rights reserved. Presenter: Haichen Shen, Yao Wang Amazon SageMaker Neo, Deep Engine Science Dynamic Model in TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Models with models© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Support dynamic model in TVM ● Support Any-dim in typing ● Use shape function to compute the type at runtime ● Virtual input_name = "data" input_shape = [tvm.relay.Any(), 3, 224, 224] dtype = "float32" block = get_model('resnet50_v1', pretrained=True) mod, params = relay.frontend.from_mxnet(block, shape={input_name:

best practices and lessons learned by these early Agile adopters. Broader, more successful Agile execution will take time, trial and error, and shaping of processes, policies, and culture, but with support locked. During sprint execution, the development team runs through the full development cycle for each user story in the sprint backlog as shown in Figure 4. Figure 4 Sprint Execution Cycle The integrated culture often run counter to those in the long-established defense acquisition enterprise. The Agile model represents a change in the way DoD conducts business, and programs must rethink how they are staffed

. . . . . . . . . . . . . . . . . . . . . . . 466 35.22 Optimize network I/O during parallel execution . . . . . . . . . . . . . . . . . . . . 467 35.23 Fix deprecation warnings . . . . . . . . . . Alternative linear algebra backends . . . . . . . . . . . . . . . . . . . . . . . . . . 476 35.31 Execution latency, package loading and package precompiling time . . . . . . . . . 476 36 Workflow Tips 479 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1629 86.3 Triggered During Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1629 86.4 Reference . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . 466 35.22 Optimize network I/O during parallel execution . . . . . . . . . . . . . . . . . . . . 467 35.23 Fix deprecation warnings . . . . . . . . . . Alternative linear algebra backends . . . . . . . . . . . . . . . . . . . . . . . . . . 476 35.31 Execution latency, package loading and package precompiling time . . . . . . . . . 476 36 Workflow Tips 479 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1629 86.3 Triggered During Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1629 86.4 Reference . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . 466 35.22 Optimize network I/O during parallel execution . . . . . . . . . . . . . . . . . . . . 467 35.23 Fix deprecation warnings . . . . . . . . . . Alternative linear algebra backends . . . . . . . . . . . . . . . . . . . . . . . . . . 476 35.31 Execution latency, package loading and package precompiling time . . . . . . . . . 476 36 Workflow Tips 479 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1629 86.3 Triggered During Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1629 86.4 Reference . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . 436 34.20 Optimize network I/O during parallel execution . . . . . . . . . . . . . . . . . . . . 437CONTENTS vii 34.21 Fix deprecation warnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1393 84.3 Triggered During Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1393 84.4 Reference . . . . . . combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own"

. . . . . . . . . . . . . . . . . . . . . . . 436 34.20 Optimize network I/O during parallel execution . . . . . . . . . . . . . . . . . . . . 437CONTENTS vii 34.21 Fix deprecation warnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1393 84.3 Triggered During Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1393 84.4 Reference . . . . . . combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own"

appears to offer predictability, control, and efficiency, the key values of the contractor-control model. But it doesn’t. Requirements: Requirements are a way of controlling the development team by constraining Characteristics of an Agile governance and oversight model: Before we dive into an Agile governance and oversight model, let’s think about what characteristics such a model should have in order to both take advantage the investment decision and the oversight process would form a seamless continuum wherein the execution of the initiative would yield valuable information by which the governance decision could be adjusted

4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 35.5 Execution latency, package loading and package precompiling time . . . . . . . . . 481 35.6 Miscellaneous . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1693 88.3 Triggered During Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1693 88.4 Reference . . . . . . combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own"

4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 35.5 Execution latency, package loading and package precompiling time . . . . . . . . . 482 35.6 Miscellaneous . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1691 88.3 Triggered During Execution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1691 88.4 Reference . . . . . . combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own"