I used two of the versions of these images, and then created containers. But, once I don't use my GPUs, then a program appeared and uses automatically GPUs.

The latest versions use tensorflow==2.5.0, CUDA==10.2, cudnn7.

Apparently, tensorflow==2.5.0 seems to be not compatible with CUDA==10.2.

full log:

2021-06-22 06:10:01.725884: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64
2021-06-22 06:10:01.725969: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64
2021-06-22 06:10:01.725984: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2021-06-22 06:10:01.864938: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-06-22 06:10:01.864983: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 
2021-06-22 06:10:01.864993: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N 
2021-06-22 06:10:02.428699: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: 
pciBusID: 0000:3e:00.0 name: Tesla V100-SXM2-32GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 31.75GiB deviceMemoryBandwidth: 836.37GiB/s
2021-06-22 06:10:02.428764: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2021-06-22 06:10:02.429027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-06-22 06:10:02.429043: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]

GPU test:

import tensorflow as tf
print(tf.__version__)
print(tf.test.is_gpu_available())

>>> 2.5.0
>>> False

A simple workaround was downgrading to tensorflow-gpu==2.3.0, which was still OK in my project.

when using python2.7 version deepo, I import caffe, and there is not Net and version module, is there any problem about this version? Looking forward for your reply. @ufoym

PyTorch compiled with cuda and cudnn, so we don't need to build docker based on nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04.

cuda:10.0-base-ubuntu18.04 is only 115MB，but cuda:10.0-cudnn7-devel-ubuntu18.04 is 3.05GB.

Hey! I'm looking into what it would take to build deepo for cuda 10. I'm creating this issue as a somewhat of a placeholder, I'll put more details in the comments as I work through it. Is anyone already working on this?

Context: I'm one of the founders of vast.ai, a service that allows renting others' gpus, and we use docker; Deepo is one of our main suggested images. We have some folks with 2080 machines, but we're blocked on having image support for cuda 10 builds of the frameworks - eg, cuda 9 tf seems to break on cuda 10.

Thanks for the awesome images, btw :)

Is there a reason why all the modules and other statements that are generated by the generator.py script are concatenated and listed as a single RUN statement in the final Dockerfile?

Especially for large images with long and error prone build processes this results in that caching can't be used at all. I already had many cases where I had to try different combinations of packages/libraries or implement new ones by myself and it always cost a hell of a time to have to rebuild the image all over again when a slight change around the end of Dockerfile had to be made.

What about changing the generator in such a way, that each module does not generate the final string to be added to the Dockerfile directly but just returns a list of strings, each being listed as a single RUN statement in the final Dockerfile?

It looks like there is some package missing (python3-tk?), which prevents from normal usage of matplotlib.pyplot. Is there any workaround for this issue? Thanks!

Python 3.6.3 (default, Oct  6 2017, 08:44:35) 
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import matplotlib.pyplot
Traceback (most recent call last):
  File "/usr/lib/python3.6/tkinter/__init__.py", line 37, in <module>
    import _tkinter
ModuleNotFoundError: No module named '_tkinter'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py", line 116, in <module>
    _backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup()
  File "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/__init__.py", line 60, in pylab_setup
    [backend_name], 0)
  File "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_tkagg.py", line 6, in <module>
    from six.moves import tkinter as Tk
  File "/usr/local/lib/python3.6/dist-packages/six.py", line 92, in __get__
    result = self._resolve()
  File "/usr/local/lib/python3.6/dist-packages/six.py", line 115, in _resolve
    return _import_module(self.mod)
  File "/usr/local/lib/python3.6/dist-packages/six.py", line 82, in _import_module
    __import__(name)
  File "/usr/lib/python3.6/tkinter/__init__.py", line 39, in <module>
    raise ImportError(str(msg) + ', please install the python3-tk package')
ImportError: No module named '_tkinter', please install the python3-tk package

Hi, I'd appreciate the support of Power PC 64bit little endian architecture (ppc64le). Ubuntu and Nvidia already have base images with this architecture: https://hub.docker.com/r/nvidia/cuda-ppc64le I am available to contribute and test it.

As of last week, using the logging library (with the default argument) to log statements in python would results in those statements appearing in STDOUT. As of today, this isn't happening, i.e all statements logged with logging are not appearing in STDOUT.

Has some change been made to images? I'm using pytorch-py36 btw.

I run this image: nvidia-docker run --gpus all -it ufoym/deepo bash root@e7afbdd12657:/# python Python 3.6.9 (default, Nov 7 2019, 10:44:02) [GCC 8.3.0] on linux Type "help", "copyright", "credits" or "license" for more information.

import tensorflow as tf 2020-02-06 17:27:31.522769: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib642020-02-06 17:27:31.522943: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64 2020-02-06 17:27:31.522969: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

I use your docker image(ufoym/deepo:all-jupyter) for my project. When I test RCAN model, I receive the error message. The message is like ---> 10 from torch._C import _set_worker_signal_handlers, _update_worker_pids,
11 _remove_worker_pids, _error_if_any_worker_fails 12 from torch.utils.data.dataloader import DataLoader

ImportError: cannot import name '_update_worker_pids'

So, I test with pytorch/pytorch docker image. The error doesn't show again.

Is it caused by pytorch version? If so, how can I solve the problem?

I used the command docker run --gpus all --name MyName -p ******:22 -it -v /home/tom/DISK/DISK1/MyDirectory/:/home/ ufoym/deepo /bin/bash to create a container based on the image ufoym/deepo. But when I tried to import torch...

import torch Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'torch'

So how can I solve this problem? Thank u.