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Bump training dependencies to TensorFlow 2.3.1

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This commit is contained in:
Reuben Morais 2021-01-02 13:28:04 +00:00
parent 08c2c348f7
commit 85b9f0fd3d
3 changed files with 5 additions and 5 deletions
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2
Dockerfile.train.tmpl
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@ -1,6 +1,6 @@
# Please refer to the TRAINING documentation, "Basic Dockerfile for training"
FROM tensorflow/tensorflow:1.15.4-gpu-py3
FROM tensorflow/tensorflow:2.3.1-gpu
ENV DEBIAN_FRONTEND=noninteractive
ENV DEEPSPEECH_REPO=#DEEPSPEECH_REPO#

6
doc/TRAINING.rst
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@ -10,7 +10,7 @@ Prerequisites for training a model
* `Python 3.6 <https://www.python.org/>`_
* Mac or Linux environment
* CUDA 10.0 / CuDNN v7.6 per `Dockerfile <https://hub.docker.com/layers/tensorflow/tensorflow/1.15.4-gpu-py3/images/sha256-a5255ae38bcce7c7610816c778244309f8b8d1576e2c0023c685c011392958d7?context=explore>`_.
* CUDA 10.1 / CuDNN v7.6 per `Dockerfile <https://hub.docker.com/layers/tensorflow/tensorflow/2.3.1-gpu/images/sha256-1d0736e46ae9a961c2111394a43e0bfd266e6151a90d613b6f86229cf01e40e5?context=explore >`_.
Getting the training code
^^^^^^^^^^^^^^^^^^^^^^^^^
@ -72,7 +72,7 @@ If you have a capable (NVIDIA, at least 8GB of VRAM) GPU, it is highly recommend
.. code-block:: bash
pip3 uninstall tensorflow
pip3 install 'tensorflow-gpu==1.15.4'
pip3 install 'tensorflow-gpu==2.3.1'
Please ensure you have the required `CUDA dependency <https://www.tensorflow.org/install/source#gpu>`_ and/or :ref:`Prerequisites <cuda-training-deps>`.
@ -83,7 +83,7 @@ It has been reported for some people failure at training:
tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node tower_0/conv1d/Conv2D}}]]
Setting the ``TF_FORCE_GPU_ALLOW_GROWTH`` environment variable to ``true`` seems to help in such cases. This could also be due to an incorrect version of libcudnn. Double check your versions with the :ref:`TensorFlow 1.15 documentation <cuda-training-deps>`.
Setting the ``TF_FORCE_GPU_ALLOW_GROWTH`` environment variable to ``true`` seems to help in such cases. This could also be due to an incorrect version of libcudnn. Double check your versions with the :ref:`TensorFlow 2.3 documentation <cuda-training-deps>`.
Basic Dockerfile for training
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

2
setup.py
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@ -74,7 +74,7 @@ def main():
]
tensorflow_pypi_dep = [
'tensorflow == 1.15.4'
'tensorflow == 2.3.1'
]
# Due to pip craziness environment variables are the only consistent way to