PlaidML을 통해 AMD GPU 사용하기 (macOS)

Published onesixx on

R로 Deep Learning을 macbook의 GPU를 활용해서 분석하기 위한 환경설정.

Miniconda 설치 (reticulate 릍 통해)

https://onesixx.com/miniconda/

가상환경 설치 (ex. sixxDL)

### create new myENV by cloning
U> conda create --name sixxDL --clone r-reticulate
U> conda activate sixxDL

condaenv 선택 (reticulate 릍 통해)

library(reticulate)
myENV='sixxDL'
use_condaenv(condaenv=myENV, required=T)

Keras 설치

#  (sixxDL) ~$➜  conda upgrade pip
#  (sixxDL) ~$➜  sudo conda update -n base conda
#  설치된 패키지 확인 ~$➜  conda list tensorflow  또는 ~$➜  pip list | grep tensorflow

(sixxDL) ~$➜  pip install tensorflow==2.2.0
(sixxDL) ~$➜  pip uninstall keras-preprocessing
(sixxDL) ~$➜  pip install keras-preprocessing==1.1.0
(sixxDL) ~$➜  pip install keras==2.2.4
(sixxDL) ~$➜  plaidml-setup

추가 모듈 설치

#library(keras)
#install_keras(method="conda")
#install_keras(method="conda", version="2.2.4")

conda_install(envname=myENV, c("matplotlib","pandas","seaborn","scikit-learn"))

Keras 란

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

#install.packages("keras")      # 'config', 'tensorflow', 'tfruns', 'zeallot'
library(keras)
> 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)

use_condaenv(condaenv='sixxDL', required=T) #cf.> use_python, use_virtualenv, use_miniconda
use_python(python="~/.local/share/r-miniconda/envs/sixxDL/bin/python" )

install_keras(method="conda", tensorflow="gpu")

설치후 확인 작업

#.rs.restartR()  # cmd+shift+F10
use_condaenv(condaenv='sixxDL', required=T) #cf.> use_python, use_virtualenv, use_miniconda
use_python(python="~/.local/share/r-miniconda/envs/sixxDL/bin/python" )
py_config()
> 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
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Building wheels for collected packages: pyyaml, termcolor, absl-py, wrapt
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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

install_keras()
Collecting tensorflow==2.0.0
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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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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
~$➜  brew install clinfo
~$➜  clinfo
Number of platforms                               1
  Platform Name                                   Apple
  Platform Vendor                                 Apple
  Platform Version                                OpenCL 1.2 (Jun  8 2020 17:36:15)
  Platform Profile                                FULL_PROFILE
  Platform Extensions                             cl_APPLE_SetMemObjectDestructor cl_APPLE_ContextLoggingFunctions cl_APPLE_clut cl_APPLE_query_kernel_names cl_APPLE_gl_sharing cl_khr_gl_event

