Thanks for the suggestion! I was waiting till the end, but memory was never freed, it just decreased to zero, and then the program stopped. True that. ModelCheckpoint would accept the optional parameter save_format='h5' in its constructor, Who will benefit from this feature? You switched accounts on another tab or window. Already on GitHub? Sign in ValueError Traceback (most recent call last) Subclassed networks are used when a user subclasses the `Model` class. It clearly mentions that. tensorflow 2.0 memory leak in LoadSavedModel. Simply train tf.keras. Have a question about this project? Already on GitHub? TF-2.0: Keras model save load memory leakage, https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/compiler/jit/xla_cpu_device.cc#L67, https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/compiler/jit/xla_device.cc#L525, Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes, OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04.3 LTS. Another way to save to H5 is to set the extension to ".h5" or ".hdf5" or ".keras". --> 416 fn.get_concrete_function(*args, **kwargs) But are you sure you need to be using TFBertModel and not TFBertMainLayer, for your hidden layer? ---> 94 serialization_cache) So when I am using the last_hidden_state I am getting this warning: @gadagashwini @jvishnuvardhan I think this issue should be reopend, as the stack overflow post you linked only worksaround the fact that 514 saving=True): pip install tf-2.0-nightly-preview. I am closing this issue as it was resolved. --> 413 trace_with_training(True) Well occasionally send you account related emails. 66 if signature is not None: ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/saved_model/save.py in list_functions(self, obj) TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)", Describe the current behavior Connect and share knowledge within a single location that is structured and easy to search. I dugged a little bit. ---> 64 functions = saveable_view.list_functions(saveable_view.root) Please let me know once the root cause is found and fixed. How much leak you are noticing now on your local machine? This is doable from a Colab Notebook as well provided you have performed the authorization steps. In my opinion, inconsistencies like this severly hurt to usability of TensorFlow especially in combination with GCS so it would be great if this issue could be reopened. 5 x, emb = inputs Yes 81 def _get_serialized_attributes(self, serialization_cache): ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/layer_serialization.py in _get_serialized_attributes(self, serialization_cache) Two types of `Networks` exist: Graph Networks and Subclass Networks. @jvishnuvardhan So do you mean that model.save can not be used if I'm writing custom training loop instead of compilation + fit? 92 Sign up for a free GitHub account to open an issue and contact its maintainers and the community. On Thu, Dec 15, 2022 at 5:54 PM gowthamkpr ***@***. 848 iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: None, TensorFlow installed from (source or binary): binary, TensorFlow version (use command below): 2.0.0-dev20190724, Bazel version (if compiling from source): None, GCC/Compiler version (if compiling from source): None. GitHub Policy, This behaviour is expected based on the API docs: A use-case where this is a problem is when you want to use ModelCheckpoint with a model that contains weights that are not instances of tf.Variable, such as tf.keras.layers.TextVectorization. Already on GitHub? You switched accounts on another tab or window. 777 self._initialize_uninitialized_variables(initializer_map) We read every piece of feedback, and take your input very seriously. 112 Sign in Yes In other words, the call function of Model should automatically call _set_inputs with the input received. Describe the current behavior I do want to stress though that the only difference is that I am providing some randomly generated x and y data, and removing layers from the custom model for simplicity. Pull requests. These functions will not be directly callable after loading. 2149 if graph_function is None: Can anyone provide another feedback on that? All rights reserved.Licensed under the Creative Commons Attribution License 3.0.Code samples licensed under the Apache 2.0 License. --> 817 self._maybe_build(inputs) privacy statement. NotImplementedError: Saving the model to HDF5 format requires the model to be a Functional model or a Sequential model. The point of this issue is that if it cannot be safely serialized then saving as an h5py either is handling it improperly, and should raise an error similar to the above, OR it works as it should with h5py (based on only this one test case and limited testing besides checking weights and accuracy) and the documentation should include this information, and probably adopt the method/recommend it. 413 trace_with_training(True), ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in get_concrete_function(self, *args, **kwargs) Note that the get_config method was implemented in the custom layer and custom model. I trained a tensorflow model in python and then loaded this model in C++ to do inference, then I detected memory leak using -fsanitize leak. You could also delete those imported models. I should call tf.saved_model.save(model), is it right? Yes Sign in The text was updated successfully, but these errors were encountered: On which version are you running? If including tracebacks, please include the full 143 return obj_functions, ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py in _list_functions_for_serialization(self, serialization_cache) Thanks! 