Blog Post

AI - Machine Learning Blog
9 MIN READ

ONNX Runtime Training Technical Deep Dive

SherlockNoMad's avatar
SherlockNoMad
Icon for Microsoft rankMicrosoft
May 19, 2020

Author: Sherlock Huang, AI Frameworks, Microsoft

This post is co-authored by Cheng Tang, Jesse Benson, Kaarthik Sivashanmugam and Alexey Svyatkovskiy

 

Today we announced the preview for new training feature in ONNX Runtime (ORT). This blog explains how we have been using it to accelerate training for large transformer models. ONNX Runtime Training is integrated with PyTorch so that existing training code can be directly accelerated for training.

In this paper, we will describe some of the key aspects of ORT design and implementation that enable us to achieve the distributed training performance improvements. We will also use BERT-L pre-training as the benchmark to illustrate the performance of ORT training. Finally, we will present a case study of training GPT-2 model for code autocompletion feature in Visual Studio IntelliCode

 

Design and Implementation

ONNX Runtime Training is built on the same open sourced code as the popular inference engine for ONNX models. Figure 1 shows the high-level architecture for ONNX Runtime’s ecosystem. ORT is a common runtime backend that supports multiple framework frontends, such as PyTorch and Tensorflow/Keras. It makes use of the Execution Provider interface to perform computation on different hardwareThis enables us to build hardware-agnostic, graph-level optimizations that are extensible across different platforms, as well as hardware specific optimization targeting platforms like NVIDIA GPUWe have also implemented additional optimizations, outlined below, to expedite training for large transformer models. 

 

Figure 1. ONNX Runtime High Level Architecture

Static Graph Optimizations

Machine learning models are commonly abstracted as computation graphs. The computation graph used by deep learning frameworks could be either static or dynamic. In the current implementation, ORT has a view of the entire static computation graph. This makes it possible to enable many common graph optimization techniques, such as constant folding, redundant operation elimination, and operator fusion. They are first applied on the forward computation graph before auto differentiation engine builds the backward graph. As ORT has the global knowledge of data dependencies, it only builds the minimal gradient graph that is needed for targeted weights. Consequently, activation tensors that are not needed for backward computation are automatically dropped after use. With a minimal training graph, it ensures that only essential computation is performed and memory consumption is minimized.

 

Memory Usage Optimizations

Over the last few years, the size of deep learning models has been growing rapidly. GPU memory consumption has become a limiting factor for large model training. ORT has made conscious efforts to preserve and reuse memory whenever possible. For example, ORT reuses the same buffer segments throughout a series of operations, including gradient accumulation, gradient scaling adjustment, allreduce communication and weight update computation (if the optimizer allows). ORT also tries to perform in-place operations if the source tensor is no longer consumed elsewhere in the computation graph. ORT’s kernel implementation also tries to minimize the use of scratch buffers, such as avoid using some memory intensive cuDNN functions, and reusing output buffer as scratch buffer if possible. As a result, ORT can train BERT with 2x the batch size as PyTorch. This enables us to utilize the GPU resources more efficiently, resulting in better performance on the same model and the ability to train larger models.

 

ZeRO Stage 1 Integration

Zero Redundancy Optimizer (ZeRO) is a memory optimization technique from Microsoft Research. ZeRO is used to save GPU memory consumption by eliminating duplicated states across workers during distributed training. ZeRO has three main optimization stages.  Currently, ONNX Runtime implemented Stage 1 of ZeRO. ZeRO Stage 1, known as the optimizer state partitioning, allows ORT to shard the optimizer states, including 1st and 2nd order moments (and fp32 copy of weights in mixed precision mode), across multiple workers with no extra communication overhead. With ZeRO, ORT can further boost batch size or train a larger model. In BERT-L pre-training, ZeRO allows batch size to further grow from 148 to 168 for phase 1 and from 23 to 27 for phase 2 in a 32GB V100. Distributed checkpointing is also introduced, as model persistent state is distributed across multiple workers. ZeRO can be enabled with a config flag.

 

Native Mixed Precision Training Support     

Unlike PyTorch’s dependency on NVIDIA Apex extension, ORT has implemented its own support for mixed precision training. Mixed precision training can be enabled with a config flag – no other code change needed. Under the hood, ORT converts the static computation graph into mixed precision mode through a series of graph transformations, i.e. running most of the computations in fp16 while keeping some numerically sensitive computation in fp32. ORT supports dynamic loss scaling by automatically inserting the computation nodes for loss scaling into the graph.

 

Highly Scaleable Distributed Training

ORT seeks to build a unified highly scaleable distributed training framework for hybrid parallelism, including a mixed of data and model parallelisms. ORT supports data parallelism, which is the most popular distributed training mode adopted by many internal teams. We are enhancing ORT to fully support training extremely large models (>100 billion parameters). It has an experimental implementation of Megatron-style horizontal parallelism and we are actively developing to support pipeline parallelism, such as PipeDream.

 

CUDA Kernel Optimizations

ORT has introduced highly optimized CUDA kernels for some key operations including Reductions, Dropout and Softmax. In addition, we have also introduced a few key operator fusions with fused kernels for LayerNormalization, Gelu and their gradients, as well as Lamb Optimizer.

 

Using ORT with PyTorch Training Code

ONNX Runtime has the capability to train existing PyTorch models through its optimized backend. For this, we have introduced a python API for PyTorch, called ORTTrainer, which can be used to switch the training backend for PyTorch models (instance of torch.nn.Module) to ORT. This requires some changes from the user, such as replacing the PyTorch optimizer, and optionally, setting flags to enable additional features such as mixed-precision training. Under the hood, as shown in Figure 2, ORTTrainer first converts the PyTorch model to ONNX format through the PyTorch-ONNX exporter. Next, ORT backend takes over and applies graph optimizations, builds a training graph, performs transformations on it as needed (e.g. mixed-precision transformation), and sets up the graph elements needed for distributed training. In this design, while all the computation-intensive workload is offloaded onto the ORT backend, users can still enjoy the rich PyTorch frontend utilities, such as data loading, checkpointing , and easy specification of loss functions. 

 

Figure 2. Workflow for converting an PyTorch model into an ORT training graph

It is important to note that the current API is experimental and expected to see significant changes in the near future. A new version of the API is under active development. Our goal is to improve the interface to provide more seamless integration with PyTorch training that requires minimal changes in users’ training code, introduce new features, and present a more flexible API to cover advanced scenarios. Please refer to the training examples for more details.

 

Benchmarking Training Acceleration with ONNX Runtime

We now present the performance evaluation of BERT-L pre-training with ONNX Runtime in a 4-node DGX-2 cluster. In AzureML, we also reproduced the pre-training convergence for BERT-Large using sample from NVIDIA’s DeepLearningExamplesle’s repo. We also validated fine tuning accuracy with SQuAD benchmarks.

 

Benchmarking on DGX-2

We compared PyTorch and ORT’s BERT-L training performance on 4 NVIDIA DGX-2 machines (each with 16x 32GB V100) interconnected with InfiniBand. PyTorch’s result was obtained with NGC 20.03-py3 docker image following Nvidia’s recipe. ORT’s result was obtained following the same recipe, except that ORT used bigger local batch sizes. As described above, ORT is able to run at a 2x batch size of PyTorch’s. ORT ran at a local batch size of 128 and 16 for phase 1 and 2 respectively, whereas PyTorch ran at batch size of 64 and 8. The effective global batch size remained unchanged in both cases. Overall, ORT achieved throughput improvement of 11.32% and 14.61% for phase 1 and 2. The total time to train was reduces by 11.16%, from 17.74 hours to 15.76 hours.

Table 1. Time to train on 4 NVIDIA DGX-2 machines

 

PyTorch 1.5 with

NGC 20.03-py3

PyTorch 1.5 with

ONNX Runtime

% Gain with

ONNX Runtime

Phase 1 Throughput (ex/sec)

11522.1

12826.2

11.32%

Phase 2 Throughput (ex/sec)

2150.0

2464.1

14.61%

Phase 1 time (hours)

11.12

9.99

10.16%

Phase 2 time (hours)

6.62

5.77

12.84%

Total time (hours)

17.74

15.76

11.16%

 

BERT-L Pre-training on AzureML

We performed BERT-L pre-training on 8x ND40rs_v2 cluster (each with 8x 32GB V100) interconnected with InfiniBand in AzureML. We used the same Nvidia’s recipe, expect that we doubled the local batch size in the same way we mentioned above. Mixed precision mode and LAMB optimizer was used throughout the training. As the end of phase 2, we achieved the training loss of 1.31. The end-to-end training time was 18.32 hours.

Table 2. Time to train on Azure ML with 8x ND40rs_v2

 

PyTorch 1.5 with ONNX Runtime

Phase 1 Throughput (ex/sec)

10751.4

Phase 2 Throughput (ex/sec)

2223.7

Phase 1 Time (hours)

11.92

Phase 2 Time (hours)

6.40

Total Time (hours)

18.32

 

Figure 3 shows a loss curve produced in a typical pre-training run. Phase 1 ends with a loss value around 1.4 after 7038 steps. Phase 2 continues with a jump of loss due to switch of sequence length, and it finally decrease to a loss value around 1.3.

­

Figure 3. ORT BERT-L pre-training loss curves

The pretrained model is then further finetuned on SQuAD dataset. Both full precision or mixed precision finetuning result in satisfactory Exact Match and F1 scores.

Table 3. BERT-L fine-tuning result on SQuAD Dataset

Accuracy Metrics

Finetuning - FP32

Finetuning -

mixed precision

Exact Match %

84.63

84.81

F1 score %

91.15

91.32

 

A Case Study with Visual Studio using GPT-2 Medium

Microsoft Visual Studio uses ONNX Runtime to accelerate pre-training a 24-layer GPT-2 Medium model to power code autocompletion in the IntelliCode of Visual Studio. Intellicode serves as a universal programming language compiler, effectively generating syntactically correct code in multiple programming languages, capable of completing an entire line of code in a couple of keystrokes. The training dataset for this task comprises over 1.2 billion lines of source code in Python, C#, JavaScript and TypeScript programming language from 52000 top-starred projects in GitHub. We treat the source code data as a sequence of tokens corresponding to the output of a lexical analyzer.

The training was performed in a DGX-2 cluster. As we use a large sequence length of 1024, the memory usage is very intensive and PyTorch is only able to fit a batch size of 2 on the 32GB V100. ORT achieved 15.8% higher throughput under the identical local batch. As ORT is more memory efficient and able to run at a bigger batch size of 3, it delivered an overall 20.5% of the throughput improvement. As a result, the overall training time is reduced from 202 hours to 168 hours (with 1.2 x higher throughput). The final evaluation metric also achieved the same production shipping bar.  

Table 4. GPT-2 medium pre-training performance.

 

Batch size / GPU

Throughput (ex/sec)

Time to train (hours)

PyTorch

2

48.7

202

PyTorch + ORT

2

56.4

174

Pytorch + ORT

3

58.7

160

 

Conclusion

Today, we announced the preview of training support in ONNX Runtime with a focus on large scale computation intensive transformer models. We have demonstrated that, on a 4 DGX-2 cluster, ONNX Runtime can achieve a throughput gain of 11.32% and 14.61% for BERT-L phase 1 and 2 pre-training over PyTorch. The total training time was reduced by 11.16%, from 17.74 hours to 15.76 hours. ONNX Runtime is able to train BERT-L at a 2x batch size as PyTorch. We have shown a similar 20.5% speedup on a GPT-2 model, saving 34 hours in total training time. ONNX Runtime Training is integrated with PyTorch so that existing PyTorch training code can be directly accelerated for transformer models training. 

 

Get Started

As a part of the announcement on using ONNX Runtime for training, we have released a Docker image with ORT and made available a repo at https://github.com/microsoft/onnxruntime-training-examples that will host examples for ORT training. The first recipe available in this repo will help you get started with ORT for BERT pretraining in Azure Machine Learning service or NVIDIA DGX-2 and see the speedup in action. This recipe shows how to use ONNX Runtime training with BERT pretraining implementation in PyTorch. You can use this example either with the two datasets used in the original implementation or with your custom dataset to pretrain a BERT model and get the performance improvements with ORT reported in this blog. We are planning to add more examples for transformer models and other models. We also welcome your contribution to this repo and feedback to improve ORT training capabilities and experience.

Updated Oct 29, 2020
Version 4.0

1 Comment

  • Hello Sherlock, thank you for your good article.

     

    I have a question about the ONNX computation graph. Is this graph a static computation graph or a dynamic one? And why?

