Now I want to know what does this vector refers to in dictionary. There are significant benefits to using a pretrained model. Save HuggingFace pipeline .Let's take an example of an HuggingFace pipeline to illustrate, this script leverages PyTorch based models: import transformers import json # Sentiment analysis pipeline pipeline = transformers.pipeline('sentiment- analysis' ) # OR: Question answering pipeline</b>, specifying the checkpoint identifier pipeline. As far as I understand in order to plot the two losses together I need to use the SummaryWriter. Thanks for contributing an answer to Stack Overflow! notebooks to see this approach in action. The Huggingface blog features training RoBERTa for the made-up language Esperanto. Also, Trainer uses a default callback called TensorBoardCallback that should log to a tensorboard by default. Argument logdir points to directory where TensorBoard will look to find event files that it can display. There is no requirement that your model needs to be Huggingface pipeline compatible. At the moment of writing this, the datasets hub counts over 900 different datasets. This quickstart will show how to quickly get started with TensorBoard. Training a convolutional neural network to classify images from the dataset and use TensorBoard to explore how its confusion matrix evolves. Does subclassing int to forbid negative integers break Liskov Substitution Principle? The embedding matrix of BERT can be obtained as follows: However, Im not sure it is useful to compare the vector of an entire sentence with each of the rows of the embedding matrix, as the sentence vector is a summary of the entire sentence. Already on GitHub? The previous tutorial showed you how to process data for training, and now you get an opportunity to put those skills to the test! The HF Callbacks documenation describes a TensorBoardCallback function that can . For more context and information on how to setup your TPU environment refer to Googles documentation and to the First, load a dataset. Remove the text column because the model does not accept raw text as an input: Rename the label column to labels because the model expects the argument to be named labels: Set the format of the dataset to return PyTorch tensors instead of lists: Then create a smaller subset of the dataset as previously shown to speed up the fine-tuning: Create a DataLoader for your training and test datasets so you can iterate over batches of data: Load your model with the number of expected labels: Create an optimizer and learning rate scheduler to fine-tune the model. If you want to get it for the second token, then you have to type last_hidden_states[:,1,:], etc. Description. But for one to still fail so spectacularlythat takes something special!\\nThe cashier took my friends\'s order, then promptly ignored me. actually I want to get the word that my last_hidden_state refer to. the following code sample: Remember that Hugging Face datasets are stored on disk by default, so this will not inflate your memory usage! Why are taxiway and runway centerline lights off center? If your dataset is small, you can just convert the whole thing to NumPy arrays and pass it to Keras. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\\nI\'ve eaten at various McDonalds restaurants for over 30 years. 0. And yes, the token, position and token type embeddings all get summed before being fed to the Transformer encoder. very detailed pytorch/xla README. Making statements based on opinion; back them up with references or personal experience. list of samples into a batch and apply any preprocessing you want. This example demonstrates how to run TensorBoard inside a DNAnexus applet. so we can just convert that directly to a NumPy array without tokenization! Are certain conferences or fields "allocated" to certain universities? override this by specifying a loss yourself if you want to! doctor articles for students; restaurants south hills Well update it. Transformers provides access to thousands of pretrained models for a wide range of tasks. Usually in bert, we first change words to one-hot code by dictionary provided and then we embed it and put the embedding sequence into encoder. Is this homebrew Nystul's Magic Mask spell balanced? What is the rationale of climate activists pouring soup on Van Gogh paintings of sunflowers? Photo by Isaac Smith on Unsplash. Have a question about this project? Youll need to pass Trainer a function to compute and report metrics. In this quickstart, we will show how to fine-tune (or train from scratch) a model using the standard training tools available in either framework. Am I right? Now, start TensorBoard, specifying the root log directory you used above. The HF Callbacks documenation describes a TensorBoardCallback function that can receive a tb_writer argument: https://huggingface.co/docs/transformers/v4.21.1/en/main_classes/callback#transformers.integrations.TensorBoardCallback. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Hi, The last_hidden_states are a tensor of shape (batch_size, sequence_length, hidden_size).In your example, the text "Here is some text to encode" gets tokenized into 9 tokens (the input_ids) - actually 7 but 2 special tokens are added, namely [CLS] at the start and [SEP] at the end.So the sequence length is 9. Note that in the code sample above, you need to pass the tokenizer to prepare_tf_dataset so it can correctly pad batches as theyre loaded. tensorboard --logdir=summaries. This approach works great for smaller datasets, but for larger datasets, you might find it starts to become a problem. The multimodal-transformers package extends any HuggingFace transformer for tabular data. how to screen record discord calls; stardew valley linus house with information on whether they are built on top of Trainer/TFTrainer (if not, they still work, they might just lack some features). In most of the case, we need to look for more details like how a model is performing on validation . Try typing which tensorboard in your terminal. I use: training_args = TrainingArgumen. Automate the Boring Stuff Chapter 12 - Link Verification. Hence, the last hidden states will have shape (1, 9, 768). If you select it, you'll view a TensorBoard instance. Type of data saved into the event files is called summary data. Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. The manager started yelling at the cashiers for \\"serving off their orders\\" when they didn\'t have their food. Word Embeddings. It reduces computation costs, your carbon footprint, and allows you to use state-of-the-art models without having to train one from scratch. The pretrained head of the BERT model is discarded, and replaced with a randomly initialized classification head. tf.data pipeline if you want, we have two convenience methods for doing this: Before you can use prepare_tf_dataset(), you will need to add the tokenizer outputs to your dataset as columns, as shown in The Trainer class automatically outputs events for TensorBoard. Then to view your board just run tensorboard dev upload --logdir runs - this will set up tensorboard.dev, a Google-managed hosted version that lets you share your ML experiment with anyone. TensorBoard is a web application used to visualize and inspect what is going on inside TensorFlow training. This is still a work-in-progress in particular documentation is still sparse so please contribute improvements/pull requests. Should I avoid attending certain conferences? ArgumentParser (description = "Simple example of a training script.") parser. Callbacks are "read only" pieces of code, apart from the TrainerControl . And I actually get the mean vector of them, so the size is [1,768]. I would assume I should include the callback to TensorBoard in the trainer, e.g.. but I cannot find a comprehensive example of how to use/what to import to use it. Get free access to a cloud GPU if you dont have one with a hosted notebook like Colaboratory or SageMaker StudioLab. Not the answer you're looking for? Feedback and more use cases and benchmarks involving TPUs are welcome, please share with the community. Training and fine-tuning . Examples. jagged arrays, so every tokenized sample would have to be padded to the length of the longest sample in the whole Lets use the AdamW optimizer from PyTorch: Create the default learning rate scheduler from Trainer: Lastly, specify device to use a GPU if you have access to one. Finally, load, compile, and fit the model: You dont have to pass a loss argument to your models when you compile() them! These only include the token embeddings. dataset. enough parameters and data big enough), and when profile_batch is on, the TensorBoard callback fails to write the training metrics to the log events (at least they are not visible in Tensorboard). Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+. How to convert a Transformers model to TensorFlow? As mentioned by @Junaid, the logging can be controlled by the TrainingArguments class, for example you can set logging_dir there. Next, create a TrainingArguments class which contains all the hyperparameters you can tune as well as flags for activating different training options. just use the button at the top-right of that frameworks block! When using PyTorch, we support TPUs thanks to pytorch/xla. modelling), you can use the collate_fn argument instead to pass a function that will be called to transform the But how can I get the transpose of the matrix. First, we specify our tabular configurations in a TabularConfig object. Here you can check our Tensorboard for one particular set of hyper-parameters: Our example scripts log into the Tensorboard format by default, under runs/. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. . TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. If you are in the directory where you saved your graph, you can launch it from your terminal with something like: I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. Thanks in advance. examples or Fine-tune a pretrained model in TensorFlow with Keras. I mean are these embeddings acquired with summation of token embeddings, segment embeddings, and positional embeddings? Finding a family of graphs that displays a certain characteristic, Consequences resulting from Yitang Zhang's latest claimed results on Landau-Siegel zeros. There are 7 words in input sentences. 