Update CUDA version

SETUP.md

@@ -157,7 +157,7 @@ In the following `3.6` should be replaced with the Python version you are using
     sudo dockerd &
     # Pull the image from the Nvidia docker hub (https://hub.docker.com/r/nvidia/cuda) that is suitable for your system
     # E.g. for Ubuntu 18.04 do
-    sudo docker run --gpus all -it --rm nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04
+    sudo docker run --gpus all -it --rm nvidia/cuda:11.2-cudnn8.1-runtime-ubuntu18.04
 
     # Within the container: 
 

docs/README.md

@@ -2,7 +2,7 @@
 
 To setup the documentation, first you need to install the dependencies of the full environment. For it please follow the [SETUP.md](../SETUP.md). Then type:
 
-    conda create -n reco_full python=3.6 cudatoolkit=10.0 "cudnn>=7.6"
+    conda create -n reco_full -c conda-forge python=3.6 cudatoolkit=11.2 cudnn=8.1
     conda activate reco_full
     pip install --no-cache --no-binary scikit-surprise .[all]
     pip install sphinx_rtd_theme

recommenders/README.md

@@ -47,7 +47,7 @@ pip install --no-cache --no-binary scikit-surprise recommenders[examples,gpu]
 
 ## GPU Support
 
-You will need CUDA Toolkit v11.2 and CuDNN = 8.1 to enable both Tensorflow and PyTorch to use the GPU. For example, if you are using a conda enviroment, this can be installed with
+You will need CUDA Toolkit v11.2 and CuDNN v8.1 to enable both Tensorflow and PyTorch to use the GPU. For example, if you are using a conda enviroment, this can be installed with
 ```bash
 conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1
 ```

setup.py

@@ -67,7 +67,7 @@ extras_require = {
     "gpu": [
         "nvidia-ml-py3>=7.352.0",
         "tensorflow>=2.6",  # compiled with CUDA 11.2, cudnn 8.1
-        "torch==1.2.0",  # last os-common version with CUDA 10.0 support
+        "torch>=1.8",  # for CUDA 11 support
         "fastai>=1.0.46,<2",
     ],
     "spark": [

tests/ci/azure_pipeline_test/dsvm_nightly_linux_gpu.yml

@@ -31,7 +31,7 @@ extends:
     task_name: "Test - Nightly Linux GPU"
     timeout: 240
     conda_env: "nightly_linux_gpu"
-    conda_opts: "python=3.6 cudatoolkit=10.0 \"cudnn>=7.6\""
+    conda_opts: "python=3.6 -c conda-forge cudatoolkit=11.2 cudnn=8.1"
     pip_opts: "[gpu,examples,dev]  -f https://download.pytorch.org/whl/cu100/torch_stable.html"
     pytest_markers: "not spark and gpu"
     pytest_params: "-x"

tests/ci/azure_pipeline_test/dsvm_notebook_linux_gpu.yml

@@ -59,6 +59,6 @@ extends:
     - unit
     task_name: "Test - Unit Notebook Linux GPU"
     conda_env: "unit_notebook_linux_gpu"
-    conda_opts: "python=3.6 cudatoolkit=10.0 \"cudnn>=7.6\""
+    conda_opts: "python=3.6 -c conda-forge cudatoolkit=11.2 cudnn=8.1"
     pip_opts: "[gpu,examples,dev]  -f https://download.pytorch.org/whl/cu100/torch_stable.html"
     pytest_markers: "notebooks and not spark and gpu"

tests/ci/azure_pipeline_test/dsvm_unit_linux_gpu.yml

@@ -59,6 +59,6 @@ extends:
     - unit
     task_name: "Test - Unit Linux GPU"
     conda_env: "unit_linux_gpu"
-    conda_opts: "python=3.6 cudatoolkit=10.0 \"cudnn>=7.6\""
+    conda_opts: "python=3.6 -c conda-forge cudatoolkit=11.2 cudnn=8.1"
     pip_opts: "[gpu,dev] -f https://download.pytorch.org/whl/cu100/torch_stable.html"
     pytest_markers: "not notebooks and not spark and gpu"

tests/ci/azure_pipeline_test/release_pipeline.yml

@@ -45,7 +45,7 @@ jobs:
     - unit
     task_name: "Test - Unit Linux GPU"
     conda_env: "release_unit_linux_gpu"
-    conda_opts: "python=3.6 cudatoolkit=10.0 \"cudnn>=7.6\""
+    conda_opts: "python=3.6 -c conda-forge cudatoolkit=11.2 cudnn=8.1"
     pip_opts: "[gpu] -f https://download.pytorch.org/whl/cu100/torch_stable.html"
     pytest_markers: "not notebooks and not spark and gpu"
     install: "release"
@@ -56,7 +56,7 @@ jobs:
     - unit
     task_name: "Test - Unit Notebook Linux GPU"
     conda_env: "release_unit_notebook_linux_gpu"
-    conda_opts: "python=3.6 cudatoolkit=10.0 \"cudnn>=7.6\""
+    conda_opts: "python=3.6 -c conda-forge cudatoolkit=11.2 cudnn=8.1"
     pip_opts: "[gpu,examples]  -f https://download.pytorch.org/whl/cu100/torch_stable.html"
     pytest_markers: "notebooks and not spark and gpu"
     install: "release"
@@ -105,7 +105,7 @@ jobs:
     task_name: "Test - Nightly Linux GPU"
     timeout: 240
     conda_env: "release_nightly_linux_gpu"
-    conda_opts: "python=3.6 cudatoolkit=10.0 \"cudnn>=7.6\""
+    conda_opts: "python=3.6 -c conda-forge cudatoolkit=11.2 cudnn=8.1"
     pip_opts: "[gpu,examples]  -f https://download.pytorch.org/whl/cu100/torch_stable.html"
     pytest_markers: "not spark and gpu"
     install: "release"

tools/docker/Dockerfile

@@ -87,7 +87,7 @@ RUN if [ "${VIRTUAL_ENV}" = "conda" ] ; then pip install --no-cache --no-binary
 ###########
 # GPU Stage
 ###########
-FROM nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04 AS gpu
+FROM nvidia/cuda:11.2-cudnn8.1-runtime-ubuntu18.04 AS gpu
 
 ARG HOME
 ARG VIRTUAL_ENV

tools/generate_conda_file.py

@@ -65,9 +65,9 @@ CONDA_PYSPARK = {"pyarrow": "pyarrow>=0.8.0", "pyspark": "pyspark>=3"}
 CONDA_GPU = {
     "fastai": "fastai==1.0.46",
     "numba": "numba>=0.38.1",
-    "pytorch": "pytorch>=1.0.0,<=1.2.0",    # For cudatoolkit=10.0
-    "cudatoolkit": "cudatoolkit=10.0",
-    "cudnn": "cudnn>=7.6"
+    "pytorch": "pytorch>=1.8.0",    # For cudatoolkit=11
+    "cudatoolkit": "cudatoolkit=11.2",
+    "cudnn": "cudnn=8.1"
 }
 
 PIP_BASE = {