Major Description
Some program requires container hostnames to be known for application to run. For example, distributed tensorflow requires launch_command that looks like:
# On ps0.example.com:
$ python trainer.py \
--ps_hosts=ps0.example.com:2222,ps1.example.com:2222 \
--worker_hosts=worker0.example.com:2222,worker1.example.com:2222 \
--job_name=ps --task_index=0
# On ps1.example.com:
$ python trainer.py \
--ps_hosts=ps0.example.com:2222,ps1.example.com:2222 \
--worker_hosts=worker0.example.com:2222,worker1.example.com:2222 \
--job_name=ps --task_index=1
# On worker0.example.com:
$ python trainer.py \
--ps_hosts=ps0.example.com:2222,ps1.example.com:2222 \
--worker_hosts=worker0.example.com:2222,worker1.example.com:2222 \
--job_name=worker --task_index=0
# On worker1.example.com:
$ python trainer.py \
--ps_hosts=ps0.example.com:2222,ps1.example.com:2222 \
--worker_hosts=worker0.example.com:2222,worker1.example.com:2222 \
--job_name=worker --task_index=1
This is a bit cumbersome to orchestrate via Distributed Shell, or YARN services launch_command. In addition, the dynamic parameters do not work with YARN flex command. This is the classic pain point for application developer attempt to automate system environment settings as parameter to end user application.
It would be great if YARN Docker integration can provide a simple option to expose hostnames of the yarn service via a mounted file. The file content gets updated when flex command is performed. This allows application developer to consume system environment settings via a standard interface. It is like /proc/devices for Linux, but for Hadoop. This may involve updating a file in distributed cache, and allow mounting of the file via container-executor.