initial commit

Dockerfile

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+FROM anibali/pytorch:2.0.0-cuda11.8-ubuntu22.04
+USER root
+# Set up time zone.
+ENV TZ=UTC
+RUN sudo ln -snf /usr/share/zoneinfo/$TZ /etc/localtime
+
+
+
+# Install torch geometric
+RUN pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu118.html
+RUN pip install torch_geometric
+RUN pip install h5py ipykernel==5.5.5 ipywidgets==7.6.3 jupyter nglview==2.7.7 pandas
+RUN pip install pytorch-lightning==1.8.3
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN apt-get update; \
+    DEBIAN_FRONTEND=noninteractive; \
+    apt-get install -y --no-install-recommends --allow-downgrades\
+        bzip2=1.0.8-5build1 cmake=3.22.1-1ubuntu1.22.04.1 csh=20110502-7 \
+        make=4.3-4.1build1 gcc=4:11.2.0-1ubuntu1 gfortran=4:11.2.0-1ubuntu1 \
+        g++=4:11.2.0-1ubuntu1 flex=2.6.4-8build2 bison=2:3.8.2+dfsg-1build1 \
+        patch=2.7.6-7build2 bc=1.07.1-3build1 libbz2-dev=1.0.8-5build1 \
+        wget=1.21.2-2ubuntu1 openmpi-bin=4.1.2-2ubuntu1 \
+        libopenmpi-dev=4.1.2-2ubuntu1 openssh-client=1:8.9p1-3 \
+        ca-certificates=20211016ubuntu0.22.04.1
+
+WORKDIR /usr/bin
+COPY AmberTools22.tar.bz2 .
+RUN tar xjvf AmberTools22.tar.bz2 && rm AmberTools22.tar.bz2
+
+WORKDIR amber22_src
+WORKDIR build
+RUN chmod +x run_cmake
+RUN ./run_cmake
+RUN make -j 4 install
+RUN echo "source /usr/bin/amber22/amber.sh" >> /etc/bash.bashrc
+SHELL ["/bin/bash", "-c"]
+
+ENV AMBERHOME="/usr/bin/amber22/"
+ENV PATH="$AMBERHOME/bin:$PATH"
+ENV PYTHONPATH="$AMBERHOME/lib/python3.10/site-packages"
+
+RUN adduser -u 5678 --disabled-password --gecos "" appuser && chown -R appuser .
+USER appuser
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
+

main.py

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+import gradio as gr
+import torch
+import requests
+from torchvision import transforms
+
+model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+
+def predict(inp):
+    inp = transforms.ToTensor()(inp).unsqueeze(0)
+    with torch.no_grad():
+        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
+        confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
+    return confidences
+
+
+def run():
+    demo = gr.Interface(
+        fn=predict,
+        inputs=gr.inputs.Image(type="pil"),
+        outputs=gr.outputs.Label(num_top_classes=3),
+    )
+
+    demo.launch(server_name="0.0.0.0", server_port=7860)
+
+
+if __name__ == "__main__":
+    run()

requirements.txt

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+gradio
+torchvision
+requests