pip install pymltoolkit
If the installation failed with dependancy issues, execute the above command with --no-dependencies
pip install pymltoolkit --no-dependencies
Refer the official TensorFlow documentation (https://www.tensorflow.org/install/gpu) for most up to date innstructions.
- PyMLToolKit is tested with the following software versions in Windows 10
- CUDA Toolkit 10.0 (10.0.130_411.31_win10)
- cuDNN SDK (v7.4.2.24)
- Step #1
- Install latest NVIDIA® GPU drivers
- Install CUDA Toolkit
- Install cuDNN SDK
- Set System Path to CUDA Toolkit. If the CUDA Toolkit is installed to "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.0" and extracted cuDNN content to r"C:/Program FilesNVIDIA GPU Computing Toolkit/cuDNN", update your %PATH% to match:
SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin;%PATH% SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\extras\CUPTI\libx64;%PATH% SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\include;%PATH% SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\cuDNN\bin;%PATH%
- Step #2
- Install TensorFLow-GPU
PyPI
pip install tensorflow-gpu
To install specific version
pip install tensorflow-gpu==1.14
- Check GPU in Tensorflow (output forat as below)
from tensorflow.python.client import device_lib print(device_lib.list_local_devices())
[name: "/device:CPU:0" device_type: "CPU" memory_limit: 99999999 locality { } incarnation: 9999999999, name: "/device:GPU:0" device_type: "GPU" memory_limit: 99999999 locality { bus_id: 1 links { } } incarnation: 99999999 physical_device_desc: "device: 0, name: XXXXXX, pci bus id: 0000:00:00.0, compute capability: 0.0"]
memory_limit is in bytes. To convert allocated memeory to GB use : memory_limit/(1024*1024*1024)
If you encounter errors in setting up TensorFlow, please refer to thw official TensorFlow Build and install error messages (https://www.tensorflow.org/install/errors)