Disable Bazel remote cache for forked PR by lsy323 · Pull Request #5259 · pytorch/xla

lsy323 · 2023-06-27T18:15:24Z

Background

Since forked PRs doesn't have access to Github Repository secrets, the gcloud credentials passed by secrets cannot be picked up by forked PRs, causing the CI to fail due to empty gcloud credential files.

Thanks to @stgpetrovic pointing out, whenever the self-hosted workers are used in CI, the workers will have access to the remote cache thus the credential won't be needed at that time.

cc @mateuszlewko who is working on using self-hosted workers in TPU CI.

Temporary Solution

Since gcloud credential is only required when Bazel remote cache is enabled, the size of the credential file is checked in setup.py, if the credential is empty (Actually the credential file is 1 byte), we assume it's triggered from a forked PR and skip the remote cache.

Next Step

After the self-hosted workers are setup for CPU/GPU CI, we can remove the gcloud credential in setup.py.

lsy323 · 2023-06-28T01:08:14Z

cc @JackCaoG @will-cromar

JackCaoG · 2023-06-28T01:09:10Z

Thanks, let me know when this pr is ready for review.

lsy323 · 2023-06-28T01:13:03Z

Hi @stgpetrovic, before self-hosted workers are set up, I think we should disable remote cache for PRs from forks. The size of the credential file from forks is 1 byte, based on this fact, I made the changes in the related files. Would you please take a pass? Feel free to take it over if you know a more elegant way to do it. Thanks

lsy323 · 2023-06-28T01:14:01Z

I tried to update setup.py, the build passed, but test failed since remote cache config is also in test.sh and test coverage files. Add the check there as well.

The CI is failing due to outage of gitlab, will trigger again later.

will-cromar · 2023-06-28T16:21:44Z

+    print("check GCLOUD_KEY_FILE: {}".format(GCLOUD_KEY_FILE))
+    print("check if file exists: {}".format(os.path.exists(GCLOUD_KEY_FILE)))
+    print("check size: {}".format(os.path.getsize(GCLOUD_KEY_FILE)))
+    if gclout_key_file_size > 1:


Won't this prevent us from using the remove cache if we don't have a GCLOUD_KEY_FILE set? I've been running the remote cache with my GCE machine's default credentials, not a key file.

Yes, there thw adc is implicit. The criterion could be iff the key file.env var is set to an existing file that has size 1 or less, turn it off.

lsy323 · 2023-06-28T20:11:21Z

Updated the logic in setup.py, so the workflow where credential file isn't explicitly set in GCLOUD_KEY_FILE can still use remote cache.

will-cromar

Thanks!

* disable bazel remote cache if gcloud key is empty * remove remote cache from setup.py * experiment with debug msg * fix flag * add more logs * skip remote chache if credential file is empty * add comment * add logs * add check in test and coverage script * fix condition in coverage test * advance branch pr * allow remote cache if gloud file isn't specified explicitly * remove dummy comment

