Job Definition File

Job Definition File#

Job Definition File (JDF) a way how to submit a complex pipeline into a HyperQueue. It is a TOML file that describes tasks of a job. JDF provides all functionalities as command line interface of HyperQueue and also adds access to additional features:

• Heterogeneous tasks -- Job may be composed of different tasks
• Dependencies -- Tasks may have dependencies
• Resource request alternatives -- Task may have alternative resource requests, e.g.: 4 cpus OR 1 cpus and 1 gpu

Note that these features are also available through Python interface.

Minimal example#

First, we create file with the following content:

[[task]]
command = ["sleep", "1"]

Let us assume that we have named this file as myfile.toml, then we can run the following command to submit a job:

$ hq job submit-file myfile.toml

The effect will be same as running:

$ hq submit sleep 1

Task configuration#

The following shows how job and task may be configured in more detail. All options except command are optional. If not said otherwise, an option in format xxx = ... is an equivalent of --xxx = ... in hq submit command. The default are the same as CLI interface.

name = "test-job"
stream = "path/to/stream/dir"  # Stdout/Stderr streaming (see --stream)
max_fails = 11

[[task]]
stdout = "testout-%{TASK_ID}
stderr = { path = "testerr-%{TASK_ID}", mode = "rm-if-finished" }
task_dir = true
time_limit = "1m 10s"
priority = -1
crash_limit = 12
command = ["/bin/bash", "-c", "echo $ABC"]

# Environment variables
env = { "ABC" = "123", "XYZ" = "aaaa" }

# Content that will be written on stdin
stdin = "Hello world!"

[[task.request]]
resources = { "cpus" = "4 compact!", "gpus" = 2 }
time_request = "10s"

More tasks#

More tasks with different configuration may be defined as follows:

[[task]]
command = ["sleep", "1"]

[[task]]
command = ["sleep", "2"]

[[task]]
command = ["sleep", "3"]

In the case above, tasks are given automatic task ids from id 0. You can also specify IDs manually:

[[task]]
id = 10
command = ["sleep", "1"]

[[task]]
id = 11
command = ["sleep", "2"]

[[task]]
id = 2
command = ["sleep", "3"]

Task arrays#

If you want to create uniform tasks you can define task array (similar to --array):

[[array]]
ids = "1,2,50-100"
command = ["sleep", "1"]

You can also specify array with content of HQ_ENTRIES:

[[array]]
entries = ["One", "Two", "Three"]
command = ["sleep", "1"]

Task dependencies#

Job Definition File allows to define a dependencies between tasks. In other words, it means that the task may be executed only if the previous tasks are already finished.

The task's option deps defines on which tasks the given task dependents. The task is addressed by their IDs.

The following example creates three tasks where the third task depends on the first two tasks.

[[task]]
id = 1
command = [...]

[[task]]
id = 3
command = [...]

[[task]]
id = 5
command = [...]
deps = [1, 3] # <---- Dependancy on tasks 1 and 3

Resource variants#

More resource configurations may be defined for a task. In this case, HyperQueue will take into account all these configurations during scheduling. When a task is started exactly one configuration is chosen. If in a given moment more configuration are possible for a given task, the configuration first defined has a higher priority.

The following configuration defines that a task may be executed on 1 cpus and 1 gpu OR on 4 cpus.

[[task]]
command = [...]
[[task.request]]
resources = { "cpus" = 1, "gpus" = 1 }
[[task.request]]
resources = { "cpus" = 4 }

In the case that many tasks with such a configuration are submitted to a worker with 16 cpus and 4 gpus then HyperQueue will run simultaneously 4 tasks in the first configuration and 3 tasks in the second one.

For a task with resource variants, HyperQueue sets variable HQ_RESOURCE_VARIANT to an index of chosen variant (counted from 0) when a task is started.

Non-integer resource amounts#

You may specify a resource number as float, e.g. resources = { "foo" = 1.5 }. It is valid but internally the type if converted to float, that may for some numbers lead to a rounding up when number is converted to 4-digit precision of resource amounts. If you want to avoid this, put the number into parentheses, e.g. resources = { "foo" = "1.5" }.