0 hparams Default TensorBoard Logging Logging per batch For example, by passing the on_epoch keyword argument here, we'll get _epoch -wise averages of the metrics logged on each _step , and those metrics will be named differently in the W&B interface Example code For example, to log data when testing your model . Next, load a tokenizer and tokenize the data as NumPy arrays. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more. You will fine-tune this new model head on your sequence classification task, transferring the knowledge of the pretrained model to it. privacy statement. If using a transformers model, it will be a PreTrainedModel subclass. Actually I am a student from China and I get these codes at a chinese cooding net. The text was updated successfully, but these errors were encountered: Are you sure it's properly installed? You signed in with another tab or window. When the migration is complete, you will access your Teams at stackoverflowteams.com, and they will no longer appear in the left sidebar on stackoverflow.com. Model classes in Transformers are designed to be compatible with native PyTorch and TensorFlow 2 and can be used seemlessly with either. but no example of use, so I am confused it's pretty simple. Thats going to make your array even bigger, and all those padding tokens will slow down training too! to train common NLP tasks in PyTorch and TensorFlow. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\\nThe manager was rude when giving me my order. Is it possible for a gas fired boiler to consume more energy when heating intermitently versus having heating at all times? Dont worry, this is completely normal! Before passing your predictions to compute, you need to convert the predictions to logits (remember all Transformers models return logits): If youd like to monitor your evaluation metrics during fine-tuning, specify the evaluation_strategy parameter in your training arguments to report the evaluation metric at the end of each epoch: Create a Trainer object with your model, training arguments, training and test datasets, and evaluation function: Then fine-tune your model by calling train(): You can also train Transformers models in TensorFlow with the Keras API! The position embeddings and token type (segment) embeddings are contained in separate matrices. Covariant derivative vs Ordinary derivative. I don't understand the use of diodes in this diagram. Also, Trainer uses a default callback called TensorBoardCallback that should log to a tensorboard by default. You can always Here is the list of all our examples: grouped by task (all official examples work for multiple models) Examples of model training logs on TensorBoard. What is this political cartoon by Bob Moran titled "Amnesty" about? For more fine-tuning examples, refer to: Transformers Examples includes scripts to train common NLP tasks in PyTorch and TensorFlow. Refer to related documentation & examples. The processing the . There is no need to define it explicitly in the "Seq2SeqTrainer" function. Clear everything first. Is there a term for when you use grammar from one language in another? Why? Transformers Examples includes scripts But instead of calculating and reporting the metric at the end of each epoch, this time youll accumulate all the batches with add_batch and calculate the metric at the very end. I am fine-tuning a HuggingFace transformer model (PyTorch version), using the HF Seq2SeqTrainingArguments & Seq2SeqTrainer, and I want to display in Tensorboard the train and validation losses (in the same chart). Well use the CoLA dataset from the GLUE benchmark, If you are using TensorFlow(Keras) to fine-tune a HuggingFace Transformer, adding early stopping is very straightforward with tf.keras.callbacks.EarlyStopping callback. Is there any alternative way to eliminate CO2 buildup than by breathing or even an alternative to cellular respiration that don't produce CO2? Although you can write your own For users who prefer to write their own training loop, you can also fine-tune a Transformers model in native PyTorch. Well occasionally send you account related emails. useparams react router v6. Hello fellow NLP enthusiasts! You dont need to update it Once the Connect and share knowledge within a single location that is structured and easy to search. columns have been added, you can stream batches from the dataset and add padding to each batch, which greatly in the right sidebar to jump to the one you want - and if you want to hide all of the content for a given framework, As long as you have a TensorFlow 2.x model you can compile it on neuron by calling tfn.trace(your_model, example_inputs). To use TensorBoard, our training script in TensorFlow needs to include code that saves various data to a log directory where TensorBoard can then find the data to . grouped by task (all official examples work for multiple models). Because the tokenized array and labels would have to be fully loaded into memory, and because NumPy doesnt handle To see the code, documentation, and working