* Skip calling as_strided in empty_strided_symint if the input has dynamic dimensions. (#5239) * Skip calling as_strided in empty_strided_symint. * only return empty_symint conditionally. * add a comment * Add XRT nightly builds (#5261) * Add XRT nightly builds * remove space * Add ToString method for both PjrtData and PjrtShardedData (#5265) * Add ToString method for both PjrtData and PjrtShardedData * on cpu same config will become replicated, dont't check actual op sharding type * fix xrt tostring * Update Sharded graph HLO dumping (#5266) * Disable Bazel remote cache for forked PR (#5259) * disable bazel remote cache if gcloud key is empty * remove remote cache from setup.py * experiment with debug msg * fix flag * add more logs * skip remote chache if credential file is empty * add comment * add logs * add check in test and coverage script * fix condition in coverage test * advance branch pr * allow remote cache if gloud file isn't specified explicitly * remove dummy comment * Suppress debug symbols in OpenXLA code (#5269) * [SPMD] Sharding n-d tensor on (n+1)-d Mesh (#5268) * Make TPU detection more robust (#5271) * Clean bazel stuff on distutils clean. (#5274) * Clean bazel stuff on distutils clean * Fix python formatting * fix conflict * Fix the error when export_torch_model is given a non-tensor (#5277) However the generated StableHLO graph still hardcodes the non-tensor value. this is not correct, will fix later. * Dsiable test_simple_model_with_different_input_shape since it is curretnly broken by pytorch (#5282) * Always do build_ext in python setup.py develop (#5273) Bazel should figure out that _XLAC.so is current or not, and trigger rebuild if any cpp files changed. * Remove or improve several hardcoded TPU test conditions (#5272) * Remove or improve several hardcoded TPU test conditions * Fix test condition * Add `runtime.host_index` (#5283) * Make it an error if calling sizes() on a dynamic tensor. (#4998) * Err if calling sizes() on dynamic tensor * try to set has_symbolic_sizes_strides_ * resolve merge conflict * enable CONTINUE_ON_ERROR * fixed the python test test_SizeEq_should_not_compile_for_identical_symints * fix test_index_types * set CONTINUE_ON_ERROR to true * remove some unwanted code. * add a print * directly set has_symbolic_sizes_strides_ = true * make some fixes. * fix empty_strided_symint * ran linter * change error type in the test. * fix comments * ran linter * Fix the error where mark_step does not materalize tensors on SPMD:0 (#5281) * Fix the error where mark_step does not materalize tensors on SPMD:0 * typo * fix test_non_tensor_scalar * Disable torch._dynamo.config.automatic_dynamic_shapes (#5285) * Set torch._dynamo.config.automatic_dynamic_shapes to False * Enable DynamoInferenceBasicTest.test_simple_model_with_different_input_shape * [Traceable Collecive] Hide token for all_gather (#5232) Summary: This pull request does the following: 1. It hides token for all_gather. 2. It folds the out-of-place all_gather into the regular all_gather. 3. It fixes an issue with the last all_reduce_in_place PR where it forgot to set the token. Test Plan: PJRT_DEVICE=TPU python test/test_mp_all_gather.py * Lower squeeze.dims (#5286) * avoid copy proto in PrepareOutputShardingPropagation (#5287) * Revert "Suppress debug symbols in OpenXLA code (#5269)" This reverts commit 3967d7b. * Revert "fix conflict" This reverts commit e91ad3a. --------- Co-authored-by: iefgnoix <isaacwxf23@gmail.com> Co-authored-by: Will Cromar <wcromar@google.com> Co-authored-by: Siyuan Liu <lsiyuan@google.com> Co-authored-by: stgpetrovic <stgpetrovic@gmail.com> Co-authored-by: Mohit Khatwani <118776932+khatwanimohit@users.noreply.github.com> Co-authored-by: qihqi <hanq@google.com> Co-authored-by: Wonjoo Lee <wonjoo@google.com> Co-authored-by: Jiewen Tan <jwtan@google.com> Co-authored-by: Baole Ai <baoleai01@gmail.com>

