tensor2tensor | deep learning models and datasets | Machine Learning library
kandi X-RAY | tensor2tensor Summary
kandi X-RAY | tensor2tensor Summary
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
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Top functions reviewed by kandi - BETA
- 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 .
tensor2tensor Key Features
tensor2tensor Examples and Code Snippets
Lines of Code : 71License : Non-SPDX (NOASSERTION)
Copydata_bin=/data2/mmyin/XLM-experiments/data-bin/xlm-data-bin/zh-en-ldc-32k
export CUDA_VISIBLE_DEVICES=1,2,3,4
export NGPU=4
python -m torch.distributed.launch --nproc_per_node=$NGPU train.py \
--exp_name Supervised_MT \
--exp_id LDC_ch-en_n import re
from gutenberg import acquire
from gutenberg import cleanup
from tensor2tensor.data_generators import problem
from tensor2tensor.data_generators import text_problems
from tensor2tensor.utils import registry
@registry.register_problem
cla echo "No specAugment"
# Set Paths
MODEL=transformer
HPARAMS=transformer_librispeech_v1
PROBLEM=librispeech_clean_small
DATA_DIR=data/no_spec
TMP_DIR=tmp
TRAIN_DIR=train/$PROBLEM
mkdir -p $DATA_DIR $TMP_DIR $TRAIN_DIR
# Generate data
t2t-datagen \
def input_fn():
def dataset_parser(self, value):
"""Parse an ImageNet record from a serialized string Tensor."""
image = self.image_preprocessing_fn(
image_bytes=image_bytes,
is_training=self.is_training,
)
pip3 install tensorflow==1.15.0
python utils/avg_checkpoints.py
--checkpoints path/to/checkpoint1,path/to/checkpoint2
--num_last_checkpoints 2
--output_path where/to/save/the/output
pip3 -V
echo $PATH
g2p-seq2seq --version
Community Discussions
Trending Discussions on tensor2tensor
QUESTION
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
Note:
- CUDA Runtime and cudnn version fits the guide from Tensorflow official documentation.
- I've also tried for TensorFlow-gpu = 2.0, still the same problem.
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?
===================================
Update
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.
...ANSWER
Answered 2021-Jun-15 at 13:04Generally, 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
QUESTION
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:
...ANSWER
Answered 2021-Mar-21 at 14:59rasa 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.
QUESTION
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:
...ANSWER
Answered 2020-Apr-24 at 17:22Apparently 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.
QUESTION
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:
...ANSWER
Answered 2020-Mar-30 at 18:31I 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":
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