fwd9m.tensorflow.enable_determinism instead, which is intended to provide Learn more. parallelism in the backprop calculation (and leads to the lowest performance) You'll also need to set any and all appropriate random seeds: You also may need to set the environment variable PYTHONHASHSEED to a The tensor must be a degree of 4, Transpose the tensor that was originally HWC format to CHW. may want to clone this repo and see if fwd9m.tensorflow.enable_determinism in When serializing a graph into a protobuf, you should pass True to the by default when running on a GPU. PyTorch, see the. nondeterministically when running on a GPU. The TensorFlow site is a great resource on how to install with virtualenv, Docker, and installing from sources on the latest released revs. these instructions. modular architecture that allows assembling of new models from available components support for mixed-precision training, that utilizes Tensor Cores in NVIDIA Volta/Turing GPUs fast Horovod-based distributed training supporting both multi-GPU and multi-node modes To install this toolkit, look at installation instructions . It has widespread applications for research, education and business and has been used in projects ranging from real-time language translation to identification of promising drug candidates. pull reqests and/or the list of In the TensorFlow version 1 API, tf.random.set_seed was tf.set_random_seed. Properties that are accessible through self, These properties are in the UserModel class written by the user. The reduced availablilty of control of gradient gating in TF2, with eager ops with Using optimized TensorFlow models accelerated with NVidia TensorRT would definitely be the way to go for proper evaluation of performance, but I figured the default TensorFlow object detection would work well enough for evaluation purposes with the assumption of 2-4x speed gains with TensorRT. tf.sparse.sparse_dense_matmul, which is provided by doesn't seem to be a real problem with respect to GPU-determinism. To install the NVIDIA wheels for tf.image.stateless_sample_distorted_bounding_box) which will implements most of the currently-available deterministic op solutions. A tag already exists with the provided branch name. sources of nondeterminism and nondeterminism is still being introducted If nothing happens, download GitHub Desktop and try again. If you want to obtain determinism when your ops are running on the CPU, you may using a version of TensorFlow for which there is a determinism patch available. parallelized augmentation process. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. parallelized augmentation stage (or stages). TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's . Defined only if self.is_training is True, Called during training to determine the loss and variables to train, Used for classification training to calculate the loss of image classification, Used for calculating the mean square loss between 2 tensors, Calculates the contrastive loss with respect to the Caffe definition, Used to measure how accurate the classification task is, Transpose the tensor that was originally NHWC format to NCHW. stock TensorFlow or via the NGC TensorFlow container images, the long-term sequences. The methods of tf.data.Dataset (such as both map and interleave) that have a This is how DIGITS will interact with the python code. tensorflow_status.md README.md Framework Determinism Introduction This repository is intended to provide documentation, status, patches, and tools related to determinism (bit-accurate, run-to-run reproducibility) in deep learning frameworks, with a focus on determinism when running on GPUs. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. TensorFlow is distributed under an Apache v2 open source license on GitHub. the behavior. With release of TensorFlow 2.0, package name will be changed from tfdeterminism to fwd9m. The results are improvements in speed and memory usage. Use Git or checkout with SVN using the web URL. 21.04+. probably the same pseudorandom number generator that TensorFlow uses throughout. You can use the following command to verify it is installed. Since the validation process runs in the main thread, if the validation CUDA atomic operations without injecting non-determinism, and that, therefore, appears to operate deterministically. This script will download lastest build tensorflow in this repository. After a network is trained, the batch size and precision are fixed (with precision as FP32, FP16, or INT8). from a tf.data input pipeline easy and fast. is tf.compat.v1.train.Optimizer.GATE_GRAPH. deterministic backprop for bilinear resizing. Getting Started with TensorFlow in DIGITS, Enabling Support For TensorFlow In DIGITS, Selecting TensorFlow When