

classmethod load_from_dict(machine_dict)[source]

classmethod load_from_json(json_path)[source]

sub_script_cmd(res)[source]

sub_script_head(res)[source]

subclasses_dict = {'DpCloudServer': <class 'dpdispatcher.dp_cloud_server.DpCloudServer'>, 'LSF': <class 'dpdispatcher.lsf.LSF'>, 'PBS': <class 'dpdispatcher.pbs.PBS'>, 'Shell': <class 'dpdispatcher.shell.Shell'>, 'Slurm': <class 'dpdispatcher.slurm.Slurm'>, 'Torque': <class 'dpdispatcher.pbs.Torque'>, 'dpcloudserver': <class 'dpdispatcher.dp_cloud_server.DpCloudServer'>, 'lsf': <class 'dpdispatcher.lsf.LSF'>, 'pbs': <class 'dpdispatcher.pbs.PBS'>, 'shell': <class 'dpdispatcher.shell.Shell'>, 'slurm': <class 'dpdispatcher.slurm.Slurm'>, 'torque': <class 'dpdispatcher.pbs.Torque'>}

dpdispatcher.pbs module

class dpdispatcher.pbs.PBS(*args, **kwargs)[source]

Bases: dpdispatcher.machine.Machine

Methods

do_submit(job)

submit a single job, assuming that no job is running there.

arginfo
bind_context
check_finish_tag
check_if_recover
check_status
default_resources
gen_command_env_cuda_devices
gen_script
gen_script_command
gen_script_custom_flags_lines
gen_script_end
gen_script_env
gen_script_header
gen_script_wait
load_from_dict
load_from_json
sub_script_cmd
sub_script_head

check_finish_tag(job)[source]

check_status(job)[source]

default_resources(resources)[source]

do_submit(job)[source]: submit a single job, assuming that no job is running there.

gen_script(job)[source]

gen_script_header(job)[source]

class dpdispatcher.pbs.Torque(*args, **kwargs)[source]

Bases: dpdispatcher.pbs.PBS

Methods

do_submit(job)

submit a single job, assuming that no job is running there.

arginfo
bind_context
check_finish_tag
check_if_recover
check_status
default_resources
gen_command_env_cuda_devices
gen_script
gen_script_command
gen_script_custom_flags_lines
gen_script_end
gen_script_env
gen_script_header
gen_script_wait
load_from_dict
load_from_json
sub_script_cmd
sub_script_head

check_status(job)[source]

dpdispatcher.shell module

class dpdispatcher.shell.Shell(*args, **kwargs)[source]

Bases: dpdispatcher.machine.Machine

Methods

do_submit(job)

submit a single job, assuming that no job is running there.

arginfo
bind_context
check_finish_tag
check_if_recover
check_status
default_resources
gen_command_env_cuda_devices
gen_script
gen_script_command
gen_script_custom_flags_lines
gen_script_end
gen_script_env
gen_script_header
gen_script_wait
load_from_dict
load_from_json
sub_script_cmd
sub_script_head

check_finish_tag(job)[source]

check_status(job)[source]

default_resources(resources)[source]

do_submit(job)[source]: submit a single job, assuming that no job is running there.

gen_script(job)[source]

gen_script_header(job)[source]

dpdispatcher.slurm module

class dpdispatcher.slurm.Slurm(*args, **kwargs)[source]

Bases: dpdispatcher.machine.Machine

Methods

do_submit(job[, retry, max_retry])

submit a single job, assuming that no job is running there.

arginfo
bind_context
check_finish_tag
check_if_recover
check_status
default_resources
gen_command_env_cuda_devices
gen_script
gen_script_command
gen_script_custom_flags_lines
gen_script_end
gen_script_env
gen_script_header
gen_script_wait
load_from_dict
load_from_json
sub_script_cmd
sub_script_head

check_finish_tag(job)[source]

check_status(job, retry=0, max_retry=3)[source]

default_resources(resources)[source]

do_submit(job, retry=0, max_retry=3)[source]: submit a single job, assuming that no job is running there.

gen_script(job)[source]

gen_script_header(job)[source]

dpdispatcher.ssh_context module

class dpdispatcher.ssh_context.SSHContext(*args, **kwargs)[source]

Bases: dpdispatcher.base_context.BaseContext

Attributes

sftp
ssh

Methods

block_checkcall(cmd[, asynchronously, ...])

