Source code for building timesfm docker.

notebooks/community/model_garden/docker_source_codes/model_oss/timesfm/dockerfile/serve.Dockerfile

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+FROM nvidia/cuda:12.3.2-devel-ubuntu22.04
+
+# Install basic libs
+RUN apt-get update && apt-get upgrade -y && apt-get install -y --no-install-recommends \
+        cmake \
+        curl \
+        wget \
+        sudo \
+        gnupg \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        lsb-release \
+        ca-certificates \
+        build-essential \
+        git \
+        software-properties-common \
+        cuda-toolkit \
+        libcudnn8 \
+        apt-transport-https
+
+RUN apt install -y --no-install-recommends python3.10 \
+        python3.10-venv \
+        python3.10-dev \
+        python3-pip
+
+Run apt-get autoremove -y
+
+RUN pip install --upgrade pip
+RUN pip install --upgrade --ignore-installed \
+        "jax[cuda12]==0.4.26" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html \
+        numpy==1.26.4 \
+        paxml==1.4.0 \
+        praxis==1.4.0 \
+        jaxlib==0.4.26 \
+        pandas==2.1.4 \
+        einshape==1.0.0 \
+        utilsforecast==0.1.10 \
+        huggingface_hub[cli]==0.23.0 \
+        google-cloud-aiplatform[prediction]==1.51.0 \
+        fastapi==0.109.1 \
+        flask==3.0.3 \
+        smart_open[gcs]==7.0.4 \
+        protobuf==3.19.6 \
+        scikit-learn==1.0.2 \
+        timesfm==1.0.1
+
+# Download license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Move scaffold.
+COPY model_oss/timesfm/main.py /app/main.py
+COPY model_oss/timesfm/predictor.py /app/predictor.py
+
+WORKDIR ..
+
+# Spin off inference server.
+CMD ["python3", "/app/main.py"]

notebooks/community/model_garden/docker_source_codes/model_oss/timesfm/main.py

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+"""Predict server for TimesFM."""
+
+import json
+import os
+import flask
+import predictor
+
+# Create the flask app.
+app = flask.Flask(__name__)
+_OK_STATUS = 200
+_INTERNAL_ERROR_STATUS = 500
+_HOST = '0.0.0.0'
+
+# Define the predictor and load the checkpoints.
+predictor = predictor.TimesFMPredictor()
+predictor.load(os.environ['AIP_STORAGE_URI'])
+
+
+@app.route(os.environ['AIP_HEALTH_ROUTE'], methods=['GET'])
+def health() -> flask.Response:
+  return flask.Response(status=_OK_STATUS)
+
+
+@app.route(os.environ['AIP_PREDICT_ROUTE'], methods=['GET', 'POST'])
+def predict() -> flask.Response:
+  """Calls TimesFM for prediction.
+
+  Returns:
+    A `flask.Response` containing the prediction result in JSON.
+  """
+  try:
+    body = flask.request.get_json(silent=True, force=True)
+    preprocessed_inputs = predictor.preprocess(body)
+    outputs = predictor.predict(preprocessed_inputs)
+    postprocessed_outputs = predictor.postprocess(outputs)
+    return flask.Response(
+        json.dumps(postprocessed_outputs),
+        status=_OK_STATUS,
+        mimetype='application/json',
+    )
+  except Exception as e:  # pylint: disable=broad-exception-caught
+    return flask.Response(
+        json.dumps({'error': str(e)}),
+        status=_INTERNAL_ERROR_STATUS,
+        mimetype='application/json',
+    )
+
+
+if __name__ == '__main__':
+  app.run(host=_HOST, port=os.environ['AIP_HTTP_PORT'])