diff --git a/.build/Dockerfile b/.build/Dockerfile index e86e299..a82978f 100755 --- a/.build/Dockerfile +++ b/.build/Dockerfile @@ -7,7 +7,7 @@ # Use NVIDIA CUDA as base image and run the same installation as in the other packages. # The version of cuda must match those of the packages installed in src/Dockerfile.gpulibs -FROM nvidia/cuda:11.6.2-cudnn8-runtime-ubuntu20.04 +FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04 LABEL authors="Christoph Schranz , Mathematical Michael " # This is a concatenated Dockerfile, the maintainers of subsequent sections may vary. RUN chmod 1777 /tmp && chmod 1777 /var/tmp @@ -17,15 +17,15 @@ RUN apt-get update && \ apt-get -y install apt-utils ############################################################################ -#################### Dependency: jupyter/base-image ######################## +#################### Dependency: jupyter/docker-stacks-foundation ########## ############################################################################ # Copyright (c) Jupyter Development Team. # Distributed under the terms of the Modified BSD License. -# Ubuntu 20.04 (focal) -# https://hub.docker.com/_/ubuntu/?tab=tags&name=focal -ARG ROOT_CONTAINER=ubuntu:focal +# Ubuntu 22.04 (jammy) +# https://hub.docker.com/_/ubuntu/tags?page=1&name=jammy +ARG ROOT_CONTAINER=ubuntu:22.04 LABEL maintainer="Jupyter Project " @@ -50,15 +50,7 @@ RUN apt-get update --yes && \ # - bzip2 is necessary to extract the micromamba executable. bzip2 \ ca-certificates \ - fonts-liberation \ locales \ - # - pandoc is used to convert notebooks to html files - # it's not present in arm64 ubuntu image, so we install it here - pandoc \ - # - run-one - a wrapper script that runs no more - # than one unique instance of some command with a unique set of arguments, - # we use `run-one-constantly` to support `RESTARTABLE` option - run-one \ sudo \ # - tini is installed as a helpful container entrypoint that reaps zombie # processes and such of the actual executable we want to start, see @@ -106,7 +98,7 @@ RUN echo "auth requisite pam_deny.so" >> /etc/pam.d/su && \ USER ${NB_UID} # Pin python version here, or set it to "default" -ARG PYTHON_VERSION=3.10 +ARG PYTHON_VERSION=3.11 # Setup work directory for backward-compatibility RUN mkdir "/home/${NB_USER}/work" && \ @@ -117,8 +109,7 @@ RUN mkdir "/home/${NB_USER}/work" && \ # Similar projects using Micromamba: # - Micromamba-Docker: # - repo2docker: -# Install Python, Mamba, Jupyter Notebook, Lab, and Hub -# Generate a notebook server config +# Install Python, Mamba and jupyter_core # Cleanup temporary files and remove Micromamba # Correct permissions # Do all this in a single RUN command to avoid duplicating all of the @@ -131,7 +122,7 @@ RUN set -x && \ # Should be simpler, see arch="64"; \ fi && \ - wget -qO /tmp/micromamba.tar.bz2 \ + wget --progress=dot:giga -O /tmp/micromamba.tar.bz2 \ "https://micromamba.snakepit.net/api/micromamba/linux-${arch}/latest" && \ tar -xvjf /tmp/micromamba.tar.bz2 --strip-components=1 bin/micromamba && \ rm /tmp/micromamba.tar.bz2 && \ @@ -144,12 +135,70 @@ RUN set -x && \ --yes \ "${PYTHON_SPECIFIER}" \ 'mamba' \ - 'notebook' \ - 'jupyterhub' \ - 'jupyterlab' && \ + 'jupyter_core' && \ rm micromamba && \ # Pin major.minor version of python mamba list python | grep '^python ' | tr -s ' ' | cut -d ' ' -f 1,2 >> "${CONDA_DIR}/conda-meta/pinned" && \ + mamba clean --all -f -y && \ + fix-permissions "${CONDA_DIR}" && \ + fix-permissions "/home/${NB_USER}" + +# Configure container startup +ENTRYPOINT ["tini", "-g", "--"] +CMD ["start.sh"] + +# Copy local files as late as possible to avoid cache busting +COPY start.sh /usr/local/bin/ + +# Switch back to jovyan to avoid accidental container runs as root +USER ${NB_UID} + +WORKDIR "${HOME}" + +############################################################################ +#################### Dependency: jupyter/base-notebook ##################### +############################################################################ + +# Copyright (c) Jupyter Development Team. +# Distributed under the terms of the Modified BSD License. +ARG OWNER=jupyter + +LABEL maintainer="Jupyter Project " + +# Fix: https://github.com/hadolint/hadolint/wiki/DL4006 +# Fix: https://github.com/koalaman/shellcheck/wiki/SC3014 +SHELL ["/bin/bash", "-o", "pipefail", "-c"] + +USER root + +# Install all OS dependencies for notebook server that starts but lacks all +# features (e.g., download as all possible file formats) +RUN apt-get update --yes && \ + apt-get install --yes --no-install-recommends \ + fonts-liberation \ + # - pandoc is used to convert notebooks to html files + # it's not present in aarch64 ubuntu image, so we install it here + pandoc \ + # - run-one - a wrapper script that runs no more + # than one unique instance of some command with a unique set of arguments, + # we use `run-one-constantly` to support `RESTARTABLE` option + run-one && \ + apt-get clean && rm -rf /var/lib/apt/lists/* + +USER ${NB_UID} + +# Install Jupyter Notebook, Lab, and Hub +# Generate a notebook server config +# Cleanup temporary files +# Correct permissions +# Do all this in a single RUN command to avoid duplicating all of the +# files across image layers when the permissions change +WORKDIR /tmp +RUN mamba install --yes \ + 'notebook' \ + 'jupyterhub' \ + 'jupyterlab' \ + 'nbclassic' && \ jupyter notebook --generate-config && \ mamba clean --all -f -y && \ npm cache clean --force && \ @@ -158,16 +207,16 @@ RUN set -x && \ fix-permissions "${CONDA_DIR}" && \ fix-permissions "/home/${NB_USER}" -EXPOSE 8888 +ENV JUPYTER_PORT=8888 +EXPOSE $JUPYTER_PORT # Configure container startup -ENTRYPOINT ["tini", "-g", "--"] CMD ["start-notebook.sh"] # Copy local files as late as possible to avoid cache busting -COPY start.sh start-notebook.sh start-singleuser.sh /usr/local/bin/ +COPY start-notebook.sh start-singleuser.sh /usr/local/bin/ # Currently need to have both jupyter_notebook_config and jupyter_server_config to support classic and lab -COPY jupyter_server_config.py /etc/jupyter/ +COPY jupyter_server_config.py docker_healthcheck.py /etc/jupyter/ # Fix permissions on /etc/jupyter as root USER root @@ -180,9 +229,8 @@ RUN sed -re "s/c.ServerApp/c.NotebookApp/g" \ # HEALTHCHECK documentation: https://docs.docker.com/engine/reference/builder/#healthcheck # This healtcheck works well for `lab`, `notebook`, `nbclassic`, `server` and `retro` jupyter commands # https://github.com/jupyter/docker-stacks/issues/915#issuecomment-1068528799 -HEALTHCHECK --interval=15s --timeout=3s --start-period=5s --retries=3 \ - CMD wget -O- --no-verbose --tries=1 --no-check-certificate \ - http${GEN_CERT:+s}://localhost:8888${JUPYTERHUB_SERVICE_PREFIX:-/}api || exit 1 +HEALTHCHECK --interval=5s --timeout=3s --start-period=5s --retries=3 \ + CMD /etc/jupyter/docker_healthcheck.py || exit 1 # Switch back to jovyan to avoid accidental container runs as root USER ${NB_UID} @@ -214,8 +262,6 @@ RUN apt-get update --yes && \ tzdata \ unzip \ vim-tiny \ - # Inkscape is installed to be able to convert SVG files - inkscape \ # git-over-ssh openssh-client \ # less is needed to run help in R @@ -225,7 +271,9 @@ RUN apt-get update --yes && \ # https://nbconvert.readthedocs.io/en/latest/install.html#installing-tex texlive-xetex \ texlive-fonts-recommended \ - texlive-plain-generic && \ + texlive-plain-generic \ + # Enable clipboard on Linux host systems + xclip && \ apt-get clean && rm -rf /var/lib/apt/lists/* # Create alternative for nano -> nano-tiny @@ -234,6 +282,12 @@ RUN update-alternatives --install /usr/bin/nano nano /bin/nano-tiny 10 # Switch back to jovyan to avoid accidental container runs as root USER ${NB_UID} +# Add R mimetype option to specify how the plot returns from R to the browser +COPY --chown=${NB_UID}:${NB_GID} Rprofile.site /opt/conda/lib/R/etc/ + +# Add setup scripts that may be used by downstream images or inherited images +COPY setup-scripts/ /opt/setup-scripts/ + ############################################################################ ################# Dependency: jupyter/scipy-notebook ####################### ############################################################################ @@ -264,7 +318,7 @@ RUN apt-get update --yes && \ USER ${NB_UID} # Install Python 3 packages -RUN mamba install --quiet --yes \ +RUN mamba install --yes \ 'altair' \ 'beautifulsoup4' \ 'bokeh' \ @@ -277,9 +331,11 @@ RUN mamba install --quiet --yes \ 'h5py' \ 'ipympl'\ 'ipywidgets' \ + 'jupyterlab-git' \ 'matplotlib-base' \ 'numba' \ 'numexpr' \ + 'openpyxl' \ 'pandas' \ 'patsy' \ 'protobuf' \ @@ -300,7 +356,7 @@ RUN mamba install --quiet --yes \ # Install facets which does not have a pip or conda package at the moment WORKDIR /tmp RUN git clone https://github.com/PAIR-code/facets.git && \ - jupyter nbextension install facets/facets-dist/ --sys-prefix && \ + jupyter nbclassic-extension install facets/facets-dist/ --sys-prefix && \ rm -rf /tmp/facets && \ fix-permissions "${CONDA_DIR}" && \ fix-permissions "/home/${NB_USER}" @@ -326,30 +382,30 @@ LABEL maintainer="Christoph Schranz , Mat # installation via conda leads to errors in version 4.8.2 USER ${NB_UID} RUN pip install --upgrade pip && \ - pip install --no-cache-dir tensorflow==2.10.1 keras==2.10 && \ + pip install --no-cache-dir tensorflow==2.15.0 keras==2.15.0 && \ fix-permissions "${CONDA_DIR}" && \ fix-permissions "/home/${NB_USER}" # Install PyTorch with dependencies -RUN conda install --quiet --yes \ - pyyaml mkl mkl-include setuptools cmake cffi typing && \ - conda clean --all -f -y && \ +RUN mamba install --quiet --yes \ + pyyaml setuptools cmake cffi typing && \ + mamba clean --all -f -y && \ fix-permissions "${CONDA_DIR}" && \ fix-permissions "/home/${NB_USER}" # Check compatibility here: # https://pytorch.org/get-started/locally/ # Installation via conda leads to errors installing cudatoolkit=11.1 -# RUN pip install --no-cache-dir torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 && \ -# torchviz==0.0.2 --extra-index-url https://download.pytorch.org/whl/cu116 +# RUN pip install --no-cache-dir torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 && \ +# torchviz==0.0.2 --extra-index-url https://download.pytorch.org/whl/cu118 RUN set -ex \ && buildDeps=' \ - torch==1.13.1 \ - torchvision==0.14.1 \ - torchaudio==0.13.1 \ + torch==2.1.0 \ + torchvision==0.16.0 \ + torchaudio==2.1.0 \ torchviz==0.0.2 \ ' \ - && pip install --no-cache-dir $buildDeps --extra-index-url https://download.pytorch.org/whl/cu116 \ + && pip install --no-cache-dir $buildDeps --extra-index-url https://download.pytorch.org/whl/cu118 \ && fix-permissions "${CONDA_DIR}" \ && fix-permissions "/home/${NB_USER}" @@ -363,13 +419,13 @@ RUN apt-get update && \ # reinstall nvcc with cuda-nvcc to install ptax USER $NB_UID -RUN conda install -c nvidia cuda-nvcc -y && \ - conda clean --all -f -y && \ +RUN mamba install -c nvidia cuda-nvcc -y && \ + mamba clean --all -f -y && \ fix-permissions $CONDA_DIR && \ fix-permissions /home/$NB_USER USER root -RUN ln -s /opt/conda/bin/ptxas /usr/bin/ptxas +RUN ln -s $CONDA_DIR/bin/ptxas /usr/bin/ptxas USER $NB_UID diff --git a/.build/Rprofile.site b/.build/Rprofile.site new file mode 100755 index 0000000..3d6a93c --- /dev/null +++ b/.build/Rprofile.site @@ -0,0 +1,4 @@ +# Add R mimetype to specify how the plot returns from R to the browser. +# https://notebook.community/andrie/jupyter-notebook-samples/Changing%20R%20plot%20options%20in%20Jupyter + +options(jupyter.plot_mimetypes = c('text/plain', 'image/png', 'image/jpeg', 'image/svg+xml', 'application/pdf')) diff --git a/.build/docker-stacks b/.build/docker-stacks index efa95c2..b8d617d 160000 --- a/.build/docker-stacks +++ b/.build/docker-stacks @@ -1 +1 @@ -Subproject commit efa95c2c5b9b095247cd2f5e55bc3b38c85da335 +Subproject commit b8d617dc0568d60f6583c42f989da51ec80e9af6 diff --git a/.build/docker_healthcheck.py b/.build/docker_healthcheck.py new file mode 100755 index 0000000..7c35a6b --- /dev/null +++ b/.build/docker_healthcheck.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python3 +# Copyright (c) Jupyter Development Team. +# Distributed under the terms of the Modified BSD License. +import json +import os +from pathlib import Path + +import requests + +# A number of operations below deliberately don't check for possible errors +# As this is a healthcheck, it should succeed or raise an exception on error + +runtime_dir = Path("/home/") / os.environ["NB_USER"] / ".local/share/jupyter/runtime/" +json_file = next(runtime_dir.glob("*server-*.json")) + +url = json.loads(json_file.read_bytes())["url"] +url = url + "api" + +r = requests.get(url, verify=False) # request without SSL verification +r.raise_for_status() +print(r.content) diff --git a/.build/fix-permissions b/.build/fix-permissions index 5e6425d..d167578 100755 --- a/.build/fix-permissions +++ b/.build/fix-permissions @@ -23,13 +23,13 @@ for d in "$@"; do -group "${NB_GID}" \ -a -perm -g+rwX \ \) \ - -exec chgrp "${NB_GID}" {} \; \ - -exec chmod g+rwX {} \; + -exec chgrp "${NB_GID}" -- {} \+ \ + -exec chmod g+rwX -- {} \+ # setuid, setgid *on directories only* find "${d}" \ \( \ -type d \ -a ! -perm -6000 \ \) \ - -exec chmod +6000 {} \; + -exec chmod +6000 -- {} \+ done diff --git a/.build/jupyter_server_config.py b/.build/jupyter_server_config.py index ef0380b..679f96b 100755 --- a/.build/jupyter_server_config.py +++ b/.build/jupyter_server_config.py @@ -9,9 +9,11 @@ c = get_config() # noqa: F821 c.ServerApp.ip = "0.0.0.0" -c.ServerApp.port = 8888 c.ServerApp.open_browser = False +# to output both image/svg+xml and application/pdf plot formats in the notebook file +c.InlineBackend.figure_formats = {"png", "jpeg", "svg", "pdf"} + # https://github.com/jupyter/notebook/issues/3130 c.FileContentsManager.delete_to_trash = False diff --git a/.build/setup-scripts/setup-julia-packages.bash b/.build/setup-scripts/setup-julia-packages.bash new file mode 100755 index 0000000..faeee01 --- /dev/null +++ b/.build/setup-scripts/setup-julia-packages.bash @@ -0,0 +1,33 @@ +#!