ReLERNN train TF2 model.fit memory leak and errors

[kr-colab]

ReLERNN train TF2 model.fit memory leak and errors

[andrewkern]

hey there-- if you are getting the library error I would guess something is wrong in your nvidia setup. How did you install tf / the cuda tools?

Also I'm not sure if ReLERNN is ready for tf2.1, but I do know @jradrion has it working on tf2....

[LZeitler]

Hi Andrew,
I installed TF 2.1 with pip. According to the readme ReLERNN is tested on TF2.1 so I thought I give it a go and tried matching dependencies as good as possible.
Do you think the library error is related to memory usage?

[andrewkern]

so this memory leak seems to be on the TF side, but the error you report has to do with nvidia tools-- how was cuda installed on your system?

[LZeitler]

not sure, it loads with python when run on a GPU node

[andrewkern]

okay so one question for your admins is what happened to nvinfer. that seems to be either installed somewhere that TF can't find it or not installed

[LZeitler]

I will ask them whats going on.
So generally would you say rather use Relernn with TF 1.x for now?

[jradrion]

@LZeitler I had not noticed a memory leak issue in my testing of ReLERNN with tf2.1. However, I testing by running only ~10 epochs for speed, and our machine has a fairly large amount of memory, so it's possible that I'll find this issue when training for more epochs. I will be testing this ASAP.

As far as the warning your first describe, I also get that warning at every epoch. I had seen a comment about it being a spurious in this thread, and temporarily ignored it since everything else appeared to be working. However, users are now saying it is connected to the memory leak issue.

The warning about Could not load dynamic library 'libnvinfer.so.6' appears to be connected to not having TensorRT installed. TensorRT should not be necessary, but the warning did go away once we installed it.

I'll do some more testing and report back.

[jradrion]

@LZeitler I have not forgotten about you. I'm still debugging a number of issues related to this move to tf2.

[jradrion]

@LZeitler I removed multiprocessing from model.fit, and I was able to run a full-sized dataset to >400 epochs without running into any memory issues. Could you try to pull these changes and reinstall. Please let me know if you are still having issues.

[LZeitler]

@jradrion I pulled and reinstalled.
For now I'm ignoring the warnings related to TensorRT.
However, testing with the example pipeline, I am now getting some other warnings:

2020-03-02 15:39:32.734313: W tensorflow/core/common_runtime/base_collective_executor.cc:217] BaseCollectiveExecutor::StartAbort Out of range: End of sequence
[[{{node IteratorGetNext}}]]
[[IteratorGetNext/_2]]
2020-03-02 15:39:32.734285: W tensorflow/core/common_runtime/base_collective_executor.cc:217] BaseCollectiveExecutor::StartAbort Out of range: End of sequence
[[{{node IteratorGetNext}}]]
WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 200 batches). You may need to use the repeat() function when building your dataset.
WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss
WARNING:tensorflow:Can save best model only with val_loss available, skipping.
Traceback (most recent call last):
File "/cluster/home/zeitlerl/.local/bin/ReLERNN_TRAIN", line 117, in <module>
main()
File "/cluster/home/zeitlerl/.local/bin/ReLERNN_TRAIN", line 107, in main
gpuID=args.gpuID)
File "/cluster/home/zeitlerl/.local/lib/python3.7/site-packages/ReLERNN/helpers.py", line 371, in runModels
model.load_weights(network[1])
File "/cluster/home/zeitlerl/.local/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py", line 234, in load_weights
return super(Model, self).load_weights(filepath, by_name, skip_mismatch)
File "/cluster/home/zeitlerl/.local/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py", line 1222, in load_weights
hdf5_format.load_weights_from_hdf5_group(f, self.layers)
File "/cluster/home/zeitlerl/.local/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/hdf5_format.py", line 699, in load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples)
File "/cluster/home/zeitlerl/.local/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py", line 3323, in batch_set_value
x.assign(np.asarray(value, dtype=dtype(x)))
File "/cluster/home/zeitlerl/.local/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py", line 819, in assign
self._shape.assert_is_compatible_with(value_tensor.shape)
File "/cluster/home/zeitlerl/.local/lib/python3.7/site-packages/tensorflow_core/python/framework/tensor_shape.py", line 1110, in assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (2610, 256) and (2036, 256) are incompatible

[LZeitler]

@jradrion Running on big dataset works now! Thanks for the fix! Memory issue is also resolved!

[jradrion]

Hi @LZeitler thanks for bringing the issue with the example scripts to my attention. I had to bump up the number of training simulations to avoid this error with a fixed number of epochs. ReLERNN/examples/example_pipeline.sh should be working now. Glad to see you are no longer having memory issues with your big dataset.

I'm going to go ahead and close this issue. Please let me know if you come across any other problems.

Best,
Jeff