  Platform Name                                   Apple
Number of devices                                 3
  Device Name                                     Intel(R) Core(TM) i7-7920HQ CPU @ 3.10GHz
  Device Vendor                                   Intel
  Device Vendor ID                                0xffffffff
  Device Version                                  OpenCL 1.2
  Driver Version                                  1.1
  Device OpenCL C Version                         OpenCL C 1.2
  Device Type                                     CPU
  Device Profile                                  FULL_PROFILE
  Device Available                                Yes
  Compiler Available                              Yes
  Linker Available                                Yes
  Max compute units                               8
  Max clock frequency                             3100MHz
  Device Partition                                (core)
    Max number of sub-devices                     0
    Supported partition types                     None
    Supported affinity domains                    (n/a)
  Max work item dimensions                        3
  Max work item sizes                             1024x1x1
  Max work group size                             1024
  Preferred work group size multiple              1
  Preferred / native vector sizes
    char                                                16 / 16
    short                                                8 / 8
    int                                                  4 / 4
    long                                                 2 / 2
    half                                                 0 / 0        (n/a)
    float                                                4 / 4
    double                                               2 / 2        (cl_khr_fp64)
  Half-precision Floating-point support           (n/a)
  Single-precision Floating-point support         (core)
    Denormals                                     Yes
    Infinity and NANs                             Yes
    Round to nearest                              Yes
    Round to zero                                 Yes
    Round to infinity                             Yes
    IEEE754-2008 fused multiply-add               Yes
    Support is emulated in software               No
    Correctly-rounded divide and sqrt operations  Yes
  Double-precision Floating-point support         (cl_khr_fp64)
    Denormals                                     Yes
    Infinity and NANs                             Yes
    Round to nearest                              Yes
    Round to zero                                 Yes
    Round to infinity                             Yes
    IEEE754-2008 fused multiply-add               Yes
    Support is emulated in software               No
  Address bits                                    64, Little-Endian
  Global memory size                              17179869184 (16GiB)
  Error Correction support                        No
  Max memory allocation                           4294967296 (4GiB)
  Unified memory for Host and Device              Yes
  Minimum alignment for any data type             128 bytes
  Alignment of base address                       1024 bits (128 bytes)
  Global Memory cache type                        Read/Write
  Global Memory cache size                        64
  Global Memory cache line size                   8388608 bytes
  Image support                                   Yes
    Max number of samplers per kernel             16
    Max size for 1D images from buffer            65536 pixels
    Max 1D or 2D image array size                 2048 images
    Base address alignment for 2D image buffers   1 bytes
    Pitch alignment for 2D image buffers          1 pixels
    Max 2D image size                             8192x8192 pixels
    Max 3D image size                             2048x2048x2048 pixels
    Max number of read image args                 128
    Max number of write image args                8
  Local memory type                               Global
  Local memory size                               32768 (32KiB)
  Max number of constant args                     8
  Max constant buffer size                        65536 (64KiB)
  Max size of kernel argument                     4096 (4KiB)
  Queue properties
    Out-of-order execution                        No
    Profiling                                     Yes
  Prefer user sync for interop                    Yes
  Profiling timer resolution                      1ns
  Execution capabilities
    Run OpenCL kernels                            Yes
    Run native kernels                            Yes
  printf() buffer size                            1048576 (1024KiB)
  Built-in kernels                                (n/a)
  Device Extensions                               cl_APPLE_SetMemObjectDestructor cl_APPLE_ContextLoggingFunctions cl_APPLE_clut cl_APPLE_query_kernel_names cl_APPLE_gl_sharing cl_khr_gl_event cl_khr_fp64 cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_byte_addressable_store cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_3d_image_writes cl_khr_image2d_from_buffer cl_APPLE_fp64_basic_ops cl_APPLE_fixed_alpha_channel_orders cl_APPLE_biased_fixed_point_image_formats cl_APPLE_command_queue_priority

  Device Name                                     Intel(R) HD Graphics 630
  Device Vendor                                   Intel Inc.
  Device Vendor ID                                0x1024500
  Device Version                                  OpenCL 1.2
  Driver Version                                  1.2(Jul  6 2020 11:56:19)
  Device OpenCL C Version                         OpenCL C 1.2
  Device Type                                     GPU
  Device Profile                                  FULL_PROFILE
  Device Available                                Yes
  Compiler Available                              Yes
  Linker Available                                Yes
  Max compute units                               24
  Max clock frequency                             1100MHz
  Device Partition                                (core)
    Max number of sub-devices                     0
    Supported partition types                     None
    Supported affinity domains                    (n/a)
  Max work item dimensions                        3
  Max work item sizes                             256x256x256
  Max work group size                             256
  Preferred work group size multiple              32
  Preferred / native vector sizes
    char                                                 1 / 1
    short                                                1 / 1
    int                                                  1 / 1
    long                                                 1 / 1
    half                                                 0 / 0        (n/a)
    float                                                1 / 1
    double                                               0 / 0        (n/a)
  Half-precision Floating-point support           (n/a)
  Single-precision Floating-point support         (core)
    Denormals                                     Yes
    Infinity and NANs                             Yes
    Round to nearest                              Yes
    Round to zero                                 Yes
    Round to infinity                             Yes
    IEEE754-2008 fused multiply-add               Yes
    Support is emulated in software               No
    Correctly-rounded divide and sqrt operations  Yes
  Double-precision Floating-point support         (n/a)
  Address bits                                    64, Little-Endian
  Global memory size                              1610612736 (1.5GiB)
  Error Correction support                        No
  Max memory allocation                           402653184 (384MiB)
  Unified memory for Host and Device              Yes
  Minimum alignment for any data type             128 bytes
  Alignment of base address                       1024 bits (128 bytes)
  Global Memory cache type                        None
  Image support                                   Yes
    Max number of samplers per kernel             16
    Max size for 1D images from buffer            25165824 pixels
    Max 1D or 2D image array size                 2048 images
    Base address alignment for 2D image buffers   4 bytes
    Pitch alignment for 2D image buffers          32 pixels
    Max 2D image size                             16384x16384 pixels
    Max 3D image size                             2048x2048x2048 pixels
    Max number of read image args                 128
    Max number of write image args                8
  Local memory type                               Local
  Local memory size                               65536 (64KiB)
  Max number of constant args                     8
  Max constant buffer size                        65536 (64KiB)
  Max size of kernel argument                     1024
  Queue properties
    Out-of-order execution                        No
    Profiling                                     Yes
  Prefer user sync for interop                    Yes
  Profiling timer resolution                      80ns
  Execution capabilities
    Run OpenCL kernels                            Yes
    Run native kernels                            No
  printf() buffer size                            1048576 (1024KiB)
  Built-in kernels                                (n/a)
  Device Extensions                               cl_APPLE_SetMemObjectDestructor cl_APPLE_ContextLoggingFunctions cl_APPLE_clut cl_APPLE_query_kernel_names cl_APPLE_gl_sharing cl_khr_gl_event cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_byte_addressable_store cl_khr_image2d_from_buffer cl_khr_gl_depth_images cl_khr_depth_images cl_khr_3d_image_writes

