tensor2tensor | deep learning models and datasets | Machine Learning library

 by   tensorflow Python Version: 1.15.7 License: Apache-2.0

kandi X-RAY | tensor2tensor Summary

kandi X-RAY | tensor2tensor Summary

tensor2tensor is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. tensor2tensor has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install tensor2tensor' or download it from GitHub, PyPI.

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

            kandi-support Support

              tensor2tensor has a highly active ecosystem.
              It has 13742 star(s) with 3287 fork(s). There are 462 watchers for this library.
              It had no major release in the last 12 months.
              There are 574 open issues and 672 have been closed. On average issues are closed in 173 days. There are 15 open pull requests and 0 closed requests.
              It has a negative sentiment in the developer community.
              The latest version of tensor2tensor is 1.15.7

            kandi-Quality Quality

              tensor2tensor has 0 bugs and 0 code smells.

            kandi-Security Security

              tensor2tensor has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              tensor2tensor code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              tensor2tensor is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              tensor2tensor releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              tensor2tensor saves you 78413 person hours of effort in developing the same functionality from scratch.
              It has 86910 lines of code, 6563 functions and 471 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed tensor2tensor and discovered the below as its top functions. This is intended to give you an instant insight into tensor2tensor implemented functionality, and help decide if they suit your requirements.
            • Perform beam search .
            • Evolve evolved Transformer decoder .
            • Multihead attention .
            • A basic hyperparameters .
            • Input function .
            • Perform a multihead attention .
            • Multi - layer transformer .
            • Transformer .
            • Define wrapper for collect .
            • Apply a convolutional layer .
            Get all kandi verified functions for this library.

            tensor2tensor Key Features

            No Key Features are available at this moment for tensor2tensor.

            tensor2tensor Examples and Code Snippets

            Pythondot img1Lines of Code : 71dot img1License : Non-SPDX (NOASSERTION)
            copy iconCopy
            export CUDA_VISIBLE_DEVICES=1,2,3,4
            export NGPU=4
            python -m torch.distributed.launch --nproc_per_node=$NGPU train.py \
                --exp_name Supervised_MT \
                --exp_id LDC_ch-en_n  
            Defining the Problem
            Pythondot img2Lines of Code : 65dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            import re
            from gutenberg import acquire
            from gutenberg import cleanup
            from tensor2tensor.data_generators import problem
            from tensor2tensor.data_generators import text_problems
            from tensor2tensor.utils import registry
            Pythondot img3Lines of Code : 58dot img3License : Permissive (Apache-2.0)
            copy iconCopy
            echo "No specAugment"
            # Set Paths
            mkdir -p $DATA_DIR $TMP_DIR $TRAIN_DIR
            # Generate data
            t2t-datagen \
            Memory reduction Tensorflow TPU v2/v3 bfloat16
            Pythondot img4Lines of Code : 35dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def input_fn():
              def dataset_parser(self, value):
                """Parse an ImageNet record from a serialized string Tensor."""
                image = self.image_preprocessing_fn(
            no attribute 'contrib' for problems()
            Pythondot img5Lines of Code : 2dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            pip3 install tensorflow==1.15.0
            How to get tensor from multiple models and average them?
            Pythondot img6Lines of Code : 5dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            python utils/avg_checkpoints.py
              --checkpoints path/to/checkpoint1,path/to/checkpoint2
              --num_last_checkpoints 2
              --output_path where/to/save/the/output
            Failed to load the native TensorFlow runtime. when running g2p-seq2seq --version
            Pythondot img7Lines of Code : 6dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            pip3 -V
            echo $PATH
            g2p-seq2seq --version

            Community Discussions


            Using TensorFlow with GPU taking a long time for loading library related to CUDA
            Asked 2021-Jun-15 at 13:04

            Machine Setting:

            • GPU: GeForce RTX 3060

            • Driver Version: 460.73.01

            • CUDA Driver Veresion: 11.2

            • Tensorflow: tensorflow-gpu 1.14.0

            • CUDA Runtime Version: 10.0

            • cudnn: 7.4.1


            1. CUDA Runtime and cudnn version fits the guide from Tensorflow official documentation.
            2. I've also tried for TensorFlow-gpu = 2.0, still the same problem.