389 'inputs with matching shapes ' While using the subclassing API for a subclassed layer and model, I was unable to use the model.save_model() function for h5 or hdf5 file types, but I could successfully save and load the model if it was saved as h5py file type. 539 Please note that Keras development has moved to a separate Keras-team/keras repository to focus entirely on only Keras. maybe in future this can add but not as of now . Also I attached output of my code running. I understand that so long as you use the save model format, which is done by calling model.save("NameOfYourModel", save_format='tf') This should be the most clear method of successfully saving a subclassed model, while also being clear of the format being used. such as the code text?Thanks, I followed this guide and it worked for me: https://www.tensorflow.org/guide/saved_model#exporting_custom_models. layers and variables). Additionally, TF does not provide any documentation regarding saving a model with custom training (without using .compile() and .fit()). You can collect some of this information using our environment capture If the issue still persists with the newer versions of TF, please feel free to open it in keras-team/keras repository by providing details about the issue and a standalone code to reproduce the issue. For other people (@ch-hristov) still having trouble with this, I wrote up an explanation and workarounds on stackoverflow: https://stackoverflow.com/questions/62482511/tfbertmainlayer-gets-less-accuracy-compared-to-tfbertmodel/64000378#64000378 57 pred, true_fn=true_fn, false_fn=false_fn, name=name) 774 if self._stateful_fn is None: # pass --> 115 signatures, options) name='input_feature' 1846 if self.input_signature: 65 signature = functions.get(DEFAULT_SIGNATURE_ATTR, None) 90 """ This guide uses tf.keras a high-level API to build and train models in TensorFlow. A model is, abstractly: A function that computes something on tensors (a forward pass) Some. 105 set_training_arg(training, training_arg_index, args, kwargs) 414 trace_with_training(False) Sign up for a free GitHub account to open an issue and contact its maintainers and the community. The pooled output produces a single vector representing the whole sequence, not separate vectors for each token of the sequence. 872 signatures = signature_serialization.canonicalize_signatures(signatures). Subclassing tf.keras.models.Model save_model saves h5py but not h5/hdf5 file types, https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model#save. callback. @aginpatrick You can store Keras model checkpoints to google storage by creating a custom GCS callback as shown here, It seems it's now possible to save model to Google Storage without the GCS We read every piece of feedback, and take your input very seriously. Check the code below to create and save model Github Issue Try the code here # import necessary modules import tensorflow as tf import tensorflow.keras as tk print (tf.__version__) # define a custom model class MyModel (tk.Model): . Sign in Furthermore, I am also not using .compile() nor .fit(), and I can't try to figure which saving method I could use. To Reproduce inner = tf.nn.relu6(inner). iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: TensorFlow installed from (source or binary): Binary, TensorFlow version (use command below): tensorflow-gpu==2.0.0-beta1. --> 358 return weak_wrapped_fn().wrapped(*args, **kwds) data_format="channels_last", to your account. If you have installed latest tf-nightly as in my google colab, you should be noticing similar performance. -> 2041 capture_by_value=self._capture_by_value), In other words, the below code should work. @julyrashchenko I just your code and I cannot reproduce the issue. 113 else: # Rank of 0 is the master node so we want that one to save the model; if hvd. You signed in with another tab or window. Have a question about this project? It is recommended that you use the layer properties to access specific variables, e.g. 556 def call_and_return_conditional_losses(inputs, *args, **kwargs): filters=self.hparams.conv_filters[i], Any luck on this issue? Steps to reproduce the behavior: Expected behavior How do I solve this ?? ***> wrote: You signed in with another tab or window. To see all available qualifiers, see our documentation. I'm sure there's some way, just a question of how much custom work you have to do (probably some, given the above quote). Already on GitHub? If save format is hdf5, and h5py is not available. Have a question about this project? Other info / logs 775 initializer_map = object_identity.ObjectIdentityDictionary() It will be closed if no further activity occurs. The file will include: The model's architecture/config The model's weight values (which were learned during training) The model's compilation information (if