Merge pull request #250 from RaulPPelaez/docs_update

Update installation instructions regarding CUDA

README.md

@@ -29,34 +29,7 @@ We recommend using [Mamba](https://github.com/conda-forge/miniforge/#mambaforge)
 
 ### Install from source  
 
-1. Clone the repository:
-    ```shell
-    git clone https://github.com/torchmd/torchmd-net.git
-    cd torchmd-net
-    ```
-
-2. Install the dependencies in environment.yml. You can do it via pip, but we recommend [Mambaforge](https://github.com/conda-forge/miniforge/#mambaforge) instead:  
-
-    ```shell
-    mamba env create -f environment.yml
-    mamba activate torchmd-net
-    ```
-
-3. CUDA enabled installation  
-   You can skip this section if you only need a CPU installation.
-
-	```shell
-	mamba install cuda-nvcc cuda-libraries-dev cuda-version "gxx<12" pytorch=*=*cuda*
-	```
-gxx<12 is required due to a [bug in GCC+CUDA12](https://github.com/pybind/pybind11/issues/4606) that prevents pybind11 from compiling correctly.
-
-4. Install TorchMD-NET into the environment:
-    ```shell
-    pip install -e .
-    ```
-This will install TorchMD-NET in editable mode, so that changes to the source code are immediately available.
-Besides making all python utilities available environment-wide, this will also install the `torchmd-train` command line utility.
-
+TorchMD-Net is installed using pip, but you will need to install some dependencies before. Check [this documentation page](https://torchmd-net.readthedocs.io/en/latest/installation.html#install-from-source).  
 
 ## Usage
 Specifying training arguments can either be done via a configuration yaml file or through command line arguments directly. Several examples of architectural and training specifications for some models and datasets can be found in [examples/](https://github.com/torchmd/torchmd-net/tree/main/examples). Note that if a parameter is present both in the yaml file and the command line, the command line version takes precedence.

docs/source/installation.rst

@@ -14,33 +14,64 @@ Install from source
 
 1. Clone the repository:
 
-   .. code-block:: shell
+.. code-block:: shell
 
       git clone https://github.com/torchmd/torchmd-net.git
       cd torchmd-net
 
-2. Install the dependencies in environment.yml. You can do it via pip, but we recommend `Mambaforge <https://github.com/conda-forge/miniforge/#mambaforge>`_ instead.
+2. Install the dependencies in environment.yml.
+
+.. code-block:: shell
+
+      conda env create -f environment.yml
+      conda activate torchmd-net
 
-3. Create an environment and activate it:
+3. CUDA enabled installation
 
-   .. code-block:: shell
+You can skip this section if you only need a CPU installation.
 
-      mamba env create -f environment.yml
-      mamba activate torchmd-net
+You will need the CUDA compiler (nvcc) and the corresponding development libraries to build TorchMD-Net with CUDA support. You can install CUDA from the `official NVIDIA channel <https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#conda-installation>`_ or from conda-forge.
 
-4. CUDA enabled installation
+The conda-forge channel `changed the way to install CUDA from versions 12 and above <https://github.com/conda-forge/conda-forge.github.io/issues/1963)`_, thus the following instructions depend on whether you need CUDA < 12. If you have a GPU available, conda-forge probably installed the CUDA runtime (not the developer tools) on your system already, you can check with conda:
+
+.. code-block:: shell
+
+   conda list | grep cuda
 
-   You can skip this section if you only need a CPU installation.
+   
+Or by asking pytorch:
+
+.. code-block:: shell
+		 
+   python -c "import torch; print(torch.version.cuda)"
 
- .. code-block:: shell
+   
+It is recommended to install the same version as the one used by torch.  
 
-   mamba install cuda-nvcc cuda-libraries-dev cuda-version "gxx<12" pytorch=*=*cuda*
+.. warning:: At the time of writing there is a `bug in Mamba <https://github.com/mamba-org/mamba/issues/3120>`_ (v1.5.6) that can cause trouble when installing CUDA on an already created environment. We thus recommend conda for this step.
+
+* CUDA>=12
 
- .. warning:: gxx<12 is required due to a `bug in GCC+CUDA12 <https://github.com/pybind/pybind11/issues/4606>`_ that prevents pybind11 from compiling correctly
+.. code-block:: shell
+
+   conda install -c conda-forge cuda-nvcc cuda-libraries-dev cuda-version "gxx<12" pytorch=*=*cuda*
+
+   
+.. warning:: gxx<12 is required due to a `bug in GCC+CUDA12 <https://github.com/pybind/pybind11/issues/4606>`_ that prevents pybind11 from compiling correctly
 
-5. Install TorchMD-NET into the environment:
 
-   .. code-block:: shell
+* CUDA<12  
+
+The nvidia channel provides the developer tools for CUDA<12.
+
+.. code-block:: shell
+		 
+    conda install -c nvidia "cuda-nvcc<12" "cuda-libraries-dev<12" "cuda-version<12" "gxx<12" pytorch=*=*cuda*
+
+
+4. Install TorchMD-NET into the environment:
+
+.. code-block:: shell
 
       pip install -e .