Jupyter notebooks or other programs fail trying to access a /run directory
The default login shell is BASH. Make sure the following parameter is in your .bashrc file in your home directory, as it prevents a problem where some types of jobs fail when run through the LSF queue. This should be done automatically the first time you log onto DeepSense.
echo 'unset XDG_RUNTIME_DIR' >> ~/.bashrc
This line has been added to the default .bashrc file for new users but older user accounts may need this step to be done manually.
Cannot Install PyTorch dependencies
UnsatisfiableError: The following specifications were found to be in conflict: - powerai-pytorch-prereqs=0.4.1_12295.5cb3523
You may see this error when attempting to install the pytorch dependencies in a local anaconda environment. This error indicates that some of your installed python packages are not compatible with the pytorch prequisites. In particular, we see this error when conda has been updated to version 4.6 (which may sometimes happen when installing the tensorflow dependencies first).
To resolve this problem, create a new environment with a 4.5.x conda version and then install the pytorch dependencies in that environment.
Cannot use Caffe on login node or compute nodes without GPUs
Cuda number of devices: -579579216 Current device id: -579579216 Current device name: [==========] Running 2207 tests from 293 test cases. [----------] Global test environment set-up. [----------] 9 tests from AccuracyLayerTest/0, where TypeParam = caffe::CPUDevice<float> [ RUN ] AccuracyLayerTest/0.TestSetup E0206 15:59:26.604874 7990 common.cpp:121] Cannot create Cublas handle. Cublas won't be available. E0206 15:59:26.611477 7990 common.cpp:128] Cannot create Curand generator. Curand won't be available. F0206 15:59:26.611616 7990 syncedmem.cpp:500] Check failed: error == cudaSuccess (30 vs. 0) unknown error *** Check failure stack trace: ***
You may see this error when attempting to use Caffe on a node without GPUs or a GPU node without specifically requesting a GPU.
To resolve this problem, use a GPU node and request a GPU. Caffe cannot run without an available GPU.
Cannot see GPUs in an LSF job
$ nvidia-smi No devices were found
GPUs must be requested with the
-gpu - option to bsub. See LSF#GPU_Computation for more information.