Setup Libraries#
These instructions install miniconda and the requisite libraries for the tutorial in a conda environment, which is an independent python installation.
Jupyter has the similar concept of kernels, which are independent execution environments. They don’t even have to be Python, kernels for other languages exist as well.
By loading a separate kernel for each project, we avoid the complication of different components/projects having weird interactions, ultimately helping reproducibility.
We first produce a new conda environment with the libraries we require, then we tell Jupyter about this new environment with the ipython executable
NOTE: This step may take a little while
%%bash
#!/bin/bash
KERNEL_NAME="machine-learning-das"
# Download mambaforge install script
if [[ ! -f $HOME/mambaforge.sh ]]; then
wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh -O $HOME/mambaforge.sh || { echo "Failed to download the mambaforge installation script."; exit $?; }
fi
# Install miniconda3
if [[ ! -d $HOME/mambaforge ]]; then
chmod u+x $HOME/mambaforge.sh
bash $HOME/mambaforge.sh -b -f -u -p $HOME/mambaforge
fi
echo "Sourcing the conda and mamba setup scripts..."
source $HOME/mambaforge/etc/profile.d/conda.sh
source $HOME/mambaforge/etc/profile.d/mamba.sh
# # Not updating miniconda since that results in an issues with mamba
# # https://github.com/mamba-org/mamba/issues/1775
# echo "Updating the miniconda3 release..."
# conda update -y -n base -c defaults conda 2>&1 || { echo "Failed to properly update miniconda3."; exit $?; }
# Create conda environment
if [[ ! -d $HOME/mambaforge/envs/$KERNEL_NAME ]]; then
echo "Creating the miniconda3 environment \"$KERNEL_NAME\"..."
mamba env create -n $KERNEL_NAME -f environment.yml || { echo "Failed to properly setup the environment \"$KERNEL_NAME\" using mamba."; exit $?; }
fi
echo "Activating the kernel \"$KERNEL_NAME\"..."
conda activate $KERNEL_NAME || { echo "Failed to activate the kernel \"$KERNEL_NAME\"."; exit $?; }
python -m ipykernel install --name $KERNEL_NAME --user
echo "Installed \"$KERNEL_NAME\"!"
Sourcing the conda and mamba setup scripts...
Creating the miniconda3 environment "machine-learning-das"...
Retrieving notices: ...working... done
Looking for: ['python==3.10', 'pip']
Transaction
Prefix: /home/cms.rkansal/mambaforge/envs/machine-learning-das
Updating specs:
- python==3.10
- pip
Package Version Build Channel Size
───────────────────────────────────────────────────────────────────────────────────────
Install:
───────────────────────────────────────────────────────────────────────────────────────
+ _libgcc_mutex 0.1 conda_forge conda-forge/linux-64 Cached
+ _openmp_mutex 4.5 2_gnu conda-forge/linux-64 Cached
+ bzip2 1.0.8 hd590300_5 conda-forge/linux-64 254kB
+ ca-certificates 2023.11.17 hbcca054_0 conda-forge/linux-64 154kB
+ ld_impl_linux-64 2.40 h41732ed_0 conda-forge/linux-64 Cached
+ libffi 3.4.2 h7f98852_5 conda-forge/linux-64 Cached
+ libgcc-ng 13.2.0 h807b86a_3 conda-forge/linux-64 774kB
+ libgomp 13.2.0 h807b86a_3 conda-forge/linux-64 422kB
+ libnsl 2.0.1 hd590300_0 conda-forge/linux-64 33kB
+ libsqlite 3.44.2 h2797004_0 conda-forge/linux-64 846kB
+ libuuid 2.38.1 h0b41bf4_0 conda-forge/linux-64 Cached
+ libzlib 1.2.13 hd590300_5 conda-forge/linux-64 Cached
+ ncurses 6.4 h59595ed_2 conda-forge/linux-64 884kB
+ openssl 3.2.0 hd590300_1 conda-forge/linux-64 3MB
+ pip 23.3.2 pyhd8ed1ab_0 conda-forge/noarch 1MB
+ python 3.10.0 h543edf9_3_cpython conda-forge/linux-64 Cached
+ readline 8.2 h8228510_1 conda-forge/linux-64 Cached
+ setuptools 69.0.3 pyhd8ed1ab_0 conda-forge/noarch 471kB
+ sqlite 3.44.2 h2c6b66d_0 conda-forge/linux-64 836kB
+ tk 8.6.13 noxft_h4845f30_101 conda-forge/linux-64 3MB
+ tzdata 2023d h0c530f3_0 conda-forge/noarch 120kB
+ wheel 0.42.0 pyhd8ed1ab_0 conda-forge/noarch 58kB
+ xz 5.2.6 h166bdaf_0 conda-forge/linux-64 Cached
Summary:
Install: 23 packages
Total download: 12MB
───────────────────────────────────────────────────────────────────────────────────────