"}},"componentScriptGroups({\"componentId\":\"custom.widget.MicrosoftFooter\"})":{"__typename":"ComponentScriptGroups","scriptGroups":{"__typename":"ComponentScriptGroupsDefinition","afterInteractive":{"__typename":"PageScriptGroupDefinition","group":"AFTER_INTERACTIVE","scriptIds":[]},"lazyOnLoad":{"__typename":"PageScriptGroupDefinition","group":"LAZY_ON_LOAD","scriptIds":[]}},"componentScripts":[]},"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/community/NavbarDropdownToggle\"]})":[{"__ref":"CachedAsset:text:en_US-components/community/NavbarDropdownToggle-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"shared/client/components/common/QueryHandler\"]})":[{"__ref":"CachedAsset:text:en_US-shared/client/components/common/QueryHandler-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/messages/MessageCoverImage\"]})":[{"__ref":"CachedAsset:text:en_US-components/messages/MessageCoverImage-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"shared/client/components/nodes/NodeTitle\"]})":[{"__ref":"CachedAsset:text:en_US-shared/client/components/nodes/NodeTitle-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/messages/MessageTimeToRead\"]})":[{"__ref":"CachedAsset:text:en_US-components/messages/MessageTimeToRead-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/messages/MessageSubject\"]})":[{"__ref":"CachedAsset:text:en_US-components/messages/MessageSubject-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/users/UserLink\"]})":[{"__ref":"CachedAsset:text:en_US-components/users/UserLink-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"shared/client/components/users/UserRank\"]})":[{"__ref":"CachedAsset:text:en_US-shared/client/components/users/UserRank-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/messages/MessageTime\"]})":[{"__ref":"CachedAsset:text:en_US-components/messages/MessageTime-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/messages/MessageBody\"]})":[{"__ref":"CachedAsset:text:en_US-components/messages/MessageBody-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/messages/MessageCustomFields\"]})":[{"__ref":"CachedAsset:text:en_US-components/messages/MessageCustomFields-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/messages/MessageRevision\"]})":[{"__ref":"CachedAsset:text:en_US-components/messages/MessageRevision-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/messages/MessageReplyButton\"]})":[{"__ref":"CachedAsset:text:en_US-components/messages/MessageReplyButton-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/messages/MessageAuthorBio\"]})":[{"__ref":"CachedAsset:text:en_US-components/messages/MessageAuthorBio-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"shared/client/components/users/UserAvatar\"]})":[{"__ref":"CachedAsset:text:en_US-shared/client/components/users/UserAvatar-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"shared/client/components/ranks/UserRankLabel\"]})":[{"__ref":"CachedAsset:text:en_US-shared/client/components/ranks/UserRankLabel-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/users/UserRegistrationDate\"]})":[{"__ref":"CachedAsset:text:en_US-components/users/UserRegistrationDate-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"shared/client/components/nodes/NodeAvatar\"]})":[{"__ref":"CachedAsset:text:en_US-shared/client/components/nodes/NodeAvatar-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"shared/client/components/nodes/NodeDescription\"]})":[{"__ref":"CachedAsset:text:en_US-shared/client/components/nodes/NodeDescription-1745505309939"}],"message({\"id\":\"message:3659381\"})":{"__ref":"BlogReplyMessage:message:3659381"},"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"components/tags/TagView/TagViewChip\"]})":[{"__ref":"CachedAsset:text:en_US-components/tags/TagView/TagViewChip-1745505309939"}],"cachedText({\"lastModified\":\"1745505309939\",\"locale\":\"en-US\",\"namespaces\":[\"shared/client/components/nodes/NodeIcon\"]})":[{"__ref":"CachedAsset:text:en_US-shared/client/components/nodes/NodeIcon-1745505309939"}]},"CachedAsset:pages-1745487429260":{"__typename":"CachedAsset","id":"pages-1745487429260","value":[{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"BlogViewAllPostsPage","type":"BLOG","urlPath":"/category/:categoryId/blog/:boardId/all-posts/(/:after|/:before)?","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"CasePortalPage","type":"CASE_PORTAL","urlPath":"/caseportal","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"CreateGroupHubPage","type":"GROUP_HUB","urlPath":"/groups/create","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"CaseViewPage","type":"CASE_DETAILS","urlPath":"/case/:caseId/:caseNumber","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"InboxPage","type":"COMMUNITY","urlPath":"/inbox","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"HelpFAQPage","type":"COMMUNITY","urlPath":"/help","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"IdeaMessagePage","type":"IDEA_POST","urlPath":"/idea/:boardId/:messageSubject/:messageId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"IdeaViewAllIdeasPage","type":"IDEA","urlPath":"/category/:categoryId/ideas/:boardId/all-ideas/(/:after|/:before)?","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"LoginPage","type":"USER","urlPath":"/signin","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"BlogPostPage","type":"BLOG","urlPath":"/category/:categoryId/blogs/:boardId/create","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"UserBlogPermissions.Page","type":"COMMUNITY","urlPath":"/c/user-blog-permissions/page","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ThemeEditorPage","type":"COMMUNITY","urlPath":"/designer/themes","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"TkbViewAllArticlesPage","type":"TKB","urlPath":"/category/:categoryId/kb/:boardId/all-articles/(/:after|/:before)?","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1730142000000,"localOverride":null,"page":{"id":"AllEvents","type":"CUSTOM","urlPath":"/Events","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"OccasionEditPage","type":"EVENT","urlPath":"/event/:boardId/:messageSubject/:messageId/edit","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"OAuthAuthorizationAllowPage","type":"USER","urlPath":"/auth/authorize/allow","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"PageEditorPage","type":"COMMUNITY","urlPath":"/designer/pages","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"PostPage","type":"COMMUNITY","urlPath":"/category/:categoryId/:boardId/create","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ForumBoardPage","type":"FORUM","urlPath":"/category/:categoryId/discussions/:boardId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"TkbBoardPage","type":"TKB","urlPath":"/category/:categoryId/kb/:boardId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"EventPostPage","type":"EVENT","urlPath":"/category/:categoryId/events/:boardId/create","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"UserBadgesPage","type":"COMMUNITY","urlPath":"/users/:login/:userId/badges","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"GroupHubMembershipAction","type":"GROUP_HUB","urlPath":"/membership/join/:nodeId/:membershipType","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"MaintenancePage","type":"COMMUNITY","urlPath":"/maintenance","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"IdeaReplyPage","type":"IDEA_REPLY","urlPath":"/idea/:boardId/:messageSubject/:messageId/comments/:replyId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"UserSettingsPage","type":"USER","urlPath":"/mysettings/:userSettingsTab","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"GroupHubsPage","type":"GROUP_HUB","urlPath":"/groups","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ForumPostPage","type":"FORUM","urlPath":"/category/:categoryId/discussions/:boardId/create","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"OccasionRsvpActionPage","type":"OCCASION","urlPath":"/event/:boardId/:messageSubject/:messageId/rsvp/:responseType","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"VerifyUserEmailPage","type":"USER","urlPath":"/verifyemail/:userId/:verifyEmailToken","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"AllOccasionsPage","type":"OCCASION","urlPath":"/category/:categoryId/events/:boardId/all-events/(/:after|/:before)?","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"EventBoardPage","type":"EVENT","urlPath":"/category/:categoryId/events/:boardId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"TkbReplyPage","type":"TKB_REPLY","urlPath":"/kb/:boardId/:messageSubject/:messageId/comments/:replyId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"IdeaBoardPage","type":"IDEA","urlPath":"/category/:categoryId/ideas/:boardId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"CommunityGuideLinesPage","type":"COMMUNITY","urlPath":"/communityguidelines","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"CaseCreatePage","type":"SALESFORCE_CASE_CREATION","urlPath":"/caseportal/create","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"TkbEditPage","type":"TKB","urlPath":"/kb/:boardId/:messageSubject/:messageId/edit","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ForgotPasswordPage","type":"USER","urlPath":"/forgotpassword","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"IdeaEditPage","type":"IDEA","urlPath":"/idea/:boardId/:messageSubject/:messageId/edit","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"TagPage","type":"COMMUNITY","urlPath":"/tag/:tagName","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"BlogBoardPage","type":"BLOG","urlPath":"/category/:categoryId/blog/:boardId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"OccasionMessagePage","type":"OCCASION_TOPIC","urlPath":"/event/:boardId/:messageSubject/:messageId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ManageContentPage","type":"COMMUNITY","urlPath":"/managecontent","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ClosedMembershipNodeNonMembersPage","type":"GROUP_HUB","urlPath":"/closedgroup/:groupHubId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"CommunityPage","type":"COMMUNITY","urlPath":"/","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ForumMessagePage","type":"FORUM_TOPIC","urlPath":"/discussions/:boardId/:messageSubject/:messageId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"IdeaPostPage","type":"IDEA","urlPath":"/category/:categoryId/ideas/:boardId/create","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1730142000000,"localOverride":null,"page":{"id":"CommunityHub.Page","type":"CUSTOM","urlPath":"/Directory","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"BlogMessagePage","type":"BLOG_ARTICLE","urlPath":"/blog/:boardId/:messageSubject/:messageId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"RegistrationPage","type":"USER","urlPath":"/register","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"EditGroupHubPage","type":"GROUP_HUB","urlPath":"/group/:groupHubId/edit","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ForumEditPage","type":"FORUM","urlPath":"/discussions/:boardId/:messageSubject/:messageId/edit","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ResetPasswordPage","type":"USER","urlPath":"/resetpassword/:userId/:resetPasswordToken","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1730142000000,"localOverride":null,"page":{"id":"AllBlogs.Page","type":"CUSTOM","urlPath":"/blogs","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"TkbMessagePage","type":"TKB_ARTICLE","urlPath":"/kb/:boardId/:messageSubject/:messageId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"BlogEditPage","type":"BLOG","urlPath":"/blog/:boardId/:messageSubject/:messageId/edit","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ManageUsersPage","type":"USER","urlPath":"/users/manage/:tab?/:manageUsersTab?","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ForumReplyPage","type":"FORUM_REPLY","urlPath":"/discussions/:boardId/:messageSubject/:messageId/replies/:replyId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"PrivacyPolicyPage","type":"COMMUNITY","urlPath":"/privacypolicy","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"NotificationPage","type":"COMMUNITY","urlPath":"/notifications","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"UserPage","type":"USER","urlPath":"/users/:login/:userId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"OccasionReplyPage","type":"OCCASION_REPLY","urlPath":"/event/:boardId/:messageSubject/:messageId/comments/:replyId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ManageMembersPage","type":"GROUP_HUB","urlPath":"/group/:groupHubId/manage/:tab?","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"SearchResultsPage","type":"COMMUNITY","urlPath":"/search","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"BlogReplyPage","type":"BLOG_REPLY","urlPath":"/blog/:boardId/:messageSubject/:messageId/replies/:replyId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"GroupHubPage","type":"GROUP_HUB","urlPath":"/group/:groupHubId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"TermsOfServicePage","type":"COMMUNITY","urlPath":"/termsofservice","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"CategoryPage","type":"CATEGORY","urlPath":"/category/:categoryId","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"ForumViewAllTopicsPage","type":"FORUM","urlPath":"/category/:categoryId/discussions/:boardId/all-topics/(/:after|/:before)?","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"TkbPostPage","type":"TKB","urlPath":"/category/:categoryId/kbs/:boardId/create","__typename":"PageDescriptor"},"__typename":"PageResource"},{"lastUpdatedTime":1745487429260,"localOverride":null,"page":{"id":"GroupHubPostPage","type":"GROUP_HUB","urlPath":"/group/:groupHubId/:boardId/create","__typename":"PageDescriptor"},"__typename":"PageResource"}],"localOverride":false},"CachedAsset:text:en_US-components/context/AppContext/AppContextProvider-0":{"__typename":"CachedAsset","id":"text:en_US-components/context/AppContext/AppContextProvider-0","value":{"noCommunity":"Cannot find community","noUser":"Cannot find current user","noNode":"Cannot find node with id {nodeId}","noMessage":"Cannot find message with id {messageId}"},"localOverride":false},"CachedAsset:text:en_US-shared/client/components/common/Loading/LoadingDot-0":{"__typename":"CachedAsset","id":"text:en_US-shared/client/components/common/Loading/LoadingDot-0","value":{"title":"Loading..."},"localOverride":false},"User:user:-1":{"__typename":"User","id":"user:-1","uid":-1,"login":"Deleted","email":"","avatar":null,"rank":null,"kudosWeight":1,"registrationData":{"__typename":"RegistrationData","status":"ANONYMOUS","registrationTime":null,"confirmEmailStatus":false,"registrationAccessLevel":"VIEW","ssoRegistrationFields":[]},"ssoId":null,"profileSettings":{"__typename":"ProfileSettings","dateDisplayStyle":{"__typename":"InheritableStringSettingWithPossibleValues","key":"layout.friendly_dates_enabled","value":"false","localValue":"true","possibleValues":["true","false"]},"dateDisplayFormat":{"__typename":"InheritableStringSetting","key":"layout.format_pattern_date","value":"MMM dd yyyy","localValue":"MM-dd-yyyy"},"language":{"__typename":"InheritableStringSettingWithPossibleValues","key":"profile.language","value":"en-US","localValue":"en","possibleValues":["en-US"]}},"deleted":false},"Theme:customTheme1":{"__typename":"Theme","id":"customTheme1"},"Category:category:AI":{"__typename":"Category","id":"category:AI","entityType":"CATEGORY","displayId":"AI","nodeType":"category","depth":3,"title":"Artificial Intelligence and Machine Learning","shortTitle":"Artificial Intelligence and Machine Learning","parent":{"__ref":"Category:category:solutions"},"categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:top":{"__typename":"Category","id":"category:top","displayId":"top","nodeType":"category","depth":0,"title":"Top","entityType":"CATEGORY","shortTitle":"Top"},"Category:category:communities":{"__typename":"Category","id":"category:communities","displayId":"communities","nodeType":"category","depth":1,"parent":{"__ref":"Category:category:top"},"title":"Communities","entityType":"CATEGORY","shortTitle":"Communities"},"Category:category:solutions":{"__typename":"Category","id":"category:solutions","displayId":"solutions","nodeType":"category","depth":2,"parent":{"__ref":"Category:category:communities"},"title":"Topics","entityType":"CATEGORY","shortTitle":"Topics"},"Blog:board:MachineLearningBlog":{"__typename":"Blog","id":"board:MachineLearningBlog","entityType":"BLOG","displayId":"MachineLearningBlog","nodeType":"board","depth":4,"conversationStyle":"BLOG","title":"AI - Machine Learning Blog","description":"","avatar":null,"profileSettings":{"__typename":"ProfileSettings","language":null},"parent":{"__ref":"Category:category:AI"},"ancestors":{"__typename":"CoreNodeConnection","edges":[{"__typename":"CoreNodeEdge","node":{"__ref":"Community:community:gxcuf89792"}},{"__typename":"CoreNodeEdge","node":{"__ref":"Category:category:communities"}},{"__typename":"CoreNodeEdge","node":{"__ref":"Category:category:solutions"}},{"__typename":"CoreNodeEdge","node":{"__ref":"Category:category:AI"}}]},"userContext":{"__typename":"NodeUserContext","canAddAttachments":false,"canUpdateNode":false,"canPostMessages":false,"isSubscribed":false},"boardPolicies":{"__typename":"BoardPolicies","canPublishArticleOnCreate":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.forums.policy_can_publish_on_create_workflow_action.accessDenied","key":"error.lithium.policies.forums.policy_can_publish_on_create_workflow_action.accessDenied","args":[]}}},"shortTitle":"AI - Machine Learning Blog","repliesProperties":{"__typename":"RepliesProperties","sortOrder":"REVERSE_PUBLISH_TIME","repliesFormat":"threaded"},"eventPath":"category:AI/category:solutions/category:communities/community:gxcuf89792board:MachineLearningBlog/","tagProperties":{"__typename":"TagNodeProperties","tagsEnabled":{"__typename":"PolicyResult","failureReason":null}},"requireTags":false,"tagType":"PRESET_ONLY"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/cmstNC05WEo0blc\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/cmstNC05WEo0blc","height":512,"width":512,"mimeType":"image/png"},"Rank:rank:4":{"__typename":"Rank","id":"rank:4","position":6,"name":"Microsoft","color":"333333","icon":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/cmstNC05WEo0blc\"}"},"rankStyle":"OUTLINE"},"User:user:670116":{"__typename":"User","id":"user:670116","uid":670116,"login":"SherlockNoMad","deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS02NzAxMTYtMTkyNzc1aTY3OUZFRkQwODM1Rjk2RTQ"},"rank":{"__ref":"Rank:rank:4"},"email":"","messagesCount":1,"biography":null,"topicsCount":1,"kudosReceivedCount":2,"kudosGivenCount":0,"kudosWeight":1,"registrationData":{"__typename":"RegistrationData","status":null,"registrationTime":"2020-05-16T12:57:01.522-07:00","confirmEmailStatus":null},"followersCount":null,"solutionsCount":0},"BlogTopicMessage:message:1398310":{"__typename":"BlogTopicMessage","uid":1398310,"subject":"ONNX Runtime Training Technical Deep Dive","id":"message:1398310","revisionNum":46,"repliesCount":1,"author":{"__ref":"User:user:670116"},"depth":0,"hasGivenKudo":false,"board":{"__ref":"Blog:board:MachineLearningBlog"},"conversation":{"__ref":"Conversation:conversation:1398310"},"messagePolicies":{"__typename":"MessagePolicies","canPublishArticleOnEdit":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","key":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","args":[]}},"canModerateSpamMessage":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","key":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","args":[]}}},"contentWorkflow":{"__typename":"ContentWorkflow","state":"PUBLISH","scheduledPublishTime":null,"scheduledTimezone":null,"userContext":{"__typename":"MessageWorkflowContext","canSubmitForReview":null,"canEdit":false,"canRecall":null,"canSubmitForPublication":null,"canReturnToAuthor":null,"canPublish":null,"canReturnToReview":null,"canSchedule":false},"shortScheduledTimezone":null},"readOnly":false,"editFrozen":false,"moderationData":{"__ref":"ModerationData:moderation_data:1398310"},"teaser":"
\n

Today we are introducing the preview of the new training feature in ONNX Runtime. This allows training a Pytorch transformer model up to ... 45% faster with 2x batch size. 

\n
","body":"

Author: Sherlock Huang, AI Frameworks, Microsoft

\n

This post is co-authored by Cheng Tang, Jesse Benson, Kaarthik Sivashanmugam and Alexey Svyatkovskiy

\n

 

\n

Today we announced the preview for new training feature in ONNX Runtime (ORT). This blog explains how we have been using it to accelerate training for large transformer models. ONNX Runtime Training is integrated with PyTorch so that existing training code can be directly accelerated for training.

