 Trends Artificial Intelligence
global regions simultaneously. Meanwhile, platform incumbents and emerging challengers are racing to build and deploy the next layers of AI infrastructure: agentic interfaces, enterprise copilots, real-world autonomous robotic vacuum cleaner that can navigate homes, is launched 10/05: A Stanford team build a driverless car named Stanley; it completes a 132-mile course, winning the DARPA Grand years. Sam Altman, CEO of OpenAI, remarked in January 2025, We are now confident we know how to build AGI as we have traditionally understood it. This is a forecast, not a dictum, but it reflects how0 码力 | 340 页 | 12.14 MB | 4 月前3 Trends Artificial Intelligence
global regions simultaneously. Meanwhile, platform incumbents and emerging challengers are racing to build and deploy the next layers of AI infrastructure: agentic interfaces, enterprise copilots, real-world autonomous robotic vacuum cleaner that can navigate homes, is launched 10/05: A Stanford team build a driverless car named Stanley; it completes a 132-mile course, winning the DARPA Grand years. Sam Altman, CEO of OpenAI, remarked in January 2025, We are now confident we know how to build AGI as we have traditionally understood it. This is a forecast, not a dictum, but it reflects how0 码力 | 340 页 | 12.14 MB | 4 月前3
 Bring Your Own Codegen to TVM= relay.testing.mobilenet.get_workload(batch_size=1) 3. Partition and build the network with an external codegen mod = relay.build_extern(mod, “dnnl”) 4. Run the inference exe = relay.create_executor(“vm” testing.mobilenet.get_workload(batch_size=1) mod[‘main’] = MyAnnotator().visit(mod[‘main’]) mod = relay.build_extern(mod, “dnnl”)© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Example: ● Implement the build logic© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Implement the Codegen ● Implement a codegen class to accept subgraphs and build binary/library/engine0 码力 | 19 页 | 504.69 KB | 5 月前3 Bring Your Own Codegen to TVM= relay.testing.mobilenet.get_workload(batch_size=1) 3. Partition and build the network with an external codegen mod = relay.build_extern(mod, “dnnl”) 4. Run the inference exe = relay.create_executor(“vm” testing.mobilenet.get_workload(batch_size=1) mod[‘main’] = MyAnnotator().visit(mod[‘main’]) mod = relay.build_extern(mod, “dnnl”)© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Example: ● Implement the build logic© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Implement the Codegen ● Implement a codegen class to accept subgraphs and build binary/library/engine0 码力 | 19 页 | 504.69 KB | 5 月前3
 OpenAI 《A practical guide to building agents》A practical guide to building agents Contents What is an agent? 4 When should you build an agent? 5 Agent design foundations 7 Guardrails 24 Conclusion 32 2 Practical guide to building agents Introduction systems known as agents. This guide is designed for product and engineering teams exploring how to build their first agents, distilling insights from numerous customer deployments into practical and actionable operating within clearly defined guardrails. 4 A practical guide to building agents When should you build an agent? Building agents requires rethinking how your systems make decisions and handle complexity0 码力 | 34 页 | 7.00 MB | 6 月前3 OpenAI 《A practical guide to building agents》A practical guide to building agents Contents What is an agent? 4 When should you build an agent? 5 Agent design foundations 7 Guardrails 24 Conclusion 32 2 Practical guide to building agents Introduction systems known as agents. This guide is designed for product and engineering teams exploring how to build their first agents, distilling insights from numerous customer deployments into practical and actionable operating within clearly defined guardrails. 4 A practical guide to building agents When should you build an agent? Building agents requires rethinking how your systems make decisions and handle complexity0 码力 | 34 页 | 7.00 MB | 6 月前3
 XDNN TVM - Nov 2019Copyright 2018 Xilinx FPGA Pipeline report in MLSuite 1.5 (animated gif of ResNet-50, view in slideshow mode) >> 14© Copyright 2018 Xilinx Quantization Tool – vai_q ˃ 4 commands in vai_q quantize ‒ Quantize0 码力 | 16 页 | 3.35 MB | 5 月前3 XDNN TVM - Nov 2019Copyright 2018 Xilinx FPGA Pipeline report in MLSuite 1.5 (animated gif of ResNet-50, view in slideshow mode) >> 14© Copyright 2018 Xilinx Quantization Tool – vai_q ˃ 4 commands in vai_q quantize ‒ Quantize0 码力 | 16 页 | 3.35 MB | 5 月前3
