author_link: https://twitter.com/arthurheinrique link: https://medium.com/@arthur393/another-boilerplate-to-fastapi-azure-pipeline-ci-pytest-3c8d9a4be0bb title: 'Another Boilerplate to FastAPI: Azure Pipeline CI + Pytest' - author: Shane Soh author_link: https://medium.com/@shane.soh link: https://medium.com/analytics-vidhya/deploy-machine-learning-models-with-keras-fastapi-redis-and-docker-4940df614ece title: Deploy Machine Learning Models with Keras, FastAPI, Redis and Docker - author: Mandy Gu author_link:...

        `max_batch_size` for building TensorRT engines. Add parameters
        `use_dynamic_shape` to enable dynamic shape support. The default is to
        disable dynamic shape support. Add `dynamic_shape_profile_strategy` for
        selecting a dynamic shape profile strategy. The default is profile
        strategy is `Range`.
    *   Issue a warning when function get_tensorrt_rewriter_config is used.

This is very useful when you need to:

* Have shared logic (the same code logic again and again).
* Share database connections.
* Enforce security, authentication, role requirements, etc.
* And many other things...

All these, while minimizing code repetition.

## First Steps

  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  const std::vector<std::unique_ptr<ParallelTensor>>& handles = *outputs;
  const std::vector<int64_t>* shape;
  absl::Status s = handles[0]->Shape(&shape);
  ASSERT_TRUE(s.ok());
  EXPECT_EQ(0, shape->size());
}

TEST(PARALLEL_DEVICE_LIB, TestCancelOnError) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);

  // devices of a ParallelDevice. If called, ParallelTensor::Shape inspects
  // `components` to determine a shape.
  static std::unique_ptr<ParallelTensor> FromTensorHandles(
      const ParallelDevice& parallel_device,
      std::vector<TensorHandlePtr> components, TF_Status* status);
  // Uses the provided shape without additional checks, which avoids blocking
  // when ParallelTensor::Shape is called.

      new ParallelTensor(parallel_device, std::move(components), dtype));
}

absl::Status ParallelTensor::Shape(const std::vector<int64_t>** shape) const {
  if (!shape_.has_value()) {
    TF_Status status;
    PartialTensorShape combined_shape;
    TF_RETURN_IF_ERROR(unwrap(tensors_[0].get())->Shape(&combined_shape));

    for (const TensorHandlePtr& component : tensors_) {
      PartialTensorShape component_shape;

// number of dimensions of a parallel tensor.
int ParallelTensorNumDims(void* data, TF_Status* status) {
  const std::vector<int64_t>* shape;
  absl::Status s = reinterpret_cast<ParallelTensor*>(data)->Shape(&shape);
  if (!s.ok()) {
    tsl::Set_TF_Status_from_Status(status, s);
    return -1;
  }
  return shape->size();
}

// Used as an argument to TFE_NewCustomDeviceTensorHandle, for computing a
// dimension of a parallel tensor.

	</modules>
	<build>
		<pluginManagement>
			<plugins>
				<plugin>
					<groupId>org.apache.maven.plugins</groupId>
					<artifactId>maven-shade-plugin</artifactId>
					<executions>
						<execution>
							<phase>package</phase>
							<goals>
								<goal>shade</goal>
							</goals>
							<configuration>
								<artifactSet>
									<includes>
										<include>org.dbflute:dbflute-runtime</include>

   * tests can suppress it with {@code FeatureSpecificTestSuiteBuilder.suppressing()} while we
   * figure out what to do with <a
   * href="https://github.com/openjdk/jdk/blob/c25c4896ad9ef031e3cddec493aef66ff87c48a7/src/java.base/share/classes/java/util/concurrent/ConcurrentHashMap.java#L4830">{@code
   * ConcurrentHashMap} support for {@code entrySet().add()}</a>.
   */
  @J2ktIncompatible
  @GwtIncompatible // reflection

   */
  public static ArrayBasedEscaperMap create(Map<Character, String> replacements) {
    return new ArrayBasedEscaperMap(createReplacementArray(replacements));
  }

  // The underlying replacement array we can share between multiple escaper
  // instances.
  private final char[][] replacementArray;

  private ArrayBasedEscaperMap(char[][] replacementArray) {
    this.replacementArray = replacementArray;
  }