Comments (2)
I added the following code just before "tf.train.MonitoredTrainingSession" in line 319 in runners.py.
gpu_config = tf.ConfigProto(gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction = 0.5), allow_soft_placement=True)
gpu_config.gpu_options.allow_growth=True
gpu_config.gpu_options.polling_inactive_delay_msecs = 10
Then simply add it as a config parameter as shown below.
with tf.train.MonitoredTrainingSession(master=config.master,
is_chief=config.task == 0,
config=gpu_config, #NEW PARAMETER ADDED
hooks=[log_hook],
checkpoint_dir=config.logdir,
save_checkpoint_secs=120,
save_summaries_steps=config.summarize_every,
log_step_count_steps=config.summarize_every) as sess:
This will make sure you use a limited amount of your memory while training. Run your training after this while keeping an eye on your task manager (GPU usage) to see weather the process still peaks or not.
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Let me know if it works for you after these fixes. Because I am experiencing similar issues and I am using RTX3090 with Cuda 11.7.
Training still crashes even after fixing my "Out of memory" issues. :/
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