\n

In this paper, we will describe some of the key aspects of ORT design and implementation that enable us to achieve the distributed training performance improvements. We will also use BERT-L pre-training as the benchmark to illustrate the performance of ORT training. Finally, we will present a case study of training GPT-2 model for code autocompletion feature in Visual Studio IntelliCode

\n

 

\n

Design and Implementation

\n

ONNX Runtime Training is built on the same open sourced code as the popular inference engine for ONNX models. Figure 1 shows the high-level architecture for ONNX Runtime’s ecosystem. ORT is a common runtime backend that supports multiple framework frontends, such as PyTorch and Tensorflow/Keras. It makes use of the Execution Provider interface to perform computation on different hardwareThis enables us to build hardware-agnostic, graph-level optimizations that are extensible across different platforms, as well as hardware specific optimization targeting platforms like NVIDIA GPUWe have also implemented additional optimizations, outlined below, to expedite training for large transformer models. 

\n

 

\n\n\n\n\n\n\n
Figure 1. ONNX Runtime High Level Architecture
\n

Static Graph Optimizations

\n

Machine learning models are commonly abstracted as computation graphs. The computation graph used by deep learning frameworks could be either static or dynamic. In the current implementation, ORT has a view of the entire static computation graph. This makes it possible to enable many common graph optimization techniques, such as constant folding, redundant operation elimination, and operator fusion. They are first applied on the forward computation graph before auto differentiation engine builds the backward graph. As ORT has the global knowledge of data dependencies, it only builds the minimal gradient graph that is needed for targeted weights. Consequently, activation tensors that are not needed for backward computation are automatically dropped after use. With a minimal training graph, it ensures that only essential computation is performed and memory consumption is minimized.

\n

 

\n

Memory Usage Optimizations

\n

Over the last few years, the size of deep learning models has been growing rapidly. GPU memory consumption has become a limiting factor for large model training. ORT has made conscious efforts to preserve and reuse memory whenever possible. For example, ORT reuses the same buffer segments throughout a series of operations, including gradient accumulation, gradient scaling adjustment, allreduce communication and weight update computation (if the optimizer allows). ORT also tries to perform in-place operations if the source tensor is no longer consumed elsewhere in the computation graph. ORT’s kernel implementation also tries to minimize the use of scratch buffers, such as avoid using some memory intensive cuDNN functions, and reusing output buffer as scratch buffer if possible. As a result, ORT can train BERT with 2x the batch size as PyTorch. This enables us to utilize the GPU resources more efficiently, resulting in better performance on the same model and the ability to train larger models.

\n

 

\n

ZeRO Stage 1 Integration

\n

Zero Redundancy Optimizer (ZeRO) is a memory optimization technique from Microsoft Research. ZeRO is used to save GPU memory consumption by eliminating duplicated states across workers during distributed training. ZeRO has three main optimization stages.  Currently, ONNX Runtime implemented Stage 1 of ZeRO. ZeRO Stage 1, known as the optimizer state partitioning, allows ORT to shard the optimizer states, including 1st and 2nd order moments (and fp32 copy of weights in mixed precision mode), across multiple workers with no extra communication overhead. With ZeRO, ORT can further boost batch size or train a larger model. In BERT-L pre-training, ZeRO allows batch size to further grow from 148 to 168 for phase 1 and from 23 to 27 for phase 2 in a 32GB V100. Distributed checkpointing is also introduced, as model persistent state is distributed across multiple workers. ZeRO can be enabled with a config flag.

\n

 

\n

Native Mixed Precision Training Support     

\n

Unlike PyTorch’s dependency on NVIDIA Apex extension, ORT has implemented its own support for mixed precision training. Mixed precision training can be enabled with a config flag – no other code change needed. Under the hood, ORT converts the static computation graph into mixed precision mode through a series of graph transformations, i.e. running most of the computations in fp16 while keeping some numerically sensitive computation in fp32. ORT supports dynamic loss scaling by automatically inserting the computation nodes for loss scaling into the graph.

\n

 

\n

Highly Scaleable Distributed Training

\n

ORT seeks to build a unified highly scaleable distributed training framework for hybrid parallelism, including a mixed of data and model parallelisms. ORT supports data parallelism, which is the most popular distributed training mode adopted by many internal teams. We are enhancing ORT to fully support training extremely large models (>100 billion parameters). It has an experimental implementation of Megatron-style horizontal parallelism and we are actively developing to support pipeline parallelism, such as PipeDream.

\n

 

\n

CUDA Kernel Optimizations

\n

ORT has introduced highly optimized CUDA kernels for some key operations including Reductions, Dropout and Softmax. In addition, we have also introduced a few key operator fusions with fused kernels for LayerNormalization, Gelu and their gradients, as well as Lamb Optimizer.

\n

 

\n

Using ORT with PyTorch Training Code

\n

ONNX Runtime has the capability to train existing PyTorch models through its optimized backend. For this, we have introduced a python API for PyTorch, called ORTTrainer, which can be used to switch the training backend for PyTorch models (instance of torch.nn.Module) to ORT. This requires some changes from the user, such as replacing the PyTorch optimizer, and optionally, setting flags to enable additional features such as mixed-precision training. Under the hood, as shown in Figure 2, ORTTrainer first converts the PyTorch model to ONNX format through the PyTorch-ONNX exporter. Next, ORT backend takes over and applies graph optimizations, builds a training graph, performs transformations on it as needed (e.g. mixed-precision transformation), and sets up the graph elements needed for distributed training. In this design, while all the computation-intensive workload is offloaded onto the ORT backend, users can still enjoy the rich PyTorch frontend utilities, such as data loading, checkpointing , and easy specification of loss functions. 

\n

 

\n\n\n\n\n\n\n
Figure 2. Workflow for converting an PyTorch model into an ORT training graph
\n

It is important to note that the current API is experimental and expected to see significant changes in the near future. A new version of the API is under active development. Our goal is to improve the interface to provide more seamless integration with PyTorch training that requires minimal changes in users’ training code, introduce new features, and present a more flexible API to cover advanced scenarios. Please refer to the training examples for more details.

\n

 

\n

Benchmarking Training Acceleration with ONNX Runtime

\n

We now present the performance evaluation of BERT-L pre-training with ONNX Runtime in a 4-node DGX-2 cluster. In AzureML, we also reproduced the pre-training convergence for BERT-Large using sample from NVIDIA’s DeepLearningExamplesle’s repo. We also validated fine tuning accuracy with SQuAD benchmarks.

\n

 

\n

Benchmarking on DGX-2

\n

We compared PyTorch and ORT’s BERT-L training performance on 4 NVIDIA DGX-2 machines (each with 16x 32GB V100) interconnected with InfiniBand. PyTorch’s result was obtained with NGC 20.03-py3 docker image following Nvidia’s recipe. ORT’s result was obtained following the same recipe, except that ORT used bigger local batch sizes. As described above, ORT is able to run at a 2x batch size of PyTorch’s. ORT ran at a local batch size of 128 and 16 for phase 1 and 2 respectively, whereas PyTorch ran at batch size of 64 and 8. The effective global batch size remained unchanged in both cases. Overall, ORT achieved throughput improvement of 11.32% and 14.61% for phase 1 and 2. The total time to train was reduces by 11.16%, from 17.74 hours to 15.76 hours.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Table 1. Time to train on 4 NVIDIA DGX-2 machines
\n

 

\n
\n

PyTorch 1.5 with

\n

NGC 20.03-py3

\n
\n

PyTorch 1.5 with

\n

ONNX Runtime

\n
\n

% Gain with

\n

ONNX Runtime

\n
\n

Phase 1 Throughput (ex/sec)

\n
\n

11522.1

\n
\n

12826.2

\n
\n

11.32%

\n
\n

Phase 2 Throughput (ex/sec)

\n
\n

2150.0

\n
\n

2464.1

\n
\n

14.61%

\n
\n

Phase 1 time (hours)

\n
\n

11.12

\n
\n

9.99

\n
\n

10.16%

\n
\n

Phase 2 time (hours)

\n
\n

6.62

\n
\n

5.77

\n
\n

12.84%

\n
\n

Total time (hours)

\n
\n

17.74

\n
\n

15.76

\n
\n

11.16%

\n
\n

 

\n

BERT-L Pre-training on AzureML

\n

We performed BERT-L pre-training on 8x ND40rs_v2 cluster (each with 8x 32GB V100) interconnected with InfiniBand in AzureML. We used the same Nvidia’s recipe, expect that we doubled the local batch size in the same way we mentioned above. Mixed precision mode and LAMB optimizer was used throughout the training. As the end of phase 2, we achieved the training loss of 1.31. The end-to-end training time was 18.32 hours.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Table 2. Time to train on Azure ML with 8x ND40rs_v2
\n

 

\n
\n

PyTorch 1.5 with ONNX Runtime

\n
\n

Phase 1 Throughput (ex/sec)

\n
\n

10751.4

\n
\n

Phase 2 Throughput (ex/sec)

\n
\n

2223.7

\n
\n

Phase 1 Time (hours)

\n
\n

11.92

\n
\n

Phase 2 Time (hours)

\n
\n

6.40

\n
\n

Total Time (hours)

\n
\n

18.32

\n
\n

 

\n

Figure 3 shows a loss curve produced in a typical pre-training run. Phase 1 ends with a loss value around 1.4 after 7038 steps. Phase 2 continues with a jump of loss due to switch of sequence length, and it finally decrease to a loss value around 1.3.

\n

­

\n\n\n\n\n\n\n
Figure 3. ORT BERT-L pre-training loss curves
\n

The pretrained model is then further finetuned on SQuAD dataset. Both full precision or mixed precision finetuning result in satisfactory Exact Match and F1 scores.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Table 3. BERT-L fine-tuning result on SQuAD Dataset
\n

Accuracy Metrics

\n
\n

Finetuning - FP32

\n
\n

Finetuning -

\n

mixed precision

\n
\n

Exact Match %

\n
\n

84.63

\n
\n

84.81

\n
\n

F1 score %

\n
\n

91.15

\n
\n

91.32

\n
\n

 

\n

A Case Study with Visual Studio using GPT-2 Medium

\n

Microsoft Visual Studio uses ONNX Runtime to accelerate pre-training a 24-layer GPT-2 Medium model to power code autocompletion in the IntelliCode of Visual Studio. Intellicode serves as a universal programming language compiler, effectively generating syntactically correct code in multiple programming languages, capable of completing an entire line of code in a couple of keystrokes. The training dataset for this task comprises over 1.2 billion lines of source code in Python, C#, JavaScript and TypeScript programming language from 52000 top-starred projects in GitHub. We treat the source code data as a sequence of tokens corresponding to the output of a lexical analyzer.

\n

The training was performed in a DGX-2 cluster. As we use a large sequence length of 1024, the memory usage is very intensive and PyTorch is only able to fit a batch size of 2 on the 32GB V100. ORT achieved 15.8% higher throughput under the identical local batch. As ORT is more memory efficient and able to run at a bigger batch size of 3, it delivered an overall 20.5% of the throughput improvement. As a result, the overall training time is reduced from 202 hours to 168 hours (with 1.2 x higher throughput). The final evaluation metric also achieved the same production shipping bar.  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Table 4. GPT-2 medium pre-training performance.
\n

 

\n
\n

Batch size / GPU

\n
\n

Throughput (ex/sec)

\n
\n

Time to train (hours)

\n
\n

PyTorch

\n
\n

2

\n
\n

48.7

\n
\n

202

\n
\n

PyTorch + ORT

\n
\n

2

\n
\n

56.4

\n
\n

174

\n
\n

Pytorch + ORT

\n
\n

3

\n
\n

58.7

\n
\n

160

\n
\n

 

\n

Conclusion

\n

Today, we announced the preview of training support in ONNX Runtime with a focus on large scale computation intensive transformer models. We have demonstrated that, on a 4 DGX-2 cluster, ONNX Runtime can achieve a throughput gain of 11.32% and 14.61% for BERT-L phase 1 and 2 pre-training over PyTorch. The total training time was reduced by 11.16%, from 17.74 hours to 15.76 hours. ONNX Runtime is able to train BERT-L at a 2x batch size as PyTorch. We have shown a similar 20.5% speedup on a GPT-2 model, saving 34 hours in total training time. ONNX Runtime Training is integrated with PyTorch so that existing PyTorch training code can be directly accelerated for transformer models training. 

\n

 

\n

Get Started

\n

As a part of the announcement on using ONNX Runtime for training, we have released a Docker image with ORT and made available a repo at https://github.com/microsoft/onnxruntime-training-examples that will host examples for ORT training. The first recipe available in this repo will help you get started with ORT for BERT pretraining in Azure Machine Learning service or NVIDIA DGX-2 and see the speedup in action. This recipe shows how to use ONNX Runtime training with BERT pretraining implementation in PyTorch. You can use this example either with the two datasets used in the original implementation or with your custom dataset to pretrain a BERT model and get the performance improvements with ORT reported in this blog. We are planning to add more examples for transformer models and other models. We also welcome your contribution to this repo and feedback to improve ORT training capabilities and experience.

","body@stringLength":"29089","rawBody":"

Author: Sherlock Huang, AI Frameworks, Microsoft

\n

This post is co-authored by Cheng Tang, Jesse Benson, Kaarthik Sivashanmugam and Alexey Svyatkovskiy

\n

 

\n

Today we announced the preview for new training feature in ONNX Runtime (ORT). This blog explains how we have been using it to accelerate training for large transformer models. ONNX Runtime Training is integrated with PyTorch so that existing training code can be directly accelerated for training.

\n

In this paper, we will describe some of the key aspects of ORT design and implementation that enable us to achieve the distributed training performance improvements. We will also use BERT-L pre-training as the benchmark to illustrate the performance of ORT training. Finally, we will present a case study of training GPT-2 model for code autocompletion feature in Visual Studio IntelliCode

\n

 

\n

Design and Implementation

\n

ONNX Runtime Training is built on the same open sourced code as the popular inference engine for ONNX models. Figure 1 shows the high-level architecture for ONNX Runtime’s ecosystem. ORT is a common runtime backend that supports multiple framework frontends, such as PyTorch and Tensorflow/Keras. It makes use of the Execution Provider interface to perform computation on different hardwareThis enables us to build hardware-agnostic, graph-level optimizations that are extensible across different platforms, as well as hardware specific optimization targeting platforms like NVIDIA GPUWe have also implemented additional optimizations, outlined below, to expedite training for large transformer models. 

\n

 

\n\n\n\n\n\n\n
Figure 1. ONNX Runtime High Level Architecture
\n

Static Graph Optimizations

\n

Machine learning models are commonly abstracted as computation graphs. The computation graph used by deep learning frameworks could be either static or dynamic. In the current implementation, ORT has a view of the entire static computation graph. This makes it possible to enable many common graph optimization techniques, such as constant folding, redundant operation elimination, and operator fusion. They are first applied on the forward computation graph before auto differentiation engine builds the backward graph. As ORT has the global knowledge of data dependencies, it only builds the minimal gradient graph that is needed for targeted weights. Consequently, activation tensors that are not needed for backward computation are automatically dropped after use. With a minimal training graph, it ensures that only essential computation is performed and memory consumption is minimized.

