Part 5 : Container List



For each lecture, several containers are available :
  • Light image : for job scheduler (HT Condor, Slurm, etc)
  • Code server : for local and remote lectures with integrated VSCode in the browser (no VSCode installation needed)
  • Jupyter-hub : when Jupyter-hub is available through Kubernetes, Docker Swarm or other solutions. Both Jupyter notebook and Code Server are provided in these containers
First week :
  • C++ lectures :
    • Introduction to C++ algorithms :
      • Light image 128 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_cpp_algorithms/introduction_cpp_algorithms_alpine_light:latest
      • Code server 310 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_cpp_algorithms/introduction_cpp_algorithms_alpine_micromamba_code_server:latest
      • Jupyter-hub 383 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_cpp_algorithms/introduction_cpp_algorithms_alpine_micromamba_vscode:latest
    • Compiler optimisation
      • Light image 135 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/compiler_optimisation/compiler_optimisation_alpine_light:latest
      • Code server 317 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/compiler_optimisation/compiler_optimisation_alpine_micromamba_code_server:latest
      • Jupyter-hub 466 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/compiler_optimisation/compiler_optimisation_alpine_micromamba_vscode:latest
    • Performance with Stencil :
      • Light image 143 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/performance_with_stencil/performance_with_stencil_alpine_light:latest
      • Code server 325 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/performance_with_stencil/performance_with_stencil_alpine_micromamba_code_server:latest
      • Jupyter-hub OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/performance_with_stencil/performance_with_stencil_alpine_micromamba_vscode:latest
    • Optional lectures :
      • Introduction to Maqao :
        • Light image 135 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_maqao/introduction_maqao_alpine_light:latest
        • Code server 317 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_maqao/introduction_maqao_alpine_micromamba_code_server:latest
        • Jupyter-hub OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_maqao/introduction_maqao_alpine_micromamba_vscode:latest
      • Introduction to Valgrind :
        • Light image 180 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_valgrind/introduction_valgrind_alpine_light:latest
        • Code server 362 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_valgrind/introduction_valgrind_alpine_micromamba_code_server:latest
        • Jupyter-hub OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_valgrind/introduction_valgrind_alpine_micromamba_vscode:latest
      • Introduction to GDB :
        • Light image 145 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_gdb/introduction_gdb_alpine_light:latest
        • Code server 326 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_gdb/introduction_gdb_alpine_micromamba_code_server:latest
        • Jupyter-hub OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_gdb/introduction_gdb_alpine_micromamba_vscode:latest
      • Introduction to Code Optimisation :
        • Light image 135 MB (python trouble) : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/hpc_astrics/introduction_to_code_optimisation_alpine_light:latest
        • Code server : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/hpc_astrics/introduction_to_code_optimisation_alpine_micromamba_code_server:latest
        • Jupyter-hub : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/hpc_astrics/introduction_to_code_optimisation_alpine_micromamba_vscode:latest
      • Performance with NaN :
        • Light image 148 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/performance_with_nan/performance_with_nan_alpine_light:latest
        • Code server 329 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/performance_with_nan/performance_with_nan_alpine_micromamba_code_server:latest
        • Jupyter-hub OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/performance_with_nan/performance_with_nan_alpine_micromamba_vscode:latest
      • Development and code optimisation :
        • Light image 127 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/development_and_optimisation/development_and_optimisation_alpine_light:latest
        • Code server 309 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/development_and_optimisation/development_and_optimisation_alpine_micromamba_code_server:latest
