Hierarchical job execution and logging
Project description
🐊 Gator
Gator is a combination of a task runner and a logging system. Every job is managed by a wrapper layer that monitors its progress, resource usage, and captures the messages that it emits via STDOUT or STDERR. Execution is managed in a hierarchical structure, with jobs at the leaves reporting back to layers of parent processes.
NOTE This project is under development is not feature complete, nor has it been battle tested.
Setting Up
Gator has been developed using poetry
for package management:
$> python3 -m pip install poetry
$> poetry install
YAML Specification
Tasks to perform are specified in a custom YAML syntax, arranged into two distinct object types:
!Job
that describes a single task to perform;!JobGroup
that describes a set of tasks to perform, also supporting layers of nested groups;!JobArray
similar to a!JobGroup
, but repeats the set of described tasks a specified number of times.
A simple specification may look like this:
!JobGroup
id : top
jobs:
# Nested layer
- !JobGroup
id : inner
jobs:
- !Job
id : say_hi
command: echo
args : ["hi"]
# Arrayed job - waits for 'say_hi' to complete
- !JobArray
id : counting
on_pass:
- say_hi
repeats: 4
jobs :
- !Job
id : echo_count
command: echo
args : ["$GATOR_ARRAY_INDEX"]
# Directly attached to root - waits for 'counting' to complete
- !Job
id : say_bye
on_pass:
- counting
command: echo
args : ["bye"]
Executing a Job Specification
To run a given job specification, use the Gator CLI:
$> python3 -m gator examples/job.yaml
[17:58:50] Starting Gator 🐊
[INFO ] Launching task: echo hey there you
[INFO ] Monitoring task
[INFO ] hey there you
[INFO ] Task completed with return code 0
[INFO ] Recorded 0 warnings and 0 errors
Hub
To run the hub:
$> poe hub
TODO
- Get hub working
- Pass artefacts between jobs and form artefact based dependencies
- Arbitrary metrics gathering - replace warning and error counts with a generalised mechanism that supports aggregation while summarising min, max, mean, sum, and count of metrics recorded
- Random number seeding
- Hooks
- Tool based log parsers
- Custom runners - currently everything is shell, perhaps support other things?
- Non-environment variable based parameters
Project details
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