Run command with arguments.

bind_submission
block_call
call
check_file_exists
check_finish
clean
close
download
get_job_root
get_return
kill
load_from_dict
read_file
upload
write_file

bind_submission(submission)[source]

block_call(cmd)[source]

block_checkcall(cmd, asynchronously=False, stderr_whitelist=None)[source]

Run command with arguments. Wait for command to complete. If the return code was zero then return, otherwise raise RuntimeError.

Parameters

cmd: str: The command to run.
asynchronously: bool, optional, default=False: Run command asynchronously. If True, nohup will be used to run the command.

call(cmd)[source]

check_file_exists(fname)[source]

check_finish(cmd_pipes)[source]

clean()[source]

close()[source]

download(submission, check_exists=False, mark_failure=True, back_error=False)[source]

get_job_root()[source]

get_return(cmd_pipes)[source]

kill(cmd_pipes)[source]

classmethod load_from_dict(context_dict)[source]

read_file(fname)[source]

property sftp

property ssh

upload(submission, dereference=True)[source]

write_file(fname, write_str)[source]

class dpdispatcher.ssh_context.SSHSession(hostname, username, password=None, port=22, key_filename=None, passphrase=None, timeout=10, totp_secret=None)[source]

Bases: object

Attributes

sftp: Returns sftp.

Methods

exec_command(cmd[, retry])

Calling self.ssh.exec_command but has an exception check.

arginfo
close
ensure_alive
get_ssh_client

static arginfo()[source]

close()[source]

ensure_alive(max_check=10, sleep_time=10)[source]

exec_command(cmd, retry=0)[source]: Calling self.ssh.exec_command but has an exception check.

get_ssh_client()[source]

property sftp: Returns sftp. Open a new one if not existing.

dpdispatcher.submission module

class dpdispatcher.submission.Job(job_task_list, *, resources, machine=None)[source]

Bases: object

Job is generated by Submission automatically. A job ususally has many tasks and it may request computing resources from job scheduler systems. Each Job can generate a script file to be submitted to the job scheduler system or executed locally.

Parameters

job_task_listlist of Task: the tasks belonging to the job
resourcesResources: the machine resources. Passed from Submission when it constructs jobs.
machinemachine: machine object to execute the job. Passed from Submission when it constructs jobs.

Methods

`deserialize`(job_dict[, machine])	convert the job_dict to a Submission class object
`get_job_state`()	get the jobs.
`serialize`([if_static])	convert the Task class instance to a dictionary.

get_hash
handle_unexpected_job_state
job_to_json
register_job_id
submit_job

classmethod deserialize(job_dict, machine=None)[source]

convert the job_dict to a Submission class object

Parameters

submission_dictdict: path-like, the base directory of the local tasks

Returns

submissionJob: the Job class instance converted from the job_dict

get_hash()[source]

get_job_state()[source]

get the jobs. Usually, this method will query the database of slurm or pbs job scheduler system and get the results.

Notes

this method will not submit or resubmit the jobs if the job is unsubmitted.

handle_unexpected_job_state()[source]

job_to_json()[source]

register_job_id(job_id)[source]

serialize(if_static=False)[source]

convert the Task class instance to a dictionary.

Parameters

if_staticbool: whether dump the job runtime infomation (job_id, job_state, fail_count, job_uuid etc.) to the dictionary.

Returns

task_dictdict: the dictionary converted from the Task class instance

submit_job()[source]

class dpdispatcher.submission.Resources(number_node, cpu_per_node, gpu_per_node, queue_name, group_size, *, custom_flags=[], strategy={'if_cuda_multi_devices': False}, para_deg=1, module_unload_list=[], module_list=[], source_list=[], envs={}, **kwargs)[source]

Bases: object

Resources is used to describe the machine resources we need to do calculations.