* initiak commit * Add test workflow for `xrt` branch (#5241) * Add test workflow for `xrt` branch * Only run for PRs targeting XRT branch * Add function to generate stablehlo based callable from pytorch model (#5216) * Add function to generate stablehlo based callable from pytorch model Added function `torch_xla.experimental.stablehlo_saved_model.export_pytorch_model`. This function will take a pytorch Module and convert it into stablehlo bytecode. * Only run the main CI workflow on PRs targeting master and release branches (#5244) * Only run main CI for master and release branches. * Disabling XRT tests on main CI * AMP for TPUs v3 (#5161) * remove duplicate autocast_test (#5246) * Remove `test_experimental_pjrt_tpu.py` from TPU CI (#5247) * Install `expecttest` in xla_test_job.yaml (#5252) * Add IAM roles for cloudbuild_editors (#5251) * [Functionalization] Remove view in view_symint (#5231) * [Functionalization] Remove view in view_symint Summary: This pull request removes views in tensor_method::view_symint. Test Plan: XLA_DISABLE_FUNCTIONALIZATION=1 PJRT_DEVICE=TPU python ../test/test_view_ops.py -v -k TestViewOpsXLA.test_view_view PJRT_DEVICE=TPU python ../test/test_view_ops.py -v -k TestViewOpsXLA.test_view_view * Fix linters * fixed the test * ran the linter --------- Co-authored-by: Xiongfei Wei <isaacwxf23@gmail.com> * Delete XRT from the main branch (#5240) * Delete XRT from the main branch * Remove dead import * formatting * Remove disable_xrt build option * Fix runtime init * Revert "Remove disable_xrt build option" This reverts commit ba312e7. * Add disable XRT option back * formatting * Prune mesh service * Remove obsolete test * Remove other run server script * Remove XRT config * Update PJRT default device test * Add a file I forgot to save * if using_pjrt -> @requires_pjrt * Remove irrelevant test case * Remove XRT env vars * fix md link * formatting * Remove extra `requires_pjrt` * merge conflicts * Add other autocast back * Add nightly build for cuda 12 (#5253) * Fix the linter command in the CI (#5254) * fix linter command * ran linter * Jack cao g/fix spmd buff is null (#5256) * Fix that non-tensor scalar can't be handled by virtual device * add test * comment * Skip calling as_strided in empty_strided_symint if the input has dynamic dimensions. (#5239) * Skip calling as_strided in empty_strided_symint. * only return empty_symint conditionally. * add a comment * Add XRT nightly builds (#5261) * Add XRT nightly builds * remove space * [OpenXLA] Migrate to pull XLA from OpenXLA (#5202) PyTorch/XLA migrate to pull XLA from OpenXLA by replacing TensorFlow with OpenXLA after deprecating XRT usage, and replace TensorFlow-pin with OpenXLA-pin to May09 * Add ToString method for both PjrtData and PjrtShardedData (#5265) * Add ToString method for both PjrtData and PjrtShardedData * on cpu same config will become replicated, dont't check actual op sharding type * Update Sharded graph HLO dumping (#5266) * Enable PjRt Client Compilation with StableHLO (#5233) * Enable xla PjRt client compilation with StableHLO * add XLA_STABLEHLO_COMPILE to configuration.yaml * fix merge conflict * dummy commit to trigger ci * Revert "dummy commit to trigger ci" This reverts commit f7aec23. * Disable Bazel remote cache for forked PR (#5259) * disable bazel remote cache if gcloud key is empty * remove remote cache from setup.py * experiment with debug msg * fix flag * add more logs * skip remote chache if credential file is empty * add comment * add logs * add check in test and coverage script * fix condition in coverage test * advance branch pr * allow remote cache if gloud file isn't specified explicitly * remove dummy comment * Suppress debug symbols in OpenXLA code (#5269) * [SPMD] Sharding n-d tensor on (n+1)-d Mesh (#5268) * Make TPU detection more robust (#5271) * Clean bazel stuff on distutils clean. (#5274) * Clean bazel stuff on distutils clean * Fix python formatting * Delete unused .so file, and .lds files (#5275) * [OpenXLA] Delete unused .so file and .lds files * Fix the error when export_torch_model is given a non-tensor (#5277) However the generated StableHLO graph still hardcodes the non-tensor value. this is not correct, will fix later. * Dsiable test_simple_model_with_different_input_shape since it is curretnly broken by pytorch (#5282) * Always do build_ext in python setup.py develop (#5273) Bazel should figure out that _XLAC.so is current or not, and trigger rebuild if any cpp files changed. * Remove or improve several hardcoded TPU test conditions (#5272) * Remove or improve several hardcoded TPU test conditions * Fix test condition * Add `runtime.host_index` (#5283) * Make it an error if calling sizes() on a dynamic tensor. (#4998) * Err if calling sizes() on dynamic tensor * try to set has_symbolic_sizes_strides_ * resolve merge conflict * enable CONTINUE_ON_ERROR * fixed the python test test_SizeEq_should_not_compile_for_identical_symints * fix test_index_types * set CONTINUE_ON_ERROR to true * remove some unwanted code. * add a print * directly set has_symbolic_sizes_strides_ = true * make some fixes. * fix empty_strided_symint * ran linter * change error type in the test. * fix comments * ran linter * Fix the error where mark_step does not materalize tensors on SPMD:0 (#5281) * Fix the error where mark_step does not materalize tensors on SPMD:0 * typo * fix test_non_tensor_scalar * Disable torch._dynamo.config.automatic_dynamic_shapes (#5285) * Set torch._dynamo.config.automatic_dynamic_shapes to False * Enable DynamoInferenceBasicTest.test_simple_model_with_different_input_shape * run linter * wrap only if sharding type is non-replicated * Handle non-tensors * run linter * Call wrap_if_sharded first * Add exception in test for unsharded tensor * fix test * Use torch.Tensor instead of torch.tensor * use .cpu() only for tensors --------- Co-authored-by: Will Cromar <wcromar@google.com> Co-authored-by: qihqi <hanq@google.com> Co-authored-by: Meghan Cowan <cowanmeg@google.com> Co-authored-by: Mateusz Lewko <mateusz.lewko@gmail.com> Co-authored-by: Jiewen Tan <jwtan@google.com> Co-authored-by: Xiongfei Wei <isaacwxf23@gmail.com> Co-authored-by: Wonjoo Lee <wonjoo@google.com> Co-authored-by: JackCaoG <59073027+JackCaoG@users.noreply.github.com> Co-authored-by: Manfei <41607353+ManfeiBai@users.noreply.github.com> Co-authored-by: Siyuan Liu <lsiyuan@google.com> Co-authored-by: stgpetrovic <stgpetrovic@gmail.com> Co-authored-by: Mohit Khatwani <118776932+khatwanimohit@users.noreply.github.com>

