refactor: Change more files to ReST (#4)

docs/how-to/3rd-party/index.rst

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+Deep learning guide
+###################
+
+The following sections cover the different framework installations for ROCm and
+deep-learning applications. The following image provides
+the sequential flow for the use of each framework. Refer to the ROCm Compatible
+Frameworks Release Notes for each framework's most current release notes at
+:doc:`/reference/3rd-party-support-matrix`
+
+.. image:: /data/install/magma-install/magma005.png
+
+Frameworks installation
+***********************
+
+- :doc:`pytorch-install`
+- :doc:`tensorflow-install`
+- :doc:`magma-install`

docs/how-to/3rd-party/magma-install.rst

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+MAGMA installation for ROCm
+###########################
+
+MAGMA for ROCm
+**************
+
+Matrix Algebra on GPU and Multicore Architectures (MAGMA) is a
+collection of next-generation dense linear algebra libraries that is designed
+for heterogeneous architectures, such as multiple GPUs and multi- or many-core
+CPUs.
+
+MAGMA provides implementations for CUDA, HIP, Intel Xeon Phi, and OpenCL™. For
+more information, refer to
+`https://icl.utk.edu/magma/index.html <https://icl.utk.edu/magma/index.html>`_.
+
+Using MAGMA for PyTorch
+=======================
+
+Tensor is fundamental to deep-learning techniques because it provides extensive
+representational functionalities and math operations. This data structure is
+represented as a multidimensional matrix. MAGMA accelerates tensor operations
+with a variety of solutions including driver routines, computational routines,
+BLAS routines, auxiliary routines, and utility routines.
+
+Building MAGMA from source
+==========================
+
+To build MAGMA from the source, follow these steps:
+
+1. In the event you want to compile only for your uarch, use:
+
+   .. code-block:: shell
+
+       export PYTORCH_ROCM_ARCH=<uarch>
+
+   ``<uarch>`` is the architecture reported by the ``rocminfo`` command.
+
+2. Use the following:
+
+   .. code-block:: shell
+
+       export PYTORCH_ROCM_ARCH=<uarch>
+
+       # "install" hipMAGMA into /opt/rocm/magma by copying after build
+       git clone https://bitbucket.org/icl/magma.git
+       pushd magma
+       # Fixes memory leaks of MAGMA found while executing linalg UTs
+       git checkout 5959b8783e45f1809812ed96ae762f38ee701972
+       cp make.inc-examples/make.inc.hip-gcc-mkl make.inc
+       echo 'LIBDIR += -L$(MKLROOT)/lib' >> make.inc
+       echo 'LIB += -Wl,--enable-new-dtags -Wl,--rpath,/opt/rocm/lib -Wl,--rpath,$(MKLROOT)/lib -Wl,--rpath,/opt/rocm/magma/lib' >> make.inc
+       echo 'DEVCCFLAGS += --gpu-max-threads-per-block=256' >> make.inc
+       export PATH="${PATH}:/opt/rocm/bin"
+       if [[ -n "$PYTORCH_ROCM_ARCH" ]]; then
+         amdgpu_targets=`echo $PYTORCH_ROCM_ARCH | sed 's/;/ /g'`
+       else
+         amdgpu_targets=`rocm_agent_enumerator | grep -v gfx000 | sort -u | xargs`
+       fi
+       for arch in $amdgpu_targets; do
+         echo "DEVCCFLAGS += --amdgpu-target=$arch" >> make.inc
+       done
+       # hipcc with openmp flag may cause isnan() on __device__ not to be found; depending on context, compiler may attempt to match with host definition
+       sed -i 's/^FOPENMP/#FOPENMP/g' make.inc
+       make -f make.gen.hipMAGMA -j $(nproc)
+       LANG=C.UTF-8 make lib/libmagma.so -j $(nproc) MKLROOT=/opt/conda
+       make testing/testing_dgemm -j $(nproc) MKLROOT=/opt/conda
+       popd
+       mv magma /opt/rocm