Parameters

number_nodeint: The number of node need for each job.
cpu_per_nodeint: cpu numbers of each node.
gpu_per_nodeint: gpu numbers of each node.
queue_namestr: The queue name of batch job scheduler system.
group_sizeint: The number of tasks in a job.
custom_flagslist of Str: The extra lines pass to job submitting script header
strategydict: strategies we use to generation job submitting scripts. if_cuda_multi_devices : bool

If there are multiple nvidia GPUS on the node, and we want to assign the tasks to different GPUS. If true, dpdispatcher will manually export environment variable CUDA_VISIBLE_DEVICES to different task. Usually, this option will be used with Task.task_need_resources variable simultaneously.
para_degint: Decide how many tasks will be run in parallel. Usually run with strategy[‘if_cuda_multi_devices’]
source_listlist of Path: The env file to be sourced before the command execution.

Methods

arginfo
deserialize
load_from_dict
load_from_json
serialize

static arginfo()[source]

classmethod deserialize(resources_dict)[source]

classmethod load_from_dict(resources_dict)[source]

classmethod load_from_json(json_file)[source]

serialize()[source]

class dpdispatcher.submission.Submission(work_base, machine=None, resources=None, forward_common_files=[], backward_common_files=[], *, task_list=[])[source]

Bases: object

A submission represents a collection of tasks. These tasks usually locate at a common directory. And these Tasks may share common files to be uploaded and downloaded.

Parameters

work_basePath: path-like, the base directory of the local tasks
machineMachine: machine class object (for example, PBS, Slurm, Shell) to execute the jobs. The machine can still be bound after the instantiation with the bind_submission method.
resourcesResources: the machine resources (cpu or gpu) used to generate the slurm/pbs script
forward_common_fileslist: the common files to be uploaded to other computers before the jobs begin
backward_common_fileslist: the common files to be downloaded from other computers after the jobs finish
task_listlist of Task: a list of tasks to be run.

Methods

`bind_machine`(machine)	bind this submission to a machine.
`check_all_finished`()	check whether all the jobs in the submission.
`deserialize`(submission_dict[, machine])	convert the submission_dict to a Submission class object
`generate_jobs`()	After tasks register to the self.belonging_tasks, This method generate the jobs and add these jobs to self.belonging_jobs.
`handle_unexpected_submission_state`()	handle unexpected job state of the submission.
`run_submission`(*[, exit_on_submit, clean])	main method to execute the submission.
`serialize`([if_static, if_none_local_root])	convert the Submission class instance to a dictionary.
`update_submission_state`()	check whether all the jobs in the submission.

clean_jobs
download_jobs
get_hash
register_task
register_task_list
submission_from_json
submission_to_json
try_recover_from_json
upload_jobs

bind_machine(machine)[source]

bind this submission to a machine. update the machine’s context remote_root and local_root.

Parameters

machineMachine: the machine to bind with

check_all_finished()[source]

check whether all the jobs in the submission.

Notes

This method will not handle unexpected job state in the submission.

clean_jobs()[source]

classmethod deserialize(submission_dict, machine=None)[source]

convert the submission_dict to a Submission class object

Parameters

submission_dictdict: path-like, the base directory of the local tasks

Returns

submissionSubmission: the Submission class instance converted from the submission_dict

download_jobs()[source]

generate_jobs()[source]: After tasks register to the self.belonging_tasks, This method generate the jobs and add these jobs to self.belonging_jobs. The jobs are generated by the tasks randomly, and there are self.resources.group_size tasks in a task. Why we randomly shuffle the tasks is under the consideration of load balance. The random seed is a constant (to be concrete, 42). And this insures that the jobs are equal when we re-run the program.

get_hash()[source]

handle_unexpected_submission_state()[source]: handle unexpected job state of the submission. If the job state is unsubmitted, submit the job. If the job state is terminated (killed unexpectly), resubmit the job. If the job state is unknown, raise an error.

register_task(task)[source]

register_task_list(task_list)[source]

run_submission(*, exit_on_submit=False, clean=True)[source]: main method to execute the submission. First, check whether old Submission exists on the remote machine, and try to recover from it. Second, upload the local files to the remote machine where the tasks to be executed. Third, run the submission defined previously. Forth, wait until the tasks in the submission finished and download the result file to local directory. if exit_on_submit is True, submission will exit.

serialize(if_static=False, if_none_local_root=False)[source]

convert the Submission class instance to a dictionary.

