chore(gpu): docs content review 2025-01-20 (scaleway#4249)

Author: bene2k1

bare-metal/elastic-metal/reference-content/shared-responsibility-model.mdx

@@ -7,7 +7,7 @@ content:
   paragraph: Learn about the shared responsibility model for Scaleway Bare Metal services, outlining the roles of Scaleway and users in managing server security, backups, and compliance.
 tags: bare metal shared responsibility
 dates:
-  validation: 2024-07-18
+  validation: 2025-01-20
   posted: 2024-07-18
 categories:
   - bare-metal

compute/gpu/how-to/use-gpu-with-docker.mdx

@@ -7,7 +7,7 @@ content:
   paragraph: Learn how to efficiently access and use GPUs with Docker on Scaleway GPU Instances.
 tags: gpu docker
 dates:
-  validation: 2024-07-16
+  validation: 2025-01-20
   posted: 2022-03-25
 categories:
   - compute
@@ -49,7 +49,7 @@ We recommend that you map volumes from your GPU Instance to your Docker containe
 
 You can map directories from your GPU Instance's Local Storage to your Docker container, using the `-v <local_storage>:<container_mountpoint>` flag. See the example command below:
 
-```
+```bash
 docker run -it --rm -v /root/mydata/:/workspace nvidia/cuda:11.2.1-runtime-ubuntu20.04
 
 # use the `exit` command for exiting this docker container
@@ -65,7 +65,7 @@ In the above example, everything in the `/root/mydata` directory on the Instance
 
   In the following example, we create a directory called `my-data`, create a "Hello World" text file inside that directory, then use the `chown` command to set appropriate ownership for the directory before running the Docker container and specifying the mapped directories. The "Hello World" file is then available inside the Docker container:
 
-  ```
+  ```bash
   mkdir -p /root/my-data/
   echo "Hello World" > /root/my-data/hello.txt
   chown -R 1000:100 /root/my-data
@@ -153,22 +153,22 @@ The possible values of the `NVIDIA_VISIBLE_DEVICES` variable are:
 ### Example commands
 
 * Starting a GPU-enabled CUDA container (using `--gpus`)
-  ```sh
+  ```bash
   docker run --runtime=nvidia -it --rm --gpus all nvidia/cuda:11.2.1-runtime-ubuntu20.04 nvidia-smi
   ```
 
 * Starting a GPU-enabled container using `NVIDIA_VISIBLE_DEVICES` and specify the nvidia runtime
-  ```
+  ```bash
   docker run --runtime=nvidia -it --rm --e NVIDIA_VISIBLE_DEVICES=all nvidia/cuda:11.2.1-runtime-ubuntu20.04 nvidia-smi
   ```
 
 * Starting a GPU-enabled [Tensorflow](https://www.tensorflow.org/) container with a Jupyter notebook using `NVIDIA_VISIBLE_DEVICES` and map the port `88888` to access the web GUI:
-  ```
+  ```bash
   docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all -it --rm -p 8888:8888 tensorflow/tensorflow:latest-gpu-jupyter
   ```
 
 * Query the GPU UUID of the first GPU using nvidia-smi and then specifying that to the container:
-  ```
+  ```bash
   nvidia-smi -i 0 --query-gpu=uuid --format=csv
   uuid
   GPU-18a3e86f-4c0e-cd9f-59c3-55488c4b0c24

compute/gpu/how-to/use-pipenv.mdx

@@ -7,7 +7,7 @@ content:
   paragraph: This guide explains how to use Pipenv to create and manage virtual environments for Python projects on Scaleway GPU Instances.
 tags: Pipenv, virtual environment, GPU, Python
 dates:
-  validation: 2024-07-17
+  validation: 2025-01-20
   posted: 2022-03-25
 categories:
   - compute

compute/gpu/reference-content/docker-images.mdx

@@ -7,7 +7,7 @@ content:
   paragraph: Discover detailed information about Scaleway's Docker images for AI development.
 tags: docker docker-image tensorflow pytorch jax rapids
 dates:
-  validation: 2024-07-16
+  validation: 2025-01-20
   posted: 2022-03-25
 categories:
   - compute