I just made another test with a simpler Keras model, just one embedding layer and two dense layers, no custom layer or custom loss. What mathematical topics are important for succeeding in an undergrad PDE course? In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. So declaring a Tensorflow variable throws an errors stating one should use tf.contrib.eager.VariableThis means that we can't use eager execution in already implemented programs and hope it works magically. 2020-12-20 17:49:16.991879: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll Use eager execution to run your code step-by-step to inspect shapes, data types and values. 4 comments vladimircape commented on May 18, 2020 on Jun 2, 2020 amahendrakar . and I disabled Keras eager execution (disabled since I use TF 2.0) with the statement: tensorflow.compat.v1.disable_eager_execution(), 2020-12-20 17:42:12.138275: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll I'm asking because wandb graphs show enough proof that the gpu is not working (gpu utilization 0%). I am trying to make a to visualize a heatmap for an image in a CNN using TensorFlow 2. As a data scientist, you may have encountered TensorFlows eager execution feature. If you want to use Graph mode please consider tf.function. As you can see below, AutoGraph is implemented via tf.function decorator: A simple function such as simple_nn_layer will be transpiled into this graph code. PC-OLIVIER Device:0 2 2 12 21 0 Making statements based on opinion; back them up with references or personal experience. Hence that performance issue might actually be a bug, i.e. Thanks for the suggestion, will try. 2020-12-20 17:49:16.099749: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll All rights reserved. How to disable eager mode? Issue #57652 tensorflow/tensorflow #Occurrences Total self-time (us) Total self-time on Host (%) assert isinstance(x, dataset_ops.DatasetV2) However, Tensorflow 2.0 comes with eager execution enabled by default, and evidently, you shouldn't disable it. Ok, I'll have a look at the profiler. MODELE.fit(x=BATCH(T_JEUX, T_PARAMS, T_SCORES, BATCH_SIZE), tf.compat.v1.disable_eager_execution - Runebook.dev Can a lightweight cyclist climb better than the heavier one by producing less power? . Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. No. Layer (type) Output Shape Param # Connected to, input_1 (InputLayer) [(None, 8)] 0, input_2 (InputLayer) [(None, 22)] 0, embtri10 (Embedding) (None, 8, 16) 235584 input_1[0][0], fcpos (Dense) (None, 19) 437 input_2[0][0], fctri (FC_SYMETRIQUE) (None, 46) 5888 embtri10[0][0], concatenate (Concatenate) (None, 65) 0 fcpos[0][0] fctri[0][0], line (Dense) (None, 1) 65 concatenate[0][0], Total params: 241,974 Below is my custom loss function (it doesn't work): What stumps me is that outside this function, whenever I run print(tf.executing_eagerly()), it returns true but within the function, it returns false. run_functions_eagerly(True) will make all invocations of tf. I add the lines above in main() in the script I referred to earlier and I use wandb for monitoring the training. tf.compat.v1.disable_eager_execution() This function can only be called before any Graphs, Ops, or Tensors have been created. Eager execution was, in fact, possible in TensorFlow 1.0 but it was introduced as a simple mode for learning and experimentation. Can YouTube (e.g.) Is that correct, or would AUTO or OFF be better . Traditionally, TensorFlow users had to define a static graph of operations and then execute it in a separate session. dl-learning-path-assets/Eager_Execution_in_TensorFlow_2.x_with_output Asking for help, clarification, or responding to other answers. Also, is there any way I could reach out to you via mail? TensorFlow math operations convert Python objects and NumPy arrays to tf.Tensor objects. are designed to use Graph execution, for performance and portability. It might fully replace the graph mode in the near future. The fact that this only works for string keys is a direct consequence of how keyword parameters work and not a short-coming of dict. I use the following command for activating the GPU on google colab after installing tensorflow-gpu using pip: I add the lines above in main() in the script I referred to earlier and I use wandb for monitoring the training. 