  Device Name                                     AMD Radeon Pro 560 Compute Engine
  Device Vendor                                   AMD
  Device Vendor ID                                0x1021c00
  Device Version                                  OpenCL 1.2
  Driver Version                                  1.2 (Aug  7 2020 15:44:49)
  Device OpenCL C Version                         OpenCL C 1.2
  Device Type                                     GPU
  Device Profile                                  FULL_PROFILE
  Device Available                                Yes
  Compiler Available                              Yes
  Linker Available                                Yes
  Max compute units                               16
  Max clock frequency                             907MHz
  Device Partition                                (core)
    Max number of sub-devices                     0
    Supported partition types                     None
    Supported affinity domains                    (n/a)
  Max work item dimensions                        3
  Max work item sizes                             256x256x256
  Max work group size                             256
  Preferred work group size multiple              64
  Preferred / native vector sizes
    char                                                 4 / 4
    short                                                2 / 2
    int                                                  1 / 1
    long                                                 1 / 1
    half                                                 0 / 0        (n/a)
    float                                                1 / 1
    double                                               1 / 1        (cl_khr_fp64)
  Half-precision Floating-point support           (n/a)
  Single-precision Floating-point support         (core)
    Denormals                                     No
    Infinity and NANs                             Yes
    Round to nearest                              Yes
    Round to zero                                 Yes
    Round to infinity                             Yes
    IEEE754-2008 fused multiply-add               No
    Support is emulated in software               No
    Correctly-rounded divide and sqrt operations  Yes
  Double-precision Floating-point support         (cl_khr_fp64)
    Denormals                                     Yes
    Infinity and NANs                             Yes
    Round to nearest                              Yes
    Round to zero                                 Yes
    Round to infinity                             Yes
    IEEE754-2008 fused multiply-add               Yes
    Support is emulated in software               No
  Address bits                                    32, Little-Endian
  Global memory size                              4294967296 (4GiB)
  Error Correction support                        No
  Max memory allocation                           1073741824 (1024MiB)
  Unified memory for Host and Device              No
  Minimum alignment for any data type             128 bytes
  Alignment of base address                       32768 bits (4096 bytes)
  Global Memory cache type                        None
  Image support                                   Yes
    Max number of samplers per kernel             16
    Max size for 1D images from buffer            65536 pixels
    Max 1D or 2D image array size                 2048 images
    Base address alignment for 2D image buffers   256 bytes
    Pitch alignment for 2D image buffers          256 pixels
    Max 2D image size                             16384x16384 pixels
    Max 3D image size                             2048x2048x2048 pixels
    Max number of read image args                 128
    Max number of write image args                8
  Local memory type                               Local
  Local memory size                               32768 (32KiB)
  Max number of constant args                     8
  Max constant buffer size                        65536 (64KiB)
  Max size of kernel argument                     1024
  Queue properties
    Out-of-order execution                        No
    Profiling                                     Yes
  Prefer user sync for interop                    Yes
  Profiling timer resolution                      40ns
  Execution capabilities
    Run OpenCL kernels                            Yes
    Run native kernels                            No
  printf() buffer size                            134217728 (128MiB)
  Built-in kernels                                (n/a)
  Device Extensions                               cl_APPLE_SetMemObjectDestructor cl_APPLE_ContextLoggingFunctions cl_APPLE_clut cl_APPLE_query_kernel_names cl_APPLE_gl_sharing cl_khr_gl_event cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_byte_addressable_store cl_khr_image2d_from_buffer cl_khr_depth_images cl_APPLE_command_queue_priority cl_APPLE_command_queue_select_compute_units cl_khr_fp64