\n

 

\n

Memory Usage Optimizations

\n

Over the last few years, the size of deep learning models has been growing rapidly. GPU memory consumption has become a limiting factor for large model training. ORT has made conscious efforts to preserve and reuse memory whenever possible. For example, ORT reuses the same buffer segments throughout a series of operations, including gradient accumulation, gradient scaling adjustment, allreduce communication and weight update computation (if the optimizer allows). ORT also tries to perform in-place operations if the source tensor is no longer consumed elsewhere in the computation graph. ORT’s kernel implementation also tries to minimize the use of scratch buffers, such as avoid using some memory intensive cuDNN functions, and reusing output buffer as scratch buffer if possible. As a result, ORT can train BERT with 2x the batch size as PyTorch. This enables us to utilize the GPU resources more efficiently, resulting in better performance on the same model and the ability to train larger models.

\n

 

\n

ZeRO Stage 1 Integration

\n

Zero Redundancy Optimizer (ZeRO) is a memory optimization technique from Microsoft Research. ZeRO is used to save GPU memory consumption by eliminating duplicated states across workers during distributed training. ZeRO has three main optimization stages.  Currently, ONNX Runtime implemented Stage 1 of ZeRO. ZeRO Stage 1, known as the optimizer state partitioning, allows ORT to shard the optimizer states, including 1st and 2nd order moments (and fp32 copy of weights in mixed precision mode), across multiple workers with no extra communication overhead. With ZeRO, ORT can further boost batch size or train a larger model. In BERT-L pre-training, ZeRO allows batch size to further grow from 148 to 168 for phase 1 and from 23 to 27 for phase 2 in a 32GB V100. Distributed checkpointing is also introduced, as model persistent state is distributed across multiple workers. ZeRO can be enabled with a config flag.

\n

 

\n

Native Mixed Precision Training Support     

\n

Unlike PyTorch’s dependency on NVIDIA Apex extension, ORT has implemented its own support for mixed precision training. Mixed precision training can be enabled with a config flag – no other code change needed. Under the hood, ORT converts the static computation graph into mixed precision mode through a series of graph transformations, i.e. running most of the computations in fp16 while keeping some numerically sensitive computation in fp32. ORT supports dynamic loss scaling by automatically inserting the computation nodes for loss scaling into the graph.

\n

 

\n

Highly Scaleable Distributed Training

\n

ORT seeks to build a unified highly scaleable distributed training framework for hybrid parallelism, including a mixed of data and model parallelisms. ORT supports data parallelism, which is the most popular distributed training mode adopted by many internal teams. We are enhancing ORT to fully support training extremely large models (>100 billion parameters). It has an experimental implementation of Megatron-style horizontal parallelism and we are actively developing to support pipeline parallelism, such as PipeDream.

\n

 

\n

CUDA Kernel Optimizations

\n

ORT has introduced highly optimized CUDA kernels for some key operations including Reductions, Dropout and Softmax. In addition, we have also introduced a few key operator fusions with fused kernels for LayerNormalization, Gelu and their gradients, as well as Lamb Optimizer.

\n

 

\n

Using ORT with PyTorch Training Code

\n

ONNX Runtime has the capability to train existing PyTorch models through its optimized backend. For this, we have introduced a python API for PyTorch, called ORTTrainer, which can be used to switch the training backend for PyTorch models (instance of torch.nn.Module) to ORT. This requires some changes from the user, such as replacing the PyTorch optimizer, and optionally, setting flags to enable additional features such as mixed-precision training. Under the hood, as shown in Figure 2, ORTTrainer first converts the PyTorch model to ONNX format through the PyTorch-ONNX exporter. Next, ORT backend takes over and applies graph optimizations, builds a training graph, performs transformations on it as needed (e.g. mixed-precision transformation), and sets up the graph elements needed for distributed training. In this design, while all the computation-intensive workload is offloaded onto the ORT backend, users can still enjoy the rich PyTorch frontend utilities, such as data loading, checkpointing , and easy specification of loss functions. 

\n

 

\n\n\n\n\n\n\n
Figure 2. Workflow for converting an PyTorch model into an ORT training graph
\n

It is important to note that the current API is experimental and expected to see significant changes in the near future. A new version of the API is under active development. Our goal is to improve the interface to provide more seamless integration with PyTorch training that requires minimal changes in users’ training code, introduce new features, and present a more flexible API to cover advanced scenarios. Please refer to the training examples for more details.

\n

 

\n

Benchmarking Training Acceleration with ONNX Runtime

\n

We now present the performance evaluation of BERT-L pre-training with ONNX Runtime in a 4-node DGX-2 cluster. In AzureML, we also reproduced the pre-training convergence for BERT-Large using sample from NVIDIA’s DeepLearningExamplesle’s repo. We also validated fine tuning accuracy with SQuAD benchmarks.

\n

 

\n

Benchmarking on DGX-2

\n

We compared PyTorch and ORT’s BERT-L training performance on 4 NVIDIA DGX-2 machines (each with 16x 32GB V100) interconnected with InfiniBand. PyTorch’s result was obtained with NGC 20.03-py3 docker image following Nvidia’s recipe. ORT’s result was obtained following the same recipe, except that ORT used bigger local batch sizes. As described above, ORT is able to run at a 2x batch size of PyTorch’s. ORT ran at a local batch size of 128 and 16 for phase 1 and 2 respectively, whereas PyTorch ran at batch size of 64 and 8. The effective global batch size remained unchanged in both cases. Overall, ORT achieved throughput improvement of 11.32% and 14.61% for phase 1 and 2. The total time to train was reduces by 11.16%, from 17.74 hours to 15.76 hours.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Table 1. Time to train on 4 NVIDIA DGX-2 machines
\n

 

\n
\n

PyTorch 1.5 with

\n

NGC 20.03-py3

\n
\n

PyTorch 1.5 with

\n

ONNX Runtime

\n
\n

% Gain with

\n

ONNX Runtime

\n
\n

Phase 1 Throughput (ex/sec)

\n
\n

11522.1

\n
\n

12826.2

\n
\n

11.32%

\n
\n

Phase 2 Throughput (ex/sec)

\n
\n

2150.0

\n
\n

2464.1

\n
\n

14.61%

\n
\n

Phase 1 time (hours)

\n
\n

11.12

\n
\n

9.99

\n
\n

10.16%

\n
\n

Phase 2 time (hours)

\n
\n

6.62

\n
\n

5.77

\n
\n

12.84%

\n
\n

Total time (hours)

\n
\n

17.74

\n
\n

15.76

\n
\n

11.16%

\n
\n

 

\n

BERT-L Pre-training on AzureML

\n

We performed BERT-L pre-training on 8x ND40rs_v2 cluster (each with 8x 32GB V100) interconnected with InfiniBand in AzureML. We used the same Nvidia’s recipe, expect that we doubled the local batch size in the same way we mentioned above. Mixed precision mode and LAMB optimizer was used throughout the training. As the end of phase 2, we achieved the training loss of 1.31. The end-to-end training time was 18.32 hours.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Table 2. Time to train on Azure ML with 8x ND40rs_v2
\n

 

\n
\n

PyTorch 1.5 with ONNX Runtime

\n
\n

Phase 1 Throughput (ex/sec)

\n
\n

10751.4

\n
\n

Phase 2 Throughput (ex/sec)

\n
\n

2223.7

\n
\n

Phase 1 Time (hours)

\n
\n

11.92

\n
\n

Phase 2 Time (hours)

\n
\n

6.40

\n
\n

Total Time (hours)

\n
\n

18.32

\n
\n

 

\n

Figure 3 shows a loss curve produced in a typical pre-training run. Phase 1 ends with a loss value around 1.4 after 7038 steps. Phase 2 continues with a jump of loss due to switch of sequence length, and it finally decrease to a loss value around 1.3.

\n

­

\n\n\n\n\n\n\n
Figure 3. ORT BERT-L pre-training loss curves
\n

The pretrained model is then further finetuned on SQuAD dataset. Both full precision or mixed precision finetuning result in satisfactory Exact Match and F1 scores.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Table 3. BERT-L fine-tuning result on SQuAD Dataset
\n

Accuracy Metrics

\n
\n

Finetuning - FP32

\n
\n

Finetuning -

\n

mixed precision

\n
\n

Exact Match %

\n
\n

84.63

\n
\n

84.81

\n
\n

F1 score %

\n
\n

91.15

\n
\n

91.32

\n
\n

 

\n

A Case Study with Visual Studio using GPT-2 Medium

\n

Microsoft Visual Studio uses ONNX Runtime to accelerate pre-training a 24-layer GPT-2 Medium model to power code autocompletion in the IntelliCode of Visual Studio. Intellicode serves as a universal programming language compiler, effectively generating syntactically correct code in multiple programming languages, capable of completing an entire line of code in a couple of keystrokes. The training dataset for this task comprises over 1.2 billion lines of source code in Python, C#, JavaScript and TypeScript programming language from 52000 top-starred projects in GitHub. We treat the source code data as a sequence of tokens corresponding to the output of a lexical analyzer.

\n

The training was performed in a DGX-2 cluster. As we use a large sequence length of 1024, the memory usage is very intensive and PyTorch is only able to fit a batch size of 2 on the 32GB V100. ORT achieved 15.8% higher throughput under the identical local batch. As ORT is more memory efficient and able to run at a bigger batch size of 3, it delivered an overall 20.5% of the throughput improvement. As a result, the overall training time is reduced from 202 hours to 168 hours (with 1.2 x higher throughput). The final evaluation metric also achieved the same production shipping bar.  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Table 4. GPT-2 medium pre-training performance.
\n

 

\n
\n

Batch size / GPU

\n
\n

Throughput (ex/sec)

\n
\n

Time to train (hours)

\n
\n

PyTorch

\n
\n

2

\n
\n

48.7

\n
\n

202

\n
\n

PyTorch + ORT

\n
\n

2

\n
\n

56.4

\n
\n

174

\n
\n

Pytorch + ORT

\n
\n

3

\n
\n

58.7

\n
\n

160

\n
\n

 

\n

Conclusion

\n

Today, we announced the preview of training support in ONNX Runtime with a focus on large scale computation intensive transformer models. We have demonstrated that, on a 4 DGX-2 cluster, ONNX Runtime can achieve a throughput gain of 11.32% and 14.61% for BERT-L phase 1 and 2 pre-training over PyTorch. The total training time was reduced by 11.16%, from 17.74 hours to 15.76 hours. ONNX Runtime is able to train BERT-L at a 2x batch size as PyTorch. We have shown a similar 20.5% speedup on a GPT-2 model, saving 34 hours in total training time. ONNX Runtime Training is integrated with PyTorch so that existing PyTorch training code can be directly accelerated for transformer models training. 

\n

 

\n

Get Started

\n

As a part of the announcement on using ONNX Runtime for training, we have released a Docker image with ORT and made available a repo at https://github.com/microsoft/onnxruntime-training-examples that will host examples for ORT training. The first recipe available in this repo will help you get started with ORT for BERT pretraining in Azure Machine Learning service or NVIDIA DGX-2 and see the speedup in action. This recipe shows how to use ONNX Runtime training with BERT pretraining implementation in PyTorch. You can use this example either with the two datasets used in the original implementation or with your custom dataset to pretrain a BERT model and get the performance improvements with ORT reported in this blog. We are planning to add more examples for transformer models and other models. We also welcome your contribution to this repo and feedback to improve ORT training capabilities and experience.

","kudosSumWeight":2,"postTime":"2020-05-19T08:00:02.301-07:00","images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk4MzEwLTE5MjQ3MGlBRTMzMUFGRDgzQkE0MDc5?revision=46\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk4MzEwLTE5MjQ2OGkxRjRFMTMxRkMwNDk0MDE1?revision=46\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk4MzEwLTE5MjQ3M2k1QzA4OUYzRUQ2MEUxRkRD?revision=46\"}"}}],"totalCount":3,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"attachments":{"__typename":"AttachmentConnection","pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null},"edges":[]},"tags":{"__typename":"TagConnection","pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null},"edges":[{"__typename":"TagEdge","cursor":"MjUuMXwyLjF8b3wxMHxfTlZffDE","node":{"__typename":"Tag","id":"tag:azure machine learning","text":"azure machine learning","time":"2016-09-06T11:34:30.244-07:00","lastActivityTime":null,"messagesCount":null,"followersCount":null}},{"__typename":"TagEdge","cursor":"MjUuMXwyLjF8b3wxMHxfTlZffDI","node":{"__typename":"Tag","id":"tag:machine learning","text":"machine learning","time":"2017-03-20T07:48:20.119-07:00","lastActivityTime":null,"messagesCount":null,"followersCount":null}}]},"timeToRead":9,"rawTeaser":"
\n

Today we are introducing the preview of the new training feature in ONNX Runtime. This allows training a Pytorch transformer model up to ... 45% faster with 2x batch size. 