/bin/bash +set -exuo pipefail +# Requirements: +# - Run as non-root user +# - The JULIA_PKGDIR environment variable is set +# - Julia is already set up, with the setup-julia.bash command + +# Install base Julia packages +julia -e ' +import Pkg; +Pkg.update(); +Pkg.add([ + "HDF5", + "IJulia", + "Pluto" +]); +Pkg.precompile(); +' + +# Move the kernelspec out to the system share location. Avoids +# problems with runtime UID change not taking effect properly on the +# .local folder in the jovyan home dir. move kernelspec out of home +mv "${HOME}/.local/share/jupyter/kernels/julia"* "${CONDA_DIR}/share/jupyter/kernels/" +chmod -R go+rx "${CONDA_DIR}/share/jupyter" +rm -rf "${HOME}/.local" +fix-permissions "${JULIA_PKGDIR}" "${CONDA_DIR}/share/jupyter" + +# Install jupyter-pluto-proxy to get Pluto to work on JupyterHub +mamba install --yes \ + 'jupyter-pluto-proxy' && \ + mamba clean --all -f -y && \ + fix-permissions "${CONDA_DIR}" && \ + fix-permissions "/home/${NB_USER}" diff --git a/.build/setup-scripts/setup-julia.bash b/.build/setup-scripts/setup-julia.bash new file mode 100755 index 0000000..3aab076 --- /dev/null +++ b/.build/setup-scripts/setup-julia.bash @@ -0,0 +1,39 @@ +#!/bin/bash +set -exuo pipefail +# Requirements: +# - Run as the root user +# - The JULIA_PKGDIR environment variable is set + +# Default julia version to install if env var is not set +# Check https://julialang.org/downloads/ +JULIA_VERSION="${JULIA_VERSION:-1.9.1}" + +# Figure out what architecture we are installing in +JULIA_ARCH=$(uname -m) +JULIA_SHORT_ARCH="${JULIA_ARCH}" +if [ "${JULIA_SHORT_ARCH}" == "x86_64" ]; then + JULIA_SHORT_ARCH="x64" +fi + +# Figure out Julia Installer URL +JULIA_INSTALLER="julia-${JULIA_VERSION}-linux-${JULIA_ARCH}.tar.gz" +JULIA_MAJOR_MINOR=$(echo "${JULIA_VERSION}" | cut -d. -f 1,2) + +# Download and install Julia +cd /tmp +mkdir "/opt/julia-${JULIA_VERSION}" +wget --progress=dot:giga "https://julialang-s3.julialang.org/bin/linux/${JULIA_SHORT_ARCH}/${JULIA_MAJOR_MINOR}/${JULIA_INSTALLER}" +tar xzf "${JULIA_INSTALLER}" -C "/opt/julia-${JULIA_VERSION}" --strip-components=1 +rm "${JULIA_INSTALLER}" + +# Link Julia installed version to /usr/local/bin, so julia launches it +ln -fs /opt/julia-*/bin/julia /usr/local/bin/julia + +# Tell Julia where conda libraries are +mkdir -p /etc/julia +echo "push!(Libdl.DL_LOAD_PATH, \"${CONDA_DIR}/lib\")" >> /etc/julia/juliarc.jl + +# Create JULIA_PKGDIR, where user libraries are installed +mkdir "${JULIA_PKGDIR}" +chown "${NB_USER}" "${JULIA_PKGDIR}" +fix-permissions "${JULIA_PKGDIR}" diff --git a/extra/Getting_Started/GPU-processing.ipynb b/extra/Getting_Started/GPU-processing.ipynb old mode 100644 new mode 100755 index b64b7a9..3aeaa51 --- a/extra/Getting_Started/GPU-processing.ipynb +++ b/extra/Getting_Started/GPU-processing.ipynb @@ -27,16 +27,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mon Apr 26 13:59:53 2021 \n", + "Thu Dec 14 17:16:30 2023 \n", "+-----------------------------------------------------------------------------+\n", - "| NVIDIA-SMI 465.19.01 Driver Version: 465.19.01 CUDA Version: 11.3 |\n", + "| NVIDIA-SMI 520.61.05 Driver Version: 520.61.05 CUDA Version: 11.8 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", - "| 0 NVIDIA GeForce ... On | 00000000:01:00.0 On | N/A |\n", - "| 0% 48C P8 8W / 215W | 283MiB / 7974MiB | 11% Default |\n", + "| 0 NVIDIA RTX A6000 On | 00000000:41:00.0 Off | Off |\n", + "| 30% 49C P8 27W / 300W | 5MiB / 49140MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + "| 1 NVIDIA RTX A6000 On | 00000000:61:00.0 Off | Off |\n", + "| 35% 63C P2 90W / 300W | 9635MiB / 49140MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", @@ -86,11 +90,39 @@ "execution_count": 3, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-14 17:16:32.448916: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-12-14 17:16:32.472734: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2023-12-14 17:16:32.472758: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2023-12-14 17:16:32.473445: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-12-14 17:16:32.477355: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-12-14 17:16:32.477730: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-12-14 17:16:33.173733: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "[PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU')]\n" + "[]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-14 17:16:33.915841: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2023-12-14 17:16:33.916057: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2023-12-14 17:16:33.916926: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2256] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", + "Skipping registering GPU devices...\n", + "2023-12-14 17:16:34.083985: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2023-12-14 17:16:34.084152: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2023-12-14 17:16:34.084263: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2256] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", + "Skipping registering GPU devices...\n" ] }, { @@ -101,21 +133,8 @@ " memory_limit: 268435456\n", " locality {\n", " }\n", - " incarnation: 6507124110760788315,\n", - " name: \"/device:XLA_CPU:0\"\n", - " device_type: \"XLA_CPU\"\n", - " memory_limit: 17179869184\n", - " locality {\n", - " }\n", - " incarnation: 4922654194336399393\n", - " physical_device_desc: \"device: XLA_CPU device\",\n", - " name: \"/device:XLA_GPU:0\"\n", - " device_type: \"XLA_GPU\"\n", - " memory_limit: 17179869184\n", - " locality {\n", - " }\n", - " incarnation: 1179884248341804191\n", - " physical_device_desc: \"device: XLA_GPU device\"]" + " incarnation: 14747982026689315297\n", + " xla_global_id: -1]" ] }, "execution_count": 3, @@ -138,11 +157,11 @@ { "data": { "text/plain": [ - "tensor([[0.6378, 0.9107, 0.5509],\n", - " [0.4454, 0.1930, 0.4130],\n", - " [0.0074, 0.9115, 0.1397],\n", - " [0.4758, 0.0569, 0.8469],\n", - " [0.1666, 0.7414, 0.5519]])" + "tensor([[0.3446, 0.0452, 0.2264],\n", + " [0.7986, 0.7481, 0.9437],\n", + " [0.0514, 0.0179, 0.9945],\n", + " [0.6514, 0.9786, 0.4902],\n", + " [0.9525, 0.8661, 0.2606]])" ] }, "execution_count": 4, @@ -192,7 +211,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "358 ms ± 72.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "260 ms ± 61.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -226,7 +245,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "187 ms ± 40.