NULL platform behavior
  clGetPlatformInfo(NULL, CL_PLATFORM_NAME, ...)  Apple
  clGetDeviceIDs(NULL, CL_DEVICE_TYPE_ALL, ...)   Success [P0]
  clCreateContext(NULL, ...) [default]            Success [P0]
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_DEFAULT)  Success (2)
    Platform Name                                 Apple
    Device Name                                   Intel(R) HD Graphics 630
    Device Name                                   AMD Radeon Pro 560 Compute Engine
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_CPU)  Success (1)
    Platform Name                                 Apple
    Device Name                                   Intel(R) Core(TM) i7-7920HQ CPU @ 3.10GHz
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_GPU)  Success (2)
    Platform Name                                 Apple
    Device Name                                   Intel(R) HD Graphics 630
    Device Name                                   AMD Radeon Pro 560 Compute Engine
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_ACCELERATOR)  No devices found in platform
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_CUSTOM)  Invalid device type for platform
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_ALL)  Success (3)
    Platform Name                                 Apple
    Device Name                                   Intel(R) HD Graphics 630
    Device Name                                   AMD Radeon Pro 560 Compute Engine
    Device Name                                   Intel(R) Core(TM) i7-7920HQ CPU @ 3.10GHz
\tNOTE:\tyour OpenCL library only supports OpenCL 1.0,
\t\tbut some installed platforms support OpenCL 1.2.
\t\tPrograms using 1.2 features may crash
\t\tor behave unexpectedly

PlaidML 설치

|➜  pip install -U plaidml-keras

Collecting plaidml-keras
  Using cached plaidml_keras-0.7.0-py2.py3-none-any.whl (19 kB)
Collecting plaidml
  Using cached plaidml-0.7.0-py2.py3-none-macosx_10_10_x86_64.whl (17.8 MB)
Requirement already satisfied, skipping upgrade: six in ./Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from plaidml-keras) (1.14.0)
Collecting keras==2.2.4
  Using cached Keras-2.2.4-py2.py3-none-any.whl (312 kB)
Collecting cffi
  Using cached cffi-1.14.0-cp36-cp36m-macosx_10_9_x86_64.whl (174 kB)
Requirement already satisfied, skipping upgrade: numpy in ./Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from plaidml->plaidml-keras) (1.18.1)
Collecting enum34>=1.1.6
  Using cached enum34-1.1.10-py3-none-any.whl (11 kB)
Requirement already satisfied, skipping upgrade: pyyaml in ./Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from keras==2.2.4->plaidml-keras) (5.3.1)
Requirement already satisfied, skipping upgrade: h5py in ./Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from keras==2.2.4->plaidml-keras) (2.10.0)
Requirement already satisfied, skipping upgrade: scipy>=0.14 in ./Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from keras==2.2.4->plaidml-keras) (1.4.1)
Requirement already satisfied, skipping upgrade: keras-preprocessing>=1.0.5 in ./Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from keras==2.2.4->plaidml-keras) (1.1.0)
Requirement already satisfied, skipping upgrade: keras-applications>=1.0.6 in ./Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages (from keras==2.2.4->plaidml-keras) (1.0.8)
Collecting pycparser
  Using cached pycparser-2.20-py2.py3-none-any.whl (112 kB)
Installing collected packages: pycparser, cffi, enum34, plaidml, keras, plaidml-keras
  Attempting uninstall: keras
    Found existing installation: Keras 2.3.1
    Uninstalling Keras-2.3.1:
      Successfully uninstalled Keras-2.3.1
Successfully installed cffi-1.14.0 enum34-1.1.10 keras-2.2.4 plaidml-0.7.0 plaidml-keras-0.7.0 pycparser-2.20