 OpenAI - AI in the EnterpriseAI-driven solutions. Getting AI into the hands of these experts can be far more powerful than trying to build generic or horizontal solutions. BBVA, the global banking leader, has more than 125,000 employees Mercado Libre, Latin America’s largest ecommerce and fintech company, partnered with OpenAI to build a development platform layer to solve that. It’s called Verdi, and it’s powered by GPT-4o and GPT-4o scalable, consistent platform that uses natural language as a central interface. Developers now build consistently high-quality apps, faster, without having to get into the source code. Security, guardrails0 码力 | 25 页 | 9.48 MB | 5 月前3 OpenAI - AI in the EnterpriseAI-driven solutions. Getting AI into the hands of these experts can be far more powerful than trying to build generic or horizontal solutions. BBVA, the global banking leader, has more than 125,000 employees Mercado Libre, Latin America’s largest ecommerce and fintech company, partnered with OpenAI to build a development platform layer to solve that. It’s called Verdi, and it’s powered by GPT-4o and GPT-4o scalable, consistent platform that uses natural language as a central interface. Developers now build consistently high-quality apps, faster, without having to get into the source code. Security, guardrails0 码力 | 25 页 | 9.48 MB | 5 月前3
 Gluon DeploymentAmazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Like GluonCV? Go build! https://gluon-cv.mxnet.io https://github.com/dmlc/gluon-cv© 2019, Amazon Web Services, Inc. or rights reserved. Amazon Trademark 1. AWS has most TVM contributors from industry. 2. We plan to build TVM team in China, based in Shanghai, Beijing and Shenzhen. 1. Applied Scientist and SDE positions0 码力 | 8 页 | 16.18 MB | 5 月前3 Gluon DeploymentAmazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Like GluonCV? Go build! https://gluon-cv.mxnet.io https://github.com/dmlc/gluon-cv© 2019, Amazon Web Services, Inc. or rights reserved. Amazon Trademark 1. AWS has most TVM contributors from industry. 2. We plan to build TVM team in China, based in Shanghai, Beijing and Shenzhen. 1. Applied Scientist and SDE positions0 码力 | 8 页 | 16.18 MB | 5 月前3
 DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language Modeldollars, to build 4 birdhouses? A: Let’s think step by step. The cost of the planks for one birdhouse is 7 * 3 = 21. And the nails are a cost of 20 * 0.05 = 1 for each birdhouse. So to build one birdhouse0 码力 | 52 页 | 1.23 MB | 1 年前3 DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language Modeldollars, to build 4 birdhouses? A: Let’s think step by step. The cost of the planks for one birdhouse is 7 * 3 = 21. And the nails are a cost of 20 * 0.05 = 1 for each birdhouse. So to build one birdhouse0 码力 | 52 页 | 1.23 MB | 1 年前3
 Deploy VTA on Intel FPGAKernel Module DEPLOY VTA ON INTEL FPGA Setup Environment Variables Navigate to 3rdparty/cma and build kernel module Copy kernel module to DE10-Nano and Install Module CMA API Reference©2019 HARMAN INTERNATIONAL0 码力 | 12 页 | 1.35 MB | 5 月前3 Deploy VTA on Intel FPGAKernel Module DEPLOY VTA ON INTEL FPGA Setup Environment Variables Navigate to 3rdparty/cma and build kernel module Copy kernel module to DE10-Nano and Install Module CMA API Reference©2019 HARMAN INTERNATIONAL0 码力 | 12 页 | 1.35 MB | 5 月前3
 TVM: Where Are We GoingMemory Subsystem TPUsTensorization Challenge Compute primitives scalar vector tensor Challenge: Build systems to support emerging tensor instructionsTensorization Challenge C = tvm.compute((m, n)0 码力 | 31 页 | 22.64 MB | 5 月前3 TVM: Where Are We GoingMemory Subsystem TPUsTensorization Challenge Compute primitives scalar vector tensor Challenge: Build systems to support emerging tensor instructionsTensorization Challenge C = tvm.compute((m, n)0 码力 | 31 页 | 22.64 MB | 5 月前3
 Deepseek R1 本地部署完全手册5. 百度云千帆:https://console.bce.baidu.com/qianfan/modelcenter/model/buildIn/list 6. 英伟达NIM:https://build.nvidia.com/deepseek-ai/deepseek-r1 7. Groq:https://groq.com/ 8. Fireworks:https://fireworks.ai/0 码力 | 7 页 | 932.77 KB | 8 月前3 Deepseek R1 本地部署完全手册5. 百度云千帆:https://console.bce.baidu.com/qianfan/modelcenter/model/buildIn/list 6. 英伟达NIM:https://build.nvidia.com/deepseek-ai/deepseek-r1 7. Groq:https://groq.com/ 8. Fireworks:https://fireworks.ai/0 码力 | 7 页 | 932.77 KB | 8 月前3
共 13 条
- 1
- 2













