install dependencies

Dockerfile

@@ -1,5 +1,4 @@
-# Use CUDA 12.4
-FROM nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04
+FROM ubuntu:22.04
 SHELL ["/bin/bash", "-c"]
 ENV SHELL=/bin/bash
 ENV DEBIAN_FRONTEND=noninteractive
@@ -27,7 +26,7 @@ RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -
 
 # Install Anaconda and JupyterLab
 RUN eval "$('/root/anaconda/bin/conda' 'shell.bash' 'hook')" && \
-    conda create -n torch python=3.11 anaconda ipywidgets nodejs -y && \
+    conda create -n torch python=3.12 anaconda ipywidgets nodejs -y && \
     echo "conda activate torch" >> /root/.bashrc && \
     conda clean -a && pip cache purge
 
@@ -55,10 +54,8 @@ COPY JupyterLabConfig/channels.condarc /root/.condarc
 # Install PyTorch and AI libs
 RUN eval "$('/root/anaconda/bin/conda' 'shell.bash' 'hook')" && conda activate torch && \
     conda install -c pytorch -c nvidia -c conda-forge --strict-channel-priority \
-        pytorch torchvision torchaudio pytorch-cuda=12.4 \
-        transformers datasets spacy xgboost \
-        django beautifulsoup4 && \
-    pip install opencv-python && \
+        pytorch torchvision torchaudio pytorch-cuda=12.4 && \
+    pip install openai==0.28 jsonlines && \
     conda clean -a && pip cache purge
 
 # Run JupyterLab on start

README.md

@@ -8,11 +8,11 @@ CUDA Docker environment is supported by [Ubuntu nvidia cuda toolkit](https://pac
 
 ## Available Tags
 
-- `latest`: Most recent build directly from the latest `main` branch.
-- `v2.x.x`: JupyterLab installed with PyTorch GPU version `2.x.x`.
-- Branch names: Snapshots of the project environment; refer to the branch README for more information.
+Docker image tag name: `env-2411b`.
 
-Full list are available on [muhac/jupyter-pytorch | DockerHub](https://hub.docker.com/r/muhac/jupyter-pytorch).
+This image is built for the development environment of the [U of T MAT1510](https://sites.google.com/view/mat1510) course (Fall 2024).
+
+Project: [Towards Understanding Chain-of-Thought Prompting](https://github.com/muhac/understanding-chain-of-thought)
 
 ## Install & Usage
 
@@ -26,7 +26,7 @@ docker run --detach \
     --ipc=host --runtime=nvidia --gpus all \
     -p $SERVER_PORT:80 \
     -v $PROJECT_DIR:/root/projects \
-    muhac/jupyter-pytorch:latest
+    muhac/jupyter-pytorch:env-2411b
 ```
 
 You can use [this notebook](JupyterLabConfig/notebooks/PyTorchGPU.ipynb) to check your PyTorch GPU environment.