doc: Remove suggestion to build extensions in parallel

As the extensions share the build folder building them in parallel can
cause failures or wrong results due to extensions overwriting
the files of other extensions.

Signed-off-by: Alexander Grund <alexander.grund@tu-dresden.de>

Author: Flamefire

docs/_tutorials/advanced-install.md

@@ -73,21 +73,13 @@ Available `DS_BUILD` options include:
 * `DS_BUILD_TRANSFORMER_INFERENCE` builds the transformer-inference op.
 * `DS_BUILD_STOCHASTIC_TRANSFORMER` builds the stochastic transformer op.
 
-To speed up the build-all process, you can parallelize the compilation process with:
-
-```bash
-DS_BUILD_OPS=1 pip install deepspeed --global-option="build_ext" --global-option="-j8"
-```
-
-This should complete the full build 2-3 times faster. You can adjust `-j` to specify how many cpu-cores are to be used during the build. In the example it is set to 8 cores.
-
 You can also build a binary wheel and install it on multiple machines that have the same type of GPUs and the same software environment (CUDA toolkit, PyTorch, Python, etc.)
 
 ```bash
-DS_BUILD_OPS=1 python -m build --wheel --no-isolation --config-setting="--build-option=build_ext" --config-setting="--build-option=-j8"
+DS_BUILD_OPS=1 python -m build --wheel --no-isolation
 ```
 
-This will create a pypi binary wheel under `dist`, e.g., ``dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl`` and then you can install it directly on multiple machines, in our example:
+This will create a PyPI binary wheel under `dist`, e.g., `dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl`, and then you can install it directly on multiple machines, in our example:
 
 ```bash
 pip install dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl

docs/_tutorials/ds4sci_evoformerattention.md

@@ -17,7 +17,7 @@ tags: training inference
 
 `DS4Sci_EvoformerAttention` is implemented based on [CUTLASS](https://github.com/NVIDIA/cutlass). You need to clone the CUTLASS repository and specify the path to it in the environment variable `CUTLASS_PATH`.
 CUTLASS setup detection can be ignored by setting ```CUTLASS_PATH="DS_IGNORE_CUTLASS_DETECTION"```, which is useful if you have a well setup compiler (e.g., compiling in a conda package with cutlass and the cuda compilers installed).
-CUTLASS location can be automatically inferred using pypi's [nvidia-cutlass](https://pypi.org/project/nvidia-cutlass/) package by setting ```CUTLASS_PATH="DS_USE_CUTLASS_PYTHON_BINDINGS"```. Note that this is discouraged as ```nvidia-cutlass``` is not maintained anymore and outdated.
+CUTLASS location can be automatically inferred using PyPI's [nvidia-cutlass](https://pypi.org/project/nvidia-cutlass/) package by setting ```CUTLASS_PATH="DS_USE_CUTLASS_PYTHON_BINDINGS"```. Note that this is discouraged as ```nvidia-cutlass``` is not maintained anymore and outdated.
 
 You can always simply clone cutlass and setup ```CUTLASS_PATH```:
 ```shell