Hi, Having done some experiments with TensorFlow on CPU I’ve installed tensorflow-gpu within Docker with nvidia-docker and the tests run nicely. Could somebody please point me to documentation or a HowTo to associate OpenNMT-tf with this set-up.
It depends how you want to use it really.
You could first build a custom image that includes OpenNMT-tf and invokes
docker run as if you were invoking an actual script:
RUN pip install OpenNMT-tf
docker build -t opennmt/opennmt-tf -f Dockerfile .
nvidia-docker run -it --rm --entrypoint onmt-main opennmt/opennmt-tf -h
Of course you need to mount the directories you want to access within the Docker container.
Thanks - that’s a good starting point!
Hi @guillaumekln , It just kept on building until it filled the disk. Can opennmt-tf really occupy 824.5 GB? And I don’t understand why it should be writing into an OpenNMT subdirectory to which no reference was made in the Dockerfile. Any idea what could be happening?
824.5GB is the size of the context passed to
docker build. See the
PATH argument in the command documentation:
You should adapt the commands I shared above to your setup.
I’m trying to install OpenNMT with Tensorflow-gpu using Docker.
My Dockerfile looks like this
RUN pip install OpenNMT-tf
The first command docker build -t opennmt / opennmt-tf -f dockerfile . do not worry me. The second one gives me error messages and I have no idea how to get them away
WARNING: Logging before flag parsing goes to stderr.
W0829 15:45:47.053921 139708002342720 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/opennmt/decoders/rnn_decoder.py:435: The name tf.nn.rnn_cell.RNNCell is deprecated. Please use tf.compat.v1.nn.rnn_cell.RNNCell instead.
W0829 15:45:47.517523 139708002342720 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/opennmt/optimizers/adafactor.py:32: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
W0829 15:45:47.518002 139708002342720 deprecation_wrapper.py:119] From /usr/local/lib/python2.7/dist-packages/opennmt/optimizers/multistep_adam.py:36: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.
My first attempt with Opennmt Lua with GPU and docker worked fine. Was probably easier too
Hi, It’s not quite a direct answer, but have you tried nmtwizard/opennmt-tf? I find that works nicely. Here’s my command for inference via a server on a laptop without a CPU:
docker run -p 5000:5000 -v e:\tf_models\serving:/tf_models/serving nmtwizard/opennmt-tf --model %1 --model_storage /tf_models/serving serve --host 0.0.0.0 --port 5000
These are warnings. You can just ignore them.
I can ignore the warnings, but I am sure the container is not available or not build
The command above uses
--rm which deletes the container when it exits.
Hi, thanks for the tip. I tried it first without mounting directories, but it does not work.
The easy way: sudo nvidia-docker run nmtwizard/opennmt-tf
I get some warnings and this error message: entrypoint.py: error: too few arguments.
Hi, Sorry: the script I gave you was for running on my Windows laptop without a GPU. The Linux GPU command is:
CUDA_VISIBLE_DEVICES=0 nvidia-docker run -p 5000:5000 -v$PWD:/home/miguel nmtwizard/opennmt-tf --model ned2eng_0104 --model_storage /home/miguel/tf_experiments/ned2eng_tf/serving serve --host 0.0.0.0 --port 5000
Both are running as I write this. You need to make sure everything is adapted to your local system.