\n
","introduction":"","coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""},"currentRevision":{"__ref":"Revision:revision:1398310_46"},"latestVersion":{"__typename":"FriendlyVersion","major":"4","minor":"0"},"metrics":{"__typename":"MessageMetrics","views":13696},"visibilityScope":"PUBLIC","canonicalUrl":null,"seoTitle":null,"seoDescription":null,"placeholder":false,"originalMessageForPlaceholder":null,"contributors":{"__typename":"UserConnection","edges":[]},"nonCoAuthorContributors":{"__typename":"UserConnection","edges":[]},"coAuthors":{"__typename":"UserConnection","edges":[]},"blogMessagePolicies":{"__typename":"BlogMessagePolicies","canDoAuthoringActionsOnBlog":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.blog.action_can_do_authoring_action.accessDenied","key":"error.lithium.policies.blog.action_can_do_authoring_action.accessDenied","args":[]}}},"archivalData":null,"replies":{"__typename":"MessageConnection","edges":[{"__typename":"MessageEdge","cursor":"MjUuMXwyLjF8aXwxMHwxMzI6MHxpbnQsMzY1OTM4MSwzNjU5Mzgx","node":{"__ref":"BlogReplyMessage:message:3659381"}}],"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"customFields":[],"revisions({\"constraints\":{\"isPublished\":{\"eq\":true}},\"first\":1})":{"__typename":"RevisionConnection","totalCount":46}},"Conversation:conversation:1398310":{"__typename":"Conversation","id":"conversation:1398310","solved":false,"topic":{"__ref":"BlogTopicMessage:message:1398310"},"lastPostingActivityTime":"2022-10-23T01:11:41.104-07:00","lastPostTime":"2022-10-23T01:11:41.104-07:00","unreadReplyCount":1,"isSubscribed":false},"ModerationData:moderation_data:1398310":{"__typename":"ModerationData","id":"moderation_data:1398310","status":"APPROVED","rejectReason":null,"isReportedAbuse":false,"rejectUser":null,"rejectTime":null,"rejectActorType":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk4MzEwLTE5MjQ3MGlBRTMzMUFGRDgzQkE0MDc5?revision=46\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk4MzEwLTE5MjQ3MGlBRTMzMUFGRDgzQkE0MDc5?revision=46","title":"SherlockNoMad_0-1589781650044.png","associationType":"BODY","width":933,"height":662,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk4MzEwLTE5MjQ2OGkxRjRFMTMxRkMwNDk0MDE1?revision=46\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk4MzEwLTE5MjQ2OGkxRjRFMTMxRkMwNDk0MDE1?revision=46","title":"SherlockNoMad_1-1589781650047.png","associationType":"BODY","width":977,"height":116,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk4MzEwLTE5MjQ3M2k1QzA4OUYzRUQ2MEUxRkRD?revision=46\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk4MzEwLTE5MjQ3M2k1QzA4OUYzRUQ2MEUxRkRD?revision=46","title":"SherlockNoMad_0-1589783446513.png","associationType":"BODY","width":2766,"height":936,"altText":null},"Revision:revision:1398310_46":{"__typename":"Revision","id":"revision:1398310_46","lastEditTime":"2020-10-29T10:00:30.170-07:00"},"CachedAsset:theme:customTheme1-1744326567591":{"__typename":"CachedAsset","id":"theme:customTheme1-1744326567591","value":{"id":"customTheme1","animation":{"fast":"150ms","normal":"250ms","slow":"500ms","slowest":"750ms","function":"cubic-bezier(0.07, 0.91, 0.51, 1)","__typename":"AnimationThemeSettings"},"avatar":{"borderRadius":"50%","collections":["default"],"__typename":"AvatarThemeSettings"},"basics":{"browserIcon":{"imageAssetName":"favicon-1730836283320.png","imageLastModified":"1730836286415","__typename":"ThemeAsset"},"customerLogo":{"imageAssetName":"favicon-1730836271365.png","imageLastModified":"1730836274203","__typename":"ThemeAsset"},"maximumWidthOfPageContent":"1300px","oneColumnNarrowWidth":"800px","gridGutterWidthMd":"30px","gridGutterWidthXs":"10px","pageWidthStyle":"WIDTH_OF_BROWSER","__typename":"BasicsThemeSettings"},"buttons":{"borderRadiusSm":"3px","borderRadius":"3px","borderRadiusLg":"5px","paddingY":"5px","paddingYLg":"7px","paddingYHero":"var(--lia-bs-btn-padding-y-lg)","paddingX":"12px","paddingXLg":"16px","paddingXHero":"60px","fontStyle":"NORMAL","fontWeight":"700","textTransform":"NONE","disabledOpacity":0.5,"primaryTextColor":"var(--lia-bs-white)","primaryTextHoverColor":"var(--lia-bs-white)","primaryTextActiveColor":"var(--lia-bs-white)","primaryBgColor":"var(--lia-bs-primary)","primaryBgHoverColor":"hsl(var(--lia-bs-primary-h), var(--lia-bs-primary-s), calc(var(--lia-bs-primary-l) * 0.85))","primaryBgActiveColor":"hsl(var(--lia-bs-primary-h), var(--lia-bs-primary-s), calc(var(--lia-bs-primary-l) * 0.7))","primaryBorder":"1px solid transparent","primaryBorderHover":"1px solid transparent","primaryBorderActive":"1px solid transparent","primaryBorderFocus":"1px solid var(--lia-bs-white)","primaryBoxShadowFocus":"0 0 0 1px var(--lia-bs-primary), 0 0 0 4px hsla(var(--lia-bs-primary-h), var(--lia-bs-primary-s), var(--lia-bs-primary-l), 0.2)","secondaryTextColor":"var(--lia-bs-gray-900)","secondaryTextHoverColor":"hsl(var(--lia-bs-gray-900-h), var(--lia-bs-gray-900-s), calc(var(--lia-bs-gray-900-l) * 0.95))","secondaryTextActiveColor":"hsl(var(--lia-bs-gray-900-h), var(--lia-bs-gray-900-s), calc(var(--lia-bs-gray-900-l) * 0.9))","secondaryBgColor":"var(--lia-bs-gray-200)","secondaryBgHoverColor":"hsl(var(--lia-bs-gray-200-h), var(--lia-bs-gray-200-s), calc(var(--lia-bs-gray-200-l) * 0.96))","secondaryBgActiveColor":"hsl(var(--lia-bs-gray-200-h), var(--lia-bs-gray-200-s), calc(var(--lia-bs-gray-200-l) * 0.92))","secondaryBorder":"1px solid transparent","secondaryBorderHover":"1px solid transparent","secondaryBorderActive":"1px solid transparent","secondaryBorderFocus":"1px solid transparent","secondaryBoxShadowFocus":"0 0 0 1px var(--lia-bs-primary), 0 0 0 4px hsla(var(--lia-bs-primary-h), var(--lia-bs-primary-s), var(--lia-bs-primary-l), 0.2)","tertiaryTextColor":"var(--lia-bs-gray-900)","tertiaryTextHoverColor":"hsl(var(--lia-bs-gray-900-h), var(--lia-bs-gray-900-s), calc(var(--lia-bs-gray-900-l) * 0.95))","tertiaryTextActiveColor":"hsl(var(--lia-bs-gray-900-h), var(--lia-bs-gray-900-s), calc(var(--lia-bs-gray-900-l) * 0.9))","tertiaryBgColor":"transparent","tertiaryBgHoverColor":"transparent","tertiaryBgActiveColor":"hsla(var(--lia-bs-black-h), var(--lia-bs-black-s), var(--lia-bs-black-l), 0.04)","tertiaryBorder":"1px solid transparent","tertiaryBorderHover":"1px solid hsla(var(--lia-bs-black-h), var(--lia-bs-black-s), var(--lia-bs-black-l), 0.08)","tertiaryBorderActive":"1px solid transparent","tertiaryBorderFocus":"1px solid transparent","tertiaryBoxShadowFocus":"0 0 0 1px var(--lia-bs-primary), 0 0 0 4px hsla(var(--lia-bs-primary-h), var(--lia-bs-primary-s), var(--lia-bs-primary-l), 0.2)","destructiveTextColor":"var(--lia-bs-danger)","destructiveTextHoverColor":"hsl(var(--lia-bs-danger-h), var(--lia-bs-danger-s), calc(var(--lia-bs-danger-l) * 0.95))","destructiveTextActiveColor":"hsl(var(--lia-bs-danger-h), var(--lia-bs-danger-s), calc(var(--lia-bs-danger-l) * 0.9))","destructiveBgColor":"var(--lia-bs-gray-200)","destructiveBgHoverColor":"hsl(var(--lia-bs-gray-200-h), var(--lia-bs-gray-200-s), calc(var(--lia-bs-gray-200-l) * 0.96))","destructiveBgActiveColor":"hsl(var(--lia-bs-gray-200-h), var(--lia-bs-gray-200-s), calc(var(--lia-bs-gray-200-l) * 0.92))","destructiveBorder":"1px solid transparent","destructiveBorderHover":"1px solid transparent","destructiveBorderActive":"1px solid transparent","destructiveBorderFocus":"1px solid transparent","destructiveBoxShadowFocus":"0 0 0 1px var(--lia-bs-primary), 0 0 0 4px hsla(var(--lia-bs-primary-h), var(--lia-bs-primary-s), var(--lia-bs-primary-l), 0.2)","__typename":"ButtonsThemeSettings"},"border":{"color":"hsla(var(--lia-bs-black-h), var(--lia-bs-black-s), var(--lia-bs-black-l), 0.08)","mainContent":"NONE","sideContent":"LIGHT","radiusSm":"3px","radius":"5px","radiusLg":"9px","radius50":"100vw","__typename":"BorderThemeSettings"},"boxShadow":{"xs":"0 0 0 1px hsla(var(--lia-bs-gray-900-h), var(--lia-bs-gray-900-s), var(--lia-bs-gray-900-l), 0.08), 0 3px 0 -1px hsla(var(--lia-bs-gray-900-h), var(--lia-bs-gray-900-s), var(--lia-bs-gray-900-l), 0.16)","sm":"0 2px 4px hsla(var(--lia-bs-gray-900-h), var(--lia-bs-gray-900-s), var(--lia-bs-gray-900-l), 0.12)","md":"0 5px 15px hsla(var(--lia-bs-gray-900-h), var(--lia-bs-gray-900-s), var(--lia-bs-gray-900-l), 0.3)","lg":"0 10px 30px hsla(var(--lia-bs-gray-900-h), var(--lia-bs-gray-900-s), var(--lia-bs-gray-900-l), 0.3)","__typename":"BoxShadowThemeSettings"},"cards":{"bgColor":"var(--lia-panel-bg-color)","borderRadius":"var(--lia-panel-border-radius)","boxShadow":"var(--lia-box-shadow-xs)","__typename":"CardsThemeSettings"},"chip":{"maxWidth":"300px","height":"30px","__typename":"ChipThemeSettings"},"coreTypes":{"defaultMessageLinkColor":"var(--lia-bs-link-color)","defaultMessageLinkDecoration":"none","defaultMessageLinkFontStyle":"NORMAL","defaultMessageLinkFontWeight":"400","defaultMessageFontStyle":"NORMAL","defaultMessageFontWeight":"400","forumColor":"#4099E2","forumFontFamily":"var(--lia-bs-font-family-base)","forumFontWeight":"var(--lia-default-message-font-weight)","forumLineHeight":"var(--lia-bs-line-height-base)","forumFontStyle":"var(--lia-default-message-font-style)","forumMessageLinkColor":"var(--lia-default-message-link-color)","forumMessageLinkDecoration":"var(--lia-default-message-link-decoration)","forumMessageLinkFontStyle":"var(--lia-default-message-link-font-style)","forumMessageLinkFontWeight":"var(--lia-default-message-link-font-weight)","forumSolvedColor":"#148563","blogColor":"#1CBAA0","blogFontFamily":"var(--lia-bs-font-family-base)","blogFontWeight":"var(--lia-default-message-font-weight)","blogLineHeight":"1.75","blogFontStyle":"var(--lia-default-message-font-style)","blogMessageLinkColor":"var(--lia-default-message-link-color)","blogMessageLinkDecoration":"var(--lia-default-message-link-decoration)","blogMessageLinkFontStyle":"var(--lia-default-message-link-font-style)","blogMessageLinkFontWeight":"var(--lia-default-message-link-font-weight)","tkbColor":"#4C6B90","tkbFontFamily":"var(--lia-bs-font-family-base)","tkbFontWeight":"var(--lia-default-message-font-weight)","tkbLineHeight":"1.75","tkbFontStyle":"var(--lia-default-message-font-style)","tkbMessageLinkColor":"var(--lia-default-message-link-color)","tkbMessageLinkDecoration":"var(--lia-default-message-link-decoration)","tkbMessageLinkFontStyle":"var(--lia-default-message-link-font-style)","tkbMessageLinkFontWeight":"var(--lia-default-message-link-font-weight)","qandaColor":"#4099E2","qandaFontFamily":"var(--lia-bs-font-family-base)","qandaFontWeight":"var(--lia-default-message-font-weight)","qandaLineHeight":"var(--lia-bs-line-height-base)","qandaFontStyle":"var(--lia-default-message-link-font-style)","qandaMessageLinkColor":"var(--lia-default-message-link-color)","qandaMessageLinkDecoration":"var(--lia-default-message-link-decoration)","qandaMessageLinkFontStyle":"var(--lia-default-message-link-font-style)","qandaMessageLinkFontWeight":"var(--lia-default-message-link-font-weight)","qandaSolvedColor":"#3FA023","ideaColor":"#FF8000","ideaFontFamily":"var(--lia-bs-font-family-base)","ideaFontWeight":"var(--lia-default-message-font-weight)","ideaLineHeight":"var(--lia-bs-line-height-base)","ideaFontStyle":"var(--lia-default-message-font-style)","ideaMessageLinkColor":"var(--lia-default-message-link-color)","ideaMessageLinkDecoration":"var(--lia-default-message-link-decoration)","ideaMessageLinkFontStyle":"var(--lia-default-message-link-font-style)","ideaMessageLinkFontWeight":"var(--lia-default-message-link-font-weight)","contestColor":"#FCC845","contestFontFamily":"var(--lia-bs-font-family-base)","contestFontWeight":"var(--lia-default-message-font-weight)","contestLineHeight":"var(--lia-bs-line-height-base)","contestFontStyle":"var(--lia-default-message-link-font-style)","contestMessageLinkColor":"var(--lia-default-message-link-color)","contestMessageLinkDecoration":"var(--lia-default-message-link-decoration)","contestMessageLinkFontStyle":"ITALIC","contestMessageLinkFontWeight":"var(--lia-default-message-link-font-weight)","occasionColor":"#D13A1F","occasionFontFamily":"var(--lia-bs-font-family-base)","occasionFontWeight":"var(--lia-default-message-font-weight)","occasionLineHeight":"var(--lia-bs-line-height-base)","occasionFontStyle":"var(--lia-default-message-font-style)","occasionMessageLinkColor":"var(--lia-default-message-link-color)","occasionMessageLinkDecoration":"var(--lia-default-message-link-decoration)","occasionMessageLinkFontStyle":"var(--lia-default-message-link-font-style)","occasionMessageLinkFontWeight":"var(--lia-default-message-link-font-weight)","grouphubColor":"#333333","categoryColor":"#949494","communityColor":"#FFFFFF","productColor":"#949494","__typename":"CoreTypesThemeSettings"},"colors":{"black":"#000000","white":"#FFFFFF","gray100":"#F7F7F7","gray200":"#F7F7F7","gray300":"#E8E8E8","gray400":"#D9D9D9","gray500":"#CCCCCC","gray600":"#717171","gray700":"#707070","gray800":"#545454","gray900":"#333333","dark":"#545454","light":"#F7F7F7","primary":"#0069D4","secondary":"#333333","bodyText":"#1E1E1E","bodyBg":"#FFFFFF","info":"#409AE2","success":"#41C5AE","warning":"#FCC844","danger":"#BC341B","alertSystem":"#FF6600","textMuted":"#707070","highlight":"#FFFCAD","outline":"var(--lia-bs-primary)","custom":["#D3F5A4","#243A5E"],"__typename":"ColorsThemeSettings"},"divider":{"size":"3px","marginLeft":"4px","marginRight":"4px","borderRadius":"50%","bgColor":"var(--lia-bs-gray-600)","bgColorActive":"var(--lia-bs-gray-600)","__typename":"DividerThemeSettings"},"dropdown":{"fontSize":"var(--lia-bs-font-size-sm)","borderColor":"var(--lia-bs-border-color)","borderRadius":"var(--lia-bs-border-radius-sm)","dividerBg":"var(--lia-bs-gray-300)","itemPaddingY":"5px","itemPaddingX":"20px","headerColor":"var(--lia-bs-gray-700)","__typename":"DropdownThemeSettings"},"email":{"link":{"color":"#0069D4","hoverColor":"#0061c2","decoration":"none","hoverDecoration":"underline","__typename":"EmailLinkSettings"},"border":{"color":"#e4e4e4","__typename":"EmailBorderSettings"},"buttons":{"borderRadiusLg":"5px","paddingXLg":"16px","paddingYLg":"7px","fontWeight":"700","primaryTextColor":"#ffffff","primaryTextHoverColor":"#ffffff","primaryBgColor":"#0069D4","primaryBgHoverColor":"#005cb8","primaryBorder":"1px solid transparent","primaryBorderHover":"1px solid transparent","__typename":"EmailButtonsSettings"},"panel":{"borderRadius":"5px","borderColor":"#e4e4e4","__typename":"EmailPanelSettings"},"__typename":"EmailThemeSettings"},"emoji":{"skinToneDefault":"#ffcd43","skinToneLight":"#fae3c5","skinToneMediumLight":"#e2cfa5","skinToneMedium":"#daa478","skinToneMediumDark":"#a78058","skinToneDark":"#5e4d43","__typename":"EmojiThemeSettings"},"heading":{"color":"var(--lia-bs-body-color)","fontFamily":"Segoe