Downloading and Extracting Packages
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
Installing pip dependencies: ...working... Ran pip subprocess with arguments:
['/home/cms.rkansal/mambaforge/envs/machine-learning-das/bin/python', '-m', 'pip', 'install', '-U', '-r', '/home/cms.rkansal/machine-learning-das/machine-learning-das/condaenv.5i5i1hst.requirements.txt', '--exists-action=b']
Pip subprocess output:
Collecting matplotlib (from -r /home/cms.rkansal/machine-learning-das/machine-learning-das/requirements.txt (line 1))
Downloading matplotlib-3.8.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.8 kB)
Collecting numpy (from -r /home/cms.rkansal/machine-learning-das/machine-learning-das/requirements.txt (line 2))
Downloading numpy-1.26.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)
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Collecting keras (from -r /home/cms.rkansal/machine-learning-das/machine-learning-das/requirements.txt (line 5))
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Collecting ipykernel (from -r /home/cms.rkansal/machine-learning-das/machine-learning-das/requirements.txt (line 11))
Downloading ipykernel-6.28.0-py3-none-any.whl.metadata (6.0 kB)
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Collecting packaging>=20.0 (from matplotlib->-r /home/cms.rkansal/machine-learning-das/machine-learning-das/requirements.txt (line 1))
Downloading packaging-23.2-py3-none-any.whl.metadata (3.2 kB)
Collecting pillow>=8 (from matplotlib->-r /home/cms.rkansal/machine-learning-das/machine-learning-das/requirements.txt (line 1))
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Collecting pyparsing>=2.3.1 (from matplotlib->-r /home/cms.rkansal/machine-learning-das/machine-learning-das/requirements.txt (line 1))
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Collecting typing-extensions>=4.1.0 (from uproot->-r /home/cms.rkansal/machine-learning-das/machine-learning-das/requirements.txt (line 3))
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Collecting flatbuffers>=23.5.26 (from tensorflow->-r /home/cms.rkansal/machine-learning-das/machine-learning-das/requirements.txt (line 6))
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Downloading ml_dtypes-0.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)
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Requirement already satisfied: setuptools in /home/cms.rkansal/mambaforge/envs/machine-learning-das/lib/python3.10/site-packages (from tensorflow->-r /home/cms.rkansal/machine-learning-das/machine-learning-das/requirements.txt (line 6)) (69.0.3)
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Installing collected packages: webencodings, wcwidth, pytz, pure-eval, ptyprocess, mpmath, libclang, json5, flatbuffers, fastjsonschema, zipp, wrapt, widgetsnbextension, websocket-client, webcolors, urllib3, uri-template, tzdata, typing-extensions, types-python-dateutil, traitlets, tqdm, tornado, tomli, tinycss2, threadpoolctl, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, sympy, soupsieve, sniffio, six, send2trash, rpds-py, rfc3986-validator, pyzmq, PyYAML, python-json-logger, pyparsing, pygments, pycparser, pyasn1, psutil, protobuf, prompt-toolkit, prometheus-client, platformdirs, pillow, pexpect, parso, pandocfilters, packaging, overrides, oauthlib, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, nest-asyncio, mistune, MarkupSafe, markdown, kiwisolver, keras, jupyterlab-widgets, jupyterlab-pygments, jsonpointer, joblib, idna, grpcio, gast, fsspec, fqdn, fonttools, filelock, executing, exceptiongroup, defusedxml, decorator, debugpy, cycler, charset-normalizer, certifi, cachetools, babel, attrs, absl-py, werkzeug, triton, terminado, scipy, rsa, rfc3339-validator, requests, referencing, qtpy, python-dateutil, pyasn1-modules, pyaml, opt-einsum, nvidia-cusparse-cu12, nvidia-cudnn-cu12, ml-dtypes, matplotlib-inline, jupyter-core, jinja2, jedi, importlib-metadata, h5py, google-pasta, contourpy, comm, cffi, bleach, beautifulsoup4, awkward-cpp, async-lru, astunparse, asttokens, anyio, xgboost, stack-data, scikit-learn, requests-oauthlib, pandas, nvidia-cusolver-cu12, matplotlib, jupyter-server-terminals, jupyter-client, jsonschema-specifications, google-auth, awkward, arrow, argon2-cffi-bindings, uproot, torch, scikit-optimize, jsonschema, isoduration, ipython, google-auth-oauthlib, argon2-cffi, tensorboard, nbformat, ipywidgets, ipykernel, tensorflow, qtconsole, nbclient, jupyter-events, jupyter-console, nbconvert, jupyter-server, notebook-shim, jupyterlab-server, jupyter-lsp, jupyterlab, notebook, jupyter
