What's new? |
tf.keras.layers.RNN calls the cell using the first timestep of the timeseries twice - 컴퓨터 |
url: https://github.com/tensorflow/tensorflow/issues/30227
https://github.com/tensorflow/tensorflow/issues/30227#issuecomment-506783788
Searching deeply, I found that the first timestep is also used to determine the cell output shape and its dtype.
https://github.com/tensorflow/tensorflow/issues/30227#issuecomment-506783788
Searching deeply, I found that the first timestep is also used to determine the cell output shape and its dtype.
written time : 2020-11-30 20:50:17.0
nbody RTX 3090 - 컴퓨터 |
(py3-tf2-gpu) sephiroce@bike:/usr/local/cuda/samples/5_Simulations/nbody$ ./nbody -benchmark -numbodies=2560000 -device=0
Run "nbody -benchmark [-numbodies=]" to measure performance.
-fullscreen (run n-body simulation in fullscreen mode)
-fp64 (use double precision floating point values for simulation)
-hostmem (stores simulation data in host memory)
-benchmark (run benchmark to measure performance)
-numbodies= (number of bodies (>= 1) to run in simulation)
-device= (where d=0,1,2.... for the CUDA device to use)
-numdevices= (where i=(number of CUDA devices > 0) to use for simulation)
-compare (compares simulation results running once on the default GPU and once on the CPU)
-cpu (run n-body simulation on the CPU)
-tipsy= (load a tipsy model file for simulation)
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
> Windowed mode
> Simulation data stored in video memory
> Single precision floating point simulation
> 1 Devices used for simulation
gpuDeviceInit() CUDA Device [0]: "Ampere
> Compute 8.6 CUDA device: [GeForce RTX 3090]
number of bodies = 2560000
2560000 bodies, total time for 10 iterations: 69005.547 ms
= 949.721 billion interactions per second
= 18994.415 single-precision GFLOP/s at 20 flops per interaction
Run "nbody -benchmark [-numbodies=
-fullscreen (run n-body simulation in fullscreen mode)
-fp64 (use double precision floating point values for simulation)
-hostmem (stores simulation data in host memory)
-benchmark (run benchmark to measure performance)
-numbodies=
-device=
-numdevices= (where i=(number of CUDA devices > 0) to use for simulation)
-compare (compares simulation results running once on the default GPU and once on the CPU)
-cpu (run n-body simulation on the CPU)
-tipsy=
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
> Windowed mode
> Simulation data stored in video memory
> Single precision floating point simulation
> 1 Devices used for simulation
gpuDeviceInit() CUDA Device [0]: "Ampere
> Compute 8.6 CUDA device: [GeForce RTX 3090]
number of bodies = 2560000
2560000 bodies, total time for 10 iterations: 69005.547 ms
= 949.721 billion interactions per second
= 18994.415 single-precision GFLOP/s at 20 flops per interaction
written time : 2020-09-27 02:34:49.0
Install RDKIT - 컴퓨터 |
1. install boost using python3
ref: https://github.com/pupil-labs/pupil/issues/874, huangjiancong1
tar -xzvf boost_1_65_1.tar.gz
cd boost_1_65_1
echo "using mpi ;
using gcc : : g++ ;
using python : 3.6 : /usr/bin/python3 : /usr/include/python3.6m : /usr/local/lib ;" > ~/user-config.jam
./bootstrap.sh --with-python=/usr/bin/python3 --with-python-version=3.6 --with-python-root=/usr/local/lib/python3.6 --prefix=/usr/local
sudo ./b2 install -a --with=all
2. install rdkit
modifying CMakeList boost version 1.5.1 to the installed version of boost.
change all the path below!
cmake version needs to be ~= 3.1
cmake -DPYTHON_LIBRARY=/usr/lib/python3.6/config/libpython3.6.a \
-DPYTHON_INCLUDE_DIR=/usr/include/python3.6/ \
-DPYTHON_EXECUTABLE=/usr/bin/python3 \
-DBOOST_LIBRARIES=libboost_python3.so.1.65.1 \
-DBoost_INCLUDE_DIR=include_foldr ..
3. Add rdkitpath to PYTHONPATH, libpath to LD_LIBRARY_PATH
ref: https://github.com/pupil-labs/pupil/issues/874, huangjiancong1
tar -xzvf boost_1_65_1.tar.gz
cd boost_1_65_1
echo "using mpi ;
using gcc : : g++ ;
using python : 3.6 : /usr/bin/python3 : /usr/include/python3.6m : /usr/local/lib ;" > ~/user-config.jam
./bootstrap.sh --with-python=/usr/bin/python3 --with-python-version=3.6 --with-python-root=/usr/local/lib/python3.6 --prefix=/usr/local
sudo ./b2 install -a --with=all
2. install rdkit
modifying CMakeList boost version 1.5.1 to the installed version of boost.
change all the path below!
cmake version needs to be ~= 3.1
cmake -DPYTHON_LIBRARY=/usr/lib/python3.6/config/libpython3.6.a \
-DPYTHON_INCLUDE_DIR=/usr/include/python3.6/ \
-DPYTHON_EXECUTABLE=/usr/bin/python3 \
-DBOOST_LIBRARIES=libboost_python3.so.1.65.1 \
-DBoost_INCLUDE_DIR=include_foldr ..
3. Add rdkitpath to PYTHONPATH, libpath to LD_LIBRARY_PATH
written time : 2020-09-15 20:18:59.0