UI","fontStyle":"NORMAL","fontWeight":"400","h1FontSize":"34px","h2FontSize":"32px","h3FontSize":"28px","h4FontSize":"24px","h5FontSize":"20px","h6FontSize":"16px","lineHeight":"1.3","subHeaderFontSize":"11px","subHeaderFontWeight":"500","h1LetterSpacing":"normal","h2LetterSpacing":"normal","h3LetterSpacing":"normal","h4LetterSpacing":"normal","h5LetterSpacing":"normal","h6LetterSpacing":"normal","subHeaderLetterSpacing":"2px","h1FontWeight":"var(--lia-bs-headings-font-weight)","h2FontWeight":"var(--lia-bs-headings-font-weight)","h3FontWeight":"var(--lia-bs-headings-font-weight)","h4FontWeight":"var(--lia-bs-headings-font-weight)","h5FontWeight":"var(--lia-bs-headings-font-weight)","h6FontWeight":"var(--lia-bs-headings-font-weight)","__typename":"HeadingThemeSettings"},"icons":{"size10":"10px","size12":"12px","size14":"14px","size16":"16px","size20":"20px","size24":"24px","size30":"30px","size40":"40px","size50":"50px","size60":"60px","size80":"80px","size120":"120px","size160":"160px","__typename":"IconsThemeSettings"},"imagePreview":{"bgColor":"var(--lia-bs-gray-900)","titleColor":"var(--lia-bs-white)","controlColor":"var(--lia-bs-white)","controlBgColor":"var(--lia-bs-gray-800)","__typename":"ImagePreviewThemeSettings"},"input":{"borderColor":"var(--lia-bs-gray-600)","disabledColor":"var(--lia-bs-gray-600)","focusBorderColor":"var(--lia-bs-primary)","labelMarginBottom":"10px","btnFontSize":"var(--lia-bs-font-size-sm)","focusBoxShadow":"0 0 0 3px hsla(var(--lia-bs-primary-h), var(--lia-bs-primary-s), var(--lia-bs-primary-l), 0.2)","checkLabelMarginBottom":"2px","checkboxBorderRadius":"3px","borderRadiusSm":"var(--lia-bs-border-radius-sm)","borderRadius":"var(--lia-bs-border-radius)","borderRadiusLg":"var(--lia-bs-border-radius-lg)","formTextMarginTop":"4px","textAreaBorderRadius":"var(--lia-bs-border-radius)","activeFillColor":"var(--lia-bs-primary)","__typename":"InputThemeSettings"},"loading":{"dotDarkColor":"hsla(var(--lia-bs-black-h), var(--lia-bs-black-s), var(--lia-bs-black-l), 0.2)","dotLightColor":"hsla(var(--lia-bs-white-h), var(--lia-bs-white-s), var(--lia-bs-white-l), 0.5)","barDarkColor":"hsla(var(--lia-bs-black-h), var(--lia-bs-black-s), var(--lia-bs-black-l), 0.06)","barLightColor":"hsla(var(--lia-bs-white-h), var(--lia-bs-white-s), var(--lia-bs-white-l), 0.4)","__typename":"LoadingThemeSettings"},"link":{"color":"var(--lia-bs-primary)","hoverColor":"hsl(var(--lia-bs-primary-h), var(--lia-bs-primary-s), calc(var(--lia-bs-primary-l) - 10%))","decoration":"none","hoverDecoration":"underline","__typename":"LinkThemeSettings"},"listGroup":{"itemPaddingY":"15px","itemPaddingX":"15px","borderColor":"var(--lia-bs-gray-300)","__typename":"ListGroupThemeSettings"},"modal":{"contentTextColor":"var(--lia-bs-body-color)","contentBg":"var(--lia-bs-white)","backgroundBg":"var(--lia-bs-black)","smSize":"440px","mdSize":"760px","lgSize":"1080px","backdropOpacity":0.3,"contentBoxShadowXs":"var(--lia-bs-box-shadow-sm)","contentBoxShadow":"var(--lia-bs-box-shadow)","headerFontWeight":"700","__typename":"ModalThemeSettings"},"navbar":{"position":"FIXED","background":{"attachment":null,"clip":null,"color":"var(--lia-bs-white)","imageAssetName":"","imageLastModified":"0","origin":null,"position":"CENTER_CENTER","repeat":"NO_REPEAT","size":"COVER","__typename":"BackgroundProps"},"backgroundOpacity":0.8,"paddingTop":"15px","paddingBottom":"15px","borderBottom":"1px solid var(--lia-bs-border-color)","boxShadow":"var(--lia-bs-box-shadow-sm)","brandMarginRight":"30px","brandMarginRightSm":"10px","brandLogoHeight":"30px","linkGap":"10px","linkJustifyContent":"flex-start","linkPaddingY":"5px","linkPaddingX":"10px","linkDropdownPaddingY":"9px","linkDropdownPaddingX":"var(--lia-nav-link-px)","linkColor":"var(--lia-bs-body-color)","linkHoverColor":"var(--lia-bs-primary)","linkFontSize":"var(--lia-bs-font-size-sm)","linkFontStyle":"NORMAL","linkFontWeight":"400","linkTextTransform":"NONE","linkLetterSpacing":"normal","linkBorderRadius":"var(--lia-bs-border-radius-sm)","linkBgColor":"transparent","linkBgHoverColor":"transparent","linkBorder":"none","linkBorderHover":"none","linkBoxShadow":"none","linkBoxShadowHover":"none","linkTextBorderBottom":"none","linkTextBorderBottomHover":"none","dropdownPaddingTop":"10px","dropdownPaddingBottom":"15px","dropdownPaddingX":"10px","dropdownMenuOffset":"2px","dropdownDividerMarginTop":"10px","dropdownDividerMarginBottom":"10px","dropdownBorderColor":"hsla(var(--lia-bs-black-h), var(--lia-bs-black-s), var(--lia-bs-black-l), 0.08)","controllerBgHoverColor":"hsla(var(--lia-bs-black-h), var(--lia-bs-black-s), var(--lia-bs-black-l), 0.1)","controllerIconColor":"var(--lia-bs-body-color)","controllerIconHoverColor":"var(--lia-bs-body-color)","controllerTextColor":"var(--lia-nav-controller-icon-color)","controllerTextHoverColor":"var(--lia-nav-controller-icon-hover-color)","controllerHighlightColor":"hsla(30, 100%, 50%)","controllerHighlightTextColor":"var(--lia-yiq-light)","controllerBorderRadius":"var(--lia-border-radius-50)","hamburgerColor":"var(--lia-nav-controller-icon-color)","hamburgerHoverColor":"var(--lia-nav-controller-icon-color)","hamburgerBgColor":"transparent","hamburgerBgHoverColor":"transparent","hamburgerBorder":"none","hamburgerBorderHover":"none","collapseMenuMarginLeft":"20px","collapseMenuDividerBg":"var(--lia-nav-link-color)","collapseMenuDividerOpacity":0.16,"__typename":"NavbarThemeSettings"},"pager":{"textColor":"var(--lia-bs-link-color)","textFontWeight":"var(--lia-font-weight-md)","textFontSize":"var(--lia-bs-font-size-sm)","__typename":"PagerThemeSettings"},"panel":{"bgColor":"var(--lia-bs-white)","borderRadius":"var(--lia-bs-border-radius)","borderColor":"var(--lia-bs-border-color)","boxShadow":"none","__typename":"PanelThemeSettings"},"popover":{"arrowHeight":"8px","arrowWidth":"16px","maxWidth":"300px","minWidth":"100px","headerBg":"var(--lia-bs-white)","borderColor":"var(--lia-bs-border-color)","borderRadius":"var(--lia-bs-border-radius)","boxShadow":"0 0.5rem 1rem hsla(var(--lia-bs-black-h), var(--lia-bs-black-s), var(--lia-bs-black-l), 0.15)","__typename":"PopoverThemeSettings"},"prism":{"color":"#000000","bgColor":"#f5f2f0","fontFamily":"var(--font-family-monospace)","fontSize":"var(--lia-bs-font-size-base)","fontWeightBold":"var(--lia-bs-font-weight-bold)","fontStyleItalic":"italic","tabSize":2,"highlightColor":"#b3d4fc","commentColor":"#62707e","punctuationColor":"#6f6f6f","namespaceOpacity":"0.7","propColor":"#990055","selectorColor":"#517a00","operatorColor":"#906736","operatorBgColor":"hsla(0, 0%, 100%, 0.5)","keywordColor":"#0076a9","functionColor":"#d3284b","variableColor":"#c14700","__typename":"PrismThemeSettings"},"rte":{"bgColor":"var(--lia-bs-white)","borderRadius":"var(--lia-panel-border-radius)","boxShadow":" var(--lia-panel-box-shadow)","customColor1":"#bfedd2","customColor2":"#fbeeb8","customColor3":"#f8cac6","customColor4":"#eccafa","customColor5":"#c2e0f4","customColor6":"#2dc26b","customColor7":"#f1c40f","customColor8":"#e03e2d","customColor9":"#b96ad9","customColor10":"#3598db","customColor11":"#169179","customColor12":"#e67e23","customColor13":"#ba372a","customColor14":"#843fa1","customColor15":"#236fa1","customColor16":"#ecf0f1","customColor17":"#ced4d9","customColor18":"#95a5a6","customColor19":"#7e8c8d","customColor20":"#34495e","customColor21":"#000000","customColor22":"#ffffff","defaultMessageHeaderMarginTop":"40px","defaultMessageHeaderMarginBottom":"20px","defaultMessageItemMarginTop":"0","defaultMessageItemMarginBottom":"10px","diffAddedColor":"hsla(170, 53%, 51%, 0.4)","diffChangedColor":"hsla(43, 97%, 63%, 0.4)","diffNoneColor":"hsla(0, 0%, 80%, 0.4)","diffRemovedColor":"hsla(9, 74%, 47%, 0.4)","specialMessageHeaderMarginTop":"40px","specialMessageHeaderMarginBottom":"20px","specialMessageItemMarginTop":"0","specialMessageItemMarginBottom":"10px","__typename":"RteThemeSettings"},"tags":{"bgColor":"var(--lia-bs-gray-200)","bgHoverColor":"var(--lia-bs-gray-400)","borderRadius":"var(--lia-bs-border-radius-sm)","color":"var(--lia-bs-body-color)","hoverColor":"var(--lia-bs-body-color)","fontWeight":"var(--lia-font-weight-md)","fontSize":"var(--lia-font-size-xxs)","textTransform":"UPPERCASE","letterSpacing":"0.5px","__typename":"TagsThemeSettings"},"toasts":{"borderRadius":"var(--lia-bs-border-radius)","paddingX":"12px","__typename":"ToastsThemeSettings"},"typography":{"fontFamilyBase":"Segoe UI","fontStyleBase":"NORMAL","fontWeightBase":"400","fontWeightLight":"300","fontWeightNormal":"400","fontWeightMd":"500","fontWeightBold":"700","letterSpacingSm":"normal","letterSpacingXs":"normal","lineHeightBase":"1.5","fontSizeBase":"16px","fontSizeXxs":"11px","fontSizeXs":"12px","fontSizeSm":"14px","fontSizeLg":"20px","fontSizeXl":"24px","smallFontSize":"14px","customFonts":[{"source":"SERVER","name":"Segoe UI","styles":[{"style":"NORMAL","weight":"400","__typename":"FontStyleData"},{"style":"NORMAL","weight":"300","__typename":"FontStyleData"},{"style":"NORMAL","weight":"600","__typename":"FontStyleData"},{"style":"NORMAL","weight":"700","__typename":"FontStyleData"},{"style":"ITALIC","weight":"400","__typename":"FontStyleData"}],"assetNames":["SegoeUI-normal-400.woff2","SegoeUI-normal-300.woff2","SegoeUI-normal-600.woff2","SegoeUI-normal-700.woff2","SegoeUI-italic-400.woff2"],"__typename":"CustomFont"},{"source":"SERVER","name":"MWF Fluent Icons","styles":[{"style":"NORMAL","weight":"400","__typename":"FontStyleData"}],"assetNames":["MWFFluentIcons-normal-400.woff2"],"__typename":"CustomFont"}],"__typename":"TypographyThemeSettings"},"unstyledListItem":{"marginBottomSm":"5px","marginBottomMd":"10px","marginBottomLg":"15px","marginBottomXl":"20px","marginBottomXxl":"25px","__typename":"UnstyledListItemThemeSettings"},"yiq":{"light":"#ffffff","dark":"#000000","__typename":"YiqThemeSettings"},"colorLightness":{"primaryDark":0.36,"primaryLight":0.74,"primaryLighter":0.89,"primaryLightest":0.95,"infoDark":0.39,"infoLight":0.72,"infoLighter":0.85,"infoLightest":0.93,"successDark":0.24,"successLight":0.62,"successLighter":0.8,"successLightest":0.91,"warningDark":0.39,"warningLight":0.68,"warningLighter":0.84,"warningLightest":0.93,"dangerDark":0.41,"dangerLight":0.72,"dangerLighter":0.89,"dangerLightest":0.95,"__typename":"ColorLightnessThemeSettings"},"localOverride":false,"__typename":"Theme"},"localOverride":false},"CachedAsset:text:en_US-components/common/EmailVerification-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/common/EmailVerification-1745505309939","value":{"email.verification.title":"Email Verification Required","email.verification.message.update.email":"To participate in the community, you must first verify your email address. The verification email was sent to {email}. To change your email, visit My Settings.","email.verification.message.resend.email":"To participate in the community, you must first verify your email address. The verification email was sent to {email}. Resend email."},"localOverride":false},"CachedAsset:text:en_US-shared/client/components/common/Loading/LoadingDot-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-shared/client/components/common/Loading/LoadingDot-1745505309939","value":{"title":"Loading..."},"localOverride":false},"CachedAsset:quilt:o365.prod:pages/blogs/BlogMessagePage:board:MachineLearningBlog-1745502712777":{"__typename":"CachedAsset","id":"quilt:o365.prod:pages/blogs/BlogMessagePage:board:MachineLearningBlog-1745502712777","value":{"id":"BlogMessagePage","container":{"id":"Common","headerProps":{"backgroundImageProps":null,"backgroundColor":null,"addComponents":null,"removeComponents":["community.widget.bannerWidget"],"componentOrder":null,"__typename":"QuiltContainerSectionProps"},"headerComponentProps":{"community.widget.breadcrumbWidget":{"disableLastCrumbForDesktop":false}},"footerProps":null,"footerComponentProps":null,"items":[{"id":"blog-article","layout":"ONE_COLUMN","bgColor":null,"showTitle":null,"showDescription":null,"textPosition":null,"textColor":null,"sectionEditLevel":"LOCKED","bgImage":null,"disableSpacing":null,"edgeToEdgeDisplay":null,"fullHeight":null,"showBorder":null,"__typename":"OneColumnQuiltSection","columnMap":{"main":[{"id":"blogs.widget.blogArticleWidget","className":"lia-blog-container","props":null,"__typename":"QuiltComponent"}],"__typename":"OneSectionColumns"}},{"id":"section-1729184836777","layout":"MAIN_SIDE","bgColor":"transparent","showTitle":false,"showDescription":false,"textPosition":"CENTER","textColor":"var(--lia-bs-body-color)","sectionEditLevel":null,"bgImage":null,"disableSpacing":null,"edgeToEdgeDisplay":null,"fullHeight":null,"showBorder":null,"__typename":"MainSideQuiltSection","columnMap":{"main":[],"side":[],"__typename":"MainSideSectionColumns"}}],"__typename":"QuiltContainer"},"__typename":"Quilt","localOverride":false},"localOverride":false},"CachedAsset:text:en_US-pages/blogs/BlogMessagePage-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-pages/blogs/BlogMessagePage-1745505309939","value":{"title":"{contextMessageSubject} | {communityTitle}","errorMissing":"This blog post cannot be found","name":"Blog Message Page","section.blog-article.title":"Blog Post","archivedMessageTitle":"This Content Has Been Archived","section.section-1729184836777.title":"","section.section-1729184836777.description":"","section.CncIde.title":"Blog