anko-intel commented on a change in pull request #20719:
URL: https://github.com/apache/incubator-mxnet/pull/20719#discussion_r741935766
##########
File path: tools/dependencies/README.md
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@@ -52,12 +52,12 @@ MXNet is built on top of many dependencies. Managing these
dependencies could be
## Overview
-The dependencies could be categorized by several groups: BLAS libraries,
CPU-based performance boost library, i.e. ONEDNN and GPU-based performance
boosting library including CUDA, cuDNN, NCCL. and others including OpenCV,
Numpy, S3-related, PS-lite dependencies. The list below shows all the
dependencies and their version. Except for CUDA, cuDNN, NCCL which the user is
required to install on their environments, we statically link those
dependencies into libmxnet.so when we build PyPi package. By doing this, the
user can take advantage of these dependencies without being worry about it.
+The dependencies could be categorized by several groups: BLAS libraries,
CPU-based performance boost library, i.e. oneDNN and GPU-based performance
boosting library including CUDA, cuDNN, NCCL. and others including OpenCV,
Numpy, S3-related, PS-lite dependencies. The list below shows all the
dependencies and their version. Except for CUDA, cuDNN, NCCL which the user is
required to install on their environments, we statically link those
dependencies into libmxnet.so when we build PyPi package. By doing this, the
user can take advantage of these dependencies without being worry about it.
| Dependencies | MXNet Version |
| :------------: |:-------------:|
|OpenBLAS| 0.3.9 |
-|ONEDNN| 2.0 |
+|oneDNN| 2.0 |
Review comment:
is this dependency still valid? as oneDNN is included in source
directory ?
##########
File path: tools/dependencies/README.md
##########
@@ -52,12 +52,12 @@ MXNet is built on top of many dependencies. Managing these
dependencies could be
## Overview
-The dependencies could be categorized by several groups: BLAS libraries,
CPU-based performance boost library, i.e. ONEDNN and GPU-based performance
boosting library including CUDA, cuDNN, NCCL. and others including OpenCV,
Numpy, S3-related, PS-lite dependencies. The list below shows all the
dependencies and their version. Except for CUDA, cuDNN, NCCL which the user is
required to install on their environments, we statically link those
dependencies into libmxnet.so when we build PyPi package. By doing this, the
user can take advantage of these dependencies without being worry about it.
+The dependencies could be categorized by several groups: BLAS libraries,
CPU-based performance boost library, i.e. oneDNN and GPU-based performance
boosting library including CUDA, cuDNN, NCCL. and others including OpenCV,
Numpy, S3-related, PS-lite dependencies. The list below shows all the
dependencies and their version. Except for CUDA, cuDNN, NCCL which the user is
required to install on their environments, we statically link those
dependencies into libmxnet.so when we build PyPi package. By doing this, the
user can take advantage of these dependencies without being worry about it.
| Dependencies | MXNet Version |
| :------------: |:-------------:|
|OpenBLAS| 0.3.9 |
-|ONEDNN| 2.0 |
+|oneDNN| 2.0 |
Review comment:
is this dependency still valid? as oneDNN is included in source
directory ?
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