minor installation instruction update

README.md

@@ -69,6 +69,10 @@ We visualized a sparse tensor network operation on a sparse tensor, convolution,
 You can install the Minkowski Engine with `pip`, with anaconda, or on the system directly. If you experience issues installing the package, please checkout the [the installation wiki page](https://github.com/NVIDIA/MinkowskiEngine/wiki/Installation).
 If you cannot find a relevant problem, please report the issue on [the github issue page](https://github.com/NVIDIA/MinkowskiEngine/issues).
 
+- [PIP](https://github.com/NVIDIA/MinkowskiEngine#pip) installation
+- [Conda](https://github.com/NVIDIA/MinkowskiEngine#anaconda) installation
+- [Python](https://github.com/NVIDIA/MinkowskiEngine#system-python) installation
+
 ### Pip
 
 The MinkowskiEngine is distributed via [PyPI MinkowskiEngine][pypi-url] which can be installed simply with `pip`.
@@ -78,15 +82,14 @@ First, install pytorch following the [instruction](https://pytorch.org). Next, i
 sudo apt install libopenblas-dev
 pip install torch
 pip install -U MinkowskiEngine --install-option="--blas=openblas" -v
+
+# For pip installation from the latest source
+# pip install -U git+https://github.com/NVIDIA/MinkowskiEngine
 ```
 
-### Pip from the latest source
+If you want to specify arguments for the setup script, please refer to the following command.
 
 ```
-sudo apt install libopenblas-dev
-pip install torch
-export CXX=g++-7
-
 # Uncomment some options if things don't work
 pip install -U git+https://github.com/NVIDIA/MinkowskiEngine \
 #                           \ # uncomment the following line if you want to force cuda installation
@@ -102,29 +105,16 @@ pip install -U git+https://github.com/NVIDIA/MinkowskiEngine \
 ### Anaconda
 
 We recommend `python>=3.6` for installation.
-
-
-#### 1. Create a conda virtual environment and install requirements.
-
 First, follow [the anaconda documentation](https://docs.anaconda.com/anaconda/install/) to install anaconda on your computer.
 
 ```
-conda create -n py3-mink python=3.7
-conda activate py3-mink
-conda install numpy mkl-include pytorch cudatoolkit=10.2 -c pytorch
-```
-
-#### 2. Compilation and installation
-
-```
-conda activate py3-mink
-git clone https://github.com/NVIDIA/MinkowskiEngine.git
-cd MinkowskiEngine
 sudo apt install libopenblas-dev
-python setup.py install --blas=openblas
+conda create -n py3-mink python=3.8
+conda activate py3-mink
+conda install numpy mkl-include pytorch cudatoolkit=11.0 -c pytorch
+pip install -U git+https://github.com/NVIDIA/MinkowskiEngine
 ```
 
-
 ### System Python
 
 Like the anaconda installation, make sure that you install pytorch with the same CUDA version that `nvcc` uses.
@@ -144,6 +134,8 @@ git clone https://github.com/NVIDIA/MinkowskiEngine.git
 cd MinkowskiEngine
 
 python setup.py install
+# To specify blas, CUDA_HOME and force CUDA installation, use the following command
+# python setup.py install --blas=openblas --cuda_home=/usr/local/cuda --force_cuda
 ```
 
 
@@ -229,6 +221,7 @@ For issues not listed on the API and feature requests, feel free to submit
 an issue on the [github issue
 page](https://github.com/NVIDIA/MinkowskiEngine/issues).
 
+
 ## Known Issues
 
 ### Too much GPU memory usage or Frequent Out of Memory
@@ -238,12 +231,12 @@ However, pytorch is implemented assuming that the number of point, or size of th
 
 Specifically, pytorch caches chunks of memory spaces to speed up allocation used in every tensor creation. If it fails to find the memory space, it splits an existing cached memory or allocate new space if there's no cached memory large enough for the requested size. Thus, every time we use different number of point (number of non-zero elements) with pytorch, it either split existing cache or reserve new memory. If the cache is too fragmented and allocated all GPU space, it will raise out of memory error.
 
-To prevent this, you must clear the cache at regular interval with `torch.cuda.empty_cache()`.
+**To prevent this, you must clear the cache at regular interval with `torch.cuda.empty_cache()`.**
+
 
 ### Running the MinkowskiEngine on nodes with a large number of CPUs
 
 The MinkowskiEngine uses OpenMP to parallelize the kernel map generation. However, when the number of threads used for parallelization is too large (e.g. OMP_NUM_THREADS=80), the efficiency drops rapidly as all threads simply wait for multithread locks to be released.
-
 In such cases, set the number of threads used for OpenMP. Usually, any number below 24 would be fine, but search for the optimal setup on your system.
 
 ```