image for tidymodels, tidyverse, tensorflow, keras with GPU passthrough

[fahadshery]

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Question

Hi,

I am looking to install RStudio in docker using portainer. What's the best image to install that bundles the following packages:

1. Tidyverse
2. Tidymodels
3. Tensorflow (GPU enabled)
4. Keras
5. DBI (for postgers)

Thanks

[cboettig]

Thanks for your question! The best way to install these packages with GPU support is to use the rocker/ml series of images.

Because python projects typically version their python modules at the project level, often requiring different and sometimes conflicting versions, we do not pre-install a globally shared version of these. Instead, these images provide the CUDA environment for GPU use with these packages, which tends to be the more finicky aspect to set up. You can easily install any of the R packages you mention in the usual way, and use their built-in installers to select your chosen version of python modules.

Some further documentation is here: https://rocker-project.org/images/versioned/cuda.html . Please let us know if you have other questions, this helps us flush out our documentation in this relatively new area.

[fahadshery]

Hi,

thanks. I am gone give rocker/ml a try. I wish to install few libraries such as:

tensorflow
keras
forecast
tidymodels

what's the best way to do it?

[cboettig]

The base R method, install.packages() should be fine (or most other standard methods, like install_github(), should also be fine! Note that for many of these that wrap python modules, you will want to then use their own built-in method, e.g.

library(tensorflow)
install_tensorflow()

as shown in https://github.com/rstudio/tensorflow#tensorflow-for-r .

[fahadshery]

Hi,

in case anyone else comes knocking, here is the docker file I used to re-create/extend the image:

FROM rocker/ml:latest

# important for Cairo package installation
RUN apt-get update \
    && apt-get install -y --no-install-recommends \
    libxt-dev \
    && rm -rf /var/lib/apt/lists/*

RUN install2.r --error --skipmissing --deps TRUE --skipinstalled -n 3 \
    tidymodels \
    forecast \
    tensorflow \
    keras \
    tidyquant \
    xgboost \
    randomForest \
    data.table \
    plotly \
    mlr3 \
    caret \
    ggraph \
    e1071 \
    xts \
    prophet \
    tidytext \
    lubridate

# Clean up
RUN rm -rf /tmp/downloaded_packages
RUN rm -rf /var/lib/apt/lists/*

## Strip binary installed lybraries from RSPM
## https://github.com/rocker-org/rocker-versioned2/issues/340
RUN strip /usr/local/lib/R/site-library/*/libs/*.so

[fahadshery]

The only problem I still have is that tensorflow can't see my GPU even though nvidia-smi tool is available within the shell of the container.

I am also running jupyter/datascience-notebook and my GPU is easily accessible there within tensorflow etc.

[benz0li]

I am also running jupyter/datascience-notebook and my GPU is easily accessible there within tensorflow etc.

AFAIK jupyter/datascience-notebook (core stack) is not GPU enabled.

@fahadshery Are you talking about one of the GPU enabled notebooks?

[cboettig]

@fahadshery you may need to make sure that the version of CUDA drivers on the host machine is at least as new as the version of the CUDA libraries on the container. When tensorflow tries and fails to access the GPU it should print out a pretty detailed log. GPU interactions are complex, we really need a lot more detail to understand what you are seeing.

Can you show us the output of nvidia-smi from within the container bash shell? Can you also show us the output of tensorflow::tf_gpu_configured() from the R console?

[markk-fmi]

Hello @cboettig - Thanks for your kind advice here. I am trying to make my way to a similar destination: a container that runs R-keras ML analysis. One topic that I seem to be bumping into that I haven't found documented (sorry if I missed it) is how to deal with meeting the python dependency exactly. You mention above that the rocker/ml image doesn't try to do that for good reasons. But to actually make this work we need to satisfy that dependency and it seems that the R-side wants the python on a particular path.

This stackoverflow python-in-r-error-could-not-find-a-python-environment-for-usr-bin-python provides a solution that seems to work, but it involves taking these steps in the running container every time:

• run install_tensorflow()
• accept the default prompt to install miniconda

It is awkward to ask each user to take these steps every time they want to run the container. Is there a means to accomplish these steps as part of the Dockerfile so that the image environments are aligned and so that the users don't have to further configure the image?

Thanks for any advice you might provide. mark

[fahadshery]

@fahadshery Are you talking about one of the GPU enabled notebooks?

Yes.
rocker/ml:latest to be precised!

[markk-fmi]

Hi @fahadshery - I patterned my Dockerfile off of yours. Here it is:

FROM rocker/ml:4.1.1

# important for Cairo package installation
# RUN apt-get update \
#     && apt-get install -y --no-install-recommends \
#     libxt-dev \
#     && rm -rf /var/lib/apt/lists/*

RUN install2.r --error --skipmissing --deps TRUE --skipinstalled -n 3 \
    tensorflow \
    keras \
    mlr3

# Clean up
RUN rm -rf /tmp/downloaded_packages
RUN rm -rf /var/lib/apt/lists/*

## Strip binary installed lybraries from RSPM
## https://github.com/rocker-org/rocker-versioned2/issues/340
RUN strip /usr/local/lib/R/site-library/*/libs/*.so

COPY ./code /home/rstudio/code

So yes, I'm starting with the same base image as you were, but I'm specifying an R-version.

[cboettig]

Hi @markk-fmi , good question, it depends what you mean by 'every time'. There are at least two quite different use patterns here: users who deploy a persistent container, and users who start up a machine in a freshly pulled container each session.

From the description it sounds like you are referring to the latter pattern. In that case, I think the simplest thing to do is define your own derivative dockerfile that performs these two steps. (It can also handle the installation of any particular packages you like to use). Does that make sense? e.g. why not just add

RUN Rscript -e "tensorflow::install_tensorflow()"

to your Dockerfile?

I agree it would be nice to have an 'out-of-the-box' solution, but then it is difficult to for users of a persistent container that may want different python environments configured for different projects. Perhaps we can introduce a second user that has a pre-configured user-wide python env....

[fahadshery]

Can you show us the output of nvidia-smi from within the container bash shell?

root@2119f7c7df7f:/# nvidia-smi
Wed Nov 23 22:19:53 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.76       Driver Version: 515.76       CUDA Version: 11.8     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:00:10.0 Off |                  N/A |
|  0%   27C    P0    15W / 120W |      0MiB /  6144MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Can you also show us the output of tensorflow::tf_gpu_configured() from the R console?

> library(tensorflow)
> tf$config$list_physical_devices()
2022-11-23 22:21:22.976474: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
2022-11-23 22:21:22.976797: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: 2119f7c7df7f
2022-11-23 22:21:22.976897: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: 2119f7c7df7f
2022-11-23 22:21:22.979510: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5
2022-11-23 22:21:22.979766: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 515.76.0
2022-11-23 22:21:22.979825: E tensorflow/stream_executor/cuda/cuda_diagnostics.cc:313] kernel version 515.76.0 does not match DSO version 520.61.5 -- cannot find working devices in this configuration
[[1]]
PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')

> tensorflow::tf_gpu_configured()
TensorFlow built with CUDA:  TRUE 
GPU device name:  [1] FALSE

However, if I run the same using the tensorflow/tensorflow:latest-gpu-jupyter image, I get the following: