[testing] rename skip targets + docs (#7863)

* rename skip targets + docs

* fix quotes

* style

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* small improvements

* fix

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Stas Bekman
2020-10-20 01:39:13 -07:00
committed by GitHub
parent c912ba5f69
commit 3e31e7f956
9 changed files with 52 additions and 35 deletions

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@@ -400,29 +400,46 @@ or if you have multiple gpus, you can specify which one is to be used by ``pytes
CUDA_VISIBLE_DEVICES="1" pytest tests/test_logging.py
This is handy when you want to run different tasks on different GPUs.
And we have these decorators that require the condition described by the marker.
``
@require_torch
@require_tf
@require_multigpu
@require_non_multigpu
@require_torch_tpu
@require_torch_and_cuda
``
Some tests must be run on CPU-only, others on either CPU or GPU or TPU, yet others on multiple-GPUs. The following skip decorators are used to set the requirements of tests CPU/GPU/TPU-wise:
* ``require_torch`` - this test will run only under torch
* ``require_torch_gpu`` - as ``require_torch`` plus requires at least 1 GPU
* ``require_torch_multigpu`` - as ``require_torch`` plus requires at least 2 GPUs
* ``require_torch_non_multigpu`` - as ``require_torch`` plus requires 0 or 1 GPUs
* ``require_torch_tpu`` - as ``require_torch`` plus requires at least 1 TPU
For example, here is a test that must be run only when there are 2 or more GPUs available and pytorch is installed:
.. code-block:: python
@require_torch_multigpu
def test_example_with_multigpu():
If a test requires ``tensorflow`` use the ``require_tf`` decorator. For example:
.. code-block:: python
@require_tf
def test_tf_thing_with_tensorflow():
These decorators can be stacked. For example, if a test is slow and requires at least one GPU under pytorch, here is how to set it up:
.. code-block:: python
@require_torch_gpu
@slow
def test_example_slow_on_gpu():
Some decorators like ``@parametrized`` rewrite test names, therefore ``@require_*`` skip decorators have to be listed last for them to work correctly. Here is an example of the correct usage:
.. code-block:: python
@parameterized.expand(...)
@require_multigpu
@require_torch_multigpu
def test_integration_foo():
There is no problem whatsoever with ``@pytest.mark.parametrize`` (but it only works with non-unittests) - can use it in any order.
This section will be expanded soon once our work in progress on those decorators is finished.
This order problem doesn't exist with ``@pytest.mark.parametrize``, you can put it first or last and it will still work. But it only works with non-unittests.
Inside tests: