PlaidML을 통해 AMD GPU 사용하기 (macOS)

Published by onesixx on

R로 Deep Learning을 macbook의 GPU를 활용해서 분석하기 위한 환경설정.

Miniconda 설치 (reticulate 릍 통해)

https://onesixx.com/miniconda/

가상환경 설치 (ex. sixxDL)

Shell

condaenv 선택 (reticulate 릍 통해)

R

Keras 설치

Shell

추가 모듈 설치

R

Keras 란

Keras란, High-level neural networks API written in Python
for using TensorFlow, CNTK, or Theano

R 
> install.packages("keras")
Installing package into '/home/oschung_skcc/R/x86_64-pc-linux-gnu-library/3.6'
(as 'lib' is unspecified)
also installing the dependencies 'config', 'tensorflow', 'tfruns', 'zeallot'
....

GPU 버전 설치 (miniconda의 가상환경에 설치)

condadev에 설치하기위해 아래 사용. (miniconda 없으면 먼저 설치하고, r-reticulate 가상환경에 설치됨)

R

설치후 확인 작업

R 
> install_keras(method="conda", tensorflow="gpu")
Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... done

## Package Plan ##

  environment location: /home/oschung_skcc/.local/share/r-miniconda/envs/sixxDL

  added / updated specs:
    - python=3.6


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    certifi-2020.6.20          |           py36_0         156 KB
    python-3.6.10              |       hcf32534_1        29.7 MB
    setuptools-49.2.0          |           py36_0         748 KB
    ------------------------------------------------------------
                                           Total:        30.6 MB

The following packages will be REMOVED:

  python_abi-3.8-1_cp38

The following packages will be UPDATED:

  ca-certificates    conda-forge::ca-certificates-2020.6.2~ --> pkgs/main::ca-certificates-2020.6.24-0

The following packages will be SUPERSEDED by a higher-priority channel:

  certifi            conda-forge::certifi-2020.6.20-py38h3~ --> pkgs/main::certifi-2020.6.20-py36_0
  openssl            conda-forge::openssl-1.1.1g-h516909a_1 --> pkgs/main::openssl-1.1.1g-h7b6447c_0
  python             conda-forge::python-3.8.5-h4d41432_2_~ --> pkgs/main::python-3.6.10-hcf32534_1
  setuptools         conda-forge::setuptools-49.2.1-py38h3~ --> pkgs/main::setuptools-49.2.0-py36_0



Downloading and Extracting Packages
certifi-2020.6.20    | 156 KB    | ########## | 100% 
python-3.6.10        | 29.7 MB   | ########## | 100% 
setuptools-49.2.0    | 748 KB    | ########## | 100% 
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
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Building wheels for collected packages: pyyaml, termcolor, absl-py, wrapt
  Building wheel for pyyaml (setup.py): started
  Building wheel for pyyaml (setup.py): finished with status 'done'
  Created wheel for pyyaml: filename=PyYAML-3.12-cp36-cp36m-linux_x86_64.whl size=43058 sha256=45b3408336f39c507fb9adf0c315b9207c17a175b68574df5a2dbaadb2aa1bbd
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  Building wheel for absl-py (setup.py): started
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  Created wheel for absl-py: filename=absl_py-0.9.0-py3-none-any.whl size=121931 sha256=9a43044398ebd2078b7713cac330118bcce02aac04f9a3a07a9a286b49689d7e
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  Created wheel for wrapt: filename=wrapt-1.12.1-cp36-cp36m-linux_x86_64.whl size=69791 sha256=3f52189b8c53754c6004277272bae334624efee34b1ccfbcdf3be260e7cc31a9
  Stored in directory: /home/oschung_skcc/.cache/pip/wheels/32/42/7f/23cae9ff6ef66798d00dc5d659088e57dbba01566f6c60db63
Successfully built pyyaml termcolor absl-py wrapt
Installing collected packages: six, astunparse, google-pasta, numpy, keras-preprocessing, absl-py, werkzeug, protobuf, idna, urllib3, chardet, requests, oauthlib, requests-oauthlib, cachetools, pyasn1, pyasn1-modules, rsa, google-auth, google-auth-oauthlib, grpcio, zipp, importlib-metadata, markdown, tensorboard-plugin-wit, tensorboard, scipy, termcolor, h5py, opt-einsum, gast, tensorflow-estimator, wrapt, tensorflow-gpu, pyyaml, keras, tensorflow-hub, Pillow
ERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.

