remove check for main process for trackers initialization (#18706)
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0f257a8774
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@ -414,14 +414,12 @@ def main():
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checkpointing_steps = None
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# We need to initialize the trackers we use, and also store our configuration.
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# We initialize the trackers only on main process because `accelerator.log`
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# only logs on main process and we don't want empty logs/runs on other processes.
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# The trackers initializes automatically on the main process.
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if args.with_tracking:
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if accelerator.is_main_process:
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("image_classification_no_trainer", experiment_config)
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("image_classification_no_trainer", experiment_config)
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# Get the metric function
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metric = evaluate.load("accuracy")
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@ -516,14 +516,12 @@ def main():
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checkpointing_steps = None
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# We need to initialize the trackers we use, and also store our configuration.
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# We initialize the trackers only on main process because `accelerator.log`
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# only logs on main process and we don't want empty logs/runs on other processes.
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# The trackers initializes automatically on the main process.
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if args.with_tracking:
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if accelerator.is_main_process:
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("clm_no_trainer", experiment_config)
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("clm_no_trainer", experiment_config)
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# Train!
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total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
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@ -560,14 +560,12 @@ def main():
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checkpointing_steps = None
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# We need to initialize the trackers we use, and also store our configuration.
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# We initialize the trackers only on main process because `accelerator.log`
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# only logs on main process and we don't want empty logs/runs on other processes.
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# The trackers initializes automatically on the main process.
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if args.with_tracking:
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if accelerator.is_main_process:
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("mlm_no_trainer", experiment_config)
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("mlm_no_trainer", experiment_config)
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# Train!
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total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
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@ -513,14 +513,12 @@ def main():
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checkpointing_steps = None
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# We need to initialize the trackers we use, and also store our configuration.
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# We initialize the trackers only on main process because `accelerator.log`
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# only logs on main process and we don't want empty logs/runs on other processes.
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# The trackers initializes automatically on the main process.
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if args.with_tracking:
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if accelerator.is_main_process:
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("swag_no_trainer", experiment_config)
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("swag_no_trainer", experiment_config)
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# Metrics
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metric = evaluate.load("accuracy")
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@ -787,14 +787,12 @@ def main():
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checkpointing_steps = None
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# We need to initialize the trackers we use, and also store our configuration.
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# We initialize the trackers only on main process because `accelerator.log`
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# only logs on main process and we don't want empty logs/runs on other processes.
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# The trackers initializes automatically on the main process.
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if args.with_tracking:
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if accelerator.is_main_process:
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("qa_no_trainer", experiment_config)
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("qa_no_trainer", experiment_config)
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# Train!
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total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
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@ -512,14 +512,12 @@ def main():
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metric = evaluate.load("mean_iou")
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# We need to initialize the trackers we use, and also store our configuration.
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# We initialize the trackers only on main process because `accelerator.log`
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# only logs on main process and we don't want empty logs/runs on other processes.
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# The trackers initializes automatically on the main process.
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if args.with_tracking:
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if accelerator.is_main_process:
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("semantic_segmentation_no_trainer", experiment_config)
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("semantic_segmentation_no_trainer", experiment_config)
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# Train!
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total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
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@ -581,14 +581,12 @@ def main():
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checkpointing_steps = None
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# We need to initialize the trackers we use, and also store our configuration.
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# We initialize the trackers only on main process because `accelerator.log`
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# only logs on main process and we don't want empty logs/runs on other processes.
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# The trackers initializes automatically on the main process.
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if args.with_tracking:
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if accelerator.is_main_process:
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("summarization_no_trainer", experiment_config)
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("summarization_no_trainer", experiment_config)
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# Metric
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metric = evaluate.load("rouge")
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@ -459,14 +459,12 @@ def main():
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checkpointing_steps = None
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# We need to initialize the trackers we use, and also store our configuration.
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# We initialize the trackers only on main process because `accelerator.log`
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# only logs on main process and we don't want empty logs/runs on other processes.
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# The trackers initializes automatically on the main process.
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if args.with_tracking:
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if accelerator.is_main_process:
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("glue_no_trainer", experiment_config)
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("glue_no_trainer", experiment_config)
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# Get the metric function
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if args.task_name is not None:
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@ -574,14 +574,12 @@ def main():
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checkpointing_steps = None
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# We need to initialize the trackers we use, and also store our configuration.
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# We initialize the trackers only on main process because `accelerator.log`
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# only logs on main process and we don't want empty logs/runs on other processes.
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# The trackers initializes automatically on the main process.
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if args.with_tracking:
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if accelerator.is_main_process:
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("ner_no_trainer", experiment_config)
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experiment_config = vars(args)
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# TensorBoard cannot log Enums, need the raw value
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experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
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accelerator.init_trackers("ner_no_trainer", experiment_config)
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# Metrics
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metric = evaluate.load("seqeval")
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