* Update inline style code to multiline (#5291) * Fix typo in _test.yml (#5172) s/metadtaa/metadata/ * [SPMD][Virtual Device]All tensors should be in SPMD:0 C++ device (#5284) * Move all tensors to SPMD:0 C++ device under spmd context * fix load shards * fix test_mark_sharding_2d by not creating placeholder for virtual device * fix the waitdeviceop for spmd case * Fix test_shard_hashing * fix spmd device casting issue * remove hacks in test_xla_virtual_device.py * add test for new virtual device usage * fix review comments * fix IsTpuDevice * linter * Revert pr #2682 (#5215) * Make README more actionable (#5262) * Make README more actionable * move profiling guide link * text wrapping * [SPMD] Use xs.Mesh in test_2d_tensor_3d_mesh (#5295) * use mesh in test_2d_tensor_3d_mesh * remove attributes patch * [SPMD] Add FSDP sharding for test_train_spmd_linear_model.py (#5299) Summary: This diff adds FSDP sharding for test_train_spmd_linear_model.py. Test Plan: PJRT_DEVICE=TPU XLA_USE_SPMD=1 python test/spmd/test_train_spmd_linear_model.py --sharding fsdp * [SPMD] Avoid recompilations in xs.mark_sharding() (#5300) Summary: This pull requests fixes the recompilation issue in xs.mark_sharding(). xtensor->GetXlaData() will compile the program if xtensor is an IR in order to get the BackendData. I believe this is not intended given the error message below suggests only data type xtensors are supported. Test Plan: PJRT_DEVICE=TPU XLA_USE_SPMD=1 python test/spmd/test_xla_sharding.py * [SPMD] Support mark_sharding on IRs (#5301) Summary: This pull requests fixes the recompilation issue in xs.mark_sharding(). xtensor->GetXlaData() will compile the program if xtensor is an IR in order to get the BackendData. I believe this is not intended given the error message below suggests only data type xtensors are supported. Test Plan: PJRT_DEVICE=TPU XLA_USE_SPMD=1 python test/spmd/test_xla_sharding.py * [SPMD] Allow dumping post optimizations hlo (#5302) Summary: This pull request partial reverts the change in #5266 to re-enble dumping post optimizations hlo. Test Plan: XLA_USE_SPMD=1 PJRT_DEVICE=TPU python test/spmd/test_xla_sharding.py -v -k test_xla_sharded_hlo_dump_post_optimizations * Add `_sharded_cpu_state_dict` for distributed checkpointing (#5288) * initiak commit * Add test workflow for `xrt` branch (#5241) * Add test workflow for `xrt` branch * Only run for PRs targeting XRT branch * Add function to generate stablehlo based callable from pytorch model (#5216) * Add function to generate stablehlo based callable from pytorch model Added function `torch_xla.experimental.stablehlo_saved_model.export_pytorch_model`. This function will take a pytorch Module and convert it into stablehlo bytecode. * Only run the main CI workflow on PRs targeting master and release branches (#5244) * Only run main CI for master and release branches. * Disabling XRT tests on main CI * AMP for TPUs v3 (#5161) * remove duplicate autocast_test (#5246) * Remove `test_experimental_pjrt_tpu.py` from TPU CI (#5247) * Install `expecttest` in xla_test_job.yaml (#5252) * Add IAM roles for cloudbuild_editors (#5251) * [Functionalization] Remove view in view_symint (#5231) * [Functionalization] Remove view in view_symint Summary: This pull request removes views in tensor_method::view_symint. Test Plan: XLA_DISABLE_FUNCTIONALIZATION=1 PJRT_DEVICE=TPU python ../test/test_view_ops.py -v -k TestViewOpsXLA.test_view_view PJRT_DEVICE=TPU python ../test/test_view_ops.py -v -k TestViewOpsXLA.test_view_view * Fix linters * fixed the test * ran the linter --------- Co-authored-by: Xiongfei Wei <isaacwxf23@gmail.com> * Delete XRT from the main branch (#5240) * Delete XRT from the main branch * Remove dead import * formatting * Remove disable_xrt build option * Fix runtime init * Revert "Remove disable_xrt build option" This reverts commit ba312e7. * Add disable XRT option back * formatting * Prune mesh service * Remove obsolete test * Remove other run server script * Remove XRT config * Update PJRT default device test * Add a file I forgot to save * if using_pjrt -> @requires_pjrt * Remove irrelevant test case * Remove XRT env vars * fix md link * formatting * Remove extra `requires_pjrt` * merge conflicts * Add other autocast back * Add nightly build for cuda 12 (#5253) * Fix the linter command in the CI (#5254) * fix linter command * ran linter * Jack cao g/fix spmd buff is null (#5256) * Fix that non-tensor scalar can't be handled by virtual device * add test * comment * Skip calling as_strided in empty_strided_symint if the input has dynamic dimensions. (#5239) * Skip calling as_strided in empty_strided_symint. * only return empty_symint conditionally. * add a comment * Add XRT nightly builds (#5261) * Add XRT nightly builds * remove space * [OpenXLA] Migrate to pull XLA from OpenXLA (#5202) PyTorch/XLA migrate to pull XLA from OpenXLA by replacing TensorFlow with OpenXLA after deprecating XRT usage, and replace TensorFlow-pin with OpenXLA-pin to May09 * Add ToString method for both PjrtData and PjrtShardedData (#5265) * Add ToString method for both PjrtData and PjrtShardedData * on cpu same config will become replicated, dont't check actual op sharding type * Update Sharded graph HLO dumping (#5266) * Enable PjRt Client Compilation with StableHLO (#5233) * Enable xla PjRt client compilation with StableHLO * add XLA_STABLEHLO_COMPILE to configuration.yaml * fix merge conflict * dummy commit to trigger ci * Revert "dummy commit to trigger ci" This reverts commit f7aec23. * Disable Bazel remote cache for forked PR (#5259) * disable bazel remote cache if