Update BUILD.md

Splitting Caffe2 / dev + C++ benchmark mode build instructions from more mundane PyTorch-only installation.

Author: nicolasvasilache

BUILD.md

@@ -1,5 +1,5 @@
 # Important notice
-***In order to uniformize and simplify the build system we had to make choices. TC is currently only officially supported on Ubuntu 16.04 with gcc 5.4.0, cuda 9.0 and cudnn 7.***
+***In order to uniformize and simplify the build system we had to make choices. TC is currently only officially supported on Ubuntu 16.04 with gcc 5.4.0.***
 Other configurations may work too but are not yet officially supported.
 For more information about setting up the config that we use to build the conda dependencies see the following [Dockerfile](conda_recipes/Dockerfile).
 
@@ -24,9 +24,13 @@ Create a new environment in which TC will be built and install core dependencies
 conda create -y --name tc_build python=3.6
 conda activate tc_build
 conda install -y pyyaml mkl-include pytest
+conda install -y -c nicolasvasilache llvm-tapir50 halide
+```
+
+Then install the PyTorch version that corresponds to your system binaries (e.g. for PyTorch with cuda 9.0):
+```
 conda install -y -c pytorch pytorch torchvision cuda90
 conda remove -y cudatoolkit --force
-conda install -y -c nicolasvasilache llvm-tapir50 halide
 ```
 
 ***Note*** As of PyTorch 0.4, PyTorch links cuda libraries dynamically and it
@@ -36,14 +40,6 @@ As a consequence cudatoolkit only contains redundant libraries and we remove it
 explicitly. In a near future, the unified PyTorch + Caffe2 build system will link
 everything statically and stop pulling the cudatoolkit dependency.
 
-# Optional dependencies
-Optionally if you want to use Caffe2 (which is necessary for building the C++ benchmarks
-since Caffe2 is our baseline):
-```
-conda install -y -c conda-forge eigen
-conda install -y -c nicolasvasilache caffe2
-```
-
 # Activate preinstalled conda in your current terminal
 
 Once the first time configuration above has been completed, one should activate conda in
@@ -54,41 +50,22 @@ equivalent)
 conda activate tc_build
 ```
 
-# Cudnn version
-***Note*** As of PyTorch 0.4, we need to package our own Caffe2. The curent PyTorch + Caffe2
-build system links cudnn dynamically. The version of cudnn that is linked dynamically
-is imposed on us by the docker image supported by NVIDIA
-[Dockerfile](conda_recipes/docker-images/tc-cuda9.0-cudnn7.1-ubuntu16.04-devel/Dockerfile).
-For now this cudnn version is cudnn 7.1.
-If for some reason, one cannot install cudnn 7.1 system-wide, one may resort to the
-following:
-```
-conda install -c anaconda cudnn
-conda remove -y cudatoolkit --force
-```
-
-***Note*** cudnn pulls a cudatoolkit dependencey but this can never replace a system
-installation because it cannot package libcuda.so (which comes with the driver,
-not the toolkit).
-As a consequence cudatoolkit only contains redundant libraries and we remove it
-explicitly. In a near future, the unified PyTorch + Caffe2 build system will link
-everything statically and we will not need to worry about cudnn anymore.
-
-# Build TC with dependencies supplied by conda (including cudnn 7.1)
+# Build TC with dependencies supplied by conda
 ```
 CLANG_PREFIX=$(${CONDA_PREFIX}/bin/llvm-config --prefix) ./build.sh
 ```
 You may need to pass the environment variable `CUDA_TOOLKIT_ROOT_DIR` pointing
 to your cuda installation (this is required for `FindCUDA.cmake` to find your cuda installation
-and can be omitted on most systems).
+and can be omitted on most systems). When required, passing `CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda`
+is generally sufficient.
 
 # Test locally
 Run C++ tests:
 ```
 ./test.sh
 ```
 
-Install the TC Python package locally to `/tmp`:
+Install the TC Python package locally to `/tmp` for smoke checking:
 ```
 python setup.py install --prefix=/tmp
 export PYTHONPATH=${PYTHONPATH}:$(find /tmp/lib -name site-packages)
@@ -103,4 +80,41 @@ python -c 'import tensor_comprehensions'
 Run Python tests:
 ```
 ./test_python/run_test.sh
-```
+```
+
+At this point, if things work as expected you can venture installing as follows
+(always a good idea to record installed files for easy removal):
+```
+python setup.py install --record tc_files.txt
+```
+
+# Advanced / development mode installation
+
+## Optional dependencies
+Optionally if you want to use Caffe2 (this is necessary for building the C++ benchmarks
+since Caffe2 is our baseline):
+```
+conda install -y -c conda-forge eigen
+conda install -y -c nicolasvasilache caffe2
+```
+
+## Cudnn version 7.1 in Caffe2 / dev mode
+***Note*** As of PyTorch 0.4, we need to package our own Caffe2. The curent PyTorch + Caffe2
+build system links cudnn dynamically. The version of cudnn that is linked dynamically
+is imposed on us by the docker image supported by NVIDIA
+[Dockerfile](conda_recipes/docker-images/tc-cuda9.0-cudnn7.1-ubuntu16.04-devel/Dockerfile).
+For now this cudnn version is cudnn 7.1.
+
+If for some reason, one cannot install cudnn 7.1 system-wide, one may resort to the
+following:
+```
+conda install -c anaconda cudnn
+conda remove -y cudatoolkit --force
+```
+
+***Note*** cudnn pulls a cudatoolkit dependencey but this can never replace a system
+installation because it cannot package libcuda.so (which comes with the driver,
+not the toolkit).
+As a consequence cudatoolkit only contains redundant libraries and we remove it
+explicitly. In a near future, the unified PyTorch + Caffe2 build system will link
+everything statically and we will not need to worry about cudnn anymore.