Fixed outdated TensorFlow-with-Spyder example

aicloud_slurm/README.md

@@ -23,7 +23,7 @@
     - [PyTorch and Anaconda](#pytorch-and-anaconda)
     - [PyTorch and multi-precision training](#pytorch-and-multi-precision-training)
     - [Multi-GPU data parallelism training with Horovod and Keras](#multi-gpu-data-parallelism-training-with-horovod-and-keras)
-    - [TensorFlow with Spyder 3 GUI](#tensorflow-with-spyder3-gui)
+    - [TensorFlow 2 with Spyder GUI](#tensorflow-2-with-spyder-gui)
     - [Matlab](#matlab)
 - [Priority](#priority)
 - [Fair usage](#fair-usage)
@@ -616,19 +616,19 @@ An example on how to adapt your PyTorch code is provided [here](https://git.its.
 The NVIDIA DGX-2 comes with specialized hardware for moving data between GPUs: [NVLinks and NVSwitches](https://www.nvidia.com/en-us/data-center/nvlink/). One approach to utilizing these links is using the MVDIA Collective Communication Library ([NCCL](https://developer.nvidia.com/NCCL)). NCCL is compatible with the Message Passing Library (MPI) used in many HPC applications and facilities. This in turn is build into the Horovod framework for data parallelism training supporting many deep learning frameworks requiring only minor changes in the source code. In [this example](https://git.its.aau.dk/CLAAUDIA/docs_aicloud/src/branch/master/aicloud_slurm/multi_gpu_keras) we show how to run Horovod on our system, including Slurm settings. You can then adapt this example for you preferred framework as described in the [Horovod documentation](https://horovod.readthedocs.io/en/stable/)
 
 
-## Tensorflow with spyder3 GUI
+## TensorFlow 2 with Spyder GUI
 
-It is possible to start a GUI in the singularity container and show graphical elements. In this example we will start the IDE Spyder 3 using X11 forwarding. First connect to the AI Cloud with X11 forwarding enabled
+It is possible to start a GUI in the Singularity container and show graphical elements. In this example we will start the IDE Spyder using X11 forwarding. First connect to the AI Cloud with X11 forwarding enabled
 
 ```console
 ssh <aau ID>@ai-pilot.srv.aau.dk -X
 ```
 
-From the above git repository, go to the folder 'aicloud_slurm/tensorflow_spyder/' with the Singularity file containing
+From the Git repository hosting this documentation, go to the folder 'aicloud_slurm/tensorflow_spyder/' with the Singularity file containing:
 
 ```console
 BootStrap: docker
-From: nvcr.io/nvidia/tensorflow:19.11-tf1-py3
+From: nvcr.io/nvidia/tensorflow:21.06-tf2-py3
 
 %post
 apt-get update
@@ -637,18 +637,18 @@ pip install pandas
 pip install spyder
 ```
 
-Build the image with
+Build the image by running:
 
 ```console
 srun --cpus-per-task=6 singularity build --fakeroot tensorflow_spyder.sif Singularity
 ```
 
-Execute with (notice the --x11 flag to enable X11 forwarding via the SLURM scheduler)
+Execute with (notice the `--x11` flag to enable X11 forwarding via the Slurm scheduler)
 ```console
-srun --gres=gpu:1 --x11 singularity exec tensorflow_spyder.sif spyder3
+srun --gres=gpu:1 --x11 singularity exec --nv tensorflow_spyder.sif spyder
 ```
 
-You should now see the Spyder 3 GUI.
+You should now see the Spyder GUI (it may take a few seconds to appear depending on your connection bandwidth).
 
 ## Matlab
 It is possible to run Matlab both with and without GUI

aicloud_slurm/tensorflow_spyder/Singularity

@@ -1,8 +1,8 @@
 BootStrap: docker
-From: nvcr.io/nvidia/tensorflow:19.11-tf1-py3
+From: nvcr.io/nvidia/tensorflow:21.06-tf2-py3
 
 %post
 apt-get update
 DEBIAN_FRONTEND=noninteractive apt-get -yq install xorg x11-apps libxkbcommon-x11-0 alsa
 pip install pandas
-pip install spyder
+pip install spyder