GitLab

* [WIP][CI] Framework and hardware-specific docker images for CI tests

* username

* fix cpu

* try out the image

* push latest

* update workspace

* no root isolation for actions

* add a flax image

* flax and onnx matrix

* fix runners

* add reports

* onnxruntime image

* retry tpu

* fix

* fix

* build onnxruntime

* naming

* onnxruntime-gpu image

* onnxruntime-gpu image, slow tests

* latest jax version

* trigger flax

* run flax tests in one thread

* fast flax tests on cpu

* fast flax tests on cpu

* trigger slow tests

* rebuild torch cuda

* force cuda provider

* fix onnxruntime tests

* trigger slow

* don't specify gpu for tpu

* optimize

* memory limit

* fix flax tests

* disable docker cache

* Fix pipelines user_agent, ignore CI requests

* fix circular import

* N/A versions

* N/A versions

Tests: upgrade PyTorch cuda to 11.7.

Otherwise the cuda versions of torch and torchvision mismatch, and
examples tests fail. We were requesting cuda 11.6 for PyTorch, and the
default torchvision (via setup.py).

Another option would be to include torchvision in the same pip install
line as torch.

* add method to enable cuda with minimal gpu usage to stable diffusion

* add test to minimal cuda memory usage

* ensure all models but unet are onn torch.float32

* move to cpu_offload along with minor internal changes to make it work

* make it test against accelerate master branch

* coming back, its official: I don't know how to make it test againt the master branch from accelerate

* make it install accelerate from master on tests

* go back to accelerate>=0.11

* undo prettier formatting on yml files

* undo prettier formatting on yml files againn

* [CI] Localize the HF cache

* pip cache

* de-env

* refactor matrix

* fix fast cache

* less onnx steps

* revert

* revert pip cache

* revert pip cache

* remove debugging trigger

* Add smoke tests for the training examples

* upd

* use a dummy dataset

* mark as slow

* cleanup

* Update test cases

* naming

* make_reports

* add test utils

* style

* style

Add datasets + transformers to test deps

* Init CI

* clarify cpu

* style

* Check scripts quality too

* Drop smi for cpu tests

* Run PR tests on cpu docker envs

* Update .github/workflows/push_tests.yml
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Try minimal python container

* Print env, install stable GPU torch

* Manual torch install

* remove deprecated platform.dist()
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>