⬆️ Update for TensorFlow 2.5+2.6

master
Gerber, Mike 3 years ago
parent 5e20d251d6
commit 5d05953b5f

@ -25,12 +25,42 @@ GPU available: True
Results
=======
As of 2020-09, the only combinations that are working:
As of 2021-10, the only combinations that are working:
* TensorFlow 1.15.3 using CUDA Toolkit 10.0
* TensorFlow 2.3.0 using CUDA Toolkit 10.1
* (TensorFlow 2.3.0 using CUDA Toolkit 10.1)
* TensorFlow 2.4.3 using CUDA Toolkit 11.0
* TensorFlow 2.5.1 & TensorFlow 2.6.0 using CUDA Toolkit 11.1 & 11.2.1
This is only for pip-installable TensorFlow, not self-compiled nor Anaconda.
We also did not test other TensorFlow versions. Note that these are the
CUDA *Toolkit* versions, not the CUDA version the driver supports (reported
by `nvidia-smi`).
This is only for pip-installable TensorFlow from PyPI, not self-compiled nor
Anaconda. Note that these are the CUDA *Toolkit* versions, not the CUDA version
the driver supports (reported by `nvidia-smi`).
Full log `run-docker-compatibility-matrix`
------------------------------------------
~~~
tensorflow-gpu 1.15 nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04 True
tensorflow-gpu 1.15 nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 False
tensorflow-gpu 1.15 nvidia/cuda:10.2-cudnn7-runtime-ubuntu18.04 False
tensorflow-gpu 1.15 nvidia/cuda:11.0-cudnn8-runtime-ubuntu18.04 False
tensorflow-gpu 1.15 nvidia/cuda:11.1-cudnn8-runtime-ubuntu18.04 False
tensorflow-gpu 1.15 nvidia/cuda:11.2.1-cudnn8-runtime-ubuntu18.04 False
tensorflow 2.4.3 nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04 False
tensorflow 2.4.3 nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 False
tensorflow 2.4.3 nvidia/cuda:10.2-cudnn7-runtime-ubuntu18.04 False
tensorflow 2.4.3 nvidia/cuda:11.0-cudnn8-runtime-ubuntu18.04 True
tensorflow 2.4.3 nvidia/cuda:11.1-cudnn8-runtime-ubuntu18.04 False
tensorflow 2.4.3 nvidia/cuda:11.2.1-cudnn8-runtime-ubuntu18.04 False
tensorflow 2.5.1 nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04 False
tensorflow 2.5.1 nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 False
tensorflow 2.5.1 nvidia/cuda:10.2-cudnn7-runtime-ubuntu18.04 False
tensorflow 2.5.1 nvidia/cuda:11.0-cudnn8-runtime-ubuntu18.04 False
tensorflow 2.5.1 nvidia/cuda:11.1-cudnn8-runtime-ubuntu18.04 True
tensorflow 2.5.1 nvidia/cuda:11.2.1-cudnn8-runtime-ubuntu18.04 True
tensorflow 2.6.0 nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04 False
tensorflow 2.6.0 nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 False
tensorflow 2.6.0 nvidia/cuda:10.2-cudnn7-runtime-ubuntu18.04 False
tensorflow 2.6.0 nvidia/cuda:11.0-cudnn8-runtime-ubuntu18.04 False
tensorflow 2.6.0 nvidia/cuda:11.1-cudnn8-runtime-ubuntu18.04 True
tensorflow 2.6.0 nvidia/cuda:11.2.1-cudnn8-runtime-ubuntu18.04 True
~~~

@ -2,7 +2,9 @@ ARG BASE_IMAGE
FROM $BASE_IMAGE
ARG tf
ARG tensorflow_package
ARG tensorflow_version
ARG test_nvidia_options
RUN apt-get update && \
apt-get install -y python3 python3-distutils curl &&\
@ -11,9 +13,9 @@ RUN apt-get update && \
curl -sSL https://bootstrap.pypa.io/get-pip.py -o get-pip.py && \
python3 get-pip.py && \
rm -f get-pip.py
COPY assets/requirements-$tf.txt /tmp
RUN pip install --no-cache-dir -r /tmp/requirements-$tf.txt
RUN pip install --no-cache-dir "$tensorflow_package == $tensorflow_version"
COPY test-nvidia /usr/bin
CMD ["/usr/bin/test-nvidia"]
ENV test_nvidia_options $test_nvidia_options
CMD /usr/bin/test-nvidia $test_nvidia_options

@ -1 +0,0 @@
tensorflow-gpu == 1.15.*

@ -1 +0,0 @@
tensorflow == 2.*

@ -1,17 +1,29 @@
#!/bin/sh
set -e
for tf in tf1 tf2; do
for tensorflow_version in 1.15 2.4.3 2.5.1 2.6.0; do
# Note: CUDA 11.0 only with CUDNN 8
for BASE_IMAGE in \
nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04 \
nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 \
nvidia/cuda:10.2-cudnn7-runtime-ubuntu18.04 \
nvidia/cuda:11.0-cudnn8-runtime-ubuntu18.04 \
nvidia/cuda:11.1-cudnn8-runtime-ubuntu18.04 \
nvidia/cuda:11.2.1-cudnn8-runtime-ubuntu18.04 \
; do
echo "== $tf $BASE_IMAGE"
# According to the docs at https://www.tensorflow.org/install/gpu, we should
# use different package names depending on the major version of TF.
if echo $tensorflow_version | grep -q ^1; then
tensorflow_package=tensorflow-gpu
else
tensorflow_package=tensorflow
fi
echo -n "$tensorflow_package $tensorflow_version $BASE_IMAGE "
work_dir=`dirname $0`
image_id=`docker build -q --build-arg tf=$tf --build-arg BASE_IMAGE=$BASE_IMAGE -f assets/Dockerfile $work_dir`
image_id=`docker build -q --build-arg test_nvidia_options="--quiet" --build-arg tensorflow_package=$tensorflow_package --build-arg tensorflow_version=$tensorflow_version --build-arg BASE_IMAGE=$BASE_IMAGE -f assets/Dockerfile $work_dir`
docker run --gpus all -it --rm -e TF_CPP_MIN_LOG_LEVEL=2 $image_id
docker rmi $image_id >/dev/null || true
done

@ -1,13 +1,23 @@
#!/usr/bin/python3
import os
import argparse
parser = argparse.ArgumentParser(description="Test if GPU is available in TensorFlow")
parser.add_argument('--quiet', dest='quiet', action='store_true')
parser.set_defaults(quiet=False)
args = parser.parse_args()
if not args.quiet:
os.system('nvidia-smi -L')
if not 'TF_CPP_MIN_LOG_LEVEL' in os.environ:
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' # '1' means >= WARN
import tensorflow as tf
if not args.quiet:
print('TensorFlow', tf.__version__)
with tf.compat.v1.Session() as sess:
hello = tf.constant('Hello, TensorFlow!')
@ -17,6 +27,12 @@ with tf.compat.v1.Session() as sess:
if hasattr(tf.config, 'list_physical_devices'):
# TensorFlow 2
is_gpu_available = len(tf.config.list_physical_devices('GPU')) > 0
if not args.quiet:
print('GPU available:', is_gpu_available)
else:
print(is_gpu_available)
else:
if not args.quiet:
print('GPU available:', tf.test.is_gpu_available(cuda_only=True))
else:
print(tf.test.is_gpu_available(cuda_only=True))

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