nnabla-ext-cuda

release-note-bugfix

• fix convolution segv caused by cudaEvent_t instance variables
• Generate random seed when seed==-1 in cudnn context

release-note-build

• Includes cuda lib in wheel

release-note-op-layer

• Add CUDA implementation of OneHot function
• RandomFlip implementation in CUDA

release-note-utility

• Skip allreduce if array is not updated

Install the latest nnabla by:

pip install nnabla
pip install nnabla-ext-cuda101  # For CUDA version 10.1 users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda101.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla-ext-cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla-ext-cuda101

Note that CUDA 10.1 and cuDNN 7.6 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.6)
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.6)
• nnabla-ext-cuda101 (CUDA 10.1 x cuDNN 7.6)

pip install nnabla
pip install nnabla-ext-cuda101  # For CUDA 10.1 x cuDNN 7.6 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

The "nnabla-ext-cuda" package is temporarily unavailable. Use of this package is not recommended. Please install nnabla-ext-cuda101, nnabla-ext-cuda100, nnabla-ext-cuda90 or nnabla-ext-cuda80 instead.
The following nnabla CUDA extension packages have been deprecated and the PyPi repository has been closed.

• nnabla-ext-cuda91
• nnabla-ext-cuda92
  The following "nnabla-ext-cuda" docker images have been deprecated.
• py37-cuda92
• py36-cuda92
• py27-cuda92
• py37-cuda92-v1.0.xx
• py36-cuda92-v1.0.xx
• py27-cuda92-v1.0.xx

We've decided to change nnabla's versioning policy to semantic versioning.
This change has been applied from version 1.1.0.

Install the latest nnabla by:

pip install nnabla
pip install nnabla-ext-cuda101  # For CUDA version 10.1 users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda101.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla-ext-cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla-ext-cuda101

Note that CUDA 10.1 and cuDNN 7.6 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.6)
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.6)
• nnabla-ext-cuda101 (CUDA 10.1 x cuDNN 7.6)

pip install nnabla
pip install nnabla-ext-cuda101  # For CUDA 10. x cuDNN 7.6 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

The "nnabla-ext-cuda" package is temporarily unavailable. Use of this package is not recommended. Please install nnabla-ext-cuda101, nnabla-ext-cuda100, nnabla-ext-cuda90 or nnabla-ext-cuda80 instead.
The following nnabla CUDA extension packages have been deprecated and the PyPi repository has been closed.

• nnabla-ext-cuda91
• nnabla-ext-cuda92
  The following "nnabla-ext-cuda" docker images have been deprecated.
• py37-cuda92
• py36-cuda92
• py27-cuda92
• py37-cuda92-v1.0.xx
• py36-cuda92-v1.0.xx
• py27-cuda92-v1.0.xx

We've decided to change nnabla's versioning policy to semantic versioning.
This change has been applied from version 1.1.0.

release-note-op-layer

• Concurrent streams in backward conv & disable nan propagation in MaxPoolingCudnn

release-note-utility

• Add CUDA stream handler class in Python (as an unsupported feature so far)
• Add NVTX utilities in Python

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 10.1 and cuDNN 7.6 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.6)
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.6)
• nnabla-ext-cuda101 (CUDA 10.1 x cuDNN 7.6)

pip install nnabla
pip install nnabla_ext_cuda101  # For CUDA 10. x cuDNN 7.6 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

release-note-bugfix

• Fix cuda kernel block calculation for zero size.
• [fix] Set mathType properly in CUDNN Convolution

release-note-build

• Fix test on ppc64le
• Add ppc64le build support.

release-note-op-layer

• Add 1-D linear and nearest neighbor interpolation.
• Add CUDA implementation of scatter_nd function

release-note-utility

• [Solver] AdamW and SGDW
• LARS (Layer-wise Adaptive Rate Scaling)

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 10.1 and cuDNN 7.6 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.6)
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.6)
• nnabla-ext-cuda101 (CUDA 10.1 x cuDNN 7.6)

pip install nnabla
pip install nnabla_ext_cuda101  # For CUDA 10. x cuDNN 7.6 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

release-note-break-compat

• [Fix] LSTM/GRU Output Shape

release-note-bugfix

• [Fix] Derivatives for Activation Layers
• [Fix] PF.lstm, PF.gru weight param mismatch

release-note-build

• Fix compile errors in nbla_cli build

release-note-op-layer

• Add 2-D and 3-D nearest neighbor interpolation.
• [Fix] log_sigmoid computation efficiency
• Add CUDNN LogSoftmax and disable fp32 Softmax
• Add CUDNN Fused Batch Normalization and utilize faster CUDNN Batch Normalization
• CUDNN Convolution and Pooling w/ NHWC layout
• Add 3D linear interpolation support.

release-note-utility

• Fix AdaBound & AMSBound for learning rate scheduling (sony/nnabla#443)

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 9.2 and cuDNN 7.4 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.5(Linux), 7.6(Win))
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.5(Linux), 7.6(Win))
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.5(Linux), 7.6(Win))
• nnabla-ext-cuda101 (CUDA 10.1 x cuDNN 7.5(Linux), 7.6(Win))

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.4 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• Support ubuntu18.04 for multi-gpu build.
• [Fix] RNN/LSTM/GRU Memory Leakage
• gather_nd and selection by index variable (advanced indexing)
• Fix bugs in min and max functions when used to return indices.
• Synchronized Batch Normalization
• Various Activation Funcs (hard_sigmoid, hard_tanh, log_sigmoid, relu6, softplus, softsign, tanh_shrink, sinc)

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 9.2 and cuDNN 7.4 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.5)
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.5)
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.5)

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.4 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• Add numeric include
• Fix numpy requirements
• Add AdaBound & AMSBound
• Add tile function (c.f. numpy.tile or torch.repeat)
• Add CUDA implemention for random_choice function.
• Add TopKDataCuda and TopKGradCuda function implementations.