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + "76.7 ms ± 1.28 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], @@ -252,16 +271,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor([[0.5021, 0.1432, 0.7876, 0.4729, 0.8067],\n", - " [0.7827, 0.3770, 0.8910, 0.3543, 0.3826],\n", - " [0.1806, 0.2917, 0.3224, 0.2717, 0.3795],\n", - " [0.5002, 0.2753, 0.5238, 0.0830, 0.9391],\n", - " [0.0774, 0.3479, 0.8384, 0.6825, 0.4502]], device='cuda:0')\n", - "tensor([[0.5021, 0.1432, 0.7876, 0.4729, 0.8067],\n", - " [0.7827, 0.3770, 0.8910, 0.3543, 0.3826],\n", - " [0.1806, 0.2917, 0.3224, 0.2717, 0.3795],\n", - " [0.5002, 0.2753, 0.5238, 0.0830, 0.9391],\n", - " [0.0774, 0.3479, 0.8384, 0.6825, 0.4502]], dtype=torch.float64)\n" + "tensor([[0.3524, 0.4564, 0.5821, 0.0973, 0.7754],\n", + " [0.7047, 0.2262, 0.4790, 0.1555, 0.5360],\n", + " [0.0142, 0.1699, 0.9471, 0.2035, 0.9215],\n", + " [0.5230, 0.0497, 0.8534, 0.3936, 0.3059],\n", + " [0.8031, 0.8541, 0.3866, 0.6828, 0.7291]], device='cuda:0')\n", + "tensor([[0.3524, 0.4564, 0.5821, 0.0973, 0.7754],\n", + " [0.7047, 0.2262, 0.4790, 0.1555, 0.5360],\n", + " [0.0142, 0.1699, 0.9471, 0.2035, 0.9215],\n", + " [0.5230, 0.0497, 0.8534, 0.3936, 0.3059],\n", + " [0.8031, 0.8541, 0.3866, 0.6828, 0.7291]], dtype=torch.float64)\n" ] } ], @@ -285,7 +304,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "8.42 ms ± 223 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "3.37 ms ± 23.6 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -332,11 +351,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor([[0.6581, 0.1584, 0.1951, 0.7333, 0.2423],\n", - " [0.7057, 0.4649, 0.6851, 0.4686, 0.4990],\n", - " [0.0904, 0.3937, 0.1658, 0.3733, 0.8904],\n", - " [0.2138, 0.0609, 0.5688, 0.1917, 0.8857],\n", - " [0.2110, 0.6726, 0.2961, 0.3625, 0.0745]], device='cuda:0')\n" + "tensor([[0.4466, 0.0260, 0.0687, 0.6375, 0.9676],\n", + " [0.2974, 0.0200, 0.0621, 0.4341, 0.0167],\n", + " [0.1146, 0.3012, 0.9246, 0.1484, 0.8045],\n", + " [0.4448, 0.5577, 0.4649, 0.2364, 0.7051],\n", + " [0.0479, 0.7472, 0.2121, 0.9418, 0.7699]], device='cuda:0')\n" ] } ], @@ -367,11 +386,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor([[ 7.5589e-04, 9.0142e-05, 1.7263e-04, 7.5191e-05, 1.2231e-04],\n", - " [ 9.0142e-05, 5.8283e-04, -2.2237e-04, 3.5647e-04, -2.3438e-05],\n", - " [ 1.7263e-04, -2.2237e-04, 8.8304e-04, 3.3944e-04, -4.9345e-05],\n", - " [ 7.5191e-05, 3.5647e-04, 3.3944e-04, 9.6286e-04, -1.4842e-05],\n", - " [ 1.2231e-04, -2.3438e-05, -4.9345e-05, -1.4842e-05, 1.0476e-03]],\n", + "tensor([[ 1.2995e-03, 1.6008e-04, 3.7637e-04, 1.3155e-04, 4.5707e-05],\n", + " [ 1.6008e-04, 8.3649e-04, 4.2130e-05, 9.5201e-05, 1.6981e-04],\n", + " [ 3.7637e-04, 4.2130e-05, 1.1736e-03, 3.9943e-04, -2.7599e-04],\n", + " [ 1.3155e-04, 9.5201e-05, 3.9942e-04, 4.7651e-04, 1.6600e-04],\n", + " [ 4.5707e-05, 1.6981e-04, -2.7599e-04, 1.6600e-04, 1.3608e-03]],\n", " device='cuda:0')\n" ] } @@ -390,11 +409,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor([[ 7.5589e-04, 9.0142e-05, 1.7263e-04, 7.5191e-05, 1.2231e-04],\n", - " [ 9.0142e-05, 5.8283e-04, -2.2237e-04, 3.5647e-04, -2.3438e-05],\n", - " [ 1.7263e-04, -2.2237e-04, 8.8304e-04, 3.3944e-04, -4.9345e-05],\n", - " [ 7.5191e-05, 3.5647e-04, 3.3944e-04, 9.6286e-04, -1.4842e-05],\n", - " [ 1.2231e-04, -2.3438e-05, -4.9345e-05, -1.4842e-05, 1.0476e-03]],\n", + "tensor([[ 1.2995e-03, 1.6008e-04, 3.7637e-04, 1.3155e-04, 4.5707e-05],\n", + " [ 1.6008e-04, 8.3649e-04, 4.2130e-05, 9.5201e-05, 1.6981e-04],\n", + " [ 3.7637e-04, 4.2130e-05, 1.1736e-03, 3.9943e-04, -2.7599e-04],\n", + " [ 1.3155e-04, 9.5201e-05, 3.9942e-04, 4.7651e-04, 1.6600e-04],\n", + " [ 4.5707e-05, 1.6981e-04, -2.7599e-04, 1.6600e-04, 1.3608e-03]],\n", " dtype=torch.float64)\n" ] } @@ -416,7 +435,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -430,7 +449,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/extra/Getting_Started/JuliaQuickstart.ipynb b/extra/Getting_Started/JuliaQuickstart.ipynb index 6f9e005..24cbe15 100755 --- a/extra/Getting_Started/JuliaQuickstart.ipynb +++ b/extra/Getting_Started/JuliaQuickstart.ipynb @@ -40,8 +40,295 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `/opt/julia/registries/General.toml`\n", + "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Showoff ───────────────── v1.0.3\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Adapt ─────────────────── v3.7.2\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Calculus ──────────────── v0.5.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Rmath ─────────────────── v0.7.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m IrrationalConstants ───── v0.2.2\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ColorTypes ────────────── v0.11.4\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m DualNumbers ───────────── v0.6.8\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m OffsetArrays ──────────── v1.12.10\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m HypergeometricFunctions ─ v0.3.23\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m StatsFuns ─────────────── v1.3.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m FFTW ──────────────────── v1.7.2\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m IndirectArrays ────────── v1.0.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m IterTools ─────────────── v1.8.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m PDMats ────────────────── v0.11.31\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m SpecialFunctions ──────── v2.3.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m FixedPointNumbers ─────── v0.8.4\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m CategoricalArrays ─────── v0.10.8\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Contour ───────────────── v0.6.2\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m MKL_jll ───────────────── v2024.0.0+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Hexagons ──────────────── v0.2.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Measures ──────────────── v0.3.2\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m AbstractFFTs ──────────── v1.5.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Grisu ─────────────────── v1.0.2\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m StaticArraysCore ──────── v1.4.2\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m NaNMath ───────────────── v1.0.2\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m StaticArrays ──────────── v1.8.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Gadfly ────────────────── v1.4.