gcloud key is empty * remove remote cache from setup.py * experiment with debug msg * fix flag * add more logs * skip remote chache if credential file is empty * add comment * add logs * add check in test and coverage script * fix condition in coverage test * advance branch pr * allow remote cache if gloud file isn't specified explicitly * remove dummy comment * Suppress debug symbols in OpenXLA code (#5269) * [SPMD] Sharding n-d tensor on (n+1)-d Mesh (#5268) * Make TPU detection more robust (#5271) * Clean bazel stuff on distutils clean. (#5274) * Clean bazel stuff on distutils clean * Fix python formatting * Delete unused .so file, and .lds files (#5275) * [OpenXLA] Delete unused .so file and .lds files * Fix the error when export_torch_model is given a non-tensor (#5277) However the generated StableHLO graph still hardcodes the non-tensor value. this is not correct, will fix later. * Dsiable test_simple_model_with_different_input_shape since it is curretnly broken by pytorch (#5282) * Always do build_ext in python setup.py develop (#5273) Bazel should figure out that _XLAC.so is current or not, and trigger rebuild if any cpp files changed. * Remove or improve several hardcoded TPU test conditions (#5272) * Remove or improve several hardcoded TPU test conditions * Fix test condition * Add `runtime.host_index` (#5283) * Make it an error if calling sizes() on a dynamic tensor. (#4998) * Err if calling sizes() on dynamic tensor * try to set has_symbolic_sizes_strides_ * resolve merge conflict * enable CONTINUE_ON_ERROR * fixed the python test test_SizeEq_should_not_compile_for_identical_symints * fix test_index_types * set CONTINUE_ON_ERROR to true * remove some unwanted code. * add a print * directly set has_symbolic_sizes_strides_ = true * make some fixes. * fix empty_strided_symint * ran linter * change error type in the test. * fix comments * ran linter * Fix the error where mark_step does not materalize tensors on SPMD:0 (#5281) * Fix the error where mark_step does not materalize tensors on SPMD:0 * typo * fix test_non_tensor_scalar * Disable torch._dynamo.config.automatic_dynamic_shapes (#5285) * Set torch._dynamo.config.automatic_dynamic_shapes to False * Enable DynamoInferenceBasicTest.test_simple_model_with_different_input_shape * run linter * wrap only if sharding type is non-replicated * Handle non-tensors * run linter * Call wrap_if_sharded first * Add exception in test for unsharded tensor * fix test * Use torch.Tensor instead of torch.tensor * use .cpu() only for tensors --------- Co-authored-by: Will Cromar <wcromar@google.com> Co-authored-by: qihqi <hanq@google.com> Co-authored-by: Meghan Cowan <cowanmeg@google.com> Co-authored-by: Mateusz Lewko <mateusz.lewko@gmail.com> Co-authored-by: Jiewen Tan <jwtan@google.com> Co-authored-by: Xiongfei Wei <isaacwxf23@gmail.com> Co-authored-by: Wonjoo Lee <wonjoo@google.com> Co-authored-by: JackCaoG <59073027+JackCaoG@users.noreply.github.com> Co-authored-by: Manfei <41607353+ManfeiBai@users.noreply.github.com> Co-authored-by: Siyuan Liu <lsiyuan@google.com> Co-authored-by: stgpetrovic <stgpetrovic@gmail.com> Co-authored-by: Mohit Khatwani <118776932+khatwanimohit@users.noreply.github.com> * Supoort unordered sharding spec correctly (#5305) * Supoort non-ordered sharding spec correctly * use permute instead of transpose * use dim > 2 to suit TPU v3(otherwise can't be divide evenly) * Support unordered sharding spec for partial replication (#5316) * Suport unordered sharding spec for partial replication * add 4d test * handle 2d tensor with 2d mesh case * refactoring * Fix mismatched GPU docker image in the doc. (#5319) * quick refactor on _get_group_assignment (#5318) * Add tf independent serialization (#5308) Create a serialization format for StableHLO graphs and weights without tf.saved_model Need to not use tensorflow because tensorflow is no longer dependency of pytorch/xla. Information saved are enough to reconstruct the tf.saved_model for serving. Information stored: * metadata on which tensor maps which input position * StableHLO version number * metadata on which tensor corresponds to user input or parameter * metadata on shape and dtype of each tensor. * Tensors themselves are saved as numpy arrays using np.save. * Disable coverage for now (#5321) * Enable Some input output aliasing under SPMD (#5320) * Use `_sharded_cpu_state_dict` functionality to Write Items for SPMD Save Planner (#5315) * initial commit * add suggested changes * add unit test * fix test * fix test * add suggested changes * remove is_sharded_tensor check * check if device type is xla in `wrap_if_sharded` * change order * update resolve_data and add more tests * run linter * use subtest * formatting fixes * run linter * handle single tensor for method send_to_device_single (#5317) * handle single tensor for method send_to_device_single * fix broadcast parameter --------- Co-authored-by: Wonjoo Lee <wonjoo@google.com> Co-authored-by: Nikita Shulga <nshulga@meta.com> Co-authored-by: iefgnoix <isaacwxf23@gmail.com> Co-authored-by: Will Cromar <wcromar@google.com> Co-authored-by: Mohit Khatwani <118776932+khatwanimohit@users.noreply.github.com> Co-authored-by: Jiewen Tan <jwtan@google.com> Co-authored-by: Yash Shah <55116947+yashs97@users.noreply.github.com> Co-authored-by: qihqi <hanq@google.com> Co-authored-by: Meghan Cowan <cowanmeg@google.com> Co-authored-by: Mateusz Lewko <mateusz.lewko@gmail.com> Co-authored-by: Manfei <41607353+ManfeiBai@users.noreply.github.com> Co-authored-by: Siyuan Liu <lsiyuan@google.com> Co-authored-by: stgpetrovic <stgpetrovic@gmail.com>

