larngroup / tag-dta Goto Github PK

Hello, I have been able to reproduce results from model training and validation. I am curious if there is a way to predict binding pocket and bindining affinity for a specific protein using all of the drugs in the dataset.

Hi there! Im getting an out of memory error when running the evalation code 'python main.py --inference_option Evaluation'. I tried it on an A10 20GB and A100 80GB but getting the same error either way.

2024-01-09 15:35:39.453000: W external/local_tsl/tsl/framework/bfc_allocator.cc:497] ******************************************************************************__________________
2024-01-09 15:35:39.453020: W tensorflow/core/framework/op_kernel.cc:1827] RESOURCE_EXHAUSTED: failed to allocate memory
Traceback (most recent call last):
File "/miniconda3/envs/TAG-DTA_mantis/lib/python3.9/site-packages/TAG-DTA/TAG-DTA/source/main.py", line 694, in
run_eval_model(FLAGS)
File "/miniconda3/envs/TAG-DTA_mantis/lib/python3.9/site-packages/TAG-DTA/TAG-DTA/source/main.py", line 664, in run_eval_model
aff_preds = model([affinity_data_val[0], affinity_data_val[1]], training=False)[1]
File "miniconda3/envs/TAG-DTA_mantis/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/miniconda3/envs/TAG-DTA_mantis/lib/python3.9/site-packages/TAG-DTA/TAG-DTA/source/transformer_encoder.py", line 174, in call
x, attn_enc_w = layer(x, mask)
File "/miniconda3/envs/TAG-DTA_mantis/lib/python3.9/site-packages/TAG-DTA/TAG-DTA/source/transformer_encoder.py", line 83, in call
attn_out, attn_w = self.mha_layer([x, x, x], mask=mask)
File "/miniconda3/envs/TAG-DTA_mantis/lib/python3.9/site-packages/TAG-DTA/TAG-DTA/source/mha_layer.py", line 154, in call
attention_output, attention_weights = self.attention([query, key, value], mask=mask)
File "/miniconda3/envs/TAG-DTA_mantis/lib/python3.9/site-packages/TAG-DTA/TAG-DTA/source/mha_layer.py", line 62, in call
scaled_attention_scores += (mask * -1e9)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: Exception encountered when calling layer 'scaled_dot_product_attention' (type scaled_dot_product_attention).

{{function_node _wrapped__AddV2_device/job:localhost/replica:0/task:0/device:GPU:0}} failed to allocate memory [Op:AddV2] name:

Call arguments received by layer 'scaled_dot_product_attention' (type scaled_dot_product_attention):
• inputs=['tf.Tensor(shape=(4867, 4, 576, 64), dtype=float32)', 'tf.Tensor(shape=(4867, 4, 576, 64), dtype=float32)', 'tf.Tensor(shape=(4867, 4, 576, 64), dtype=float32)']
• mask=tf.Tensor(shape=(4867, 1, 1, 576), dtype=float32)