New Singularity version + deleted tracking

Singularity.v0.2.def

@@ -18,6 +18,8 @@ From: ghcr.io/sinzlab/pytorch-singularity:v3.9-torch1.10.2-dj0.12.7.def
                           # dvipng \
 
 
+
+
     python3.9 -m pip --no-cache-dir install \
         ipykernel \
         requests \

Singularity.v0.4.def

@@ -0,0 +1,114 @@
+BootStrap: docker
+From: ubuntu:21.10
+
+%labels
+    MAINTAINER Arne Nix <[email protected]>
+%files
+    ../bias_transfer /src/bias_transfer
+    ../nntransfer /src/nntransfer
+    ../neuralpredictors /src/neuralpredictors
+    ../pytorch_warmup /src/pytorch_warmup
+    ../nnfabrik /src/nnfabrik
+    ../ffcv /src/ffcv
+
+%environment
+    export PATH="/opt/conda/bin:${PATH}"
+
+%post
+    apt update && apt install -y software-properties-common
+    add-apt-repository universe
+
+    apt update && apt install -y \
+        build-essential \
+        git \
+        wget \
+        vim \
+        curl \
+        zip \
+        zlib1g-dev \
+        unzip \
+        pkg-config \
+        libblas-dev \
+        liblapack-dev \
+        python3-tk \
+        python3-wheel \
+        graphviz \
+        libhdf5-dev \
+        texlive-latex-extra \
+        texlive-fonts-recommended \
+        texlive-base \
+        dvipng
+
+
+    # apt-get update && apt-get install -y wget && rm -rf /var/lib/apt/lists/*
+    # wget  https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+    #         && mkdir /root/.conda \
+    #         && bash Miniconda3-latest-Linux-x86_64.sh -b \
+    #         && rm -f Miniconda3-latest-Linux-x86_64.sh
+
+    curl -fsSL -v -o ~/miniconda.sh -O  https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh  && \
+    chmod +x ~/miniconda.sh && \
+    ~/miniconda.sh -b -p /opt/conda && \
+    rm ~/miniconda.sh && \
+    /opt/conda/bin/conda install -y python=${PYTHON_VERSION} conda-build pyyaml numpy ipython&& \
+    /opt/conda/bin/conda clean -ya
+
+    export PATH="/opt/conda/bin:${PATH}"
+
+    # conda create -y -n ffcv python=3.9 ipython cupy pkg-config compilers libjpeg-turbo opencv pytorch torchvision cudatoolkit=11.3 numba -c pytorch -c conda-forge
+    conda create -y -n ffcv python=3.9 ipython cupy pkg-config compilers libjpeg-turbo opencv numba  -c conda-forge
+
+    echo "source activate" >> ~/.bashrc
+    echo "conda activate ffcv" >> ~/.bashrc
+
+    conda run -n ffcv pip --no-cache-dir  install \
+        torch==1.10.2+cu113 \
+        torchvision==0.11.3+cu113 \
+        torchaudio==0.10.2+cu113 \
+        -f https://download.pytorch.org/whl/cu113/torch_stable.html
+
+
+    conda run -n ffcv pip install --no-cache-dir jupyterlab_vim \
+                                        checkout_code \
+                                        requests \
+                                        imageio \
+                                        scikit-image \
+                                        einops \
+                                        vit-pytorch \
+                                        blackcellmagic\
+        pytest \
+        pytest-cov \
+        numpy \
+        matplotlib \
+        scipy \
+        pandas \
+        jupyter \
+        scikit-learn \
+        scikit-image \
+        seaborn \
+        graphviz \
+        gpustat \
+        h5py \
+        gitpython \
+        Pillow==8.0.1 \
+        jupyterlab \
+        ipykernel \
+        opencv-python \
+        datajoint==0.12.7
+
+    conda run -n ffcv pip install -e /src/bias_transfer
+    conda run -n ffcv pip install -e /src/nntransfer
+    conda run -n ffcv pip install -e /src/nnfabrik
+    conda run -n ffcv pip install -e /src/neuralpredictors
+    conda run -n ffcv pip install -e /src/pytorch_warmup
+    conda run -n ffcv pip install -e /src/ffcv
+
+    conda run -n ffcv python -m ipykernel install --user --name=ffcv
+
+%startscript
+    exec "$@"
+
+%runscript
+    exec "$@"
+
+

nntransfer/analysis/plot.py

@@ -53,10 +53,10 @@ def plot_wrapper(*args, **kwargs):
         if "talk" in style or "beamer" in style:
             sns.set_context("talk")
             font = "sans-serif"
-            small_fontsize = 13
-            xsmall_fontsize = 13
-            normal_fontsize = 14
-            large_fontsize = 17
+            small_fontsize = 14
+            xsmall_fontsize = 14
+            normal_fontsize = 15
+            large_fontsize = 16
         else:
             sns.set_context("paper")
             font = "serif"
@@ -106,7 +106,9 @@ def plot_wrapper(*args, **kwargs):
             for i in range(len(ax)):
                 ax[i] = [ax[i]]
 
-        plt.grid(True, linestyle=":", linewidth=0.5)
+        for i in range(len(ax)):
+            for j in range(len(ax[i])):
+                ax[i][j].grid(True, linestyle=":", linewidth=0.8)
 
         # execute the actual plotting
         plot_function(*args, fig=fig, ax=ax, **kwargs)
@@ -215,11 +217,13 @@ def set_size(style, ratio=None, fraction=1, subplots=(1, 1), gridspec_kw=None):
 
 def save_plot(fig, name, types=("pgf", "pdf", "png")):
     for file_type in types:
+        fig.patch.set_alpha(0.0)
         fig.savefig(
             name + "." + file_type,
             facecolor=fig.get_facecolor(),
             edgecolor=fig.get_edgecolor(),
             bbox_inches="tight",
+            transparent=True
         )
     plt.close(fig)
 

nntransfer/configs/trainer/base.py

@@ -27,10 +27,10 @@ def __init__(self, **kwargs):
             "keep_selection": (),
         }
         self.keep_checkpoints: bool = False
-        self.patience: int = 10
+        self.patience: int = None
         self.threshold: float = 0.0001
         self.verbose: bool = False
-        self.lr_milestones: Tuple = (30, 60)
+        self.lr_milestones: Tuple = ()
         self.min_lr: float = 0.00001  # lr scheduler min learning rate
         self.threshold_mode: str = "rel"
         self.train_cycler: str = "LongCycler"

nntransfer/trainer/trainer.py

@@ -331,6 +331,7 @@ def train(self):
         test_result = self.test_final_model(epoch)
 
         copy_ensemble_param_to_buffer(self.model, self.config.ensemble_iteration)
+        print(self.model.state_dict().keys())
 
         if self.config.switch_teacher:
             return (