bump BSP versions (#140)

Author: pythops

Containerfile.image.l4t32

@@ -3,7 +3,7 @@ FROM ubuntu:jammy
 LABEL org.opencontainers.image.authors="Badr @pythops"
 
 # Define the BSP version
-ARG BSP=https://developer.download.nvidia.com/embedded/L4T/r32_Release_v7.4/T210/Jetson-210_Linux_R32.7.4_aarch64.tbz2
+ARG BSP=https://developer.nvidia.com/downloads/embedded/l4t/r32_release_v7.5/t210/jetson-210_linux_r32.7.5_aarch64.tbz2
 
 ARG L4T_PACKAGES
 

Containerfile.image.l4t35

@@ -3,7 +3,7 @@ FROM ubuntu:jammy
 LABEL org.opencontainers.image.authors="Badr @pythops"
 
 # Define the BSP version
-ARG BSP=https://developer.download.nvidia.com/embedded/L4T/r35_Release_v5.0/release/Jetson_Linux_R35.5.0_aarch64.tbz2
+ARG BSP=https://developer.nvidia.com/downloads/embedded/l4t/r35_release_v6.0/release/jetson_linux_r35.6.0_aarch64.tbz2
 
 ARG L4T_PACKAGES
 
@@ -59,7 +59,7 @@ RUN chroot /build/Linux_for_Tegra/rootfs/ /bin/bash -c "chown -R jetson:jetson /
 RUN mount -t proc proc rootfs/proc/ && \
     mount -t sysfs sys rootfs/sys && \
     mount -o bind /dev rootfs/dev && \
-    chroot rootfs /bin/bash -c "echo 'nameserver 8.8.8.8' > /etc/resolv.conf && apt update; apt install -y cuda-toolkit $L4T_PACKAGES"
+    chroot rootfs /bin/bash -c "echo 'nameserver 8.8.8.8' > /etc/resolv.conf && apt update; apt install -y cuda-toolkit-11-4 $L4T_PACKAGES"
 
 # Edit initrd
 WORKDIR /tmp/pythops

Containerfile.image.l4t36

@@ -3,7 +3,7 @@ FROM ubuntu:jammy
 LABEL org.opencontainers.image.authors="Badr @pythops"
 
 # Define the BSP version
-ARG BSP=https://developer.nvidia.com/downloads/embedded/l4t/r36_release_v3.0/release/jetson_linux_r36.3.0_aarch64.tbz2
+ARG BSP=https://developer.nvidia.com/downloads/embedded/l4t/r36_release_v4.0/release/Jetson_Linux_R36.4.0_aarch64.tbz2
 
 ARG L4T_PACKAGES
 
@@ -27,7 +27,8 @@ RUN apt update && \
         python3-yaml \
         build-essential \
         lz4 \
-        cpio
+        cpio \
+        xmlstarlet
 
 # Extract the BSP
 RUN mkdir /build && \

Containerfile.rootfs.24_04

@@ -42,7 +42,10 @@ RUN apt update && apt install -y \
         iso-codes \
         wget \
         cpio \
-        busybox
+        busybox \
+        nvme-cli \
+        libseat1 \
+        efibootmgr
 
 # Additional tools
 RUN apt install -y \

Readme.md

@@ -1,5 +1,10 @@
 # Nvidia Jetson Minimalist Images
 
+[![Discord][discord-badge]][chat-url]
+
+[discord-badge]: https://img.shields.io/badge/Discord-chat-5865F2?style=for-the-badge&logo=discord
+[chat-url]: https://discord.gg/wA7Pg9H6
+
 ## Motivation
 
 The need for the minimalist images came from the official jetson images being large in size and containing pre-installed packages that are not necessary, resulting in the consumption of valuable disk space and memory.
@@ -23,22 +28,7 @@ The need for the minimalist images came from the official jetson images being la
 >
 > check this [link](https://www.jetson-ai-lab.com/initial_setup_jon.html) for more information.
 
-## Download the prebuilt images
-
-The prebuilt images are available in the GitHub release page 👉 [here](https://github.com/pythops/jetson-image/releases)
-
-To be able to decompress the images, you must have [lrzip](https://github.com/ckolivas/lrzip) installed.
-
-After the image is decompressed, follow the section below to flash the image to your board.
-
-The default login is:
-
-```
-username: jetson
-password: jetson
-```
-
-## Build the jetson image yourself
+## Build the jetson image
 
 > [!NOTE]
 > Building the jetson image has been tested only on Linux machines.
@@ -62,10 +52,10 @@ Then create a new rootfs with the desired ubuntu version.
 > [!NOTE]
 > Only the orin family boards can use ubuntu 24.04
 
-For ubuntu 22.04
+For ubuntu 24.04
 
 ```
-just build-jetson-rootfs 20.04
+just build-jetson-rootfs 24.04
 ```
 
 This will create the rootfs in the `rootfs` directory.
@@ -126,11 +116,9 @@ $ sudo apt install -y libcudnn8 libcudnn8-dev ...
 
 ## Result
 
-For the jetson nano for instance with the new image, only 150MB of RAM is used, which leaves you with 3.85 GB for your projects !
-
-## Looking for professional support ?
+For the `jetson orin nano` for instance with the new image, only 220MB of RAM is used, which leaves plenty of RAM for your projects !
 
-If you need more advanced configuration or a custom setup, you can contact me on this address support@pythops.com
+![](https://github.com/user-attachments/assets/7404e20f-3ccd-42c7-b8d6-e93c635aa6f0)
 
 ## License