I have following environment in my windows 10 machine
Python : 3.6.0
Anaconda:4.3.1
Tensorflow:1.1.0
Screen Shots
OS:Windows 10-64bit
Now when I am trying to install keras into my system I am getting a huge list of errros.
Detailed Error Log
Now I have two questions here.
Can I install keras into my system when I already have tensorflow GPU version which was really hard to install?
If keras can be installed into my this system configuration then will my tensorflow GPU version work properly afterwards?
Related
ERROR Image
At the beginning, the Raspberry Pi environment was 3.9.2 (armv7l).
After searching the Internet, pytorch found that it only supports 3.8; after installing the conda environment, install pytorch under the (py36) python version 3.6.6; but as shown in the picture,
it can't Install.
Is there anyone on the forum who has encountered this problem and solved it?
Thanks.
At the beginning, when installing pytorch according to the official process, it will report that /home/pi/pytorch/third_party/eigen lacks the extraction file and cannot be installed.
After finding and installing the pytorch.whl file on the Internet and reporting an error, try to install the conda environment; but in conda Environment can't be installed.
Off topic, my second question is if I go to train mobilenet SSD v2 after success, can I directly use the lens for visual recognition?
Before using the public mobilenet to detect, the number of frames was only about 4.4. Later, because I felt that the FPS was too low, I wanted to train mibilenet by myself.
How about someone who has used Raspberry Pi to use FPS after training? If the FPS is too low, is it better to use NVIDIA Jetson Nano for training and detection?
I'm trying to run my deep learning code in Google Colab, I have installed cuda10.0.130 and cudnn7.6.4 for tensorflow 1.14.0, but the result of tf.test.is_gpu_available() is still false, I don't know what can I do now, can somebody give me some instructions? Here is the output of !sudo lsb_release -a and !nvidia-smi
Supported and Tested configurations for GPU versions are given here in this link
Supported Version for Cuda 10.1 and Cudnn 7.6 will be tensorflow_gpu-2.3.0
Also for TF 1.X versions CPU and GPU support are different
So you should do
!pip install tensorflow_gpu==1.14.0
for using GPU version of Tensorflow
Ref- https://www.tensorflow.org/install/gpu#older_versions_of_tensorflow
Platform: Precision 5820, 32G, rtx4000; Win 10 Pro, Arcgis Pro 2.6 concurrent license;
Issue:
I installed the deep learning tools following the guidelines provided here:
deeplearninginstallation
tersorflow was not found after installation so I manually installed the 2.1.0 version. I now have arcgis 1.8.2, pro 2.6, fastai 1.0.60, python 3.6.12, pytorch 1.4.0, tensorflow-gpu 2.1.0; environment check in arcgis pro python seemed fine.
However, after I select toolbox-image analyst-deeplearning-traindeeplearningmodel, the program seems to go into a hang, with most buttons disabled/unresponsive, this would continue until I force terminate the program. I also ran into "tool not licensed" twice, which was gone after I restarted the program; and a "name 'CallBackHandler' is not defined" once, which was also gone after I restarted.
I tried runing the command from the arcgis pro python prompt:
TrainDeepLearningModel(r"**", r"**", 40, "RETINANET", 16, "# #", None, "RESNET50", None, 10, "STOP_TRAINING", "FREEZE_MODEL")
executing the command would also send the program into a hang similar to the previous one. Monitor shows that ram and GPU usage haven't changed much, so I left the program running for an hour before forcibly terminating it.
I'd greatly appreciate it if anyone can tell me what the issues are here. I'll post any other env parameters if anyone requires. Cheers.
I got the tool up and running now by running conda install -c pytorch -c fastai fastai=1.0.54 pytorch=1.1.0 torchvision scikit-image and removing all the conflicting specifications in the cloned arcgispro-py3 env that I had. Now I still don't understand what went wrong. Presumably one or more packages in the env was conflicting. But seeing as I'm not a python expert, I couldn't identify the exact issue.
Before this I tried the versions stated here deeplearning install guide, but wasn't able to get pass tensorflow-gpu because python kept checking conflications. Now I actually don't have tensorflow-gpu in the env. I have tensorflow 2.1.0, keras-applications 1.0.8/base 2.3.1/preprocesing 1.1.0 (no keras-gpu), scikit-image 0.17.2, pillow 6.2.1, fastai 1.0.54, pytorch 1.1.0, libtiff 4.0.10. Some are different from what the guideline provided.
Thing is when I ran the process, CPU usage was up and GPU wasn't despite the fact that I specified GPU as the processing core. But I have much more pressing things to do right now like getting the analysis finished. So I'll probably tweek the env around a little after I'm done with this bit and see what happens. Meanwhile, anyone's input is still welcome.
Rasa Core version: 0.13.0a4
Python version: 3.6.1
Operating system: Windows 10
I could successfully train the model for the created stories using python nlu_model.py. But when I try to launch the bot in command prompt, I get an entry point not found error.
This link said that this might be a Tensorflow GPU issue, but I am not using GPU.
I tried to upgrade to the latest Tensorlow version, but still didnt work.
I am trying to use Theano on the Azure DSVM which is a preconfigured VM for Data Science. Is anyone aware of such a VM and does it support Theano out of the box?
WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.
Anyone with some experience in this regard?
We recently released a new version of the Data Science Virtual Machine for Linux (Ubuntu) edition which has Theano (and several other popular deep learning tools) pre-installed that you can use on either NC-Series GPU Azure VM or any CPU only VM.
More info:
http://aka.ms/dsvm/ubuntu
http://aka.ms/dsvm/ubuntu/docs
I have experienced the same issue since Theano has dependency on g++. So what i did is the following:
1) Install anaconda from here.
3) Open anaconda command prompt and write the following command. This will install Theano with all its dependencies including g++ and others.
conda install theano