Post","section.tifEmD.description":"","section.tifEmD.title":""},"localOverride":false},"CachedAsset:quiltWrapper:o365.prod:Common:1745505311093":{"__typename":"CachedAsset","id":"quiltWrapper:o365.prod:Common:1745505311093","value":{"id":"Common","header":{"backgroundImageProps":{"assetName":null,"backgroundSize":"COVER","backgroundRepeat":"NO_REPEAT","backgroundPosition":"CENTER_CENTER","lastModified":null,"__typename":"BackgroundImageProps"},"backgroundColor":"transparent","items":[{"id":"community.widget.navbarWidget","props":{"showUserName":true,"showRegisterLink":true,"useIconLanguagePicker":true,"useLabelLanguagePicker":true,"className":"QuiltComponent_lia-component-edit-mode__0nCcm","links":{"sideLinks":[],"mainLinks":[{"children":[],"linkType":"INTERNAL","id":"gxcuf89792","params":{},"routeName":"CommunityPage"},{"children":[],"linkType":"EXTERNAL","id":"external-link","url":"/Directory","target":"SELF"},{"children":[{"linkType":"INTERNAL","id":"microsoft365","params":{"categoryId":"microsoft365"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"windows","params":{"categoryId":"Windows"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"Common-microsoft365-copilot-link","params":{"categoryId":"Microsoft365Copilot"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"microsoft-teams","params":{"categoryId":"MicrosoftTeams"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"microsoft-securityand-compliance","params":{"categoryId":"microsoft-security"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"azure","params":{"categoryId":"Azure"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"Common-content_management-link","params":{"categoryId":"Content_Management"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"exchange","params":{"categoryId":"Exchange"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"windows-server","params":{"categoryId":"Windows-Server"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"outlook","params":{"categoryId":"Outlook"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"microsoft-endpoint-manager","params":{"categoryId":"microsoftintune"},"routeName":"CategoryPage"},{"linkType":"EXTERNAL","id":"external-link-2","url":"/Directory","target":"SELF"}],"linkType":"EXTERNAL","id":"communities","url":"/","target":"BLANK"},{"children":[{"linkType":"INTERNAL","id":"a-i","params":{"categoryId":"AI"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"education-sector","params":{"categoryId":"EducationSector"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"partner-community","params":{"categoryId":"PartnerCommunity"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"i-t-ops-talk","params":{"categoryId":"ITOpsTalk"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"healthcare-and-life-sciences","params":{"categoryId":"HealthcareAndLifeSciences"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"microsoft-mechanics","params":{"categoryId":"MicrosoftMechanics"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"public-sector","params":{"categoryId":"PublicSector"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"s-m-b","params":{"categoryId":"MicrosoftforNonprofits"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"io-t","params":{"categoryId":"IoT"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"startupsat-microsoft","params":{"categoryId":"StartupsatMicrosoft"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"driving-adoption","params":{"categoryId":"DrivingAdoption"},"routeName":"CategoryPage"},{"linkType":"EXTERNAL","id":"external-link-1","url":"/Directory","target":"SELF"}],"linkType":"EXTERNAL","id":"communities-1","url":"/","target":"SELF"},{"children":[],"linkType":"EXTERNAL","id":"external","url":"/Blogs","target":"SELF"},{"children":[],"linkType":"EXTERNAL","id":"external-1","url":"/Events","target":"SELF"},{"children":[{"linkType":"INTERNAL","id":"microsoft-learn-1","params":{"categoryId":"MicrosoftLearn"},"routeName":"CategoryPage"},{"linkType":"INTERNAL","id":"microsoft-learn-blog","params":{"boardId":"MicrosoftLearnBlog","categoryId":"MicrosoftLearn"},"routeName":"BlogBoardPage"},{"linkType":"EXTERNAL","id":"external-10","url":"https://learningroomdirectory.microsoft.com/","target":"BLANK"},{"linkType":"EXTERNAL","id":"external-3","url":"https://docs.microsoft.com/learn/dynamics365/?WT.mc_id=techcom_header-webpage-m365","target":"BLANK"},{"linkType":"EXTERNAL","id":"external-4","url":"https://docs.microsoft.com/learn/m365/?wt.mc_id=techcom_header-webpage-m365","target":"BLANK"},{"linkType":"EXTERNAL","id":"external-5","url":"https://docs.microsoft.com/learn/topics/sci/?wt.mc_id=techcom_header-webpage-m365","target":"BLANK"},{"linkType":"EXTERNAL","id":"external-6","url":"https://docs.microsoft.com/learn/powerplatform/?wt.mc_id=techcom_header-webpage-powerplatform","target":"BLANK"},{"linkType":"EXTERNAL","id":"external-7","url":"https://docs.microsoft.com/learn/github/?wt.mc_id=techcom_header-webpage-github","target":"BLANK"},{"linkType":"EXTERNAL","id":"external-8","url":"https://docs.microsoft.com/learn/teams/?wt.mc_id=techcom_header-webpage-teams","target":"BLANK"},{"linkType":"EXTERNAL","id":"external-9","url":"https://docs.microsoft.com/learn/dotnet/?wt.mc_id=techcom_header-webpage-dotnet","target":"BLANK"},{"linkType":"EXTERNAL","id":"external-2","url":"https://docs.microsoft.com/learn/azure/?WT.mc_id=techcom_header-webpage-m365","target":"BLANK"}],"linkType":"INTERNAL","id":"microsoft-learn","params":{"categoryId":"MicrosoftLearn"},"routeName":"CategoryPage"},{"children":[],"linkType":"INTERNAL","id":"community-info-center","params":{"categoryId":"Community-Info-Center"},"routeName":"CategoryPage"}]},"style":{"boxShadow":"var(--lia-bs-box-shadow-sm)","controllerHighlightColor":"hsla(30, 100%, 50%)","linkFontWeight":"400","dropdownDividerMarginBottom":"10px","hamburgerBorderHover":"none","linkBoxShadowHover":"none","linkFontSize":"14px","backgroundOpacity":0.8,"controllerBorderRadius":"var(--lia-border-radius-50)","hamburgerBgColor":"transparent","hamburgerColor":"var(--lia-nav-controller-icon-color)","linkTextBorderBottom":"none","brandLogoHeight":"30px","linkBgHoverColor":"transparent","linkLetterSpacing":"normal","collapseMenuDividerOpacity":0.16,"dropdownPaddingBottom":"15px","paddingBottom":"15px","dropdownMenuOffset":"2px","hamburgerBgHoverColor":"transparent","borderBottom":"1px solid var(--lia-bs-border-color)","hamburgerBorder":"none","dropdownPaddingX":"10px","brandMarginRightSm":"10px","linkBoxShadow":"none","collapseMenuDividerBg":"var(--lia-nav-link-color)","linkColor":"var(--lia-bs-body-color)","linkJustifyContent":"flex-start","dropdownPaddingTop":"10px","controllerHighlightTextColor":"var(--lia-yiq-dark)","controllerTextColor":"var(--lia-nav-controller-icon-color)","background":{"imageAssetName":"","color":"var(--lia-bs-white)","size":"COVER","repeat":"NO_REPEAT","position":"CENTER_CENTER","imageLastModified":""},"linkBorderRadius":"var(--lia-bs-border-radius-sm)","linkHoverColor":"var(--lia-bs-body-color)","position":"FIXED","linkBorder":"none","linkTextBorderBottomHover":"2px solid var(--lia-bs-body-color)","brandMarginRight":"30px","hamburgerHoverColor":"var(--lia-nav-controller-icon-color)","linkBorderHover":"none","collapseMenuMarginLeft":"20px","linkFontStyle":"NORMAL","controllerTextHoverColor":"var(--lia-nav-controller-icon-hover-color)","linkPaddingX":"10px","linkPaddingY":"5px","paddingTop":"15px","linkTextTransform":"NONE","dropdownBorderColor":"hsla(var(--lia-bs-black-h), var(--lia-bs-black-s), var(--lia-bs-black-l), 0.08)","controllerBgHoverColor":"hsla(var(--lia-bs-black-h), var(--lia-bs-black-s), var(--lia-bs-black-l), 0.1)","linkBgColor":"transparent","linkDropdownPaddingX":"var(--lia-nav-link-px)","linkDropdownPaddingY":"9px","controllerIconColor":"var(--lia-bs-body-color)","dropdownDividerMarginTop":"10px","linkGap":"10px","controllerIconHoverColor":"var(--lia-bs-body-color)"},"showSearchIcon":false,"languagePickerStyle":"iconAndLabel"},"__typename":"QuiltComponent"},{"id":"community.widget.breadcrumbWidget","props":{"backgroundColor":"transparent","linkHighlightColor":"var(--lia-bs-primary)","visualEffects":{"showBottomBorder":true},"linkTextColor":"var(--lia-bs-gray-700)"},"__typename":"QuiltComponent"},{"id":"custom.widget.community_banner","props":{"widgetVisibility":"signedInOrAnonymous","useTitle":true,"usePageWidth":false,"useBackground":false,"title":"","lazyLoad":false},"__typename":"QuiltComponent"},{"id":"custom.widget.HeroBanner","props":{"widgetVisibility":"signedInOrAnonymous","usePageWidth":false,"useTitle":true,"cMax_items":3,"useBackground":false,"title":"","lazyLoad":false,"widgetChooser":"custom.widget.HeroBanner"},"__typename":"QuiltComponent"}],"__typename":"QuiltWrapperSection"},"footer":{"backgroundImageProps":{"assetName":null,"backgroundSize":"COVER","backgroundRepeat":"NO_REPEAT","backgroundPosition":"CENTER_CENTER","lastModified":null,"__typename":"BackgroundImageProps"},"backgroundColor":"transparent","items":[{"id":"custom.widget.MicrosoftFooter","props":{"widgetVisibility":"signedInOrAnonymous","useTitle":true,"useBackground":false,"title":"","lazyLoad":false},"__typename":"QuiltComponent"}],"__typename":"QuiltWrapperSection"},"__typename":"QuiltWrapper","localOverride":false},"localOverride":false},"CachedAsset:text:en_US-components/common/ActionFeedback-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/common/ActionFeedback-1745505309939","value":{"joinedGroupHub.title":"Welcome","joinedGroupHub.message":"You are now a member of this group and are subscribed to updates.","groupHubInviteNotFound.title":"Invitation Not Found","groupHubInviteNotFound.message":"Sorry, we could not find your invitation to the group. The owner may have canceled the invite.","groupHubNotFound.title":"Group Not Found","groupHubNotFound.message":"The grouphub you tried to join does not exist. It may have been deleted.","existingGroupHubMember.title":"Already Joined","existingGroupHubMember.message":"You are already a member of this group.","accountLocked.title":"Account Locked","accountLocked.message":"Your account has been locked due to multiple failed attempts. Try again in {lockoutTime} minutes.","editedGroupHub.title":"Changes Saved","editedGroupHub.message":"Your group has been updated.","leftGroupHub.title":"Goodbye","leftGroupHub.message":"You are no longer a member of this group and will not receive future updates.","deletedGroupHub.title":"Deleted","deletedGroupHub.message":"The group has been deleted.","groupHubCreated.title":"Group Created","groupHubCreated.message":"{groupHubName} is ready to use","accountClosed.title":"Account Closed","accountClosed.message":"The account has been closed and you will now be redirected to the homepage","resetTokenExpired.title":"Reset Password Link has Expired","resetTokenExpired.message":"Try resetting your password again","invalidUrl.title":"Invalid URL","invalidUrl.message":"The URL you're using is not recognized. Verify your URL and try again.","accountClosedForUser.title":"Account Closed","accountClosedForUser.message":"{userName}'s account is closed","inviteTokenInvalid.title":"Invitation Invalid","inviteTokenInvalid.message":"Your invitation to the community has been canceled or expired.","inviteTokenError.title":"Invitation Verification Failed","inviteTokenError.message":"The url you are utilizing is not recognized. Verify your URL and try again","pageNotFound.title":"Access Denied","pageNotFound.message":"You do not have access to this area of the community or it doesn't exist","eventAttending.title":"Responded as Attending","eventAttending.message":"You'll be notified when there's new activity and reminded as the event approaches","eventInterested.title":"Responded as Interested","eventInterested.message":"You'll be notified when there's new activity and reminded as the event approaches","eventNotFound.title":"Event Not Found","eventNotFound.message":"The event you tried to respond to does not exist.","redirectToRelatedPage.title":"Showing Related Content","redirectToRelatedPageForBaseUsers.title":"Showing Related Content","redirectToRelatedPageForBaseUsers.message":"The content you are trying to access is archived","redirectToRelatedPage.message":"The content you are trying to access is archived","relatedUrl.archivalLink.flyoutMessage":"The content you are trying to access is archived View Archived Content"},"localOverride":false},"CachedAsset:component:custom.widget.community_banner-en-1744400827622":{"__typename":"CachedAsset","id":"component:custom.widget.community_banner-en-1744400827622","value":{"component":{"id":"custom.widget.community_banner","template":{"id":"community_banner","markupLanguage":"HANDLEBARS","style":".community-banner {\n a.top-bar.btn {\n top: 0px;\n width: 100%;\n z-index: 999;\n text-align: center;\n left: 0px;\n background: #0068b8;\n color: white;\n padding: 10px 0px;\n display: block;\n box-shadow: none !important;\n border: none !important;\n border-radius: none !important;\n margin: 0px !important;\n font-size: 14px;\n }\n}\n","texts":null,"defaults":{"config":{"applicablePages":[],"description":"community announcement text","fetchedContent":null,"__typename":"ComponentConfiguration"},"props":[],"__typename":"ComponentProperties"},"components":[{"id":"custom.widget.community_banner","form":null,"config":null,"props":[],"__typename":"Component"}],"grouping":"CUSTOM","__typename":"ComponentTemplate"},"properties":{"config":{"applicablePages":[],"description":"community announcement text","fetchedContent":null,"__typename":"ComponentConfiguration"},"props":[],"__typename":"ComponentProperties"},"form":null,"__typename":"Component","localOverride":false},"globalCss":{"css":".custom_widget_community_banner_community-banner_1x9u2_1 {\n a.custom_widget_community_banner_top-bar_1x9u2_2.custom_widget_community_banner_btn_1x9u2_2 {\n top: 0;\n width: 100%;\n z-index: 999;\n text-align: center;\n left: 0;\n background: #0068b8;\n color: white;\n padding: 0.625rem 0;\n display: block;\n box-shadow: none !important;\n border: none !important;\n border-radius: none !important;\n margin: 0 !important;\n font-size: 0.875rem;\n }\n}\n","tokens":{"community-banner":"custom_widget_community_banner_community-banner_1x9u2_1","top-bar":"custom_widget_community_banner_top-bar_1x9u2_2","btn":"custom_widget_community_banner_btn_1x9u2_2"}},"form":null},"localOverride":false},"CachedAsset:component:custom.widget.HeroBanner-en-1744400827622":{"__typename":"CachedAsset","id":"component:custom.widget.HeroBanner-en-1744400827622","value":{"component":{"id":"custom.widget.HeroBanner","template":{"id":"HeroBanner","markupLanguage":"REACT","style":null,"texts":{"searchPlaceholderText":"Search this community","followActionText":"Follow","unfollowActionText":"Following","searchOnHoverText":"Please enter your search term(s) and then press return key to complete a search.","blogs.sidebar.pagetitle":"Latest Blogs | Microsoft Tech Community","followThisNode":"Follow this node","unfollowThisNode":"Unfollow this node"},"defaults":{"config":{"applicablePages":[],"description":null,"fetchedContent":null,"__typename":"ComponentConfiguration"},"props":[{"id":"max_items","dataType":"NUMBER","list":false,"defaultValue":"3","label":"Max Items","description":"The maximum number of items to display in the carousel","possibleValues":null,"control":"INPUT","__typename":"PropDefinition"}],"__typename":"ComponentProperties"},"components":[{"id":"custom.widget.HeroBanner","form":{"fields":[{"id":"widgetChooser","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"title","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"useTitle","validation":null,"noValidation":null,"dataType":"BOOLEAN","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"useBackground","validation":null,"noValidation":null,"dataType":"BOOLEAN","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"widgetVisibility","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"moreOptions","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"cMax_items","validation":null,"noValidation":null,"dataType":"NUMBER","list":false,"control":"INPUT","defaultValue":"3","label":"Max