            I am using Tensorflow for an objection detection task. My situation is that the program will stuck at

            2021-06-05 12:16:54.099778: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10

            for several minutes.

            And then stuck at next loading process

            2021-06-05 12:21:22.212818: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7

            for even longer time. You may check log.txt for log details.

            After waiting for around 30 mins, the program will start to running and WORK WELL.

            However, whenever program invoke self.session.run(...), it will load the same two library related to cuda (libcublas and libcudnn) again, which is time-wasted and annoying.

            I am confused that where the problem comes from and how to resolve it. Anyone could help?

            Discussion Issue on Github



            After @talonmies 's help, the problem was resolved by resetting the environment with correct version matching among GPU, CUDA, cudnn and tensorflow. Now it works smoothly.



            Answered 2021-Jun-15 at 13:04

            Generally, if there are any incompatibility between TF, CUDA and cuDNN version you can observed this behavior.

            For GeForce RTX 3060, support starts from CUDA 11.x. Once you upgrade to TF2.4 or TF2.5 your issue will be resolved.

            For the benefit of community providing tested built configuration

            CUDA Support Matrix

            Source https://stackoverflow.com/questions/67847219


            Uncomplete installation of the RASA package with the issue: FileNotFoundError: [Errno 2] No such file or directory: 'HISTORY.rst'
            Asked 2021-Apr-01 at 14:19

            i have been using rasa for the past few weeks without problems. But recently i had issues with the installation of Spacy, leading me to uninstall an reinstall python. The issue may have occurred because of some dualities between python3.8 and 3.9 which i wasnt abled to pinpoint.

            After deleting all python version from my computer, i just reinstalled python 3.9.2. and reinstall rasa with:



            Answered 2021-Mar-21 at 14:59

            rasa 2.4 declares compatibility with Python 3.6, 3.7 and 3.8 but not 3.9 so pip is trying to find one compatible with 3.9 or at least one that doesn't declare any restriction. It finds such release at version 0.0.5.

            To use rasa 2.4 downgrade to Python 3.8.

            PS. Don't hurry up to upgrade to the latest Python — 3rd-party packages are usually not so fast. Currently Python 3.7 and 3.8 are the best.

            Source https://stackoverflow.com/questions/66733525


            Getting ModuleNotFoundError only if debug mode is enabled
            Asked 2020-Apr-24 at 17:22

            I have a Flask server which loads Tensorflow models on startup in an external service module.

            The problem is if debug mode is enabled, so FLASK_DEBUG = 1, the app crashes because it is not able to load a certain module from Tensorflow. tensorflow_core.keras to be precise.

            However, running the application without debugging works.

            The project structure looks like this:



            Answered 2020-Apr-24 at 17:22

            Apparently there is a bug in werkzeug which is used by flask to serve flask apps, when running flask apps in debug mode with python -m.

            To prevent this from happening, you can start the app without the -m option, e.g with flask run.

            Source https://stackoverflow.com/questions/61404032


            Cant import Tensorflow 2.2.0rc2 in Google Colab when installed from setup.py
            Asked 2020-Mar-31 at 11:25

            Im trying to import the latest rc2 version of Tensorflow (2.2.0rc2 at this date) in Google Colab, but cant do it when installed from my setup.py install script.

            When i install Tensorflow manually using !pip install tensorflow==2.2.0rc2 from a Colab cell, everything is ok and im able to import Tensorflow.

            The next is how i have my dependencies installation setup in Google Colab:



            Answered 2020-Mar-30 at 18:31

            I found a work around, but this is not the solution to this problem by far, so this will not be accepted as solution, but will help people in same trouble to keep going with their work:

            Install your requirements manually before installing your custom package, in my case, this is pip install -r "/content/deep-deblurring/requirements.txt":

            Source https://stackoverflow.com/questions/60936032

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install tensor2tensor

            This iPython notebook explains T2T and runs in your browser using a free VM from Google, no installation needed. Alternatively, here is a one-command version that installs T2T, downloads MNIST, trains a model and evaluates it:.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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