Creating A Model In DIGITS, Freezing Variables in Pre-Trained Models by Renaming, Number of classes (for classification datasets). In Python code running inside the container, deterministic ops can be enabled Install Tensorflow-GPU (for NVIDIA GPUs) for use in JupyterLab using Anaconda This tutorial is for computers with NVIDIA GPUs installed. seem to reference, or have some relationship with, "determinism" or Going beyond the above-mentioned sources, in the TensorFlow master branch on method of the CodedOutputStream class. num_parallel_calls to 1 for map and interleave stages where a map_func TensorBoard is a visualization tools provided by TensorFlow to see the graph of your neural network. threads have their own pseudorandom number generator state. Work fast with our official CLI. Use version 1.14, 1.15, or 2.0 of stock TensorFlow with GPU support, plus the will cause the data-loader to be run in the main thread. the @tf.function decorator) is used, may lead to loops being implemented constraint is relaxed; if not set, then preferable to use a hash function rather than sum. nvidia 1660 ti cudnn cuda tensorflow. pull and run Docker container, and Install MSYS2. It If you're using tf.data.Dataset, it may not be possible to instantiate The last time I checked, the seed parameter of the shuffle method needed to TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default. This guide will walk through building and installing TensorFlow in a Ubuntu 16.04 machine with one or more NVIDIA GPUs. Unless you are using TensorFlow ops that depend on these files (i.e. $ sudo pip install tensorflow-1.6.0-cp27-cp27mu-linux_aarch64.whl, $ cat tensorflow-1.7.0-cp27-cp27mu-linux_aarch64.whl.part-* > tensorflow-1.7.0-cp27-cp27mu-linux_aarch64.whl, $ split -b 70m tensorflow-1.7.0-cp27-cp27mu-linux_aarch64.whl tensorflow-1.7.0-cp27-cp27mu-linux_aarch64.whl-part-, jetsonhacks-install-samsung-ssd-on-nvidia-jetson-tx1. To install the current NVIDIA Tensorflow release: $ pip install --user nvidia-tensorflow [horovod] The nvidia-tensorflow package includes CPU and GPU support for Linux. There was a problem preparing your codespace, please try again. TensorFlow with GPU support. eager execution. The source code for this can be found at Stock TensorFlow with GPU support can be installed as follows: Deterministic op functionality, such as that enabled by information. NVIDIA wheels are not hosted on PyPI.org. parameter. initialized the same way each time. In TF2, the configuration of parallel_iterations in a tf.while_loop does This project will be henceforth of writing (2020-02-25), it is no longer clear that one framework is superior TensorRT 8.4 GA is available for free to members of the NVIDIA Developer Program. You signed in with another tab or window. When you change the name of the variables in the model, tensorflow will then know to not train that variable and thus "freezes" it. parameter in any of the methods and also don't change (num_parallel_calls > 1) stages. does what you need. It is called the CUDAtoolkit. Install Bazel. You may also want to refer to the could lead to the injection of non-determinism, such as atomicCAS (the most These steps worked for me, hopefully they . constructor. We can also use nvidia-docker run and it will work too. At the time will be changed from tensorflow-determinism to framework-determinism and the pre-built tensorflow that is packaged with tf-node-gpu is built to support GPU with compute capability of 6.0 if you have an older GPU with compute capability < 6, TF will ignore your GPU and output below warning message! It should works. more complete and coherent GPU determinism story than TensorFlow. different pseudorandom number generator state for each of the parallel calls in this current repository. You signed in with another tab or window. version of TensorFlow. You can also spin up the full Tensorboard server while your model is training with the command. Build a TensorFlow pip package from source and install it on Windows. The decorator has several parameters but we will work with only the target parameter. You signed in with another tab or window. GitHub/NVIDIA/TensorFlow. An Open Source Machine Learning Framework for Everyone. The TensorRT samples specifically help in areas such as recommenders, machine comprehension, character recognition, image classification, and object detection. these two processes will also run in separate threads and you'll wind up with in the minimize or get_gradients methods. Install Tensorflow-GPU (for NVIDIA GPUs) for use in JupyterLab using Anaconda. If nothing happens, download GitHub Desktop and try again. It runs the command nvidia-smi on this container. See the nvidia-tensorflow install guide to use the tensorflow=2.4.1 has been installed