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* Add CUDA binary builds

* Add "cuda_version": "cpu" to doc build jobs

* Add required cu_versions to pkg_helpers

* Regenerate config.yml

All macOS CI jobs were failing at `brew`.
The issue was the macOS version `10.13`, which is no longer supported by `brew`.
The macOS version is indirectly controlled by `xcode` version as listed [here](https://circleci.com/docs/2.0/testing-ios/#supported-xcode-versions).

Updating the `xcode` to `"12.0"` as in PyTorch core bump macOS version to `10.15` and it resolves the issue.
`Python 3.9` is still failing but it's packaging issue on upstream, which needs to be resolved separately.

Should be moved back to stable once
https://github.com/pytorch/pytorch/issues/56654 is closed

Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* Switch to cmake for build
* Hide symbols

* Do not install sox system-wide, so that it will never get mixed up with static one torchaudio builds
* Do not install cmake system-wide
* Move libsndfile installation to Docker image

Changing the test Docker image for GPU tests to use Nvidia Docker `devel` image. This is necessary for #1187. 

TorchScript BC test checks that if dumped object from the previous version is compatible with the latest master. However, PyTorch does not guarantee backward compatibility of serialized TorchScript object across versions. Domain libraries do not guarantee this either.

* Support file-like object in save func

* Disable CircleCI cache for TP artifacts for cleaner build

There's an issue with the CUDA 11.0 package for conda with
conda-package-handling that relates to https://github.com/conda/conda-package-handling/issues/71



This should fix issues with the conda smoke testing we encountered previously.
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

Upstream is on 9.4.1 and we were experiencing issues when conda-build
was attempting to check for overlinking and failed out due to some files
not existing in xcode 9.0
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

When running smoke test, proper way to install conda package is to
install from workspace, instead of installing from archive.
Otherwise the dependencies are not properly installed

https://github.com/conda/conda/issues/1884

For this, we pass the whole `conda-bld` directory from
conda package build job to upload job then smoke test job.

`torchaudio` does not have a mandatory third-party python package dependency,
so this was not an issue.

See also https://github.com/pytorch/text/pull/803

This CC adds CI test to check the backward compatibility of Torchscript functions/object dumps.

The job first dumps Torchscript objects into file from torchaudio 0.6.0 release environment (x Python 3.6, 3.7, 3,8), then load & run the function in master build (in Python 3.6, 3.7, 3.8).

If there is a BC-breaking change in master build, (registration schema change), then the test should fail.

At this moment, `info` function does not work due to suspected bug in torch side, so the test is disabled for `info` function. See https://github.com/pytorch/pytorch/issues/42258 for the detail. Once https://github.com/pytorch/pytorch/issues/42258 is resolved we can enable it.

* Use cmake for third party

* Apply patch to libmad

* Update gitignore

* Update docker test image

* packaging: Add test channels to pytorch dependency resolution
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* .circleci: Add test channel to smoke tests
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* .circleci: Put pytorch-test into a higher priority

pytorch-nightly was getting prioritized over pytorch-nightly which
shouldn't be the case
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

This PR refactors cache generation mechanism by introducing dedicated command
and bust cache on daily basis.

At this moment, Windows unittest job for 3.6 and 3.7 are broken because of
broken scipy but the environment is cached this persists until the next week.

As we have nightly build, we do not need to keep cache for one week.

* Add caching to binary build for more reliable build

* Apply same logic to unittest workflow