compile () was called) Thanks! Star 176k. Perhaps docstring for the save_format is unclear? @andreclaudino @aingo03304 @AlexFuster Can you please open a new issue with details of your model and share a simple standalone code to reproduce the issue. ---> 57 outputs, losses = fn(inputs, *args, **kwargs) It is possible to reproduce with the MNIST/ fashion MNIST or really any other dataset, simply change the 10 from the line Besides I tested it on one other machine and got the same results. Mobile device (e.g. 2152 return graph_function, args, kwargs, ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) By clicking Sign up for GitHub, you agree to our terms of service and It is saved there with GPU versions 1.15 and 2.2.4 for both respectively. In general, more Keras features are supported with Graph Networks than with Subclassed Networks, specifically: - Model cloning (`keras . Any suggestions? model compilation and fitting steps are missing your code. Thanks! For other approaches see the TensorFlow Save and Restore guide or Saving in eager. Now I want to load them in my remote ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in add_trace(self, *args, **kwargs) You signed in with another tab or window. Provide a reproducible test case that is the bare minimum necessary to generate the problem. `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. We read every piece of feedback, and take your input very seriously. my environment : docker image - tensorflow/tensorflow-latest-py3 (tensorflow 2.1.0, python 3.6.9). Already on GitHub? However I think you are referring to the model.save() method, whereas I am trying to export the Keras-Core model to the SavedModel format using tf.saved_model.save(), which is supposed to work (confirmed by @fchollet in personal communication). Is it possible that this fix fixed your issue? I'll refer to the link above, thanks. So the question is, should it not allow h5py save format with subclassed models, or if it does work correctly, then shouldn't this be a standard? Have a question about this project? To see all available qualifiers, see our documentation. No, Hi @Ryandry1st, may I have help on this similar issue please, using tf==2.2.0 Thus the saved model can be reinstantiated in the exact same state, without any of the code used for model definition or training. @julyrashchenko Can you try !pip install -q tf-nightly-gpu-2.0-preview and let us know. Would love to jump in. Usage del imported didn't changed situation (~100 Mb per 100 steps, too). to your account. I am closing this issue as it was resolved in the tf-nightly. 392 Thanks! 307 # Return shapes from fn as TensorShapes. The SavedModel serialization path uses tf.saved_model.save to save the model and all trackable objects attached to the model (e.g. The text was updated successfully, but these errors were encountered: @julyrashchenko Can you please provide the github gist of the issue as I am unable to reproduce it on my side. Thank you for clearing that up, appreciate it! Defaults to 'tf' in TF 2.X, and 'h5' in TF 1.X. 1850, ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs) iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: TensorFlow installed from (source or binary): TensorFlow version (use command below): tensorflow-gpu==2.0.0. But can not get a frozen model. privacy statement. however I can't call the function from_pretrained. 778. 559. Well occasionally send you account related emails. --> 515 ret = method(*args, **kwargs) Yes, the checkpoint is definitely capable of saving the model in HDF5 format, my point is that the message may not be obvious. padding="same", When I am getting model back from virtual worker, it has not been saving it with following error. Desktop (please complete the following information): The text was updated successfully, but these errors were encountered: @noumanriazkhan would you mind to attach / upload to colab your minimum reproducible example? ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save.py in save(model, filepath, overwrite, include_optimizer, signatures, options) Sign in Currently, tf.keras.callbacks.ModelCheckpoint does not accept a save_model parameter, while tf.Keras.Model.save does accept it to specify whether to use Tensorflow SavedModel or HDF5 file. You signed in with another tab or window. Step 1: Save an arbitrary trained model in Python Python code that I used to save a TF model to a directory. 913 converted_func) 530 def call(self, *args, **kwargs): Looks like KerasVariable needs to inherit from tensorflow.python.trackable.base.Trackable to work with tf.saved_model.save? 54 functions['_default_save_signature'] = default_signature 89 ConcreteFunction. ValueError: Exception encountered when calling layer "dcn" (type DCN). This only works if I'm able to get the model to be saved as h5 but I get the error about not being able to save a non-sequential or functional model. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 77 def functions_to_serialize(self, serialization_cache): (The fix in #3103 is for a problem with using TF*MainLayer classes within a Keras model, so it doesn't address this.). #if self.hparams.down_sample > 1: Describe the feature and the current behavior/state. the problem. