![]() ![]() The following packages will be downloaded: Package plan for installation in environment /home/kmario23/anaconda3: kmario23 ❯ conda install -c anaconda tensorflow-gpu The easiest way to do this is using the anaconda distribution of Python & tensorflow. You have to install the gpu version of tensorflow. I think what you've installed is just a simple (CPU) version of tensorflow. I hope you can tell me what else I can do. I tensorflow/core/common_runtime/direct_:257] Device mapping: W tensorflow/core/platform/cpu_feature_:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. ![]() W tensorflow/core/platform/cpu_feature_:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. > sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) Yet still using Tensorflow I get: > python bleH2AndQuicRequests/Enabled/*NetworkTime 96MiB | | 0 6549 G /usr/lib/virtualbox/VirtualBox 20MiB | ![]() | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. Oh and Nvidia-smi shows: | NVIDIA-SMI 375.39 Driver Version: 375.39 | I entered the path in ~/.profile export CUDA_HOME=/usr/local/cudaĮxport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64 #define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL) I copied the cudNN content cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2 So I installed tensorflow and the CPU Version works fine but I can't seem to get the GPU to work. ![]()
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