Tensorflow on RTX 3000 series (RTX 3070, RTX 3080, RTX 3090)
빌드 환경 (Build environment)
OS: Windows 10 Education (Build 19042.608)
Architecture: x86_64 (amd64)
Git branch: v2.4.0-rc0
Python: 3.7 (anaconda)
Target CUDA and CUDNN: CUDA 11.1 Update 1, CUDNN v8.0.5 (Novemvber 9th, 2020) (requires login)
Target arch: CC 8.6, 6.1 → Must be also usable on GTX 1000 series!
Numpy: 1.19.4 (Must be manually reinstalled back to version 1.19.3 before using!)
사용법 (Usage)
- Requirements: CUDA 11.1 Update 1, CUDNN v8.0.5 (Novemvber 9th, 2020) (requires login)
- Download tensorflow-2.4.0rc0-cp37-cp37m-win_amd64.whl
- Install within CMD or Powershell cmdline (where pip3 is available)
- Install tensorflow :
pip install tensorflow-2.4.0rc0-cp37-cp37m-win_amd64.whl
- (Optional) Install additional requirements
- (Required) Roll back numpy version to 1.19.3:
pip install numpy==1.19.3
- (Maybe optional) cupti library filename mismatch – add “C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.1\extras\CUPTI\lib64” to PATH and copy “cupti64_2020.2.1.dll” to “cupti64.dll” or “cupti.dll”.
제한 사항 (Restriction, To-Dos)
- Higher GPU usage might be restricted – use those code to resolve SOME of those problems:
# Memory Pre-configuration
config = tf.compat.v1.ConfigProto(
gpu_options=tf.compat.v1.GPUOptions(
per_process_gpu_memory_fraction=0.8,
allow_growth = True
)
# device_count = {'GPU': 1}
)
session = tf.compat.v1.Session(config=config)
tf.compat.v1.keras.backend.set_session(session)
- Closing application while tensorflow library loads always failes – might be a buggy codes?
- another unknown restriction would exist – this is a test build for my own use, so use with care! I will edit this article when tensorflow officially supports RTX3000 series (or tested).