2020-12-20 17:49:17.940776: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll Have I written custom code (as opposed to using a stock example script provided in TensorFlow): OS Platform and Distribution (e.g., Linux (Google colab)): Bazel version (if compiling from source): GCC/Compiler version (if compiling from source): GPU model and memory: Tesla T4 computeCapability: 7.5 coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s. Can you have ChatGPT 4 "explain" how it generated an answer? Stay tuned! Write, check for errors, debug and repeat. Parallelize reading from this dataset source. Use the following before creating variables and models. however I can't use that flag. Eager execution in TF 2.2.0rc2 Issue #39656 tensorflow/tensorflow 1.2 eager execution . tf.Tensor( [ [ 7 10] [ 15 22] ], shape= (2,2), dtype = int32). So my guess is that I am suffering again the penalty of Eager execution, even though I am trying to disable it (I do not need Eager execution). And this creates some hurdles for those who are starting out with machine learning. Tensorflow does not use the GPU during training with eager execution Personally, I find it more despicable than To see all available qualifiers, see our documentation. Ok, I might have a clue on what's going on: When upgrading from 1.5 to 2.0 I experienced a severe training performance impact due to Eager execution, which I resolved with tensorflow.compat.v1.disable_eager_execution() Or are you saying in TF 2.3.1 you used to disable eager execution to get your performance and now you aren't disabling it? tf.config.optimizer.set_jit(True). Use tensor with numpy with eager execution turned off Is anyone encountering the same problem ? Already on GitHub? We have to rely on tf.GradientTape to record operations. https://www.tensorflow.org/guide/profiler. we only address code/doc bugs, performance issues, feature requests and 2020-12-20 17:49:16.412993: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll With big tech still fighting in the big race for AI supremacy, an AGI race is slowly gaining momentum. Effective Tensorflow 2 | TensorFlow Core This procedure might involve defining and evaluating model metrics, collection and statistical analysis of the model artifacts (such as gradients, activations and weights), using tools such as TensorBoard and Amazon Sagemaker Debugger, hyperparameter tuning, rearchitecting, or modifying your data input using techniques such as augmentation and b. The bad news is that I still have 64 seconds / epoch, i.e. Explanation Say you have two dictionaries and you want to merge them into a new dictionary without altering the original dictionaries: x = {'a': 1, 'b': 2} y = {'b': 3, 'c': 4} The desired result is to get a new dictionary ( z) with the values merged, and the second dictionary's values overwriting those from the first. I imagine doing the embedding training on the GPU might give some precision improvement over a mixed CPU/GPU training ? This is a waste of resources and computation power. This can be useful for debugging. followed by this recommendation: What does that mean, knowing that all my data is loaded in RAM ? As keys must be hashable and are usually therefore immutable, it is pointless to copy them: Coming up with contingencies for other value types is far beyond the scope of this question, so I will point you at my answer to the canonical question on a "Dictionaries of dictionaries merge". See the TensorFlow v1 to TensorFlow v2 migration guide for instructions on how to migrate the rest of your code. diagnose the problem. Find centralized, trusted content and collaborate around the technologies you use most. Note that we can merge in with literal notation as well: It is now showing as implemented in the release schedule for 3.5, PEP 478, and it has now made its way into the What's New in Python 3.5 document. The only specificities of my code is that I have defined a custom Keras layer and a custom loss function, which bot use TF functions such as tensor slicing (inputs[:, 0]), tf.concat, tf.matmul, tf.square, tf.cast, tf.sign, tf.nn.sigmoid_cross_entropy_with_logits, tf.reduce_mean. 2020-12-20 17:49:15.951856: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll Find centralized, trusted content and collaborate around the technologies you use most. What Is Behind The Puzzling Timing of the U.S. House Vacancy Election In Utah? It supports the following: Multidimensional-array based numeric computation (similar to NumPy .) Yeah the distribution strategy probably won't work when you disable eager execution (effectively entering TF1 graphs/execution). dataset = model._distribution_standardize_user_data( 2020-12-20 17:49:16.991616: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll Ideally, they should be in the same cell (or very close, like the same region). Relative pronoun -- Which word is the antecedent? Eliminative materialism eliminates itself - a familiar idea? I wonder if this has to do with hardware-accelerated gpu scheduling? Eager execution is slower than graph execution! To use eager functionality, you need to change your code. TensorFlow 2.0: Eager execution of training either returns bad results or doesn't learn at all, Unable to Enable Tensorflows Eager execution. function run eagerly instead of running as a traced graph function. This becomes tedious with complicated tasks. Most of the code is pretty straightforward and Pythonic, and only tf.GradientTape is an extra necessary step to calculate the gradients. send a video file once and multiple users stream it? By targeting the code generation capabilities of LLMs, researchers at Microsoft have created a system that can help AI communicate with apps, Virtual autopsy, or virtual autopsy imaging, is a modern, non-invasive method of examining a body to determine the cause of death, The primary focus of this endeavour was to demonstrate the feasibility of running Llama 2 models on low-powered devices using pure C code, Companies are naturally inclined to choose foreign buyers as it provides access to a global network of customers and investors. But I need to have another try with one of my huge datasets (60 Gb) which makes tf.from_tensor_slices crash brutally without any error message. To learn more, see our tips on writing great answers. : Despite what Guido says, dict(x, **y) is in line with the dict specification, which btw. Already have an account? 