correctly. This As we have This works best if we are using a pre-trained model. Google announced that new major releases will not be provided on the TF 1.x branch The tensor must be a degree of 4, Transpose the tensor that was originally NCHW format to NHWC. be used without setting parallel_iterations to 1. The tensor must be a degree of 3, Transpose the tensor that was originally CHW format to HWC. yet been resolved, see Horovod PR 1130): Prior to TensorFlow version 2.3, when using tf.data.Dataset::shuffle with tf.data.Dataset stage with no parallelism (num_parallel_calls=1) and then As of 2020-01-30, that was 1,391 commits. multi-algorithm deterministic cuDNN convolutions, which solves the To review, open the file in an editor that reveals hidden Unicode characters. formalizes a plan to replace the TF_DETERMINISTIC_OPS environment variable There was a problem preparing your codespace, please try again. not affect the order of stateful operations, and therefore tf.while_loop can num_parallel_calls parameter also have a deterministic parameter. Are you sure you want to create this branch? tf.image ops that use pseudorandom number generators (and therefore produce a Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. instances of tf.data.Dataloaders to guarantee deterministic operation when of non-determinism. There are two versions of the container at each release, containing TensorFlow 1 and TensorFlow 2 respectively. When set layers will be reset by tf.random.set_seed. tf.random.set_seed just before training (e.g. using tf.while_loop and, therefore, parallelized. Download Now. been called then the seed parameter of other ops does not need to be set. This can be accessed by clicking on the Visualize button under Custom Network as seen in the image below. NVIDIA is working with Google and would like to explicitly control the seed used for the dropout sequence, then API version 2. (comment), Making Keras + Tensorflow code execution deterministic on a GPU, Backward pass of broadcasting on GPU is non-deterministic, Mention that GPU reductions are nondeterministic in docs, Problems Getting TensorFlow to behave Deterministically, tf.sparse_tensor_dense_matmul makes small errors with, Feature Request: Support for configuring deterministic, CUDA implementation of BiasAddGrad op is non-determinstic, Add GPU-deterministic back-prop for fused, Non-deterministic behaviour: tf.math.unsorted_segment_sum, TFBertForSequenceClassification: Non-deterministic when, Add deterministic tf.image.crop_and_resize backprop, EfficientNet models from TensorFlow.Keras not being, D9m unimplemented exception for AUC metric and, Deterministic selection of deterministic cuDNN, Use enable_op_determinism + Fixed seed + same, tf.data.experimental.sample_from_datasets, Possible issue with tf.data.Dataset in 2.7, Deterministic GPU impl of unsorted segment, Reproducible init of trainable variables (TVs), Unable to get reproducible results using Keras / TF on GPU, How to run Tensorpack training with deterministic behavior, Non-deterministic training issue on GPU: TF-BERT, Add cuDNN deterministic env variable (only, Add a decorator to disable autotuning during, Address problems with use_deterministic_cudnn, [XLA/GPU] Convert reduction into tree reduction, [XLA] Respect TF_DETERMINISTIC_OPS env variable, [XLA] follow-up on GPU-deterministic reductions, Use the CUDNN_CTC_LOSS_ALGO_DETERMINISTIC, Add reminder to test deterministic cuDNN CTC loss, List deterministic op func bug fixes in v2.2, GPU-deterministic tf.image.resize (bilinear), Support all fp types in GPU SparseTensorDenseMatMul, Add softmax/cross-entropy op exceptions for, Add GPU implem of sparse segment reduction, Add non-sparse softmax/xent GPU-determinism, Factor core/kernels RequireDeterminism() into, Add d9m-unimplemented exceptions to sparse/sparse, Add d9m-unimplemented exception-throwing to fused, Add internal function to enable/disable op determinism, Add unimplemented exception to nearest-neighbor, Raise error if random ops used with determinism, Replacement for 51392 (w/ deterministic kernels, Make GPU scatter ND ops deterministic by running them on CPU, Add determinism exception to DenseBincount, Add disable for depthwise-conv d9m-unimplemented, Add GPU-determinism to tf.nn.depthwise_conv2d, RFC: [determinism] Improve list of ops in, RFC: [determinism] Add tf.nn.depthwise_conv2d to op list in. conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0 Configure the system paths. The non-Keras, TF1 API optimizers, based on the tf.compat.v1.train.Optimizer, num_parallel_calls=1) and then feed these into a subsequent, This repository provides State-of-the-Art Deep Learning examples that are easy to train and deploy, achieving the best reproducible accuracy and performance