update

Dockerfile

@@ -1,4 +1,4 @@
-FROM ucsdets/scipy-ml-notebook:2023.1-stable
+FROM ucsdets/scipy-ml-notebook:2024.2-stable
 LABEL maintainer="Javier Duarte <jduarte@ucsd.edu>"
 
 USER root
@@ -11,22 +11,21 @@ USER jovyan
 
 RUN mamba install -c conda-forge uproot xrootd root
 
-RUN pip install --no-cache-dir 'xgboost==1.7.3' 'scikit-learn==1.2.1' 'spektral==1.2.0' 'gdown==4.6.0' 'mplhep==0.3.26' && \
+RUN pip install --no-cache-dir 'xgboost==2.0.3' 'scikit-learn==1.4.1' 'spektral==1.3.1' 'gdown==5.1.0' 'mplhep==0.3.43' && \
  fix-permissions /opt/conda && \
  fix-permissions /home/jovyan
 
-RUN pip install --no-cache-dir --no-index torch-scatter torch-sparse torch-cluster torch-spline-conv -f https://data.pyg.org/whl/torch-1.9.0+cu111.html && \
- pip install --no-cache-dir torch-geometric && \
+RUN pip install --no-cache-dir --no-index pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.1.0+cu118.html && \
  pip install --no-cache-dir typing-extensions --upgrade
 
 # RUN pip install --no-cache-dir 'jetnet==0.2.2'
 
-USER $NB_UID:$NB_GID
-RUN mkdir -p /tmp/nvvm && mkdir -p /tmp/nvvm/libdevice && cp /opt/conda/lib/libdevice.10.bc /tmp/nvvm/libdevice/
-ENV XLA_FLAGS="--xla_gpu_cuda_data_dir=/tmp"
-ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/opt/conda/lib
-ENV PATH=${PATH}:/usr/local/nvidia/bin:/opt/conda/bin:/datasets/software/R2019a/sys/cuda/glnxa64/cuda/bin
+# USER $NB_UID:$NB_GID
+# RUN mkdir -p /tmp/nvvm && mkdir -p /tmp/nvvm/libdevice && cp /opt/conda/lib/libdevice.10.bc /tmp/nvvm/libdevice/
+# ENV XLA_FLAGS="--xla_gpu_cuda_data_dir=/tmp"
+# ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/opt/conda/lib
+# ENV PATH=${PATH}:/usr/local/nvidia/bin:/opt/conda/bin:/datasets/software/R2019a/sys/cuda/glnxa64/cuda/bin
 
 # larcv2 build
-ADD build_larcv2.sh /home/jovyan/build_larcv2.sh
-RUN source build_larcv2.sh
+# ADD build_larcv2.sh /home/jovyan/build_larcv2.sh
+# RUN source build_larcv2.sh

notebooks/environment.yml

@@ -4,7 +4,7 @@ channels:
  - pytorch
  - conda-forge
 dependencies:
- - python=3.9
+ - python=3.10.10
  - numpy
  - uproot
  - tensorflow
@@ -19,14 +19,14 @@ dependencies:
  - mplhep
  - tqdm
  - xgboost
+ - jupyter-book==0.15.1
+ - jupyter_contrib_nbextensions==0.7.0
  - pip
  - pip:
- - jupyter-book
  - jetnet
  - gdown
  - spektral
  - zenodo_get
  - requests
  - ipywidgets
  - widgetsnbextension
- - jupyter_contrib_nbextensions

syllabus/syllabus.tex

@@ -142,7 +142,7 @@
 \end{center}
 
 \noindent\textbf{Drop policy}: The lowest homework score is dropped automatically.
-This drop policy is designed to account for any and all illnesses, family, medical, mental, or other emergencies.
+This drop policy is designed to account for any illnesses, family, medical, mental, or other emergencies.
 
 If you have an extended emergency (e.g., a long hospital stay) that hinders your ability to turn complete assignments beyond the emergency policy allowance, contact the professor directly as soon as the situation arises.
 
@@ -159,9 +159,9 @@
 \noindent\textbf{Homework}: Each homework will consist of a set of conceptual and programming problems.
 The assignments will be submitted as Jupyter notebooks or GitHub repositories.
 
-There will be a first deadline (on Fridays at 5:00pm) to submit a ``draft'' version of the homework, which will be graded based on effort.
+There will be a first deadline (on Fridays at 8:00pm) to submit the homework, which will be graded based on effort and completeness.
 
-There will be a second deadline (on Wednesdays at 5:00pm) to submit a ``final'' version of the homework, which will be graded based on effort and correctness.
+There will be a second deadline (on Wednesdays at 8:00pm) to submit corrections for the homework, which will be graded based on effort and correctness.
 
 \begin{center}
  \rule{\textwidth}{0.5pt}
@@ -171,7 +171,7 @@
 For the final project, students will work in groups of $\sim$4 to reproduce or extend the results of an ML in physics research article.
 Some candidate articles are listed at the end of the syllabus.
 The final project deliverables are: (1) a 4-page paper on the project, (2) code provided as a public GitHub repository, (3) a 10-minute presentation by all members of the group during finals week, and (4) self and peer evaluations for group contributions.
-Students will also be required to submit a 2-page written proposal for the project in Week 7.
+Students will also be required to submit a 1-page written proposal for the project in Week 7.
 This is to ensure the project is feasible and to receive feedback from the instructors.
 
 \begin{center}