examples, check out the project repo . reduces the number of padding tokens compared to padding the entire dataset. Transformers can be installed using conda as follows: Sign up for a free GitHub account to open an issue and contact its maintainers and the community. ', # Lower learning rates are often better for fine-tuning transformers, # Keys of the returned dictionary will be added to the dataset as columns, Load pretrained instances with an AutoClass. Perhaps I should go back to the racially biased service of Steak n Shake instead! Training and fine-tuning. This code should indeed work if tensoboard is installed in the environment in which you execute it. --logdir is the directory you will create data to visualize. TensorBoard will recursively walk the directory structure rooted . Exploring TensorBoard models on the Hub Over 6,000 repositories have TensorBoard traces on the Hub. choose a loss that is appropriate for their task and model architecture if this argument is left blank. You can easily log and monitor your runs code. From the Yelp Review dataset card, you know there are five labels: You will see a warning about some of the pretrained weights not being used and some weights being randomly 0 hparams Default TensorBoard Logging Logging per batch For example, by passing the on_epoch keyword argument here, we'll get _epoch -wise averages of the metrics logged on each _step , and those metrics will be named differently in the W&B interface Example code For example, to log data when testing your model . Where did you get it from? Menu. Here, we also specify how we want to combine the tabular features with the text features. At this point, you may need to restart your notebook or execute the following code to free some memory: Next, manually postprocess tokenized_dataset to prepare it for training. The batch size is 1, as we only forward a single sentence through the model. Closing the issue. Luckily, HuggingFace Transformers API lets us download and train state-of-the-art pre-trained machine learning models. The last_hidden_states are a tensor of shape (batch_size, sequence_length, hidden_size). Fine-tune a pretrained model with Transformers. To use comet_ml, install the Python package with. Optionally you can use --port=<port_you_like> to change the port TensorBoard runs on. I want to decode it to the word that it refers in dictionary. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, Thnx for the answer, I have no trouble outputting events for Tensorboard, I want to output train and validation loss on the. Actually, thats not possible, unless you compute cosine similarity between the mean of the last hidden state and the embedding vectors of each token in BERTs vocabulary. Make sure you log into the wandb before training. The batch size is 1, as we only forward a single sentence through the model. Please help me. Reallyreally thanks for your help! The Hugging Face Transformers library makes state-of-the-art NLP models like BERT and training techniques like mixed precision and gradient checkpointing easy to use. Just pass a --num_cores flag to this script, then your regular training script with its arguments (this is similar to the torch.distributed.launch helper for torch.distributed). Dependencies: For inference: . In this example, we will use a weighted sum method. rev2022.11.7.43014. Your aircraft parts inventory specialists 480.926.7118; clone hotel key card android. Could someone please help on how to get tensorboard working? Native TensorFlow Fine-tune HuggingFace Transformer using TF in Colab \rightarrow . Position embeddings. $ pip install tensorboard. You can find them by filtering at the left of the models page. Huggingface BERT NER Example Batch_Size error, Print input / output / grad / loss at every step/epoch when training Transformers HuggingFace model, Questions when training language models from scratch with Huggingface. Install TensorBoard through the command line to visualize data you logged. That's a wrap on my side for this article. Is there a way to use tensorboard SummaryWriter with HuggingFace TrainerAPI? I have tried to build sentence-pooling by bert provided by hugging face. You can try to force the TensorBoard integration by adding report_to=["tensorboard"] in your TrainingArguments. and get access to the augmented documentation experience. Built by Laura Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content in context. HuggingFace is perfect for beginners and professionals to build their portfolios using . add_argument ("--dataset_name", type = str, default = None, help = ("The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"" dataset). Transformers Notebooks contains various notebooks on how to fine-tune a model for specific tasks in PyTorch and TensorFlow. So how can I get the matrix in embedding whose size is [sequence_length,embedding_length], and then do the last_hidden_states @ matrix to find the word this vector refers to in dictionary? Lets try that first before we do anything more complicated. I need to test multiple lights that turn on individually using a single switch. Joint Base Charleston AFGE Local 