Successfully installed MarkupSafe-2.1.3 PyYAML-6.0.1 absl-py-2.0.0 anyio-4.2.0 argon2-cffi-23.1.0 argon2-cffi-bindings-21.2.0 arrow-1.3.0 asttokens-2.4.1 astunparse-1.6.3 async-lru-2.0.4 attrs-23.2.0 awkward-2.5.1 awkward-cpp-27 babel-2.14.0 beautifulsoup4-4.12.2 bleach-6.1.0 cachetools-5.3.2 certifi-2023.11.17 cffi-1.16.0 charset-normalizer-3.3.2 comm-0.2.1 contourpy-1.2.0 cycler-0.12.1 debugpy-1.8.0 decorator-5.1.1 defusedxml-0.7.1 exceptiongroup-1.2.0 executing-2.0.1 fastjsonschema-2.19.1 filelock-3.13.1 flatbuffers-23.5.26 fonttools-4.47.0 fqdn-1.5.1 fsspec-2023.12.2 gast-0.5.4 google-auth-2.26.1 google-auth-oauthlib-1.2.0 google-pasta-0.2.0 grpcio-1.60.0 h5py-3.10.0 idna-3.6 importlib-metadata-7.0.1 ipykernel-6.28.0 ipython-8.20.0 ipywidgets-8.1.1 isoduration-20.11.0 jedi-0.19.1 jinja2-3.1.2 joblib-1.3.2 json5-0.9.14 jsonpointer-2.4 jsonschema-4.20.0 jsonschema-specifications-2023.12.1 jupyter-1.0.0 jupyter-client-8.6.0 jupyter-console-6.6.3 jupyter-core-5.7.1 jupyter-events-0.9.0 jupyter-lsp-2.2.1 jupyter-server-2.12.2 jupyter-server-terminals-0.5.1 jupyterlab-4.0.10 jupyterlab-pygments-0.3.0 jupyterlab-server-2.25.2 jupyterlab-widgets-3.0.9 keras-2.15.0 kiwisolver-1.4.5 libclang-16.0.6 markdown-3.5.1 matplotlib-3.8.2 matplotlib-inline-0.1.6 mistune-3.0.2 ml-dtypes-0.2.0 mpmath-1.3.0 nbclient-0.9.0 nbconvert-7.14.0 nbformat-5.9.2 nest-asyncio-1.5.8 networkx-3.2.1 notebook-7.0.6 notebook-shim-0.2.3 numpy-1.26.3 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.18.1 nvidia-nvjitlink-cu12-12.3.101 nvidia-nvtx-cu12-12.1.105 oauthlib-3.2.2 opt-einsum-3.3.0 overrides-7.4.0 packaging-23.2 pandas-2.1.4 pandocfilters-1.5.0 parso-0.8.3 pexpect-4.9.0 pillow-10.2.0 platformdirs-4.1.0 prometheus-client-0.19.0 prompt-toolkit-3.0.43 protobuf-4.23.4 psutil-5.9.7 ptyprocess-0.7.0 pure-eval-0.2.2 pyaml-23.12.0 pyasn1-0.5.1 pyasn1-modules-0.3.0 pycparser-2.21 pygments-2.17.2 pyparsing-3.1.1 python-dateutil-2.8.2 python-json-logger-2.0.7 pytz-2023.3.post1 pyzmq-25.1.2 qtconsole-5.5.1 qtpy-2.4.1 referencing-0.32.1 requests-2.31.0 requests-oauthlib-1.3.1 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 rpds-py-0.16.2 rsa-4.9 scikit-learn-1.3.2 scikit-optimize-0.9.0 scipy-1.11.4 send2trash-1.8.2 six-1.16.0 sniffio-1.3.0 soupsieve-2.5 stack-data-0.6.3 sympy-1.12 tensorboard-2.15.1 tensorboard-data-server-0.7.2 tensorflow-2.15.0.post1 tensorflow-estimator-2.15.0 tensorflow-io-gcs-filesystem-0.35.0 termcolor-2.4.0 terminado-0.18.0 threadpoolctl-3.2.0 tinycss2-1.2.1 tomli-2.0.1 torch-2.1.2 tornado-6.4 tqdm-4.66.1 traitlets-5.14.1 triton-2.1.0 types-python-dateutil-2.8.19.20240106 typing-extensions-4.9.0 tzdata-2023.4 uproot-5.2.1 uri-template-1.3.0 urllib3-2.1.0 wcwidth-0.2.13 webcolors-1.13 webencodings-0.5.1 websocket-client-1.7.0 werkzeug-3.0.1 widgetsnbextension-4.0.9 wrapt-1.14.1 xgboost-2.0.3 zipp-3.17.0
done
#
# To activate this environment, use
#
# $ conda activate machine-learning-das
#
# To deactivate an active environment, use
#
# $ conda deactivate
Activating the kernel "machine-learning-das"...
Installed kernelspec machine-learning-das in /home/cms.rkansal/.local/share/jupyter/kernels/machine-learning-das
Installed "machine-learning-das"!
Results#
If successful, you should see something similar to the following:
Installed "machine-learning-das"!
The new kernel you just made will then show up in the various Jupyter notebooks after refreshing the page.