Spark/Hadoop workloads which use Hadoop MR Committer v1/v2 algorithm upload objects to a temporary prefix in a bucket. These objects are 'renamed' to a different prefix on Job commit. Object storage admins are forced to configure separate ILM policies to expire these objects and their versions to reclaim space.

### Solution

versions becoming available through `rules-python`. \
See
[here](https://github.com/tensorflow/tensorflow/commit/f91457f258fdd78f693044a57efa63a38335d1de),
and
[here](https://github.com/tensorflow/tensorflow/commit/052445e04ce20fd747657e0198a1bcec2b6dff5b),
for an example.

See
[this commit](https://github.com/tensorflow/tensorflow/commit/5f7f05a80aac9b01325a78ec3fcff0dbedb1cc23)
as a rough example of the steps below.

  - IMPORTANT NOTE: if the first import fails, you have to select and apply `Synchronization disabled`, then repeat the step above.
    Otherwise, TeamCity complains "Can't find the previous revision, please commit current settings first".
  - If anything bad happens, feel free to delete the project you created and retry (you may need to apply `Synchronization disabled` before deleting it).  
    

from the `envs` directory that is filled with configuration options:

-   Nightly jobs (Run nightly on the `nightly` branch)
    -   Uses `wheel.sh`, `libtensorflow.sh`, `code_check_full.sh`
-   Continuous jobs (Run on every GitHub commit)
    -   Uses `pycpp.sh`
-   Presubmit jobs (Run on every GitHub PR)
    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers: