Docker build

Dockerfile

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+FROM nvidia/cuda:11.7.0-devel-ubuntu20.04
+LABEL name="unified-io-inference"
+
+WORKDIR /root/.conda
+WORKDIR /root
+RUN apt-get update && apt-get -y install wget nano
+RUN wget \
+    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+    && bash Miniconda3-latest-Linux-x86_64.sh -b -p /opt/conda \
+    && rm -f Miniconda3-latest-Linux-x86_64.sh 
+ENV PATH=/opt/conda/bin:${PATH}
+RUN bash -c "conda update -n base -c defaults conda"
+
+RUN wget -nv https://ai2-prior-uio.s3.us-west-2.amazonaws.com/public/model-weights-bin/xl_1000k.bin \
+ -O xl.bin
+RUN wget -nv https://ai2-prior-uio.s3.us-west-2.amazonaws.com/public/model-weights-bin/large_1000k.bin \
+ -O large.bin
+RUN wget -nv https://ai2-prior-uio.s3.us-west-2.amazonaws.com/public/model-weights-bin/base_1000k.bin \
+ -O base.bin
+RUN wget -nv https://ai2-prior-uio.s3.us-west-2.amazonaws.com/public/model-weights-bin/small_1000k.bin \
+ -O small.bin
+RUN wget -nv https://farm2.staticflickr.com/1362/1261465554_95741e918b_z.jpg -O dbg_img.png
+
+COPY uioi.yml .
+RUN bash -c "conda env create -f uioi.yml"
+COPY requirements.txt .
+RUN bash -c ". activate uioi && pip install --upgrade pip \
+ && pip install --upgrade "jax[cuda]" \
+ -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html \
+ && python3 -m pip install -r requirements.txt"
+
+COPY . .
+RUN bash -c ". activate uioi && python ./uio/test/check.py"
+ENV INPUT_FILE=demo.list
+ENTRYPOINT bash -c ". activate uioi && python ./run.py xl xl.bin $INPUT_FILE"

README.docker.md

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+
+## Docker
+To build a docker image:
+```bash
+docker build -t unified-io-inference .
+```
+To run the docker demo:
+```
+docker run -it --gpus=1 unified-io-inference
+INFO:absl:Setting up model...
+...
+INFO:absl:Model is ready
+INFO:absl:Running model text_inputs=['what color is the sofa?']
+green
+```
+
+To run a list of queries construct an input file where each line is a file path
+and a text input, separated by `:`.  See example: [demo.list](https://github.com/isi-vista/unified-io-inference/blob/docker-build/demo.list)
+
+Prepare a directory containing image files.  `cd` to that directory.
+The steps below will write example input files and docker execution with the 
+host images mounted to the `/image-data` directory.
+
+```
+ls -1 | grep -E 'jpg|png' > files.txt
+awk '{print "/image-data/" $0 ":What-does-the-image-describe?"}' ./files.txt > caption.txt
+awk '{print "/image-data/" $0 ":Locate all objects in the image."}' ./files.txt > locate.txt
+
+#Choose an input file to process:
+export INPUT_FILE=[caption.txt or locate.txt or other]
+export HOSTPATH=$(pwd)
+
+docker run -it --gpus=1 -e INPUT_FILE=/image-data/${INPUT_FILE} \ 
+  -v /${HOSTPATH}:/image-data unified-io-inference
+```

README.md

@@ -79,6 +79,8 @@ Then it can be run with:
 jupyter notebook demo.ipynb
 ```
 
+## Docker 
+To build and run a unified-io-inference docker image see: README.docker.md 
 
 ## Just-in-time compilation
 By default `ModelRunner` compiles the underlying inference calls the first time they are used,

demo.list

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+/root/dbg_img.png:what color is the couch?

run.py

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+import argparse
+from os.path import exists
+from PIL import Image
+from uio import runner
+from uio.configs import CONFIGS
+import numpy as np
+from absl import logging
+import warnings
+# flax kicks up a lot of future warnings at the moment, ignore them
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+# To see INFO messages from `ModelRunner`
+logging.set_verbosity(logging.INFO)
+
+def main():
+  parser = argparse.ArgumentParser()
+  parser.add_argument("model_size", choices=list(CONFIGS))
+  parser.add_argument("model_weights")
+  parser.add_argument("input_file")
+  args = parser.parse_args()
+
+  model = runner.ModelRunner(args.model_size, args.model_weights)
+  input_file = open(args.input_file, 'r')
+  lines = input_file.readlines()
+  for line in lines:
+    image_path, question = line.strip().split(":")
+    print(image_path)
+    print(question)
+    with Image.open(image_path) as img:
+      image = np.array(img.convert('RGB'))
+      output = model.vqa(image, question)
+      print(output["text"])
+
+
+if __name__ == "__main__":
+  main()

uio/test/check.py

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+from functools import partial
+from jax import grad, lax
+import jax.numpy as jnp

uio/test/run.py

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+from functools import partial
+from jax import grad, lax
+import jax.numpy as jnp
+import jax as jax
+print('<<< jax test >>>')
+print(jax.devices())
+
+def tanh(x):  # Define a function
+  y = jnp.exp(-2.0 * x)
+  return (1.0 - y) / (1.0 + y)
+
+grad_tanh = grad(tanh)  # Obtain its gradient function
+print(grad_tanh(1.0)) 
+print('<<< end >>>')

uioi.yml

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+name: uioi
+channels:
+  - defaults
+  - conda-forge
+  - nvidia
+  - anaconda
+dependencies:
+  - python=3.9
+  - cudnn