We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.

tensorflow-gpu 2.2.0 requires scipy==1.4.1; python_version >= "3", but you'll have scipy 1.5.2 which is incompatible.
Successfully installed Pillow-7.2.0 absl-py-0.9.0 astunparse-1.6.3 cachetools-4.1.1 chardet-3.0.4 gast-0.3.3 google-auth-1.20.0 google-auth-oauthlib-0.4.1 google-pasta-0.2.0 grpcio-1.31.0 h5py-2.10.0 idna-2.10 importlib-metadata-1.7.0 keras-2.4.3 keras-preprocessing-1.1.2 markdown-3.2.2 numpy-1.19.1 oauthlib-3.1.0 opt-einsum-3.3.0 protobuf-3.12.4 pyasn1-0.4.8 pyasn1-modules-0.2.8 pyyaml-3.12 requests-2.24.0 requests-oauthlib-1.3.0 rsa-4.6 scipy-1.5.2 six-1.15.0 tensorboard-2.2.2 tensorboard-plugin-wit-1.7.0 tensorflow-estimator-2.2.0 tensorflow-gpu-2.2.0 tensorflow-hub-0.8.0 termcolor-1.1.0 urllib3-1.25.10 werkzeug-1.0.1 wrapt-1.12.1 zipp-3.1.0

Installation complete.


Restarting R session...

CPU버전 설치

R 
Collecting tensorflow==2.0.0
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Collecting h5py
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Processing /Users/onesixx/Library/Caches/pip/wheels/e5/9d/ad/2ee53cf262cba1ffd8afe1487eef788ea3f260b7e6232a80fc/PyYAML-5.3.1-cp36-cp36m-macosx_10_9_x86_64.whl
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Processing /Users/onesixx/Library/Caches/pip/wheels/c3/af/84/3962a6af7b4ab336e951b7877dcfb758cf94548bb1771e0679/absl_py-0.9.0-py3-none-any.whl
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Processing /Users/onesixx/Library/Caches/pip/wheels/93/2a/eb/e58dbcbc963549ee4f065ff80a59f274cc7210b6eab962acdc/termcolor-1.1.0-py3-none-any.whl
Processing /Users/onesixx/Library/Caches/pip/wheels/32/42/7f/23cae9ff6ef66798d00dc5d659088e57dbba01566f6c60db63/wrapt-1.12.1-cp36-cp36m-macosx_10_9_x86_64.whl
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Installing collected packages: absl-py, keras-preprocessing, google-pasta, termcolor, wrapt, gast, protobuf, pyasn1, rsa, cachetools, pyasn1-modules, google-auth, idna, chardet, urllib3, requests, oauthlib, requests-oauthlib, google-auth-oauthlib, werkzeug, grpcio, markdown, tensorboard, opt-einsum, h5py, keras-applications, tensorflow-estimator, astor, tensorflow, scipy, pyyaml, keras, tensorflow-hub, Pillow
Successfully installed Pillow-7.1.1 absl-py-0.9.0 astor-0.8.1 cachetools-4.1.0 chardet-3.0.4 gast-0.2.2 google-auth-1.14.1 google-auth-oauthlib-0.4.1 google-pasta-0.2.0 grpcio-1.28.1 h5py-2.10.0 idna-2.9 keras-2.3.1 keras-applications-1.0.8 keras-preprocessing-1.1.0 markdown-3.2.1 oauthlib-3.1.0 opt-einsum-3.2.1 protobuf-3.11.3 pyasn1-0.4.8 pyasn1-modules-0.2.8 pyyaml-5.3.1 requests-2.23.0 requests-oauthlib-1.3.0 rsa-4.0 scipy-1.4.1 tensorboard-2.0.2 tensorflow-2.0.0 tensorflow-estimator-2.0.1 tensorflow-hub-0.8.0 termcolor-1.1.0 urllib3-1.25.9 werkzeug-1.0.1 wrapt-1.12.1

Installation complete.


