best-placed to improve 
 it with AI. 06 Unblock your
 developers Automating the software development lifecycle can multiply 
 AI dividends. 07 Set bold 
 automation goals Most processes involve a lot of rote help guide your own thinking. Product Note: Operator Operator is an example of OpenAI’s agentic approach. Leveraging its own virtual browser, Operator can navigate the web, click on buttons, fill that previously required human intervention, such as: Automating software testing and QA using Operator to interact with web apps 
 like a real user, flagging any UI issues. Updating systems of record

subgraphs 1. Implement an operator-level annotator, OR 2. Implement a graph-level annotator© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Option 1: Operator-Level Annotation ● Implement Boolean functions in the template def conv2d(attrs, args): return is_float32(args) Relay operator name Operator attributes and args (inputs) can be checked as well Return True/False for this op After Device General Devices (CPU/GPU/FPGA) Mark supported operators or subgraphs 1. Implement extern operator functions, OR 2. Implement a graph annotator© 2019, Amazon Web Services, Inc. or its Affiliates

scratch • New Relay passes and TVM schedules required • AlterOpLayout, Graph Fusion etc require work/operator • No reuse of existing Relay and TVM infrastructure. Option 2 – Lower to a sequence of existing 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Lowering of QNN Quantize Operator fn (%input_data: Tensor[(2, 5), float32]) { qnn.quantize(%input_data, out_dtype="uint8", output_zero_point=127 Affiliates. All rights reserved. QNN Conv2D Operator • Calculations are different from FP32 Conv2D https://discuss.tvm.ai/t/tf-lite-quantized-conv2d-operator-conversion/2651/8 𝑟𝑒𝑎𝑙_𝑣𝑎𝑙𝑢𝑒 = 𝒔𝒄𝒂𝒍𝒆

function ● Relax type inference/checking for Any at compilation time ● Register a shape function for operator to check the type and compute the output shape© 2019, Amazon Web Services, Inc. or its Affiliates function ● Relax type inference/checking for Any at compilation time ● Register a shape function for operator to check the type and compute the output shape ● Shape function has two modes (op_attrs, input_tensors function ● Relax type inference/checking for Any at compilation time ● Register a shape function for operator to check the type and compute the output shape ● Shape function has two modes (op_attrs, input_tensors

cuDNN Offload to heavily optimized DNN operator library FrameworksLimitations of Existing Approach cuDNN Frameworks New operator introduced by operator fusion optimization potential benefit: System High-level data flow graph and optimizations Directly generate optimized program for new operator workloads and hardware Hardware FrameworksWhy Automation is the Future Clear winner on

3400us (baseline), 40us (target) - 85x speedup - Uh ohEnter, TVM and model co-design - PyTorch operator overhead makes interpreter infeasible - Reduce FLOPs with block-sparsified weight matrices -

ticket to a human. Orchestration Agents themselves can serve as tools for other agents—see the Manager Pattern in the Orchestration section. Refund agent, Research agent, Writing agent. 9 A practical requirements, our experience with customers highlights two broadly applicable categories: Manager (agents as tools) A central “manager” agent coordinates multiple specialized agents via tool calls, each handling specializations. Multi-agent systems can be modeled as graphs, with agents represented as nodes. In the manager pattern, edges represent tool calls whereas in the decentralized pattern, edges represent handoffs

`f` string syntax for string interpolation is more readable and concise than the traditional `+` operator. 4. The code doesn’t handle errors that might occur during the renaming process. It would be better

Although training an MoE model will introduce additional commu- nication overheads, through our operator and communication optimizations, the training for DeepSeek-V2 can attain a relatively high Model

下和全于由 loss = loss_fn() opt = tf.Adamoptimizer(learning_rate=...) # Choose a 1oss Scale manager which decides how to pick the right loss scale # throughout the training process. 1oss_scale_manger original optimizer in a LossScale0ptimizer . loss_scale_optimizer = LossScaleOptimizer(opt，1oss_scale_manager) # Call minimize() on the loss scale optimizer. train_op = loss_scale_optimizer.minimize(1oss) Loss