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
class dpdispatcher.dpcloudserver.api.API(email, password)[source]

Bases: object

Methods

download

get

get_jobs

get_tasks

get_tasks_v2

job_create

job_create_v2

post

refresh_token

upload
download(oss_file, save_file, endpoint, bucket_name)[source]
get(url, params, retry=0)[source]
get_jobs(page=1, per_page=10)[source]
get_tasks(job_id, page=1, per_page=10)[source]
get_tasks_v2(job_id, group_id, page=1, per_page=10)[source]
job_create(job_type, oss_path, input_data, program_id=None)[source]
job_create_v2(job_type, oss_path, input_data, program_id=None, group_id=None)[source]
post(url, params, retry=0)[source]
refresh_token()[source]
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'
NODATA = '2300'
OK = '0000'
PARAMERR = '2101'
PWDERR = '2104'
REQERR = '2200'
ROLEERR = '2103'
THIRDERR = '2001'
TOKENINVALID = '2100'
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'>, 'HDFS': <class 'dpdispatcher.hdfs_context.HDFSContext'>, 'HDFSContext': <class 'dpdispatcher.hdfs_context.HDFSContext'>, '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'>, 'hdfs': <class 'dpdispatcher.hdfs_context.HDFSContext'>, 'hdfscontext': <class 'dpdispatcher.hdfs_context.HDFSContext'>, '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.distributed_shell module

class dpdispatcher.distributed_shell.DistributedShell(*args, **kwargs)[source]

Bases: dpdispatcher.machine.Machine

Methods

do_submit(job)

submit th job to yarn using distributed shell

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]
do_submit(job)[source]

submit th job to yarn using distributed shell

Parameters
jobJob class instance

job to be submitted

Returns
job_id: string

submit process id

gen_script_end(job)[source]
gen_script_env(job)[source]
gen_script_header(job)[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.hdfs_cli module

class dpdispatcher.hdfs_cli.HDFS[source]

Bases: object

Fundamental class for HDFS basic manipulation

Methods

copy_from_local(local_path, to_uri)

Returns: True on success Raises: on unexpected error

exists(uri)

Check existence of hdfs uri Returns: True on exists Raises: RuntimeError

mkdir(uri)

Make new hdfs directory Returns: True on success Raises: RuntimeError

remove(uri)

Check existence of hdfs uri Returns: True on exists Raises: RuntimeError

copy_to_local

move

read_hdfs_file
static copy_from_local(local_path, to_uri)[source]

Returns: True on success Raises: on unexpected error

static copy_to_local(from_uri, local_path)[source]
static exists(uri)[source]

Check existence of hdfs uri Returns: True on exists Raises: RuntimeError

static mkdir(uri)[source]

Make new hdfs directory Returns: True on success Raises: RuntimeError

static move(from_uri, to_uri)[source]
static read_hdfs_file(uri)[source]
static remove(uri)[source]

Check existence of hdfs uri Returns: True on exists Raises: RuntimeError

dpdispatcher.hdfs_context module

class dpdispatcher.hdfs_context.HDFSContext(*args, **kwargs)[source]

Bases: dpdispatcher.base_context.BaseContext

Methods

download(submission[, check_exists, ...])

download backward files from HDFS root dir

upload(submission[, dereference])

upload forward files and forward command files to HDFS root dir

bind_submission

check_file_exists

check_finish

clean

get_job_root

kill

load_from_dict

read_file

write_file
bind_submission(submission)[source]
check_file_exists(fname)[source]
clean()[source]
download(submission, check_exists=False, mark_failure=True, back_error=False)[source]

download backward files from HDFS root dir

Parameters
submissionSubmission class instance

represents a collection of tasks, such as backward file names

Returns
none
get_job_root()[source]
classmethod load_from_dict(context_dict)[source]
read_file(fname)[source]
upload(submission, dereference=True)[source]

upload forward files and forward command files to HDFS root dir

Parameters
submissionSubmission class instance

represents a collection of tasks, such as forward file names

Returns
none
write_file(fname, write_str)[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 = {'DistributedShell': <class 'dpdispatcher.distributed_shell.DistributedShell'>, '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'>, 'distributedshell': <class 'dpdispatcher.distributed_shell.DistributedShell'>, '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.utils.run_cmd_with_all_output(cmd)[source]

Indices and tables