disable bazel remote cache if gcloud key is empty

0456f70

lsy323 force-pushed the disable-remote-cache-for-forks branch from dea3ebc to 0456f70 Compare June 27, 2023 18:24

lsy323 added 8 commits June 27, 2023 18:53

remove remote cache from setup.py

1f70931

experiment with debug msg

b84bb3a

fix flag

1b90566

add more logs

0ddf443

skip remote chache if credential file is empty

bf3b83e

add comment

d04b818

add logs

fa1fc38

add check in test and coverage script

478dd80

lsy323 requested a review from stgpetrovic June 28, 2023 01:14

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lsy323 added 2 commits June 28, 2023 15:35

fix condition in coverage test

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advance branch pr

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allow remote cache if gloud file isn't specified explicitly

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remove dummy comment

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lsy323 requested review from JackCaoG and will-cromar June 28, 2023 20:11

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[TEST] Make sure bazel remote cache is available after #5259 #5263

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lsy323 marked this pull request as ready for review June 28, 2023 23:09

lsy323 merged commit 81c5cc0 into pytorch:master Jun 28, 2023

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PR from forked branch fails CI #5255

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lsy323 deleted the disable-remote-cache-for-forks branch March 20, 2024 02:56

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Disable Bazel remote cache for forked PR#5259

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