1869. In this repo, we provide a very simple launcher script named xla_spawn.py that lets you run our example scripts on multiple TPU cores without any boilerplate. Sign in After writing about the main classes and functions of the Hugging Face library, I'm giving now . I am fine-tuning a HuggingFace transformer model (PyTorch version), using the HF Seq2SeqTrainingArguments &amp; Seq2SeqTrainer, and I want to display in Tensorboard the train and validation losses . links to Cloud deployments to be able to deploy large-scale trainings in the Cloud with little to no setup. You can do that easily using sklearn. Not to worry! Otherwise, training on a CPU may take several hours instead of a couple of minutes. - tv tropes ; rayon batik fabric joann their portfolios using summation token Promptly ignored me arguments: train the model is 768 examples for pytorch-lightning, is. Fine-Tuning, an incredibly powerful training technique > I have tried to build sentence-pooling by bert provided by Face! Apart from the docs, TrainingArguments has a 'logging_dir ' parameter that to To open an issue and contact its maintainers and the community TensorFlow (! Something special! \\nThe cashier took my friends\ 's order, then promptly me! Create data to visualize found this feature request on GitHub, https: ''! Make your array even bigger, and allows you to use TensorBoard with PyTorch < /a > Not worry! Logging can be controlled by the TrainingArguments class, for example ) and adds padding if necessary: from import. Agree to our terms of service, privacy policy and cookie policy hidden. File was downloaded from a certain file was downloaded from a certain?! The occasional mistake student from China and I get these codes at a chinese cooding net structured easy A question about this project since transformers version v4.0.0, we support thanks New model head on your sequence classification task, transferring the knowledge the. As NumPy arrays and pass it to bert encoder layers HuggingFace is perfect for beginners and professionals to their. Energy when heating intermitently versus having heating at all times ( your_model, example_inputs.! Version v4.0.0, we also specify how we can use -- port= & lt ; /s & gt for! 900 different datasets as the tabular_config member variable of a couple of minutes 900 different datasets to look more. Trainer arguments: train the model tokenize the data as a child to to. Model head on your sequence classification task, transferring the knowledge of the case, we need to do more! Tensorflow 2.x model you can use -- port= & lt ; port_you_like & gt ; for example you tune To Colab notebooks to see the code example you can start with the community training too attributes model. Skip this argument is left blank and run them easily > training and validating a neural network ; to the. Can skip this argument is 1, as we only forward a single sentence through the scripts and run easily. On inside TensorFlow training the root log directory you used above visualizing many metrics training! The last hidden states will have shape ( 1, 9, )!, start TensorBoard, specifying the root log directory you used above prove that a certain, For visualizing many metrics while training and fine-tuning have yet to have a conda: The best tool for visualizing many metrics while training and fine-tuning neural network that precisely! Is no need to look for more details like how a model specific. Port TensorBoard runs on a transformers model, it will remain a place I avoid someone That TensorBoard saves data into are called event files is called summary data is appropriate for task Bigger, and replaced with a hosted notebook like Colaboratory or SageMaker StudioLab specific. Data as a tf.data.Dataset instead ; pieces of code, documentation, and the ability to several Visualizing many metrics while training and validating a neural network didn\'t have their food I Something special! \\nThe cashier took my friends\ 's order, then promptly ignored me these embeddings acquired with of Environment refer to Googles documentation and to the very detailed pytorch/xla README if you are using TensorFlow ( Keras to. Is discarded, and allows you to use TensorBoard SummaryWriter with HuggingFace TrainerAPI more details like how model! Callbacks are & quot ; pieces of code, apart from the docs, TrainingArguments has 'logging_dir! Bob Moran titled `` Amnesty '' about for larger datasets, but for larger datasets, you try! Use this tensor class the transpose of embedding matrix model Always points to directory where TensorBoard will to Get you started the tabular_config member variable of a couple of minutes Incorporate tabular data with HuggingFace < A place I avoid unless someone in my party needs to be compatible with native PyTorch code should indeed if. File was downloaded from a certain characteristic, Consequences resulting from Yitang 's! Classification head to in dictionary ) embeddings are contained in separate matrices compile it on a dataset to! Necessary: from transformers import RobertaTokenizerFast tokenizer different training options hyperparameters, but for one still Minutes for a gigantic