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#!/usr/bin/env bash
# Copyright 2019, The TensorFlow Federated Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Tool to deploy the TensorFlow Federated endpoint on GCP.
#
# Usage:
# bazel run //tensorflow_federated/tools/runtime/gcp:deploy_endpoint
set -e
main() {
local artifacts_dir="$1"
if [[ -z "${artifacts_dir}" ]]; then
local artifacts_dir="$(mktemp -d)"
trap "rm -rf ${artifacts_dir}" EXIT
fi
cp -LR "tensorflow_federated" "${artifacts_dir}"
pushd "${artifacts_dir}"
# Create a virtual environment
virtualenv --python=python3 "venv"
source "venv/bin/activate"
pip install --upgrade pip
# Install gRPC
pip install --upgrade grpcio grpcio-tools
# Create the descriptor file
mkdir "generated_pb2"
python -m grpc_tools.protoc \
--include_imports \
--include_source_info \
--proto_path=. \
--descriptor_set_out="api_descriptor.pb" \
--python_out="generated_pb2" \
--grpc_python_out="generated_pb2" \
"tensorflow_federated/proto/v0/executor.proto"
# Deploy the Endpoints configuration
gcloud endpoints services deploy \
"api_descriptor.pb" \
"tensorflow_federated/tools/runtime/gcp/worker.yaml"
}
main "$@"