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow. I don't think that is problematic to configure. Notifications. 977 def save_weights(self, filepath, overwrite=True, save_format=None): ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options) the model. ---> 74 save_lib.save(model, filepath, signatures, options) Earlier, it used to leak ~100MB per 100 iterations. tf.keras.callbacks.ModelCheckpoint only saves weights for a subclassed model, although save_weights_only=False. Sign in Either the save_model should always work (I believe this is a feature goal) and the documentation should reflect this, or if the save is likely to produce incorrect results it should raise an error and the documentation should continue to suggest that custom models can only be saved with the save_weights feature. inner = layers.BatchNormalization()(inner) The prediction got in the first session from model and the prediction got from the second session model1 is completely different. 411 fn.get_concrete_function(*args, **kwargs) 533 return super(LayerCall, self).call(*args, **kwargs) I don't think this is the root cause. As you are using an older version of tensorflow, we are checking to see if you still need help on this issue. Thanks! If you have installed libtensorflow and configed using pkg_config, you may alternative use: The above compiled executable run to complete successfully, however, memory leak detected. ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in save(self, filepath, overwrite, include_optimizer, save_format, signatures, options) filepath: One of the following: - String or `pathlib.Path` object, path where to save the model - `h5py.File` object where to save the model: overwrite: Whether we should overwrite any existing model at the target To manually set the shapes, call model._set_inputs(inputs). but the model seems train however, when I load it I am getting: won't load pretrained parameters as opposed to TFBertModel.from_pretrained(), right? It seems like it would be useful to smooth out this workflow, as many people using keras will run into this issue when they try to save their model. Thanks, Found it, Thanks 55 return objects, functions. --> 847 outputs = call_fn(cast_inputs, *args, **kwargs) I want to save / load my keras model to / from Google storage bucket. Hence, only HDF5 models will be stored by the callback. model = res_mod(10, activation='softmax') -> 2150 graph_function = self._create_graph_function(args, kwargs) ---> 54 return true_fn() 304 if input_shape is not None: diagnose the problem. View in Colab GitHub source Introduction KerasCV is an extension of Keras for computer vision tasks. By clicking Sign up for GitHub, you agree to our terms of service and I have been able to serialize SavedModels to GCS Bucket folders directly from AI Platform Notebooks in the following manner: where export_module_dir is either a GCS Bucket or a SavedModel name inside a GCs Bucket. Maybe the model is not large enough? This may be some of the premise behind the warning, but it did work when using the beta version and no longer works with the nightly preview. ---> 59 pred, true_fn=true_fn, false_fn=false_fn, name=name) That said, I do agree that an explicit example for exporting a sub-classed keras model with a custom training loop is missing on the tf page. subclassed model needs to be called on some data in order to create its weights. To see all available qualifiers, see our documentation. 819. Does anyone solve it? shape=data_descriptions.sample_shape["input"], Please find the gist here.Thanks! Well occasionally send you account related emails. 2043 # Tell the ConcreteFunction to clean up its graph once it goes out of. 868 if signatures is None: I am running my processes inside dockers and after a while dockers shut down my processes because they acquire memory and never release it. I've been running OP's snippet and the bug is clearly reproducible. !!! 55 inputs = args[inputs_arg_index] Hi @jordisoler, As per the description mentioned here, or, are there any alternatives in tensorflow? Could not find matching concrete function to call loaded from the SavedModel. The error: privacy statement. How to save and load a model If you only have 10 seconds to read this guide, here's what you need to know. Instead, you need to save the weights in h5 format then make a new model the same way, train_on_batch as shown above, and then load the weights. model.save("NameOfYourModel", save_format='tf') Have a question about this project? Already on GitHub? .h5, .keras, then it will not work for a subclassed model. Thanks! Those who are trying to use ModelCheckpoint with a layer that is not storable in HDF5 format. Is the class implicitly set by providing the config options from BERTConfig ? Note that the model weights may have different scoped names after being loaded. @gugarosa One workaround is that after finished custom training, call model.predict on some input tensors and then call model.save to save your model as SavedModel format. --> 110 lambda: replace_training_and_call(True), I attach the logs from my terminal. You signed in with another tab or window. No. 410 with K.learning_phase_scope(value):
2333 Carlisle Ct El Dorado Hills Ca 95762,
Articles T