onesixx:~|➜  conda list keras
# packages in environment at /Users/onesixx/Library/r-miniconda/envs/r-reticulate:
#
# Name                    Version                   Build  Channel
keras                     2.2.4                    pypi_0    pypi
keras-applications        1.0.8                    pypi_0    pypi
keras-preprocessing       1.1.0                    pypi_0    pypi
plaidml-keras             0.7.0                    pypi_0    pypi

PlaidML 설정

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

|➜  plaidml-setup
n
3 : metal_amd_radeon_pro_560.0
y
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!
|➜  subl ~/.plaidml
{
    "PLAIDML_DEVICE_IDS":[
        "metal_amd_radeon_pro_560.0"
    ],
    "PLAIDML_EXPERIMENTAL":false
}

테스트

MobileNet in plaidbench를 설치하고, 이를 활용하여 TEST한다.

|➜  pip install plaidml-keras plaidbench
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
|➜ plaidbench keras mobilenet
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

library(reticulate)
py_config()
#use_condaenv(condaenv="r-reticulate", required=TRUE) # use_python
use_miniconda(condaenv="r-reticulate", required=TRUE)    
conda_list()
> 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
library(tensorflow) 
library(keras)                                
tf_config()
is_keras_available()
use_backend(backend="plaidml") # Set PlaidML as backend
> 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/
library(reticulate)
library(tensorflow) 
library(keras)  

use_miniconda(condaenv="r-reticulate", required=TRUE)    
use_backend(backend="plaidml")

ex)

library(reticulate)
library(tensorflow) 
library(keras)  

use_miniconda(condaenv="r-reticulate", required=TRUE)    
use_backend(backend="plaidml")

# Set Parameter ----------------------------------------------------------------
batch_size  <- 128
num_classes <- 10
epochs      <- 5

img_rows <- 28
img_cols <- 28

# Load Data & set train/test ---------------------------------------------------
mnist <- dataset_mnist()

x_train <- mnist$train$x
y_train <- mnist$train$y

x_test <- mnist$test$x
y_test <- mnist$test$y

# reshape - Image Flatten ------------------------------------------------------
x_train <- array_reshape(x_train, c(nrow(x_train), img_rows, img_cols, 1))
x_test  <- array_reshape(x_test,  c(nrow(x_test),  img_rows, img_cols, 1))
input_shape <- c(img_rows, img_cols, 1)

# rescale - convert the grayscale values ---------------------------------------
x_train <- x_train / 255
x_test  <- x_test  / 255

# Prepare Y data for training  -------------------------------------------------
y_train <- to_categorical(y_train, 10)
y_test  <- to_categorical(y_test,  10)

# Defining the Model : CNN -----------------------------------------------------
model <- keras_model_sequential() %>%
  layer_conv_2d(filters=32, kernel_size=c(3,3), activation='relu', input_shape=input_shape) %>% 
  layer_conv_2d(filters=64, kernel_size=c(3,3), activation='relu') %>% 
  layer_max_pooling_2d(pool_size=c(2, 2)) %>% 
  layer_dropout(rate=0.25) %>% 
  layer_flatten() %>% 
  layer_dense(units=128, activation='relu') %>% 
  layer_dropout(rate=0.5) %>% 
  layer_dense(units=num_classes, activation='softmax')

model %>% summary()

# compile the model ------------------------------------------------------------
model %>% compile(
  loss      = loss_categorical_crossentropy,
  optimizer = optimizer_adadelta(),
  metrics   = c('accuracy')
)

# Fitting - Training and Evaluation --------------------------------------------
# **** Train model using plaidml (GPU) **** ------------------------------------
model %>% fit(
  x_train, 
  y_train,
  batch_size = batch_size,
  epochs = epochs,
  validation_split = 0.2
)

Categories: DeepLearning

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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