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Version: 1.1

Job Configuration

The job configuration resides in hydra.job. The Structured Config is below, the latest definition is here.

Expand definition
# job runtime information will be populated here
class JobConf:
# Job name, populated automatically unless specified by the user (in config or cli)
name: str = MISSING
# Concatenation of job overrides that can be used as a part
# of the directory name.
# This can be configured in hydra.job.config.override_dirname
override_dirname: str = MISSING
# Job ID in underlying scheduling system
id: str = MISSING
# Job number if job is a part of a sweep
num: int = MISSING
# The config name used by the job
config_name: Optional[str] = MISSING
# Environment variables to set remotely
env_set: Dict[str, str] = field(default_factory=dict)
# Environment variables to copy from the launching machine
env_copy: List[str] = field(default_factory=list)
# Job config
class JobConfig:
# configuration for the ${hydra.job.override_dirname} runtime variable
class OverrideDirname:
kv_sep: str = "="
item_sep: str = ","
exclude_keys: List[str] = field(default_factory=list)
override_dirname: OverrideDirname = OverrideDirname()
config: JobConfig = JobConfig()


The job name is used by different things in Hydra, such as the log file name (${}.log). It is normally derived from the Python file name (The job name of the file is train). You can override it via the command line, or your config file.


Enables the creation of an output directory which is based on command line overrides. Learn more at the Customizing Working Directory page.

The job ID is populated by the active Hydra launcher. For the basic launcher, the job ID is just a serial job number. Other launchers will set it to an ID that makes sense like SLURM job ID.


Serial job number within this current sweep run. (0 to n-1).


The config name used by the job, this is populated automatically to match the config name in @hydra.main().


A Dict[str, str] that is used to set the environment variables of the running job. Some common use cases are to automatically set environment variables that are affecting underlying libraries. For example, the following will disables multithreading in Intel IPP and MKL:


Another example, is to use interpolation to automatically set the rank for Torch Distributed run to match the job number in the sweep.

RANK: ${hydra:job.num}


In some cases you want to automatically copy local environment variables to the running job environment variables. This is particularly useful for remote runs.

Last updated on by Jasha10