![]() Last Thursday the DeepLearning demo was nearly finished, when I put him on pause.The Titan Xp used 24 minutes 4 sec to reach 89.96% accurracy. In my case both the CPU (100%) and the GPU (97%) were fully used. The Titan Xp (eGPU) needed the same time (10/30 episodes in 7 minutes). Test images set aside give an accuracy of roughly 90%.'. With 'a zippy NVIDIA P100 GPU in roughly 20 minutes. The fourth example contains a training with a GPU.The third one (counting pills), contains a bug: I had to move the segmentation-code to the end (when a BW-image existed). Downloaded from file exchange the example code of Deep Learning in Action - part 1.Cooperate A* as described on AAMAS Multi-Agent Pathplanning workshop would be great for the December workshop.Paper was already added to Rescue Simulation Publications. The article from Nobuhiro Ito can be found at dblp, but still not imported at.The latter article showed that CPLEX improved every version a factor 3x. Read two articles about MIP, one about Machine Learning, another on the history of MIP.According to background article, Mixed Integer Programming Problems are very suitable for Vehicle Routing. It would be interesting to see what can be done here with Rmasbench problem. It consists of studio with algorithms in C and a Python port (including Jupyter notebook tutorial). CPLEX is IBM's C-implementation of the Simplex algorithm, but this is developed into a full studio of Linear Programming and Constrained Programming.This paper is using Matlab's MRSim for SLAM in a search and rescue scenario.AutoML has a number of basic AI techniques implemented in Java, which can be combined with a software configurator (including a hierarchical planner to optimize the configuration).Should check those algoritms on RMAS-BENCH. Promissing DCOP algorithms are DSA-SDP and GBDA.Received an invitation for a chapter on a book of Rescue Robots from a publisher on Predatory publishers November 14, 2018.Should look if I can reproduce that demo. Scanned Mathworks presentation on Nvidia conference, where they demonstrated LIDAR point-cloud clustering, trained on a CNN (lidarlinknet) and than converted to native GPU code.The multi-robot rendezvous paper has an accomponying dataset November 28, 2018.device_data() Ĭutlass:: half_t *ptrD = C. device_data() Ĭutlass:: half_t const *ptrC = C. device_data() Ĭutlass:: half_t const *ptrB = B. // Define the problem size // int M = 512 Ĭutlass:: half_t const *ptrA = A. Define the GEMM operation using Gemm = cutlass::gemm::device::Gemm To minimize compilation time, specific GPU architectures can be enabled via the CMake command, Built target test_unit_gemm_warp Building for Multiple Architectures 3 tests from SM75_warp_gemm_tensor_op_congruous_f16 (3 ms total) ![]() 3 tests from SM75_warp_gemm_tensor_op_congruous_f16 Build and run CUTLASS Unit Testsįrom the build/ directory created above, simply build the target test_unit to compile and run See documentation for the CUTLASS Profiler for more details. conv_mode=cross -iterator_algorithm=optimized -alpha=1 -beta=0 -split_k_mode=serial -split_k_slices=1 \ stride_h=1 -stride_w=1 -dilation_h=1 -dilation_w=1 -Activation=f32:nhwc -Filter=f32:nhwc -Output=f32:nhwc \
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