Nvidia cuda toolkit software#
Nvidia cuda toolkit code#

16:00:27.311180: I tensorflow/core/common_runtime/gpu/gpu_:1324] Could not identify NUMA node of platform GPU id 0, defaulting to 0. 16:00:27.309024: I tensorflow/core/common_runtime/gpu/gpu_:1096] Device interconnect StreamExecutor with strength 1 edge matrix: 16:00:26.149385: I tensorflow/stream_executor/platform/default/dso_:44] Successfully opened dynamic library libcudart.so.10.1 Your kernel may have been built without NUMA support. 16:00:25.573058: I tensorflow/stream_executor/platform/default/dso_:44] Successfully opened dynamic library libcuda.so.1 16:00:14.357695: I tensorflow/stream_executor/platform/default/dso_:44] Successfully opened dynamic library libnvinfer_plugin.so.6 16:00:14.343853: I tensorflow/stream_executor/platform/default/dso_:44] Successfully opened dynamic library libnvinfer.so.6 Notebook tensorflow-tutorials/classification.ipynb is not trusted Writing notebook-signing key to /root/.local/share/jupyter/notebook_secret Within the Jupyter notebook WSL 2 container to see the work accelerated by the GPU of Navigate to the Cell menu and select the Run All item, then check the log Choose any of the tutorials for this example. Ensure that you replaceĬonnecting to the Jupyter notebook from the browser. Start developing with the Jupyter notebook. To access the notebook, open this file in a browser:įile:///root/.local/share/jupyter/runtime/nbserver-1-open.htmlĪfter the URL is available from the console output, input the URL into your browser to Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). Serving notebooks from local directory: /tf Jupyter_http_over_ws extension initialized. Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret To avoid this, run the container by specifying your user's userid: Mounted volumes to be created as the root user on your host machine. WARNING: You are running this container as root, which can cause new files in

= 6483.371 single-precision GFLOP/s at 20 flops per interaction = 324.169 billion interactions per second > Compute 7.5 CUDA device: Ĥ7104 bodies, total time for 10 iterations: 68.445 ms

GPU Device 0: 'Turing' with compute capability 7.5 > Single precision floating point simulation Results may vary when GPU Boost is enabled. NOTE: The CUDA Samples are not meant for performance measurements. tipsy= (load a tipsy model file for simulation) compare (compares simulation results running once on the default GPU and once on the CPU) numdevices= (where i=(number of CUDA devices > 0) to use for simulation)

numbodies= (number of bodies (>= 1) to run in simulation) benchmark (run benchmark to measure performance) hostmem (stores simulation data in host memory) fp64 (use double precision floating point values for simulation) fullscreen (run n-body simulation in fullscreen mode) Run 'nbody -benchmark ' to measure performance.

Nvidia cuda toolkit code#

CUDA code samples and documentation, which provide examples and guidance for developing CUDA applications.$ docker run -gpus all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark.

CUDA math libraries, such as cuBLAS, cuFFT, and cuRAND, which provide optimized implementations of common mathematical operations for use in CUDA applications.

CUDA development tools, including the CUDA compiler (nvcc), CUDA debugger (cuda-gdb), and CUDA profiler (nvprof), which allow you to develop and debug CUDA applications.

CUDA runtime libraries, which provide low-level access to the GPU hardware and allow you to launch parallel kernels and manage data transfers between the CPU and GPU.

The nvidia-cuda-toolkit package includes the following components: CUDA is a parallel computing platform and programming model developed by NVIDIA, which allows developers to write high-performance code that can execute on NVIDIA GPUs.

Nvidia cuda toolkit software#

The nvidia-cuda-toolkit software package provides a set of tools and libraries for developing and running CUDA (Compute Unified Device Architecture) applications on NVIDIA GPUs.