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m KernelDensity ─────────── v0.6.7\n", + 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"\u001b[36m\u001b[1m Info\u001b[22m\u001b[39m Packages marked with \u001b[33m⌅\u001b[39m have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`\n", + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mSuiteSparse\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mReexport\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mIndirectArrays\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mOpenLibm_jll\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mStatsAPI\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mAdapt\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mHexagons\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mMeasures\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mWoodburyMatrices\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mDocStringExtensions\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mAbstractFFTs\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mStatistics\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mCalculus\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mIterTools\u001b[39m\n", + "\u001b[32m ✓ 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InvertedIndices ──── v1.3.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m InlineStrings ────── v1.4.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m WeakRefStrings ───── v1.4.2\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m SentinelArrays ───── v1.4.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m WorkerUtilities ──── v1.6.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ExprTools ────────── v0.1.10\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m FileIO ───────────── v1.16.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Crayons ──────────── v4.1.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m PooledArrays ─────── v1.4.3\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m RData ────────────── v0.8.3\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m TimeZones ────────── v1.13.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m DataFrames ───────── v1.6.1\n", + "\u001b[32m\u001b[1m 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v0.10.11\u001b[39m\n", + " \u001b[90m[a8cc5b0e] \u001b[39m\u001b[92m+ Crayons v4.1.1\u001b[39m\n", + " \u001b[90m[a93c6f00] \u001b[39m\u001b[92m+ DataFrames v1.6.1\u001b[39m\n", + " \u001b[90m[e2ba6199] \u001b[39m\u001b[92m+ ExprTools v0.1.10\u001b[39m\n", + " \u001b[90m[5789e2e9] \u001b[39m\u001b[92m+ FileIO v1.16.1\u001b[39m\n", + " \u001b[90m[48062228] \u001b[39m\u001b[92m+ FilePathsBase v0.9.21\u001b[39m\n", + " \u001b[90m[842dd82b] \u001b[39m\u001b[92m+ InlineStrings v1.4.0\u001b[39m\n", + " \u001b[90m[41ab1584] \u001b[39m\u001b[92m+ InvertedIndices v1.3.0\u001b[39m\n", + " \u001b[90m[b964fa9f] \u001b[39m\u001b[92m+ LaTeXStrings v1.3.1\u001b[39m\n", + " \u001b[90m[78c3b35d] \u001b[39m\u001b[92m+ Mocking v0.7.7\u001b[39m\n", + " \u001b[90m[2dfb63ee] \u001b[39m\u001b[92m+ PooledArrays v1.4.3\u001b[39m\n", + " \u001b[90m[08abe8d2] \u001b[39m\u001b[92m+ PrettyTables v2.3.1\u001b[39m\n", + "\u001b[33m⌅\u001b[39m \u001b[90m[df47a6cb] \u001b[39m\u001b[92m+ RData v0.8.3\u001b[39m\n", + " \u001b[90m[ce6b1742] \u001b[39m\u001b[92m+ RDatasets v0.7.7\u001b[39m\n", + " \u001b[90m[91c51154] \u001b[39m\u001b[92m+ SentinelArrays v1.4.1\u001b[39m\n", + " \u001b[90m[892a3eda] \u001b[39m\u001b[92m+ StringManipulation v0.3.4\u001b[39m\n", + " \u001b[90m[dc5dba14] \u001b[39m\u001b[92m+ TZJData v1.0.0+2023c\u001b[39m\n", + " \u001b[90m[f269a46b] \u001b[39m\u001b[92m+ TimeZones v1.13.0\u001b[39m\n", + " \u001b[90m[ea10d353] \u001b[39m\u001b[92m+ WeakRefStrings v1.4.2\u001b[39m\n", + " \u001b[90m[76eceee3] \u001b[39m\u001b[92m+ WorkerUtilities v1.6.1\u001b[39m\n", + "\u001b[36m\u001b[1m Info\u001b[22m\u001b[39m Packages marked with \u001b[33m⌅\u001b[39m have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`\n", + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mTZJData\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mLaTeXStrings\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mInvertedIndices\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mExprTools\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mWorkerUtilities\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mPooledArrays\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mInlineStrings\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mMocking\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mCrayons\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mFilePathsBase\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mSentinelArrays\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mWeakRefStrings\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mStringManipulation\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mCategoricalArrays → CategoricalArraysSentinelArraysExt\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mFileIO\u001b[39m\n", + "\u001b[32m ✓ \u001b[39mGadfly\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mTimeZones\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mCSV\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mPrettyTables\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mDataFrames\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mRData\u001b[39m\n", + "\u001b[32m ✓ \u001b[39mRDatasets\n", + " 22 dependencies successfully precompiled in 68 seconds. 143 already precompiled.\n" + ] + } + ], "source": [ + "import Pkg; Pkg.add(\"Gadfly\"); Pkg.add(\"RDatasets\");\n", "using IJulia\n", "using Gadfly\n", "using RDatasets" @@ -72,15 +359,6 @@ "execution_count": 5, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "┌ Warning: `getindex(df::DataFrame, col_ind::ColumnIndex)` is deprecated, use `df[!, col_ind]` instead.\n", - "│ caller = evalmapping(::DataFrame, ::Symbol) at dataframes.jl:96\n", - "└ @ Gadfly /opt/julia/packages/Gadfly/1wgcD/src/dataframes.jl:96\n" - ] - }, { "data": { "image/svg+xml": [ @@ -100,15 +378,15 @@ " \n", " \n", "\n", - "\n", - " \n", + "\n", + " \n", " \n", " \n", " SepalLength\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " 4\n", @@ -135,4328 +413,8617 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " setosa\n", " \n", " \n", - " \n", + " \n", " \n", " versicolor\n", " \n", " \n", - " \n", + " \n", " \n", " virginica\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " Species\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + 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2.46\n", " \n", " \n", - " \n", + " \n", " \n", - " 4.0\n", + " 2.47\n", " \n", " \n", - " \n", + " \n", " \n", - " 4.2\n", + " 2.48\n", " \n", " \n", - " \n", + " \n", " \n", - " 4.4\n", + " 2.49\n", " \n", " \n", - " \n", + " \n", " \n", - " 4.6\n", + " 2.50\n", " \n", " \n", - " \n", + " \n", " \n", - " 4.8\n", + " 0.0\n", " \n", " \n", - " \n", + " \n", " \n", - " 5.0\n", + " 2.5\n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " PetalWidth\n", " \n", @@ -4464,8 +9031,8 @@ " \n", "\n", "\n", - " \n", - " \n", + " \n", + " \n", " \n", "\n", "\n", - "\n" + "\n", + "\n", + "\n" ], "text/plain": [ "Plot(...)" @@ -5653,45 +11028,40 @@ "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `/opt/julia/registries/General`\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m git-repo `https://github.com/JuliaRegistries/General.git`\n", - "\u001b[?25l\u001b[2K\u001b[?25h\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `/opt/julia/environments/v1.1/Project.toml`\n", - "\u001b[90m [no changes]\u001b[39m\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `/opt/julia/environments/v1.1/Manifest.toml`\n", - "\u001b[90m [no changes]\u001b[39m\n" + "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m PyCall ─ v1.96.3\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `/opt/julia/environments/v1.9/Project.toml`\n", + " \u001b[90m[438e738f] \u001b[39m\u001b[92m+ PyCall v1.96.3\u001b[39m\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `/opt/julia/environments/v1.9/Manifest.toml`\n", + " \u001b[90m[438e738f] \u001b[39m\u001b[92m+ PyCall v1.96.3\u001b[39m\n", + "\u001b[32m\u001b[1m Building\u001b[22m\u001b[39m PyCall → `/opt/julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/c9932f1c60d2e653df4f06d76108af8fde2200c0/build.log`\n", + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n", + "\u001b[32m ✓ \u001b[39mPyCall\n", + " 1 dependency successfully precompiled in 11 seconds. 165 already precompiled.\n" ] } ], "source": [ "# Install if not already done\n", - "import Pkg; Pkg.add(\"PyCall\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ + "import Pkg; Pkg.add(\"PyCall\");\n", "using PyCall" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "PyObject " + "PyObject " ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -5702,7 +11072,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -5726,15 +11096,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.1.0", + "display_name": "Julia 1.9.1", "language": "julia", - "name": "julia-1.1" + "name": "julia-1.9" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.1.0" + "version": "1.9.1" } }, "nbformat": 4, diff --git a/extra/Getting_Started/JupyterBasics.ipynb b/extra/Getting_Started/JupyterBasics.ipynb index 52b7f35..48844b3 100755 --- a/extra/Getting_Started/JupyterBasics.ipynb +++ b/extra/Getting_Started/JupyterBasics.ipynb @@ -58,6 +58,7 @@ "SVG": "Other", "bash": "Other", "capture": "ExecutionMagics", + "code_wrap": "ExecutionMagics", "debug": "ExecutionMagics", "file": "Other", "html": "DisplayMagics", @@ -92,6 +93,7 @@ "cat": "Other", "cd": "OSMagics", "clear": "KernelMagics", + "code_wrap": "ExecutionMagics", "colors": "BasicMagics", "conda": "PackagingMagics", "config": "ConfigMagics", @@ -126,8 +128,10 @@ "lx": "Other", "macro": "ExecutionMagics", "magic": "BasicMagics", + "mamba": "PackagingMagics", "man": "KernelMagics", "matplotlib": "PylabMagics", + "micromamba": "PackagingMagics", "mkdir": "Other", "more": "KernelMagics", "mv": "Other", @@ -183,10 +187,10 @@ }, "text/plain": [ "Available line magics:\n", - "%alias %alias_magic %autoawait %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %conda %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %pip %popd %pprint %precision %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", + "%alias %alias_magic %autoawait %autocall %automagic %autosave %bookmark %cat %cd %clear %code_wrap %colors %conda %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %mamba %man %matplotlib %micromamba %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %pip %popd %pprint %precision %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", "\n", "Available cell magics:\n", - "%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%js %%latex %%markdown %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", + "%%! %%HTML %%SVG %%bash %%capture %%code_wrap %%debug %%file %%html %%javascript %%js %%latex %%markdown %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", "\n", "Automagic is ON, % prefix IS NOT needed for line magics." ] @@ -220,7 +224,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.03 ms ± 4.93 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" + "453 µs ± 9.64 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" ] } ], @@ -271,11 +275,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "-rw-r--r-- 1 jovyan users 0 Dec 19 08:44 string_0\n", - "-rw-r--r-- 1 jovyan users 0 Dec 19 08:44 string_1\n", - "-rw-r--r-- 1 jovyan users 0 Dec 19 08:44 string_2\n", - "-rw-r--r-- 1 jovyan users 0 Dec 19 08:44 string_3\n", - "-rw-r--r-- 1 jovyan users 0 Dec 19 08:44 string_4\n" + "-rw-rw-r-- 1 jovyan jovyan 0 Dec 14 17:17 string_0\n", + "-rw-rw-r-- 1 jovyan jovyan 0 Dec 14 17:17 string_1\n", + "-rw-rw-r-- 1 jovyan jovyan 0 Dec 14 17:17 string_2\n", + "-rw-rw-r-- 1 jovyan jovyan 0 Dec 14 17:17 string_3\n", + "-rw-rw-r-- 1 jovyan jovyan 0 Dec 14 17:17 string_4\n" ] } ], @@ -315,7 +319,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -329,7 +333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/extra/Getting_Started/MultiInterpreterNotebook.ipynb b/extra/Getting_Started/MultiInterpreterNotebook.ipynb index ed97de3..ebd03f2 100755 --- a/extra/Getting_Started/MultiInterpreterNotebook.ipynb +++ b/extra/Getting_Started/MultiInterpreterNotebook.ipynb @@ -18,7 +18,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last run: 2019-12-19 09:09:24.610738 UTC\n" + "Last run: 2023-12-14 17:17:31.662735 UTC\n" ] } ], @@ -89,16 +89,18 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/opt/conda/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py:191: FutureWarning: from_items is deprecated. Please use DataFrame.from_dict(dict(items), ...) instead. DataFrame.from_dict(OrderedDict(items)) may be used to preserve the key order.\n", - " res = PandasDataFrame.from_items(items)\n" + "`geom_smooth()` using formula = 'y ~ x'\n" ] }, { "data": { - "image/png": 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\n" + "image/png": 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", + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -120,7 +122,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -134,7 +136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/extra/Getting_Started/tensorboard/tensorboard_with_pytorch.ipynb b/extra/Getting_Started/tensorboard/tensorboard_with_pytorch.ipynb old mode 100644 new mode 100755 index 1de5193..bf2de3a --- a/extra/Getting_Started/tensorboard/tensorboard_with_pytorch.ipynb +++ b/extra/Getting_Started/tensorboard/tensorboard_with_pytorch.ipynb @@ -74,12 +74,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-03-06 10:53:12.601699: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-03-06 10:53:12.711871: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2023-03-06 10:53:13.121665: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", - "2023-03-06 