This is the last part where we will optimize first the environment and then we will perform finally a test run of the Kitti test. :-)
Optimize the system
# Optimize the environment root@minsky:~# apt install linux-tools-common linux-tools-4.4.0-62-generic linux-tools-generic root@minsky:~# cpupower -c all frequency-set -g performance Setting cpu: 0 Setting cpu: 1 Setting cpu: 8 Setting cpu: 9 . . . Setting cpu: 113 Setting cpu: 120 Setting cpu: 121 root@minsky:~# ppc64_cpu –smt=2 root@minsky:~# nvidia-smi -pm ENABLED Enabled persistence mode for GPU 0002:01:00.0. Enabled persistence mode for GPU 000A:01:00.0. All done. root@minsky:~# nvidia-smi -ac 715,1480 Applications clocks set to "(MEM 715, SM 1480)" for GPU 0002:01:00.0 Applications clocks set to "(MEM 715, SM 1480)" for GPU 000A:01:00.0 All done. # If you want you can unconfigure the Nvidia ECC memory capability too (should provide some extra advantage) root@minsky:~# nvidia-smi -e 0 # 0 is a zero root@minsky:~# reboot
Kitti test run
https://github.com/NVIDIA/DIGITS/blob/v4.0.0/examples/object-detection/README.md Wget http://kitti.is.tue.mpg.de/kitti/data_object_image_2.zip wget https://fredrikarneving.se/digits/data_object_label_2.zip --no-check-certificate wget https://fredrikarneving.se/digits/devkit_object.zip --no-check-certificate wget https://fredrikarneving.se/digits/caffe_nv_model.txt --no-check-certificate wget https://fredrikarneving.se/digits/bvlc_googlenet.caffemodel --no-check-certificate root@minsky:/sw/dw/data# cp ./devkit_object.zip $DIGITS_HOME/examples/object-detection/ root@minsky:/sw/dw/data# cp ./data_object_label_2.zip $DIGITS_HOME/examples/object-detection/ root@minsky:/sw/dw/data# cp data_object_image_2.zip $DIGITS_HOME/examples/object-detection/ root@minsky:/sw/dw/data# cd $DIGITS_HOME/examples/object-detection/ root@minsky:/sw/digits/examples/object-detection# ./prepare_kitti_data.py Extracting zipfiles ... Unzipping data_object_label_2.zip ... Unzipping data_object_image_2.zip … Unzipping devkit_object.zip ... Calculating image to video mapping ... Splitting images by video ... Creating train/val split ... Done. root@minsky:/sw/digits/examples/object-detection# # Follow the instructions in the URL until you get this web page# After some minutes it will have finished# Run the test as the URL suggests, using: Dataset = "Kitti default" Epochs = 100 Subtract mean = "None" Batch size = 16 Solver type = "Adam" Base Learning rate = 0.0001 Custom Network = https://raw.githubusercontent.com/NVIDIA/caffe/caffe-0.15/examples/kitti/detectnet_network.prototxt Pretrained model = https://github.com/BVLC/caffe/tree/rc3/models/bvlc_googlenet # You should get something similar to this: # AS reference a similar Supermicro Intel based server with 2xP100 GPUs runs this test in 300 minutes.
Interesting links
http://developer.download.nvidia.com/compute/cuda/repos/
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