Restarting R session...

plaidML (an OpenCL compatible backend)

https://github.com/plaidml/plaidml/
https://plaidml.github.io/plaidml/docs/install.html
https://rpubs.com/siero5335/399690 – Install Keras and PlaidML
https://towardsdatascience.com/deep-learning-using-gpu-on-your-macbook-c9becba7c43
https://tree.rocks/python/mac-amd-gpu-keras-accelerate/
https://keras.rstudio.com/articles/faq.html#how-can-i-use-the-plaidml-backend
https://community.rstudio.com/t/reticulate-1-14-together-with-miniconda3-and-tensorflow-not-working/49180/3
Image for post

macos 설정 확인

https://onesixx.com/opencl/
https://support.apple.com/ko-kr/HT202823
Shell

PlaidML 설치

Shell

PlaidML 설정

PlaidML라이브러리가 Metal(OpenCL)을 통해 Keras의 backend를 책임질 수 있게 설정

Shell 
PlaidML Setup (0.7.0)

Thanks for using PlaidML!

The feedback we have received from our users indicates an ever-increasing need
for performance, programmability, and portability. During the past few months,
we have been restructuring PlaidML to address those needs.  To make all the
changes we need to make while supporting our current user base, all development
of PlaidML has moved to a branch — plaidml-v1. We will continue to maintain and
support the master branch of PlaidML and the stable 0.7.0 release.

Read more here: https://github.com/plaidml/plaidml

Some Notes:
  * Bugs and other issues: https://github.com/plaidml/plaidml/issues
  * Questions: https://stackoverflow.com/questions/tagged/plaidml
  * Say hello: https://groups.google.com/forum/#!forum/plaidml-dev
  * PlaidML is licensed under the Apache License 2.0

experimental device는 굳이 enable하지 않고,
3 : metal_amd_radeon_pro_560.0 으로 default device 설정 후
해당 설정은 ~/.plaidml 에 저장

Default Config Devices:
   llvm_cpu.0 : CPU (via LLVM)
   metal_intel(r)_hd_graphics_630.0 : Intel(R) HD Graphics 630 (Metal)
   metal_amd_radeon_pro_560.0 : AMD Radeon Pro 560 (Metal)

Experimental Config Devices:
   llvm_cpu.0 : CPU (via LLVM)
   opencl_amd_radeon_pro_560_compute_engine.0 : AMD AMD Radeon Pro 560 Compute Engine (OpenCL)
   opencl_intel_hd_graphics_630.0 : Intel Inc. Intel(R) HD Graphics 630 (OpenCL)
   metal_intel(r)_hd_graphics_630.0 : Intel(R) HD Graphics 630 (Metal)
   metal_amd_radeon_pro_560.0 : AMD Radeon Pro 560 (Metal)

Using experimental devices can cause poor performance, crashes, and other nastiness.

Enable experimental device support? (y,n)[n]:n

Multiple devices detected (You can override by setting PLAIDML_DEVICE_IDS).
Please choose a default device:

   1 : llvm_cpu.0
   2 : metal_intel(r)_hd_graphics_630.0
   3 : metal_amd_radeon_pro_560.0

Default device? (1,2,3)[1]:3

Selected device:
    metal_amd_radeon_pro_560.0

Almost done. Multiplying some matrices...
Tile code:
  function (B[X,Z], C[Z,Y]) -> (A) { A[x,y : X,Y] = +(B[x,z] * C[z,y]); }
Whew. That worked.

Save settings to /Users/onesixx/.plaidml? (y,n)[y]:y
Success!
Shell 
{
    "PLAIDML_DEVICE_IDS":[
        "metal_amd_radeon_pro_560.0"
    ],
    "PLAIDML_EXPERIMENTAL":false
}

테스트

MobileNet in plaidbench를 설치하고, 이를 활용하여 TEST한다.