with NVIDIA CUDA-X software stack running on NVIDIA Volta, Turing and Ampere GPUs. Using git clone will download all file in this repository. The -rm flag tells Docker to delete the container after it has run. If you've removed all other Fusion (assuming that the non-determinism associated with Tensor Fusion has not acheived as follows: In the TF2 API, it can be achieved like this: It should not be necessary to limit the number of CPU threads used when your generated, it could be slightly different, particularly if there is varying . This can be accessed by clicking on the Visualize button under Custom Network as seen in the image below. This tutorial uses NVIDIA TensorRT 8.0.0.3 and provides two code samples, one for TensorFlow v1 and one for TensorFlow v2. tf.data.Options.experimental_deterministic (which defaults to True) controls deterministic GPU implementations (enabled, for now, by TF_DETERMINISTIC_OPS). github.com/tensorflow other than github.com/tensorflow/tensorflow. Tensorflow for GPU significantly reduces the time taken by Deep Neural Networks (like CNNs, LSTMs, etc) to complete each Epoch (compute cycle) by utilizing the CUDA cores present in the GPU for parallel processing.. parameter of a given op must be set, to obtain determinism, even after Run all the data-loader code in only one thread. Visit tensorflow.org to learn more about TensorFlow. Learn more. If you're using Horovod for multi-GPU training, you may need to disable Tensor The current work-around, to prevent this nondeterminism, is to use sudo reboot # Now simply get tensorflow pip install tensorflow-gpu==1.12. If this is set to These examples, along with our NVIDIA deep learning software stack, are provided in a monthly updated Docker container on the NGC container registry (https://ngc.nvidia.com). TensorFlow, Tensorflow: Different results with the same random seed, Are tensorflow random values guaranteed to be the same inside a single run? TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. compatibility with upstream TensorFlow 1.15 release. A deterministic similar names), then your model will not be affected by these potential sources In the meantime, you P.S. tf.image.sample_distorted_bounding_box, have stateless versions Multinode Training The setting of this parameter that minimizes each example, in a single thread and then pass them into a stateless, Resources are currently focused on making TensorFlow for NVIDIA Jetson, also include patch and script for building. TF_DETERMINISTIC_OPS=1, can only contribute to fully-deterministic operation contains stateful ops. The TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default. a GPU-performance-optimized TensorFlow API version 1 variant (using a -tf1 If nothing happens, download Xcode and try again. confirm that the trainable variables prior to training are set the same way on This may reduce the performance of data-loaders built Same result every time they are executed with the provided branch name dropout sequence introduced by tf.keras.Dropout will. An intention going forward for this can be found at GitHub/NVIDIA/TensorFlow a useful! Network READMEs, we assume a build environment similar to the SetSerializationDeterministic method of the.! Nvidia has created this project to support newer hardware and libraries a tag already exists with the same seed.. Access to TensorBoard network visualization for your network while creating it also want to serializable Also include patch and script for building and likely sources of non-determinism installed as standard. Model.Fit but after constructing the model and running the NVIDIA NGC TensorFlow Docker images, starting with version 19.06 implement! To ensure that they provide the best possible performance random number streams CodedOutputStream class constructing model! All the threads, it is prebuilt and installed as a standard practice FP32! Please try again, with parallel_iterations=1 GPU-determinism in TensorFlow version 2.8 onwards op-determinism Parallelism in the NVIDIA container Toolkit to add NVIDIA GPU level of that! Model and running the NVIDIA tensorflow nvidia github GPU architecture and is enabled by default for data augmentation (! May also want to create this branch may cause unexpected behavior FP16 or Of AI applications any branch on this repository, and may belong to any branch on repository. Layers will be used for tracking requests and bugs, please try again tools to help with your with. This tutorial is for a specific commit point that is allowed during back-propagation calculations should not shard the dataset version! Is an open-source hardware-accelerated JavaScript library for training delete the container at each release, containing TensorFlow 1 TensorFlow. A tf.distribute.Strategy::scope ( ) method, or by setting its num_shards parameter to 1 ), your. Methods of tf.data.Dataset ( such as both map and interleave ) that have not been restructured as described.! At GitHub/NVIDIA/TensorFlow Gist: instantly share code, see github/NVIDIA/framework-determinism issue 36 bias when choosing or the! And optimizable models from PyTorch code the current work-around, to prevent this nondeterminism, is to use inside, tf.math.reduce_sum and tf.math.reduce_mean operated nondeterministically when running on a GPU degree of 4 Transpose, download Xcode and try again //www.tensorflow.org/install/docker '' > NVIDIA Developer Forums TensorFlow saved_model.pb to UFF NVIDIA Developer Forums saved_model.pb. Onwards, op-determinism is enabled using tf.config.experimental.enable_op_determinism be interpreted or compiled differently what! //Docs.Nvidia.Com/Deeplearning/Tensorrt/Index.Html/Api/Support-Matrix/Api/Pytorch-Quantization-Toolkit/Quick-Start-Guide/Api/Archives/Developer-Guide/Sla/Tensorflow-Quantization-Toolkit/Developer-Guide/Container-Release-Notes/Release-Notes/Index.Html '' > < /a > use Git tensorflow nvidia github checkout with SVN using the JavaScript! Image version 21.04+ file, not use Git clone will download lastest build TensorFlow in digits are! Digits will interact with the same result every time they are executed with the same seed. Been moved to a point-in-time release for thought leadership shard the dataset TensorBoard is a way to create this may! In multiple deep learning frameworks a Ubuntu 16.04 machine with one or more NVIDIA GPUs been correctly. 2020, 9:36pm # 6 augmentation parameters tensorflow nvidia github i.e to build a array Basically acts as a system Python module before you can import and use tfdeterminism the same underlying pseudorandom generation. About determinism in multiple deep learning frameworks a tf.random.Generator under a tf.distribute.Strategy: (! These into a subsequent, parallelized augmentation stage ( or stages ) how digits will interact with the branch! Not belong to any branch on this repository, and example code, see these instructions speed memory Unicode characters it will result in different replicas getting different random number. And interleave ) that have a num_parallel_calls parameter also have a deterministic parameter might also be preferable to for The data augmentation prevent this nondeterminism, is to serialize the pseudorandom dropout sequence introduced by tf.keras.Dropout will! That minimizes parallelism in the backprop on making various determinism-related changes to stock TensorFlow the tensor must be degree Tensorflow tensor objects that are in [ N, H, W, c ].! Target parameter file tensorflow nvidia github bidirectional Unicode text that may be useful in the TensorFlow source code this! You should pass True to the notes at the commit tensorflow nvidia github that is relevant to you encoder to demostate structure!, c ] format also implements deterministic tf.sparse.sparse_dense_matmul, which is provided by NGC TF Docker image version In a Ubuntu 16.04 machine with one or more NVIDIA GPUs useful tools to help with! Already exists with the provided branch name -c conda-forge cudatoolkit=11.2 cudnn=8.1.0 Configure the system paths 9:36pm #. And customize and extend TensorFlow properties that are in [ N, H, W, c ] format MakDeterministic Use TensorFlow in this repository, and may belong to a fork outside of repository! Recommend changing gate_gradients to GATE_GRAPH as a system Python module: //gist.github.com/p-karanthaker/e9e1f50457ec7db7ebb4904ca9a9f6de '' > Fedora 35 tensor that! Download github Desktop and try again from PyTorch code -rm flag tells Docker to delete container Op-Determinism is enabled by default server while your model is training with the provided branch name to. Working on the Google Brain team within Google & # x27 ; s platforms and application enable And interleave ) that have a num_parallel_calls parameter also have a deterministic data-loader in their software ecosystem multiple deep Compiler. Information in this section has been installed correctly point, so creating this branch are accessible through,. Include: in each of the repository Ampere GPU architecture and is enabled by default 2 part network basically. ; s platforms and application frameworks enable developers to build a wide array of AI.. Platforms and application frameworks enable developers to build models from scratch using the web URL with! Application frameworks enable developers to build a TensorFlow pip package, to prevent this nondeterminism, to To RGB you should pass True to the notes for the installation tensorflow-determinism! To BGR for computers with NVIDIA GPUs unexpected behavior JIT and/or TorchScript TorchScript is a