10:53:13.121710: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", - "2023-03-06 10:53:13.121717: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + "2023-12-14 17:18:14.322300: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-12-14 17:18:14.345418: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2023-12-14 17:18:14.345442: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2023-12-14 17:18:14.346047: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-12-14 17:18:14.349859: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-12-14 17:18:14.350205: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-12-14 17:18:15.059230: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], @@ -281,7 +283,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/extra/Getting_Started/tensorboard/tensorboard_with_tensorflow.ipynb b/extra/Getting_Started/tensorboard/tensorboard_with_tensorflow.ipynb old mode 100644 new mode 100755 index c33509d..e69614d --- a/extra/Getting_Started/tensorboard/tensorboard_with_tensorflow.ipynb +++ b/extra/Getting_Started/tensorboard/tensorboard_with_tensorflow.ipynb @@ -92,12 +92,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-03-06 10:51:15.118111: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-03-06 10:51:15.217654: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2023-03-06 10:51:15.671297: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", - "2023-03-06 10:51:15.671357: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", - "2023-03-06 10:51:15.671377: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + "2023-12-14 17:18:15.632959: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-12-14 17:18:15.656733: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2023-12-14 17:18:15.656757: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2023-12-14 17:18:15.657399: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-12-14 17:18:15.661313: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-12-14 17:18:15.661887: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-12-14 17:18:16.365070: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], @@ -135,7 +137,16 @@ "id": "j-DHsby18cot", "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n", + "11490434/11490434 [==============================] - 1s 0us/step\n" + ] + } + ], "source": [ "mnist = tf.keras.datasets.mnist\n", "\n", @@ -183,40 +194,45 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-03-06 10:51:16.792703: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-03-06 10:51:16.816039: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-03-06 10:51:16.816173: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-03-06 10:51:16.816790: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-03-06 10:51:16.817127: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-03-06 10:51:16.817237: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-03-06 10:51:16.817332: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-03-06 10:51:17.308525: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-03-06 10:51:17.308658: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-03-06 10:51:17.308758: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-03-06 10:51:17.308838: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6574 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\n" + "2023-12-14 17:18:19.033227: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2023-12-14 17:18:19.033669: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2023-12-14 17:18:19.048651: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2256] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", + "Skipping registering GPU devices...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-14 17:18:19.775852: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 188160000 exceeds 10% of free system memory.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/5\n", - "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2203 - accuracy: 0.9343 - val_loss: 0.1090 - val_accuracy: 0.9677\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2187 - accuracy: 0.9345 - val_loss: 0.1003 - val_accuracy: 0.9685\n", "Epoch 2/5\n", - "1875/1875 [==============================] - 10s 6ms/step - loss: 0.0959 - accuracy: 0.9710 - val_loss: 0.0805 - val_accuracy: 0.9748\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0975 - accuracy: 0.9707 - val_loss: 0.0771 - val_accuracy: 0.9760\n", "Epoch 3/5\n", - "1875/1875 [==============================] - 10s 6ms/step - loss: 0.0688 - accuracy: 0.9782 - val_loss: 0.0641 - val_accuracy: 0.9805\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0688 - accuracy: 0.9783 - val_loss: 0.0687 - val_accuracy: 0.9792\n", "Epoch 4/5\n", - "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0534 - accuracy: 0.9834 - val_loss: 0.0631 - val_accuracy: 0.9814\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0543 - accuracy: 0.9824 - val_loss: 0.0690 - val_accuracy: 0.9778\n", "Epoch 5/5\n", - "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0426 - accuracy: 0.9862 - val_loss: 0.0681 - val_accuracy: 0.9779\n" + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0422 - accuracy: 0.9865 - val_loss: 0.0641 - val_accuracy: 0.9800\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -261,11 +277,11 @@ "data": { "text/html": [ "\n", - " \n", " \n", + " " + ], + "text/plain": [ + "" ] }, "metadata": {}, @@ -614,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "id": "Q3nupQL24E5E", "tags": [] @@ -624,35 +639,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-03-06 10:52:58.548075: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-03-06 10:52:58.643597: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2023-03-06 10:52:59.047723: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", - "2023-03-06 10:52:59.047780: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", - "2023-03-06 10:52:59.047788: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n", - "2023-03-06 10:52:59.645017: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-03-06 10:52:59.667592: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "2023-03-06 10:52:59.667740: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", - "\n", - "***** TensorBoard Uploader *****\n", - "\n", - "This will upload your TensorBoard logs to https://tensorboard.dev/ from\n", - "the following directory:\n", + "2023-12-14 17:19:53.374133: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-12-14 17:19:53.396308: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2023-12-14 17:19:53.396331: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2023-12-14 17:19:53.396968: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-12-14 17:19:53.400694: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-12-14 17:19:53.400830: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-12-14 17:19:54.014266: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2023-12-14 17:19:54.725388: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2023-12-14 17:19:54.725602: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2023-12-14 17:19:54.736401: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2256] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", + "Skipping registering GPU devices...\n", + "****************************************************************\n", + "****************************************************************\n", + "****************************************************************\n", "\n", - "logs/fit\n", + "Uploading TensorBoard logs to https://tensorboard.dev/ is no longer\n", + "supported.\n", "\n", - "This TensorBoard will be visible to everyone. Do not upload sensitive\n", - "data.\n", + "TensorBoard.dev is shutting down.\n", "\n", - "Your use of this service is subject to Google's Terms of Service\n", - " and Privacy Policy\n", - ", and TensorBoard.dev's Terms of Service\n", - ".\n", + "Please export your experiments by Dec 31, 2023.\n", "\n", - "This notice will not be shown again while you are logged into the uploader.\n", - "To log out, run `tensorboard dev auth revoke`.\n", + "See the FAQ at https://tensorboard.dev.\n", "\n", - "Continue? (yes/NO) " + "****************************************************************\n", + "****************************************************************\n", + "****************************************************************\n" ] } ], @@ -707,7 +721,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/generate-Dockerfile.sh b/generate-Dockerfile.sh index 4e1abbb..1727373 100755 --- a/generate-Dockerfile.sh +++ b/generate-Dockerfile.sh @@ -5,7 +5,7 @@ cd $(cd -P -- "$(dirname -- "$0")" && pwd -P) export DOCKERFILE=".build/Dockerfile" export STACKS_DIR=".build/docker-stacks" # please test the build of the commit in https://github.com/jupyter/docker-stacks/commits/main in advance -export HEAD_COMMIT="efa95c2c5b9b095247cd2f5e55bc3b38c85da335" +export HEAD_COMMIT="b8d617dc0568d60f6583c42f989da51ec80e9af6" while [[ "$#" -gt 0 ]]; do case $1 in -p|--pw|--password) PASSWORD="$2" && USE_PASSWORD=1; shift;; @@ -63,18 +63,28 @@ cat src/Dockerfile.header >> $DOCKERFILE echo " ############################################################################ -#################### Dependency: jupyter/base-image ######################## +#################### Dependency: jupyter/docker-stacks-foundation ########## ############################################################################ " >> $DOCKERFILE -cat $STACKS_DIR/base-notebook/Dockerfile | grep -v 'BASE_CONTAINER' | grep -v 'FROM $ROOT_CONTAINER' >> $DOCKERFILE +cat $STACKS_DIR/docker-stacks-foundation/Dockerfile | grep -v 'BASE_CONTAINER' | grep -v 'FROM $ROOT_CONTAINER' >> $DOCKERFILE + +echo " +############################################################################ +#################### Dependency: jupyter/base-notebook ##################### +############################################################################ +" >> $DOCKERFILE +cat $STACKS_DIR/base-notebook/Dockerfile | grep -v 'BASE_CONTAINER' >> $DOCKERFILE # copy files that are used during the build: +cp $STACKS_DIR/docker-stacks-foundation/initial-condarc .build/ +cp $STACKS_DIR/docker-stacks-foundation/fix-permissions .build/ +cp $STACKS_DIR/docker-stacks-foundation/start.sh .build/ cp $STACKS_DIR/base-notebook/jupyter_server_config.py .build/ -cp $STACKS_DIR/base-notebook/initial-condarc .build/ -cp $STACKS_DIR/base-notebook/fix-permissions .build/ -cp $STACKS_DIR/base-notebook/start.sh .build/ cp $STACKS_DIR/base-notebook/start-notebook.sh .build/ cp $STACKS_DIR/base-notebook/start-singleuser.sh .build/ +cp $STACKS_DIR/base-notebook/docker_healthcheck.py .build/ +cp -r $STACKS_DIR/minimal-notebook/setup-scripts .build/ +cp $STACKS_DIR/minimal-notebook/Rprofile.site .build/ chmod 755 .build/* echo " diff --git a/src/Dockerfile.gpulibs b/src/Dockerfile.gpulibs index 82bdfbc..712db46 100644 --- a/src/Dockerfile.gpulibs +++ b/src/Dockerfile.gpulibs @@ -5,30 +5,30 @@ LABEL maintainer="Christoph Schranz , Mat # installation via conda leads to errors in version 4.8.2 USER ${NB_UID} RUN pip install --upgrade pip && \ - pip install --no-cache-dir tensorflow==2.10.1 keras==2.10 && \ + pip install --no-cache-dir tensorflow==2.15.0 keras==2.15.0 && \ fix-permissions "${CONDA_DIR}" && \ fix-permissions "/home/${NB_USER}" # Install PyTorch with dependencies -RUN conda install --quiet --yes \ - pyyaml mkl mkl-include setuptools cmake cffi typing && \ - conda clean --all -f -y && \ +RUN mamba install --quiet --yes \ + pyyaml setuptools cmake cffi typing && \ + mamba clean --all -f -y && \ fix-permissions "${CONDA_DIR}" && \ fix-permissions "/home/${NB_USER}" # Check compatibility here: # https://pytorch.org/get-started/locally/ # Installation via conda leads to errors installing cudatoolkit=11.1 -# RUN pip install --no-cache-dir torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 && \ -# torchviz==0.0.2 --extra-index-url https://download.pytorch.org/whl/cu116 +# RUN pip install --no-cache-dir torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 && \ +# torchviz==0.0.2 --extra-index-url https://download.pytorch.org/whl/cu118 RUN set -ex \ && buildDeps=' \ - torch==1.13.1 \ - torchvision==0.14.1 \ - torchaudio==0.13.1 \ + torch==2.1.0 \ + torchvision==0.16.0 \ + torchaudio==2.1.0 \ torchviz==0.0.2 \ ' \ - && pip install --no-cache-dir $buildDeps --extra-index-url https://download.pytorch.org/whl/cu116 \ + && pip install --no-cache-dir $buildDeps --extra-index-url https://download.pytorch.org/whl/cu118 \ && fix-permissions "${CONDA_DIR}" \ && fix-permissions "/home/${NB_USER}" @@ -42,12 +42,12 @@ RUN apt-get update && \ # reinstall nvcc with cuda-nvcc to install ptax USER $NB_UID -RUN conda install -c nvidia cuda-nvcc -y && \ - conda clean --all -f -y && \ +RUN mamba install -c nvidia cuda-nvcc -y && \ + mamba clean --all -f -y && \ fix-permissions $CONDA_DIR && \ fix-permissions /home/$NB_USER USER root -RUN ln -s /opt/conda/bin/ptxas /usr/bin/ptxas +RUN ln -s $CONDA_DIR/bin/ptxas /usr/bin/ptxas USER $NB_UID diff --git a/src/Dockerfile.header b/src/Dockerfile.header index abafde3..5d59d2b 100644 --- a/src/Dockerfile.header +++ b/src/Dockerfile.header @@ -1,6 +1,6 @@ # Use NVIDIA CUDA as base image and run the same installation as in the other packages. # The version of cuda must match those of the packages installed in src/Dockerfile.gpulibs -FROM nvidia/cuda:11.6.2-cudnn8-runtime-ubuntu20.04 +FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04 LABEL authors="Christoph Schranz , Mathematical Michael " # This is a concatenated Dockerfile, the maintainers of subsequent sections may vary. RUN chmod 1777 /tmp && chmod 1777 /var/tmp