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Merge pull request #259 from deephealthproject/develop
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docker/.dockerignore renamed to .dockerignore

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.git/
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cmake-*/
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build/
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/[Bb]uild*
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_build/
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Dockerfile

‎Dockerfile‎

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FROM ubuntu:20.04
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# Install minimum dependencies ******************
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RUN apt-get update
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RUN apt-get install -y build-essential ca-certificates apt-utils checkinstall # Essentials
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RUN apt-get install -y git wget vim
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# Install miniconda
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RUN wget \
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https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
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&& mkdir /root/.conda \
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&& bash Miniconda3-latest-Linux-x86_64.sh -b \
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&& rm -f Miniconda3-latest-Linux-x86_64.sh
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# Activate conda
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ENV PATH="/root/miniconda3/bin:$PATH"
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RUN conda --version
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# Set working directory
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WORKDIR /eddl
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# Environment first (to reduce the building time if something has change)
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COPY environment.yml .
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# Install dependencies
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RUN conda update conda && \
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conda env create -f environment.yml
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# Copy repo
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COPY . .
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# Make RUN commands use the new environment:
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SHELL ["conda", "run", "-n", "eddl", "/bin/bash", "-c"]
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# Build EDDL
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RUN mkdir build
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RUN cd build && \
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cmake .. \
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-DCMAKE_PREFIX_PATH=$CONDA_PREFIX -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX \
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-DBUILD_SUPERBUILD=OFF \
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-DBUILD_TARGET=CPU \
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-DBUILD_HPC=OFF -DBUILD_TESTS=ON \
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-DBUILD_DIST=OFF -DBUILD_RUNTIME=OFF
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RUN cd build && \
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make -j$(nproc) && \
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make install
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# Test EDDL
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RUN cd build/bin/ && ./unit_tests
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# Build docs (optional, check .dockerignore)
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RUN cd docs/doxygen/ && doxygen
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RUN cd docs/sphinx/source/ && make clean && make html
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‎docker/Dockerfile‎

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This file was deleted.

