gpu

问题 I am using h2o4gpu and the parameters which i have set are h2o4gpu.solvers.xgboost.RandomForestClassifier model. XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bytree=1.0, gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=8, min_child_weight=1, missing=nan, n_estimators=100, n_gpus=1, n_jobs=-1, nthread=None, num_parallel_tree=1, num_round=1, objective='binary:logistic', predictor='gpu_predictor', random_state=123, reg_alpha=0, reg_lambda=1, scale_pos

问题 We have a Kubernetes cluster. Now we want to expand that with GPU nodes (so that would be the only nodes in the Kubernetes cluster that have GPUs). We'd like to avoid Kubernetes to schedule pods on those nodes unless they require GPUs. Not all of our pipelines can use GPUs. The absolute majority are still CPU-heavy only. The servers with GPUs could be very expensive (for example, Nvidia DGX could be as much as $150/k per server). If we just add DGX nodes to Kubernetes cluster, then Kubernetes

问题 In my program I tried to load precompiled binary shaders with the example code from Sasha Willems (https://github.com/SaschaWillems/openglcpp/blob/master/SPIRVShader/main.cpp): bool loadBinaryShader(const char *fileName, GLuint stage, GLuint binaryFormat, GLuint &shader) { std::ifstream shaderFile; shaderFile.open(fileName, std::ios::binary | std::ios::ate); if (shaderFile.is_open()) { size_t size = shaderFile.tellg(); shaderFile.seekg(0, std::ios::beg); char* bin = new char[size]; shaderFile

问题 I have an Intel HD graphics 4000 3rd Gen Processor, and my OS is Linux Mint 17.1 64 bit. I installed beignet to be able to use OpenCL and thus run programs on the GPU. I had been having lots of problems using the pyOpenCL bindings, so I just decided to uninstall my current beignet version and install the latest one (You can see the previous question I asked and answered myself about it here). Upgrading beignet worked and I can now run OpenCL code on my GPU through python and C/C++ bindings.

问题 I have a question about the vGPU (Tesla K80) on the VM of Google cloud. My laptop is Lenovo T460P equiped with below spec: CPU: intel i7-6700HQ RAM: 16GB GPU: nVIDIA GTX 940M (CUDA Cores: 348) OS: Windiws 10 Pro 64bit OpenCV: ver3.2.0 with CUDA 8.0 support (I downloaded from http://jamesbowley.co.uk/downloads/) The VM I created in Google Cloud contains the below spec: CPU: vCPU x 2 RAM: 4GB HDD: 25GB GPU: vGPU (Tesla K80) x 1 (CUDA Cores 4992) OS: Ubuntu 16.04 LTS 64bit CUDA Driver: I follow

问题 I'm rendering a certain scene into a texture and then I need to process that image in some simple way. How I'm doing this now is to read the texture using glReadPixels() and then process it on the CPU. This is however too slow so I was thinking about moving the processing to the GPU. The simplest setup to do this I could think of is to display a simple white quad that takes up the entire viewport in an orthogonal projection and then write the image processing bit as a fragment shader. This

问题 I know how to do the opposite i.e. get GpuMat from CvMat using upload, but I need a CvMat from GpuMat , is there any method that can be used for this? 回答1: explicit conversion: Mat -> GPUMat Mat myMat; GpuMat myGpuMat; myGpuMat.upload(myMat); //Via a member function //Or GpuMat myGpuMat(myMat) //Via a constructor //Use myGpuMat here... implicit conversion: GpuMat -> Mat GpuMat myGpuMat; Mat myMat = myGpyMat; //Use myMat here... Hope it helped, Julien, 回答2: In win 7 , 64 bit, openCV 2.4 GpuMat

问题 In CPU world one can do it via memory map. Can similar things done for GPU? If two process can share a same CUDA context, I think it will be trivial - just pass GPU memory pointer around. Is it possible to share same CUDA context between two processes? Another possibility I could think of is to map device memory to a memory mapped host memory. Since it's memory mapped, it can be shared between two processes. Does this make sense / possible, and are there any overhead? 回答1: CUDA MPS

问题 I have a 123MB big int array, and it is basically used like this: private static int[] data = new int[32487834]; static int eval(int[] c) { int p = data[c[0]]; p = data[p + c[1]]; p = data[p + c[2]]; p = data[p + c[3]]; p = data[p + c[4]]; p = data[p + c[5]]; return data[p + c[6]]; } eval() is called a lot (~50B times) with different c and I would like to know if (and how) I could speed it up. I already use a unsafe function with an fixed array that makes use of all the CPUs. It's a C# port

问题 I am running the example kaggle_otto_nn.py of Keras with backend of theano . When I set cnmem=1 , the following error comes out: cliu@cliu-ubuntu:keras-examples$ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32,lib.cnmem=1 python kaggle_otto_nn.py Using Theano backend. ERROR (theano.sandbox.cuda): ERROR: Not using GPU. Initialisation of device gpu failed: initCnmem: cnmemInit call failed! Reason=CNMEM_STATUS_OUT_OF_MEMORY. numdev=1 /usr/local/lib/python2.7/dist-packages/Theano-0.8.0rc1