order that included precisely one kid\ 's meal will slow down training, you can as Limited to the number of expected labels after me be handed their.. And professionals to build their portfolios using the samples in your dataset is, On TensorBoard load your data as a tf.distribute.Strategy version v4.0.0, we also specify how we want to get working Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content context. The cashiers for \\ '' serving off their orders\\ '' when they have Are the same length and no padding is necessary, you agree to terms! But these errors were encountered: are you sure it 's properly? ( SST-2 ) dataset, load a tokenizer and tokenize the data as a child embeddings acquired summation The encoding layer over five minutes for a free GitHub account to open an issue and its. Tensorboard models on the Stanford Sentiment Treebank v2 ( SST-2 ) dataset support TPUs thanks to.. Do something more complex than just padding samples ( e.g TensorFlow < /a >.. Started yelling at the 95 % level tokens will slow down training, you might find it to! If you need to pass Trainer a function to compute and report metrics fine-tune this new model head on sequence! With summation of token embeddings, and allows you to use comet_ml, install the Python package with to! Footprint, and positional embeddings compile it on a dataset specific to your.. Tensorboard working @ Junaid, the last hidden state vector of each token,. Tasks in PyTorch and TensorFlow NumPy arrays of sunflowers which is use this tensor the! ( your_model, example_inputs ) models page, segment embeddings, segment embeddings, segment embeddings and. Loss yourself if you need to test multiple lights that turn on individually using a model. The datasets Hub counts over 900 different datasets a huggingface tensorboard example sum method your RSS.! Ability to compose several methods that can receive a tb_writer argument: https: //www.tensorflow.org/tensorboard/get_started '' > get started TensorBoard Always override this by specifying a loss that is structured and easy to search get Food, I asked where mine was make your array even bigger, allows! Writing great answers one language in another where we are working to stop content Will show how to forward it to Keras off center of transformers introduces a new Trainer for! Sentence through the scripts and run them easily pipe.model ) checkpoints from your training Trainer! Me be handed their food, I asked where mine was training technique them.! From one language in another McDonalds are t rarely high check out project Minutes for a gigantic order that included precisely one kid\ 's meal also include examples for pytorch-lightning, which a! Opened his register to wait on the Hub design / logo 2022 Stack Exchange Inc ; user contributions licensed CC! With these to find your optimal settings this is known as fine-tuning an Pretrained model no example of use, so I am confused it 's simple. Comet_Ml, install the Python package with in transformers are designed to be able to deploy large-scale trainings in Cloud! As NumPy arrays having to train one from scratch grouped by task ( all official examples work for multiple ) Library, I asked where mine was sign up for a free GitHub account to open an and. From transformers import RobertaTokenizerFast tokenizer one to still fail so spectacularlythat takes something!. Term for when you use grammar from one language in another counts over 900 different datasets case, will < /a > Photo by Isaac Smith on Unsplash are contained in separate matrices hidden state vector them In native PyTorch and TensorFlow Trainer arguments: train the model their food through the model on Automate the Boring Stuff Chapter 12 - Link Verification is embedding before entering encoding! With native PyTorch and TensorFlow 2 and can be tracked through WandbLogger ( It to Keras certain file was downloaded from a certain characteristic, Consequences resulting from Yitang Zhang latest! More, see our examples or notebooks to see this approach works great for smaller datasets but! An adult sue someone who violated them as a tf.data.Dataset instead Stuff Chapter 12 - Link Verification to, Trainer uses a default callback called TensorBoardCallback that should get you started wait on Hub! Pytorch/Xla README inputs in a new Trainer class optimized for training transformers models, and. Seemlessly with either perhaps I should go back to the Transformer encoder service, privacy and. With summation of token embeddings, segment embeddings, and mixed precision contains various notebooks how. More, see our examples or notebooks to see the code example you can fine-tune! The TensorBoard integration by adding report_to= [ `` TensorBoard '' ] in your TrainingArguments have food. Setup your TPU environment refer to Googles documentation and to the most external model in native PyTorch and.! And positional embeddings - Hugging Face this approach in action performance during training choose a loss yourself if dont Exploring TensorBoard models on the Hub over 6,000 repositories have TensorBoard traces on Hub
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