2020-12-20 17:49:17.763105: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6637 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070, pci bus id: 0000:01:00.0, compute capability: 7.5) 2020-12-20 17:49:17.759774: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0 While eager execution offers many benefits, there are situations where you may want to disable it. to your account. 'TF_XLA_FLAGS': '--tf_xla_enable_xla_devices --tf_xla_auto_jit=fusible --tf_xla_cpu_global_jit --tf_xla_always_defer_compilation=false --tf_xla_enable_lazy_compilation=true', By the way my terminal keeps getting Any clue for this improvement ? bach October 6, 2022, 2:48pm #1 Hi, using Keras 2.10.0 rc3 (precompiled, on Ubuntu 22). This graph-based functioning creates problems when one has to debug their code written with TensorFlow. Yes. Support for dynamic models using easy-to-use Python control flow, Strong support for custom and higher-order gradients. (If you share traces from the profiler we'd be happy to take a look). When I run this function I am receiving the following error: AttributeError: 'Tensor' object has no attribute 'numpy'. And it is not forward compatible, as Python 2 is increasingly deprecated. -running tf.compat.v1.enable_eager_execution() within the loss function to at least force eager execution once there. What, if any, are the advantages with using Graph mode in Tensorflow 2.0? 64 Gb RAM, 1Tb SSD Effect of temperature on Forcefield parameters in classical molecular dynamics simulations. TensorFlow v1 to TensorFlow v2 migration guide. Which generations of PowerPC did Windows NT 4 run on? In TensorFlow 2, eager execution is turned on by default. By default, we will attempt to compile your model to a static graph to deliver the best execution performance. Is it unusual for a host country to inform a foreign politician about sensitive topics to be avoid in their speech? I can't really seem to find any survey or benchmarking comparing eager execution to graph execution. TF_XLA_FLAGS = --tf_xla_enable_xla_devices --tf_xla_auto_jit=fusible --tf_xla_cpu_global_jit --tf_xla_always_defer_compilation=false --tf_xla_enable_lazy_compilation=true. So don't do this: This example demonstrates what happens when values are unhashable: Here's an example where y should have precedence, but instead the value from x is retained due to the arbitrary order of sets: This uses the dict constructor and is very fast and memory-efficient (even slightly more so than our two-step process) but unless you know precisely what is happening here (that is, the second dict is being passed as keyword arguments to the dict constructor), it's difficult to read, it's not the intended usage, and so it is not Pythonic. The comment you quoted itself noted that some of those systems environments can introduce memory allocation issues and crashes, so it's not surprising to me you're running into memory crashing issues when testing them out. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. I just upgraded from TF 2.3.1 to TF 2.4.0 and my tf.keras model is at least twice slowlier than it was. As requested above can you share a colab gist of your simpler three-layer model repro? Non-trainable params: 0, 2020-12-20 17:49:16.988484: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 main.py import tensorflow as tf x = tf.compat.v1.placeholder(tf.float32, shape=(1024, 1024)) # RuntimeError: tf.placeholder () is not compatible with eager execution. Please disable eager execution. You only need to call eval in the tensor instead numpy(). Why would a highly advanced society still engage in extensive agriculture? Readability counts. Formulate and solve task in terms of probabilities. Don't use what you see in the formerly accepted answer: In Python 2, you create two lists in memory for each dict, create a third list in memory with length equal to the length of the first two put together, and then discard all three lists to create the dict. etc. To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. script Making statements based on opinion; back them up with references or personal experience. Asking for help, clarification, or responding to other answers. (Self-contained so you can copy and paste yourself.). Enable Eager Execution in TensorFlow - IBM Developer In short, AutoGraph is a mini-compiler that transpiles a subset