Shell 
Requirement already satisfied: plaidml-keras in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (0.7.0)
Collecting plaidbench
  Using cached plaidbench-0.7.0-py2.py3-none-any.whl (10.1 MB)
Requirement already satisfied: six           in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from plaidml-keras) (1.14.0)
Requirement already satisfied: keras==2.2.4  in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from plaidml-keras) (2.2.4)
Requirement already satisfied: plaidml       in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from plaidml-keras) (0.7.0)
Requirement already satisfied: enum34>=1.1.6 in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from plaidbench) (1.1.10)
Collecting click>=6.0.0
  Using cached click-7.1.1-py2.py3-none-any.whl (82 kB)
Collecting colorama
  Using cached colorama-0.4.3-py2.py3-none-any.whl (15 kB)
Requirement already satisfied: numpy         in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from plaidbench)(1.18.1)
Requirement already satisfied: h5py>=2.7.0   in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from plaidbench) (2.10.0)
Requirement already satisfied: keras-preprocessing>=1.0.5 in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from keras==2.2.4->plaidml-keras) (1.1.0)
Requirement already satisfied: scipy>=0.14   in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from keras==2.2.4->plaidml-keras) (1.4.1)
Requirement already satisfied: pyyaml        in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from keras==2.2.4->plaidml-keras) (5.3.1)
Requirement already satisfied: keras-applications>=1.0.6 in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from keras==2.2.4->plaidml-keras) (1.0.8)
Requirement already satisfied: cffi          in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from plaidml->plaidml-keras) (1.14.0)
Requirement already satisfied: pycparser     in /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from cffi->plaidml->plaidml-keras) (2.20)
Installing collected packages: click, colorama, plaidbench
Successfully installed click-7.1.1 colorama-0.4.3 plaidbench-0.7.0
Shell 
Running 1024 examples with mobilenet, batch size 1, on backend plaid
INFO:plaidml:Opening device "metal_amd_radeon_pro_560.0"
Compiling network... Warming up... Running...
Example finished, elapsed: 0.494s (compile), 8.796s (execution)

-----------------------------------------------------------------------------------------
Network Name         Inference Latency         Time / FPS
-----------------------------------------------------------------------------------------
mobilenet            8.59 ms                   0.00 ms / 1000000000.00 fps
Correctness: PASS, max_error: 1.675534622336272e-05, max_abs_error: 7.674098014831543e-07, fail_ratio: 0.0

Activity Monitor로 GPU사용 확인

Windows – GPU history ( cmd+4 )
(Mac용 활성 상태 보기 앱> 윈도우 > GPU 기록)

GPU사용 환경 확인 in R

R 
> py_config()
python:         /Users/onesixx/Library/r-miniconda/envs/r-reticulate/bin/python
libpython:      /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/libpython3.6m.dylib
pythonhome:     /Users/onesixx/Library/r-miniconda/envs/r-reticulate:/Users/onesixx/Library/r-miniconda/envs/r-reticulate
version:        3.6.10 |Anaconda, Inc.| (default, Mar 25 2020, 18:53:43)  [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]
numpy:          /Users/onesixx/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/numpy
numpy_version:  1.18.1

> conda_list()
          name                                                          python
1  r-miniconda                   /Users/onesixx/Library/r-miniconda/bin/python
2 r-reticulate /Users/onesixx/Library/r-miniconda/envs/r-reticulate/bin/python
R 
> tf_config()
TensorFlow v2.0.0 ()
Python v3.6 (~/Library/r-miniconda/envs/r-reticulate/bin/python)

> is_keras_available()
[1] TRUE

TEST using mnist_cnn

https://rpubs.com/siero5335/399690
https://onesixx.com/tutorial-started-with-keras/
R

ex)

R

Categories: DeepLearning
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onesixx

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matt_unt
matt_unt
20-06-29 17:52

thank you very much. It worked for me!
감사합니다

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