Parameters

if_staticbool: whether dump the job runtime infomation (like job_id, job_state, fail_count) to the dictionary.

Returns

submission_dictdict: the dictionary converted from the Submission class instance

classmethod submission_from_json(json_file_name='submission.json')[source]

submission_to_json()[source]

try_recover_from_json()[source]

update_submission_state()[source]

check whether all the jobs in the submission.

Notes

this method will not handle unexpected (like resubmit terminated) job state in the submission.

upload_jobs()[source]

class dpdispatcher.submission.Task(command, task_work_path, forward_files=[], backward_files=[], outlog='log', errlog='err')[source]

Bases: object

A task is a sequential command to be executed, as well as the files it depends on to transmit forward and backward.

Parameters

commandStr: the command to be executed.
task_work_pathPath: the directory of each file where the files are dependent on.
forward_fileslist of Path: the files to be transmitted to remote machine before the command execute.
backward_fileslist of Path: the files to be transmitted from remote machine after the comand finished.
outlogStr: the filename to which command redirect stdout
errlogStr: the filename to which command redirect stderr

Methods

deserialize(task_dict)

convert the task_dict to a Task class object

arginfo
get_hash
load_from_json
serialize

static arginfo()[source]

classmethod deserialize(task_dict)[source]

convert the task_dict to a Task class object

Parameters

task_dictdict: the dictionary which contains the task information
Returns
——-
taskTask: the Task class instance converted from the task_dict

get_hash()[source]

classmethod load_from_json(json_file)[source]

serialize()[source]

dpdispatcher.utils module

dpdispatcher.utils.generate_totp(secret: str, period: int = 30, token_length: int = 6) → int[source]

Generate time-based one time password (TOTP) from the secret.

Some HPCs use TOTP for two-factor authentication for safety.

Parameters

secret: str: The encoded secret provided by the HPC. It’s usually extracted from a 2D code and base32 encoded.
period: int, default=30: Time period where the code is valid in seconds.
token_length: int, default=6: The token length.

Returns

token: int: The generated token.

References

https://github.com/lepture/otpauth/blob/49914d83d36dbcd33c9e26f65002b21ce09a6303/otpauth.py#L143-L160

dpdispatcher.utils.get_sha256(filename)[source]

Get sha256 of a file.

Parameters

filename: str: The filename.

Returns

sha256: str: The sha256.

DPDispatcher’s documentation

Install DPDispatcher

Getting Started

Machine parameters

Resources parameters

Task parameters

DPDispatcher API

dpdispatcher package

Subpackages

dpdispatcher.dpcloudserver package

Submodules

dpdispatcher.dpcloudserver.api module

dpdispatcher.dpcloudserver.config module

dpdispatcher.dpcloudserver.retcode module

dpdispatcher.dpcloudserver.temp_test module

dpdispatcher.dpcloudserver.zip_file module

Submodules

dpdispatcher.JobStatus module

dpdispatcher.base_context module

dpdispatcher.dp_cloud_server module

dpdispatcher.dp_cloud_server_context module

dpdispatcher.dpdisp module

dpdispatcher.lazy_local_context module

dpdispatcher.local_context module

dpdispatcher.lsf module

dpdispatcher.machine module

dpdispatcher.pbs module

dpdispatcher.shell module

dpdispatcher.slurm module

dpdispatcher.ssh_context module

dpdispatcher.submission module

dpdispatcher.utils module

Indices and tables