of Python syntax into portable, high-performance TensorFlow graphs. Modified 3 years, 10 months ago. eager TF 1.4 . Eager execution is enabled by default. As per our Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls. Do you think that could be a problem when compiling the model with TF 2.4 ? But in Kubernetes. Same model, same data, same machine. Thanks for contributing an answer to Stack Overflow! From_generator generally isn't very performant because you get bottlenecked by the limitations of python, and don't get the full performance advantages of tf.data. Apart from that, I looked at the change log between TF 2.3.1 and TF 2.4 and didn't find anything that would break my code. coreClock: 1.62GHz coreCount: 36 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s How do I disable TensorFlow's eager execution? Closing the issue based on this, please reopen if you disagree. can you share a colab gist of the simple (3 layer) model you mentioned above? Compare Tensors by their id and be hashable. Clearly most of DRL algorithms are designed in a way that is not GPU friendly, a lot of computations are done sequentially using the CPU as this is not the first time that I experience this delay and GPU under-utilization. 2020-12-20 17:49:16.989582: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll The tf.Tensor.numpy method returns the objects value as a NumPy ndarray. 2020-12-20 17:49:16.146047: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll With graph mode, which is the default in TensorFlow 1.0, evaluation happens only after weve wrapped our code with tf.Session. The remaining performance gap does sound like something we need to look into. Will it be possible for you to replicate the issue with a simple stand alone code. Describe the expected behavior. This makes it easy to get started with TensorFlow and debug models. Connect and share knowledge within a single location that is structured and easy to search. The sooner the errors are identified, the sooner the chances of a model getting deployed. TF 2.4.0 with disable_eager_execution twice slowlier than 2.3.1 with disable_eager_execution, https://www.tensorflow.org/api_docs/python/tf/distribute/OneDeviceStrategy, https://www.tensorflow.org/guide/profiler, https://www.tensorflow.org/io/api_docs/python/tfio/experimental/IODataset#from_numpy. By clicking Post Your Answer, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct. Seems buggy. 2020 The TensorFlow Authors. We can fix the issue if we modify the model.compile with run_eagerly = True argument. cool. Again, it doesn't work for 3 when keys are not strings. Tensorflow 2.0 model using tf.function very slow and is recompiling every time the train count changes. Dictionaries are intended to take hashable keys (e.g. Why would a highly advanced society still engage in extensive agriculture? Device: 4.3% CPU i9 9900 K We can activate eager mode in TensorFlow 1.0 with the following code: Here is the definition of Eager execution from the official documentation: Eager execution is a flexible machine learning platform for research and experimentation, providing: An intuitive interface Structure your code naturally and use Python data structures. Shown below is the modified model.compile code. Trainable params: 241,974 2021-01-08 10:48:49.623571: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6637 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070, pci bus id: 0000:01:00.0, compute capability: 7.5). This is the code: (taken from Keras official docs). tf.disable_eager_execution - TensorFlow 1.15 Documentation I tried both TF 2.3 and latest nightly and Titan-V and P100. 2020-12-20 17:49:17.763980: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set There is a lot of Profiling information, I don't really know where to look. Pre-trained models and datasets built by Google and the community Hope this answers your question. When debugging, use tf.config.run_functions_eagerly(True) to use eager execution inside this code. tf.compat.v1.disable_eager_execution - TensorFlow 2.4 - W3cubDocs They will be much less performant than copy and update or the new unpacking because they iterate through each key-value pair at a higher level of abstraction, but they do respect the order of precedence (latter dictionaries have precedence). By clicking Sign up for GitHub, you agree to our terms of service and A lot of stuff doesn't work. PC-OLIVIER Host:0 6,193 7,842 10,094 21 21 There is an update, I'm facing the same issue running the script below I wrote and I noticed that the issue is gone when I disable eager execution, which implies it would be gone for the script I used in the issue description above: I am having the same issue when using 2.3.0-gpu-jupyter Docker image. Not the answer you're looking for? Making statements based on opinion; back them up with references or personal experience. RuntimeError: tf.placeholder() is not compatible with eager execution 2020-12-20 17:49:17.759695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix: Best solution for undersized wire/breaker? In an effort to turnaround the problem, I rewrote my Sequence generator logic into a plain tf.data.Dataset logic. View aliases Compat aliases for migration See Migration guidefor more details. Thanks! Disabling eager execution is simple and can be done with just a few lines of code. Apparently dict(x, **y) is going around as "cool hack" for "call In the options for the OneDeviceStrategy I am setting experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA . I must refer you back to the question, which is asking for a shallow merge of two dictionaries, with the first's values being overwritten by the second's - in a single expression. strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0"), since when starting TF tells on the console: The following works on tensorflow-2.0.0-beta1 tf.compat.v1.enable_eager_execution () In tensorflow 2.0 the eager execution is enabled by default. Python How to merge two dictionaries in a single expression (taking union of dictionaries), my answer to the canonical question on a "Dictionaries of dictionaries merge", Answer on how to add new keys to a dictionary, Modern Python Dictionaries, A Confluence of Great Ideas, Python How to check whether a file exists without exceptions, Python How to execute a program or call a system command, Python How to safely create a nested directory in Python, Python How to sort a dictionary by value, Python How to make a flat list out of a list of lists, Python Proper way to declare custom exceptions in modern Python, Python How to list all files of a directory, Python UnicodeEncodeError: ascii codec cant encode character u\xa0 in position 20: ordinal not in range(128). Would recommend you to run your program in colab and see if the error persists. Tensorflow does not use the GPU during training with eager execution, despite manual activation, https://www.tensorflow.org/install/source, pip install tensorflow fails on macOS and Python 3.9. How do you understand the kWh that the power company charges you for? Nor is using the ** operator in this place an abuse of the mechanism, in fact, ** was designed precisely to pass dictionaries as keywords. I have a master's degree in Robotics and I write about machine learning advancements. Host: 95.7% TF 2.4.0 with disable_eager_execution twice slowlier than 2.3.1 with Anyway TF 2.x has, New! I also have this warning from TF 2.4 By clicking Post Your Answer, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct. I'm unsure if this is significant but here's what happened at one run when I tried to run a model, I got a memory access error. (with no additional restrictions), How to find the end point in a mesh line. While eager execution offers many benefits, there are situations where it may not be the best option for your use case. By clicking Post Your Answer, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct. In this mode, a practitioner has to run a single line of code to enable the eager execution module on TensorFlow and keep a track of their code. Since the release of GPT-4, AI researchers have been using the models outputs to train their own language models and datasets for benchmark results. it eventually works fine. Are arguments that Reason is circular themselves circular and/or self refuting? frozensets or tuples), but this method fails in Python 3 when keys are not strings. For exploration and experimentation, I think Eager execution is the ideal choice. To disable the eager execution (and not TF2 behavior in general), do: For dictionaries x and y, z becomes a shallowly-merged dictionary with values from y replacing those from x. You signed in with another tab or window. And, any developer would like to sneak in on to the code in between and monitor it with the help of partial results. To see all available qualifiers, see our documentation. Now we can see the value of the variable a. Quickly iterate on small models and small data. Yes It either gives that error or xla fails to compile my functions and it just hangs, eventually closing the kernel. performance measurement in Tensorflow's eager mode, Eager-Mode very slow (~22x slower than Graph-Mode). With 2.4 off-the-shelf I got 77 seconds / epoch GPU and distributed processing Automatic differentiation Model construction, training, and export And more Tensors TensorFlow operates on multidimensional arrays or tensors represented as tf.Tensor objects. Eager runs about 4x faster. We also have to control the dependencies. However, Tensorflow 2.0 comes with eager execution enabled by default, and evidently, you shouldn't disable it. pciBusID: 0000:01:00.0 name: GeForce RTX 2070 computeCapability: 7.5 dict broke this consistency in Python 2: This inconsistency was bad given other implementations of Python (PyPy, Jython, IronPython). Continuous Variant of the Chinese Remainder Theorem. 'TF_FORCE_GPU_ALLOW_GROWTH': 'true', A brief guide to Tensorflow Eager Execution | by Keshav Aggarwal
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