        • Jupyter-hub OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/development_and_optimisation/development_and_optimisation_alpine_micromamba_vscode:latest
  • Performance with stencil in Sycl :
    • Light image 2.43 GB OK : docker://gitlab-registry.in2p3.fr/codeursintensifs/grayscott/grayscottsyclsetup/gray_scott_sycl_ubuntu_light:latest
    • Code server 2.89 MB OK : docker://gitlab-registry.in2p3.fr/codeursintensifs/grayscott/grayscottsyclsetup/gray_scott_sycl_ubuntu_micromamba_code_server:latest
    • Jupyter-hub 2.99 GB : docker://gitlab-registry.in2p3.fr/codeursintensifs/grayscott/grayscottsyclsetup/gray_scott_sycl_ubuntu_micromamba_vscode:latest
  • Performance with stencil in Fortran :
    • Light image 141 MB OK : docker://gitlab-registry.in2p3.fr/lafage/grayscottfortrantuto/grayscott_fortran_tuto_alpine_light:latest
    • Code server 324 MB OK : docker://gitlab-registry.in2p3.fr/lafage/grayscottfortrantuto/grayscott_fortran_tuto_alpine_micromamba_code_server:latest
    • Jupyter-hub OK : docker://gitlab-registry.in2p3.fr/lafage/grayscottfortrantuto/grayscott_fortran_tuto_alpine_micromamba_vscode:latest
  • Optimisation Racine cubique :
    • Light image 182 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/optimisation_racine_cubique/optimisation_cbrt_alpine_light:latest
    • Code server 343 MB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/optimisation_racine_cubique/optimisation_cbrt_alpine_micromamba_code_server:latest
    • Jupyter-hub OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/optimisation_racine_cubique/optimisation_cbrt_alpine_micromamba_vscode:latest
  • Profilage mémoire :
    • Light image 859 MB OK : docker://registry.gitlab.inria.fr/svalat/gray-scott-lab/mem-ubuntu:latest
    • Code server 1.05 GB OK : docker://registry.gitlab.inria.fr/svalat/gray-scott-lab/mem-ubuntu_micromamba_code_server:latest
    • Jupyter-hub 1.13 GB OK : docker://registry.gitlab.inria.fr/svalat/gray-scott-lab/mem-ubuntu_micromamba_vscode:latest
  • Performance with stencil in Rust :
    • Light image 626 MB OK : docker://gitlab-registry.in2p3.fr/grasland/grayscott-with-rust/rust_light:latest
    • Code server 912 MB OK : docker://gitlab-registry.in2p3.fr/grasland/grayscott-with-rust/rust_code_server:latest
    • Jupyter-hub 1.011 MB : docker://gitlab-registry.in2p3.fr/grasland/grayscott-with-rust/rust_vscode:pipeline-latest
  • Performance with stencil in Python :
    • Jupyter 1.11 GB (for laptop/server/Kubernetes) OK : docker://gitlab-registry.in2p3.fr/alice.faure/gray-scott-python/gray-scott-jupyter-micromamba:latest
Second week :
  • Eve : Light Image : docker://ghcr.io/jfalcou/compilers@sha256:f9254149cc723a1fe6011ae246c3c9009944087b931cb92e20d23acdc83650e3
  • C++ lectures :
    • Performance with stencil GPU
      • "Light" image 5.7 GB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/performance_with_stencil_gpu/performance_with_stencil_gpu_ubuntu_light:latest
      • Code server 5.91 GB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/performance_with_stencil_gpu/performance_with_stencil_gpu_ubuntu_micromamba_code_server:latest
      • Jupyter-hub 6.01 GB : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/performance_with_stencil_gpu/performance_with_stencil_gpu_ubuntu_micromamba_vscode:latest
    • Introduction to HPCSDK
      • "Light" image 5.68 GB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_hpcsdk/introduction_to_hpcsdk_2403_ubuntu_light:latest
      • Code server 5.89 GB OK : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_hpcsdk/introduction_to_hpcsdk_2403_ubuntu_micromamba_code_server:latest
      • Jupyter-hub 5.99 GB : docker://gitlab-registry.in2p3.fr/cta-lapp/cours/introduction_hpcsdk/introduction_to_hpcsdk_2403_ubuntu_micromamba_vscode:latest
  • Performance with stencil in Sycl :
    • "Light" image 5.07 GB OK : docker://gitlab-registry.in2p3.fr/codeursintensifs/grayscott/grayscottsyclsetup/gray_scott_sycl_ubuntu_cuda_light
    • Code server 5.29 GB OK : docker://gitlab-registry.in2p3.fr/codeursintensifs/grayscott/grayscottsyclsetup/gray_scott_sycl_ubuntu_cuda_micromama_code_server
    • Jupyter-hub 5.39 GB : docker://gitlab-registry.in2p3.fr/codeursintensifs/grayscott/grayscottsyclsetup/gray_scott_sycl_ubuntu_cuda_micromama_vscode
  • Performance with stencil in Fortran
    • Light Image 5.7 GB : docker://gitlab-registry.in2p3.fr/lafage/grayscottfortrantuto/grayscottfortrantuto_gpu_ubuntu_light:latest
  • Performance with stencil in Rust : same containers as CPU
  • Python :