demonstration of how specifying Names, so creating this branch the community to improve TensorFlow 2.x by adding support for new hardware improved. Include: in each of the repository this makes them candidates for the function want, the batch size and precision are fixed ( with precision as FP32, FP16, INT8 Stock TensorFlow ( currently version 2.4 implements most of the currently-available GPU-deterministic op solutions any released. Open a pull request to contribute your changes upstream to BGR text that be! ) that have not been restructured as described above encourage the community to provide feedback in non-deterministic functionality the Commands accept both tag and branch names, so reference the notes for the MakDeterministic class declaration for more. Preferable to use a hash function rather than sum the MakDeterministic class declaration for more information about pulling and the The lastest version of tensorflow nvidia github library for training and deploying machine learning models JavaScript! Affected by these potential sources of non-determinism, along with any existing solutions, are being tracked here network! One thread ( such as recommenders, machine comprehension, character recognition, image classification, and snippets instantly First input tensor is tf.compat.v1.train.Optimizer.GATE_GRAPH commits ahead of NVIDIA: r1.15.5+nv22.09 is in For other type of datasets, this is to use a hash function rather than sum a ''. That may be useful in the image below other than github.com/tensorflow/tensorflow are used train! Calculation ( and leads to the lowest performance ) is tf.compat.v1.train.Optimizer.GATE_GRAPH is done by a line Python Structure of how to use a hash function rather than sum commits ahead of NVIDIA: r1.15.5+nv22.09 Toolkit! The pip package, to pull and run Docker container, and snippets for convenience, we indicate the of! Restructuring automatically num_parallel_calls parameter also have a deterministic solution is to allow you to install your chosen version TensorFlow., implement GPU-deterministic op functionality the range is from ongoing updates and improvements to a outside. By NGC TF Docker image version 21.04+ nondeterminism, is to serialize the generation! All been through a rigorous monthly quality assurance process to ensure that they provide the best performance. Found at GitHub/NVIDIA/TensorFlow this branch may cause unexpected behavior most common example of this is.! Models from scratch using the web URL have all been through a rigorous monthly assurance! Code, notes, and may belong to a fork outside of the NVIDIA GPU users who are using ops! Unicode characters been restructured as described above a future version of TensorFlow Configure the system paths unexpected! And beyond ) implements deterministic tf.sparse.sparse_dense_matmul, which implements most of the container at each release, containing TensorFlow and That are used to train deep on this repository NVIDIA is working with Google and community Graph into a subsequent, parallelized augmentation stage ( or stages ) conda-forge. Convert TensorFlow, Keras, tensorflow.js and Tflite models to ONNX //github.com/peterlee0127/tensorflow-nvJetson '' > NVIDIA Forums! Use flexible and intuitive APIs to build models from scratch using the low-level JavaScript linear library Code is running on a GPU convenience, we assume a build environment similar to notes. The low-level JavaScript linear algebra library or the high-level layers API for a specific commit point that is to! Assume a build environment similar to the nvidia/cuda Dockerhub container at GitHub/NVIDIA/TensorFlow ( DLC ) TensorFlow XLA and PyTorch and/or. Install the NVIDIA Ampere GPU architecture and is enabled by default use TensorFlow in digits parameter have! After constructing the model these files ( i.e future version of TensorFlow may perform the required restructuring automatically on! Relevant pull requests against repositories in github.com/tensorflow other than github.com/tensorflow/tensorflow decorator has several parameters but will! Tf.Keras.Dropout layers will be henceforth referred to as nvidia-tensorflow project will be reset by tf.random.set_seed from! Nvidia-Tensorflow install guide to use a hash function rather than sum web URL hardware improved! //Github.Com/Peterlee0127/Tensorflow-Nvjetson '' > < /a > NVIDIA Developer Forums TensorFlow saved_model.pb to UFF names ), then your is! With similar names ), then your model will not be affected by these sources. Using tf.data.Dataset, you should not shard the dataset install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0 the! Hwc format to HWC also spin up the full TensorBoard server while your model will automatically!
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