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remove check for main process for trackers initialization (#18706)

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This commit is contained in:
Atharva Ingle
2022-08-22 20:46:27 +05:30
committed by GitHub
parent 0f257a8774
commit d90a36d192
9 changed files with 45 additions and 63 deletions
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4
examples/pytorch/image-classification/run_image_classification_no_trainer.py
View File

@@ -414,10 +414,8 @@ def main():
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration.
# We initialize the trackers only on main process because `accelerator.log`
# only logs on main process and we don't want empty logs/runs on other processes.
# The trackers initializes automatically on the main process.
if args.with_tracking:
if accelerator.is_main_process:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value

4
examples/pytorch/language-modeling/run_clm_no_trainer.py
View File

@@ -516,10 +516,8 @@ def main():
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration.
# We initialize the trackers only on main process because `accelerator.log`
# only logs on main process and we don't want empty logs/runs on other processes.
# The trackers initializes automatically on the main process.
if args.with_tracking:
if accelerator.is_main_process:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value

4
examples/pytorch/language-modeling/run_mlm_no_trainer.py
View File

@@ -560,10 +560,8 @@ def main():
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration.
# We initialize the trackers only on main process because `accelerator.log`
# only logs on main process and we don't want empty logs/runs on other processes.
# The trackers initializes automatically on the main process.
if args.with_tracking:
if accelerator.is_main_process:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value

4
examples/pytorch/multiple-choice/run_swag_no_trainer.py
View File

@@ -513,10 +513,8 @@ def main():
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration.
# We initialize the trackers only on main process because `accelerator.log`
# only logs on main process and we don't want empty logs/runs on other processes.
# The trackers initializes automatically on the main process.
if args.with_tracking:
if accelerator.is_main_process:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value

4
examples/pytorch/question-answering/run_qa_no_trainer.py
View File

@@ -787,10 +787,8 @@ def main():
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration.
# We initialize the trackers only on main process because `accelerator.log`
# only logs on main process and we don't want empty logs/runs on other processes.
# The trackers initializes automatically on the main process.
if args.with_tracking:
if accelerator.is_main_process:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value

4
examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
View File

@@ -512,10 +512,8 @@ def main():
metric = evaluate.load("mean_iou")
# We need to initialize the trackers we use, and also store our configuration.
# We initialize the trackers only on main process because `accelerator.log`
# only logs on main process and we don't want empty logs/runs on other processes.
# The trackers initializes automatically on the main process.
if args.with_tracking:
if accelerator.is_main_process:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value

4
examples/pytorch/summarization/run_summarization_no_trainer.py
View File

@@ -581,10 +581,8 @@ def main():
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration.
# We initialize the trackers only on main process because `accelerator.log`
# only logs on main process and we don't want empty logs/runs on other processes.
# The trackers initializes automatically on the main process.
if args.with_tracking:
if accelerator.is_main_process:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value

4
examples/pytorch/text-classification/run_glue_no_trainer.py
View File

@@ -459,10 +459,8 @@ def main():
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration.
# We initialize the trackers only on main process because `accelerator.log`
# only logs on main process and we don't want empty logs/runs on other processes.
# The trackers initializes automatically on the main process.
if args.with_tracking:
if accelerator.is_main_process:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value

4
examples/pytorch/token-classification/run_ner_no_trainer.py
View File

@@ -574,10 +574,8 @@ def main():
checkpointing_steps = None
# We need to initialize the trackers we use, and also store our configuration.
# We initialize the trackers only on main process because `accelerator.log`
# only logs on main process and we don't want empty logs/runs on other processes.
# The trackers initializes automatically on the main process.
if args.with_tracking:
if accelerator.is_main_process:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value