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 9.2 and cuDNN 7.4 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.5(win), 7.4(linux))
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.5(win), 7.4(linux))
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.5)

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.4 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• [Bug] Image Augmentation

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 9.2 and cuDNN 7.3 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.3(win), 7.4(linux))
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.3(win), 7.4(linux))
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.3(win), 7.4(linux))

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.3 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 9.2 and cuDNN 7.3 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.3(win), 7.4(linux))
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.3(win), 7.4(linux))
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.3(win), 7.4(linux))

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.3 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• Improve memory allocator
• [Function] Add GELU Activation
• [Fix] Minor English errors
• Change Docker image names
• C++ computation graph building APIs
• Apply source code format rules.

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 9.2 and cuDNN 7.3 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.3(win), 7.4(linux))
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.3(win), 7.4(linux))
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.3(win), 7.4(linux))

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.3 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• Add CUDNN RNN/LSTM/GRU
• [function] atan2

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 9.2 and cuDNN 7.3 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.3(win), 7.4(linux))
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.3(win), 7.4(linux))
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.3(win), 7.4(linux))

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.3 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• Fix pointer arithmetic and handling of half type
• Add functions: IsInf, IsNaN, ResetNaN, ResetInf, and Where
• Feature/20190121 build with python35
• Print error for cuda ver less than 7
• Use dedicated function to determine workspace size for alogorithm.
• Add CuDNN max and average pooling for 3D case.
• Serialization of SolverState
• Fix CuDNN reduction for output shape equal input shape.
• Fix binary functions

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 9.2 and cuDNN 7.3 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.3)
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.3)
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.3)

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.3 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• Add openmpi-bin to Multi-GPU docker.
• Build with Python3.7
• Get version info from VERSION.txt

• Add reflection padding and generalize for N-D input.
• Add CuDNN versions of sum/mean/prod reduction
• Add CUDA10 support
• Add implementation of arange function.
• Add option for choosing deterministic algorithms in cuDNN convolution
• [fix] Add include in sort.cu
• Add options for F.min() and F.max() to return indices
• [doc] Avoid unnecessary upgrade of dependent packages
• [function] Prune function
• Add the CUDA implementation for sort function.
• [format] Add auto-format make commands

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 9.2 and cuDNN 7.3 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.1)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.3)
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.3)
• nnabla-ext-cuda100 (CUDA 10.0 x cuDNN 7.3)

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.3 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• Fix link.
• Numpy-like basic indexing (negative indexing)
• Create ~/.ccache before prepare docker build.
• Delete try-catch-all logic.

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Users with python <= 3.4 may experience errors with pip install nnabla and pip install nnabla-ext-cuda.
■ Workaround
Please install matplotlib == 2.2.3 and re-install nnabla, nnabla_ext_cuda.

pip install matplotlib==2.2.3
pip install nnabla
pip install nnabla_ext_cuda

Note that CUDA 9.2 and cuDNN 7.2 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.2)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.2)
• nnabla-ext-cuda91 (CUDA 9.1 x cuDNN 7.1)
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.2)

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.2 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• Fix misspell

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Note that CUDA 9.2 and cuDNN 7.2 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.2)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.2)
• nnabla-ext-cuda91 (CUDA 9.1 x cuDNN 7.1)
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.2)

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.2 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• Get cmake 3.x from EPEL repository.
• Discard MULTI_GPU_NAME and MULTI_GPU, and enable MULTI_GPU_SUFFIX and…

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Note that CUDA 9.2 and cuDNN 7.2 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.2)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.2)
• nnabla-ext-cuda91 (CUDA 9.1 x cuDNN 7.1)
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.2)

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.2 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• Feature/20180907 multi gpu package
• Resize function with interpolation (add only 2D bilinear)
• Fix -u option.
• Multi-GPU docker build
• Remove unnecessary print
• LeakyReLU with inplace
• Update numpy version to 1.12 and install scikit-image by PIP instead of conda
• Remove MAKE_MANYLINUX_WHEEL from build-with-docker
• [c++] Enable Group in (De)ConvolutionCudaCudnn
• Fix missing SCL package issue
• Fix bug for division of collectives

Install the latest nnabla by:

pip install nnabla
pip install nnabla_ext_cuda  # For CUDA users

Note that CUDA 9.2 and cuDNN 7.2 are set as default if versions are not specified. You can also install the cuda extension with specific versions from one of the following. See also FAQ

• nnabla-ext-cuda80 (CUDA 8.0 x cuDNN 7.2)
• nnabla-ext-cuda90 (CUDA 9.0 x cuDNN 7.2)
• nnabla-ext-cuda91 (CUDA 9.1 x cuDNN 7.1)
• nnabla-ext-cuda92 (CUDA 9.2 x cuDNN 7.2)

pip install nnabla
pip install nnabla_ext_cuda92  # For CUDA 9.2 x cuDNN 7.2 users

Additional setup may be required depending on your OS or environment. Please check Python Package Installation Guide for details.

Install Guide

To use C++ inference feature, follow the demonstration on MNIST inference in C++.

Demonstration

For distributed training, you need to build a binary from source. See the guide for building multi-GPU training package.

Build Guide

• Fix/20180802 eigen download source
• Properly check architecture in cublas gemm and batched gemm