‎docs/markdown/benchmarks.md‎

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**Default flags:**
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**Version: v0.9**
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```
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Setup
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-------
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VERSION: v0.9
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TARGET: CUDNN
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CORES: 16
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EPOCHS: 5
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-- C++ flags: -fopenmp
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-- C++ flags (release): -O3 -march=native -mtune=native -Ofast -msse -mfpmath=sse -ffast-math -ftree-vectorize
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-- C++ flags (debug): -O0 -g
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Training/Evaluation:
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--------------------
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Epoch 1
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Batch 300 softmax4 ( loss[softmax_cross_entropy]=0.2405 metric[categorical_accuracy]=0.9279 ) -- 0.0049 secs/batch
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1.4780 secs/epoch
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Epoch 2
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Batch 300 softmax4 ( loss[softmax_cross_entropy]=0.0799 metric[categorical_accuracy]=0.9760 ) -- 0.0043 secs/batch
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1.2766 secs/epoch
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Epoch 3
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Batch 300 softmax4 ( loss[softmax_cross_entropy]=0.0507 metric[categorical_accuracy]=0.9841 ) -- 0.0041 secs/batch
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1.2286 secs/epoch
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Memory:
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--------
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PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
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4516 salvaca+ 20 0 12,3g 1,8g 544700 R 350,0 11,7 0:13.12 cifar_conv
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GPU Memory:
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Wed Feb 17 12:56:16 2021
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+-----------------------------------------------------------------------------+
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| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
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|-------------------------------+----------------------+----------------------+
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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| | | MIG M. |
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|===============================+======================+======================|
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| 0 GeForce GTX 1070 On | 00000000:09:00.0 On | N/A |
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| 49% 67C P2 100W / 190W | 1939MiB / 8118MiB | 83% Default |
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| | | N/A |
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+-------------------------------+----------------------+----------------------+
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```
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**Version: v0.7**
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```
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Setup
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-------
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VERSION: v0.7
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TARGET: CPU
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TARGET: GPU
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CORES: 16
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EPOCHS: 1
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| 0 GeForce GTX 1070 Off | 00000000:09:00.0 On | N/A |
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| 54% 71C P2 76W / 190W | 1188MiB / 8118MiB | 71% Default |
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+-------------------------------+----------------------+----------------------+
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```
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#### CPU only
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**Default flags:**
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**Version: v0.9**
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```
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Setup
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-------
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VERSION: v0.7
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TARGET: CPU
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CORES: 16
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EPOCHS: 5
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-- C++ flags: -fopenmp
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-- C++ flags (release): -O3 -march=native -mtune=native -Ofast -msse -mfpmath=sse -ffast-math -ftree-vectorize
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-- C++ flags (debug): -O0 -g
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Training/Evaluation:
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--------------------
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Epoch 1
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Batch 300 softmax4 ( loss[softmax_cross_entropy]=0.2374 metric[categorical_accuracy]=0.9297 ) -- 0.0406 secs/batch
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12.1930 secs/epoch
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Epoch 2
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Batch 300 softmax4 ( loss[softmax_cross_entropy]=0.0790 metric[categorical_accuracy]=0.9764 ) -- 0.0337 secs/batch
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10.1122 secs/epoch
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Epoch 3
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Batch 300 softmax4 ( loss[softmax_cross_entropy]=0.0515 metric[categorical_accuracy]=0.9839 ) -- 0.0485 secs/batch
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14.5351 secs/epoch
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Memory:
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--------
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PID USER PRI NI VIRT RES S CPU% MEM% TIME+ Command
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309406 salvaca+ 20 0 872076 318928 13228 R 1444 1,9 11:58.60 mnist_mlp
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```
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**Version: v0.7**
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**Default flags:**
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**Version: v0.9**
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```
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Setup
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-------
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VERSION: v0.9
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TARGET: CUDNN
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CORES: 16
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EPOCHS: 1
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-- C++ flags: -fopenmp
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-- C++ flags (release): -O3 -march=native -mtune=native -Ofast -msse -mfpmath=sse -ffast-math -ftree-vectorize
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-- C++ flags (debug): -O0 -g
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Training/Evaluation:
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--------------------
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5 epochs of 500 batches of size 100
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Epoch 1
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Batch 500 softmax6 ( loss[softmax_cross_entropy]=1.6524 metric[categorical_accuracy]=0.3853 ) -- 0.0074 secs/batch
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3.6942 secs/epoch
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Epoch 2
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Batch 500 softmax6 ( loss[softmax_cross_entropy]=1.1562 metric[categorical_accuracy]=0.5863 ) -- 0.0069 secs/batch
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3.4294 secs/epoch
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Epoch 3
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Batch 500 softmax6 ( loss[softmax_cross_entropy]=0.9170 metric[categorical_accuracy]=0.6756 ) -- 0.0067 secs/batch
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3.3702 secs/epoch
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Memory:
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--------
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PID USER PRI NI VIRT RES S CPU% MEM% TIME+ Command
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12366 salvaca+ 20 0 11,137g 1,011g 191004 R 1562 6,5 0:28.25 cifar_conv
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GPU Memory:
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+-----------------------------------------------------------------------------+
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| NVIDIA-SMI 440.82 Driver Version: 440.82 CUDA Version: 10.2 |
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|-------------------------------+----------------------+----------------------+
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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|===============================+======================+======================|
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| 0 GeForce GTX 1070 Off | 00000000:09:00.0 On | N/A |
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| 58% 77C P2 88W / 190W | 1369MiB / 8118MiB | 94% Default |
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+-------------------------------+----------------------+----------------------+
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```
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**Version: v0.7**
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**Version: v0.9**
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```
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Setup
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-------
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VERSION: v0.7
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TARGET: CPU
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CORES: 16
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EPOCHS: 3
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-- C++ flags: -fopenmp
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-- C++ flags (release): -O3 -march=native -mtune=native -Ofast -msse -mfpmath=sse -ffast-math -ftree-vectorize
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-- C++ flags (debug): -O0 -g
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Training/Evaluation:
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--------------------
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3 epochs of 500 batches of size 100
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Epoch 1
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Batch 500 softmax6 ( loss[softmax_cross_entropy]=1.6671 metric[categorical_accuracy]=0.3816 ) -- 0.1481 secs/batch
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74.0601 secs/epoch
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Epoch 2
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Batch 500 softmax6 ( loss[softmax_cross_entropy]=1.1738 metric[categorical_accuracy]=0.5817 ) -- 0.1678 secs/batch
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83.8880 secs/epoch
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Epoch 3
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Batch 500 softmax6 ( loss[softmax_cross_entropy]=0.9311 metric[categorical_accuracy]=0.6737 ) -- 0.1466 secs/batch
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73.3027 secs/epoch
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Evaluate with batch size 100
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Batch 100 softmax6 ( loss[softmax_cross_entropy]=0.9291 metric[categorical_accuracy]=0.6796 ) --
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Memory:
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--------
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PID USER PRI NI VIRT RES S CPU% MEM% TIME+ Command
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335469 salvaca+ 20 0 10,4g 1,4g 127808 R 1406 9,2 32:51.72 cifar_conv
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```
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**Version: v0.7**
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‎docs/markdown/development.md‎

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./autogen.sh && ./configure && make
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sudo make install
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```
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### Docker
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```
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cd eddl/
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docker build -f Dockerfile .
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```

‎docs/sphinx/source/bundle/computing_service.rst‎

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**Not implemented yet**
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Serialization
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==============
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A computing service configuration can be stored and loaded to create a
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new equivalent computing service. To do it we serialize the configuration
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using protocol buffers and the ONNX standard definition.
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Export to file
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------------------
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.. doxygenfunction:: save_compserv_to_onnx_file
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Example:
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.. code-block:: c++
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compserv cs = CS_GPU({1});
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save_compserv_to_onnx_file(cs, "my_cs.onnx");
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Import from file
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------------------
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.. doxygenfunction:: import_compserv_from_onnx_file
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Example:
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.. code-block:: c++
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compserv cs = import_compserv_from_onnx_file("my_cs.onnx");

‎docs/sphinx/source/bundle/optimizers.rst‎

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Export to file
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------------------
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.. doxygenfunction:: save_optimizer_to_onnx_file
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.. code-block:: c++
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optimizer opt = sgd(0.001, 0.9);
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save_optimizer_to_onnx_file(opt, "my_opt.onnx");
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Import from file
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------------------
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.. doxygenfunction:: import_optimizer_from_onnx_file
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Example:
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optimizer opt = import_optimizer_from_onnx_file("my_opt.onnx");

‎docs/sphinx/source/intro/installation.rst‎

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cmake .. -DBUILD_SUPERBUILD=ON -DBUILD_TARGET=CUDNN -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc -DCMAKE_CXX_COMPILER=/usr/bin/g++-7``
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cmake .. -DBUILD_SUPERBUILD=ON -DBUILD_TARGET=CUDNN -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc -DCMAKE_CXX_COMPILER=/usr/bin/g++-7
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Including EDDL in your project

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