Items","description":"The maximum number of items to display in the carousel","possibleValues":null,"__typename":"FormField"}],"layout":{"rows":[{"id":"widgetChooserGroup","type":"fieldset","as":null,"items":[{"id":"widgetChooser","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"titleGroup","type":"fieldset","as":null,"items":[{"id":"title","className":null,"__typename":"FormFieldRef"},{"id":"useTitle","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"useBackground","type":"fieldset","as":null,"items":[{"id":"useBackground","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"widgetVisibility","type":"fieldset","as":null,"items":[{"id":"widgetVisibility","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"moreOptionsGroup","type":"fieldset","as":null,"items":[{"id":"moreOptions","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"componentPropsGroup","type":"fieldset","as":null,"items":[{"id":"cMax_items","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"}],"actionButtons":null,"className":"custom_widget_HeroBanner_form","formGroupFieldSeparator":"divider","__typename":"FormLayout"},"__typename":"Form"},"config":null,"props":[],"__typename":"Component"}],"grouping":"CUSTOM","__typename":"ComponentTemplate"},"properties":{"config":{"applicablePages":[],"description":null,"fetchedContent":null,"__typename":"ComponentConfiguration"},"props":[{"id":"max_items","dataType":"NUMBER","list":false,"defaultValue":"3","label":"Max Items","description":"The maximum number of items to display in the carousel","possibleValues":null,"control":"INPUT","__typename":"PropDefinition"}],"__typename":"ComponentProperties"},"form":{"fields":[{"id":"widgetChooser","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"title","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"useTitle","validation":null,"noValidation":null,"dataType":"BOOLEAN","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"useBackground","validation":null,"noValidation":null,"dataType":"BOOLEAN","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"widgetVisibility","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"moreOptions","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"cMax_items","validation":null,"noValidation":null,"dataType":"NUMBER","list":false,"control":"INPUT","defaultValue":"3","label":"Max Items","description":"The maximum number of items to display in the carousel","possibleValues":null,"__typename":"FormField"}],"layout":{"rows":[{"id":"widgetChooserGroup","type":"fieldset","as":null,"items":[{"id":"widgetChooser","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"titleGroup","type":"fieldset","as":null,"items":[{"id":"title","className":null,"__typename":"FormFieldRef"},{"id":"useTitle","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"useBackground","type":"fieldset","as":null,"items":[{"id":"useBackground","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"widgetVisibility","type":"fieldset","as":null,"items":[{"id":"widgetVisibility","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"moreOptionsGroup","type":"fieldset","as":null,"items":[{"id":"moreOptions","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"componentPropsGroup","type":"fieldset","as":null,"items":[{"id":"cMax_items","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"}],"actionButtons":null,"className":"custom_widget_HeroBanner_form","formGroupFieldSeparator":"divider","__typename":"FormLayout"},"__typename":"Form"},"__typename":"Component","localOverride":false},"globalCss":null,"form":{"fields":[{"id":"widgetChooser","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"title","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"useTitle","validation":null,"noValidation":null,"dataType":"BOOLEAN","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"useBackground","validation":null,"noValidation":null,"dataType":"BOOLEAN","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"widgetVisibility","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"moreOptions","validation":null,"noValidation":null,"dataType":"STRING","list":null,"control":null,"defaultValue":null,"label":null,"description":null,"possibleValues":null,"__typename":"FormField"},{"id":"cMax_items","validation":null,"noValidation":null,"dataType":"NUMBER","list":false,"control":"INPUT","defaultValue":"3","label":"Max Items","description":"The maximum number of items to display in the carousel","possibleValues":null,"__typename":"FormField"}],"layout":{"rows":[{"id":"widgetChooserGroup","type":"fieldset","as":null,"items":[{"id":"widgetChooser","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"titleGroup","type":"fieldset","as":null,"items":[{"id":"title","className":null,"__typename":"FormFieldRef"},{"id":"useTitle","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"useBackground","type":"fieldset","as":null,"items":[{"id":"useBackground","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"widgetVisibility","type":"fieldset","as":null,"items":[{"id":"widgetVisibility","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"moreOptionsGroup","type":"fieldset","as":null,"items":[{"id":"moreOptions","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"},{"id":"componentPropsGroup","type":"fieldset","as":null,"items":[{"id":"cMax_items","className":null,"__typename":"FormFieldRef"}],"props":null,"legend":null,"description":null,"className":null,"viewVariant":null,"toggleState":null,"__typename":"FormFieldset"}],"actionButtons":null,"className":"custom_widget_HeroBanner_form","formGroupFieldSeparator":"divider","__typename":"FormLayout"},"__typename":"Form"}},"localOverride":false},"CachedAsset:component:custom.widget.MicrosoftFooter-en-1744400827622":{"__typename":"CachedAsset","id":"component:custom.widget.MicrosoftFooter-en-1744400827622","value":{"component":{"id":"custom.widget.MicrosoftFooter","template":{"id":"MicrosoftFooter","markupLanguage":"HANDLEBARS","style":".context-uhf {\n min-width: 280px;\n font-size: 15px;\n box-sizing: border-box;\n -ms-text-size-adjust: 100%;\n -webkit-text-size-adjust: 100%;\n & *,\n & *:before,\n & *:after {\n box-sizing: inherit;\n }\n a.c-uhff-link {\n color: #616161;\n word-break: break-word;\n text-decoration: none;\n }\n &a:link,\n &a:focus,\n &a:hover,\n &a:active,\n &a:visited {\n text-decoration: none;\n color: inherit;\n }\n & div {\n font-family: 'Segoe UI', SegoeUI, 'Helvetica Neue', Helvetica, Arial, sans-serif;\n }\n}\n.c-uhff {\n background: #f2f2f2;\n margin: -1.5625;\n width: auto;\n height: auto;\n}\n.c-uhff-nav {\n margin: 0 auto;\n max-width: calc(1600px + 10%);\n padding: 0 5%;\n box-sizing: inherit;\n &:before,\n &:after {\n content: ' ';\n display: table;\n clear: left;\n }\n @media only screen and (max-width: 1083px) {\n padding-left: 12px;\n }\n .c-heading-4 {\n color: #616161;\n word-break: break-word;\n font-size: 15px;\n line-height: 20px;\n padding: 36px 0 4px;\n font-weight: 600;\n }\n .c-uhff-nav-row {\n .c-uhff-nav-group {\n display: block;\n float: left;\n min-height: 1px;\n vertical-align: text-top;\n padding: 0 12px;\n width: 100%;\n zoom: 1;\n &:first-child {\n padding-left: 0;\n @media only screen and (max-width: 1083px) {\n padding-left: 12px;\n }\n }\n @media only screen and (min-width: 540px) and (max-width: 1082px) {\n width: 33.33333%;\n }\n @media only screen and (min-width: 1083px) {\n width: 16.6666666667%;\n }\n ul.c-list.f-bare {\n font-size: 11px;\n line-height: 16px;\n margin-top: 0;\n margin-bottom: 0;\n padding-left: 0;\n list-style-type: none;\n li {\n word-break: break-word;\n padding: 8px 0;\n margin: 0;\n }\n }\n }\n }\n}\n.c-uhff-base {\n background: #f2f2f2;\n margin: 0 auto;\n max-width: calc(1600px + 10%);\n padding: 30px 5% 16px;\n &:before,\n &:after {\n content: ' ';\n display: table;\n }\n &:after {\n clear: both;\n }\n a.c-uhff-ccpa {\n font-size: 11px;\n line-height: 16px;\n float: left;\n margin: 3px 0;\n }\n a.c-uhff-ccpa:hover {\n text-decoration: underline;\n }\n ul.c-list {\n font-size: 11px;\n line-height: 16px;\n float: right;\n margin: 3px 0;\n color: #616161;\n li {\n padding: 0 24px 4px 0;\n display: inline-block;\n }\n }\n .c-list.f-bare {\n padding-left: 0;\n list-style-type: none;\n }\n @media only screen and (max-width: 1083px) {\n display: flex;\n flex-wrap: wrap;\n padding: 30px 24px 16px;\n }\n}\n\n.social-share {\n position: fixed;\n top: 60%;\n transform: translateY(-50%);\n left: 0;\n z-index: 1000;\n}\n\n.sharing-options {\n list-style: none;\n padding: 0;\n margin: 0;\n display: block;\n flex-direction: column;\n background-color: white;\n width: 43px;\n border-radius: 0px 7px 7px 0px;\n}\n.linkedin-icon {\n border-top-right-radius: 7px;\n}\n.linkedin-icon:hover {\n border-radius: 0;\n}\n.social-share-rss-image {\n border-bottom-right-radius: 7px;\n}\n.social-share-rss-image:hover {\n border-radius: 0;\n}\n\n.social-link-footer {\n position: relative;\n display: block;\n margin: -2px 0;\n transition: all 0.2s ease;\n}\n.social-link-footer:hover .linkedin-icon {\n border-radius: 0;\n}\n.social-link-footer:hover .social-share-rss-image {\n border-radius: 0;\n}\n\n.social-link-footer img {\n width: 40px;\n height: auto;\n transition: filter 0.3s ease;\n}\n\n.social-share-list {\n width: 40px;\n}\n.social-share-rss-image {\n width: 40px;\n}\n\n.share-icon {\n border: 2px solid transparent;\n display: inline-block;\n position: relative;\n}\n\n.share-icon:hover {\n opacity: 1;\n border: 2px solid white;\n box-sizing: border-box;\n}\n\n.share-icon:hover .label {\n opacity: 1;\n visibility: visible;\n border: 2px solid white;\n box-sizing: border-box;\n border-left: none;\n}\n\n.label {\n position: absolute;\n left: 100%;\n white-space: nowrap;\n opacity: 0;\n visibility: hidden;\n transition: all 0.2s ease;\n color: white;\n border-radius: 0 10 0 10px;\n top: 50%;\n transform: translateY(-50%);\n height: 40px;\n border-radius: 0 6px 6px 0;\n display: flex;\n align-items: center;\n justify-content: center;\n padding: 20px 5px 20px 8px;\n margin-left: -1px;\n}\n.linkedin {\n background-color: #0474b4;\n}\n.facebook {\n background-color: #3c5c9c;\n}\n.twitter {\n background-color: white;\n color: black;\n}\n.reddit {\n background-color: #fc4404;\n}\n.mail {\n background-color: #848484;\n}\n.bluesky {\n background-color: white;\n color: black;\n}\n.rss {\n background-color: #ec7b1c;\n}\n#RSS {\n width: 40px;\n height: 40px;\n}\n\n@media (max-width: 991px) {\n .social-share {\n display: none;\n }\n}\n","texts":{"New tab":"What's New","New 1":"Surface Laptop Studio 2","New 2":"Surface Laptop Go 3","New 3":"Surface Pro 9","New 4":"Surface Laptop 5","New 5":"Surface Studio 2+","New 6":"Copilot in Windows","New 7":"Microsoft 365","New 8":"Windows 11 apps","Store tab":"Microsoft Store","Store 1":"Account Profile","Store 2":"Download Center","Store 3":"Microsoft Store Support","Store 4":"Returns","Store 5":"Order tracking","Store 6":"Certified Refurbished","Store 7":"Microsoft Store Promise","Store 8":"Flexible Payments","Education tab":"Education","Edu 1":"Microsoft in education","Edu 2":"Devices for education","Edu 3":"Microsoft Teams for Education","Edu 4":"Microsoft 365 Education","Edu 5":"How to buy for your school","Edu 6":"Educator Training and development","Edu 7":"Deals for students and parents","Edu 8":"Azure for students","Business tab":"Business","Bus 1":"Microsoft Cloud","Bus 2":"Microsoft Security","Bus 3":"Dynamics 365","Bus 4":"Microsoft 365","Bus 5":"Microsoft Power Platform","Bus 6":"Microsoft Teams","Bus 7":"Microsoft Industry","Bus 8":"Small Business","Developer tab":"Developer & IT","Dev 1":"Azure","Dev 2":"Developer Center","Dev 3":"Documentation","Dev 4":"Microsoft Learn","Dev 5":"Microsoft Tech Community","Dev 6":"Azure Marketplace","Dev 7":"AppSource","Dev 8":"Visual Studio","Company tab":"Company","Com 1":"Careers","Com 2":"About Microsoft","Com 3":"Company News","Com 4":"Privacy at Microsoft","Com 5":"Investors","Com 6":"Diversity and inclusion","Com 7":"Accessiblity","Com 8":"Sustainibility"},"defaults":{"config":{"applicablePages":[],"description":"The Microsoft Footer","fetchedContent":null,"__typename":"ComponentConfiguration"},"props":[],"__typename":"ComponentProperties"},"components":[{"id":"custom.widget.MicrosoftFooter","form":null,"config":null,"props":[],"__typename":"Component"}],"grouping":"CUSTOM","__typename":"ComponentTemplate"},"properties":{"config":{"applicablePages":[],"description":"The Microsoft Footer","fetchedContent":null,"__typename":"ComponentConfiguration"},"props":[],"__typename":"ComponentProperties"},"form":null,"__typename":"Component","localOverride":false},"globalCss":{"css":".custom_widget_MicrosoftFooter_context-uhf_105bp_1 {\n min-width: 17.5rem;\n font-size: 0.9375rem;\n box-sizing: border-box;\n -ms-text-size-adjust: 100%;\n -webkit-text-size-adjust: 100%;\n & *,\n & *:before,\n & *:after {\n box-sizing: inherit;\n }\n a.custom_widget_MicrosoftFooter_c-uhff-link_105bp_12 {\n color: #616161;\n word-break: break-word;\n text-decoration: none;\n }\n &a:link,\n &a:focus,\n &a:hover,\n &a:active,\n &a:visited {\n text-decoration: none;\n color: inherit;\n }\n & div {\n font-family: 'Segoe UI', SegoeUI, 'Helvetica Neue', Helvetica, Arial, sans-serif;\n }\n}\n.custom_widget_MicrosoftFooter_c-uhff_105bp_12 {\n background: #f2f2f2;\n margin: -1.5625;\n width: auto;\n height: auto;\n}\n.custom_widget_MicrosoftFooter_c-uhff-nav_105bp_35 {\n margin: 0 auto;\n max-width: calc(100rem + 10%);\n padding: 0 5%;\n box-sizing: inherit;\n &:before,\n &:after {\n content: ' ';\n display: table;\n clear: left;\n }\n @media only screen and (max-width: 1083px) {\n padding-left: 0.75rem;\n }\n .custom_widget_MicrosoftFooter_c-heading-4_105bp_49 {\n color: #616161;\n word-break: break-word;\n font-size: 0.9375rem;\n line-height: 1.25rem;\n padding: 2.25rem 0 0.25rem;\n font-weight: 600;\n }\n .custom_widget_MicrosoftFooter_c-uhff-nav-row_105bp_57 {\n .custom_widget_MicrosoftFooter_c-uhff-nav-group_105bp_58 {\n display: block;\n float: left;\n min-height: 0.0625rem;\n vertical-align: text-top;\n padding: 0 0.75rem;\n width: 100%;\n zoom: 1;\n &:first-child {\n padding-left: 0;\n @media only screen and (max-width: 1083px) {\n padding-left: 0.75rem;\n }\n }\n @media only screen and (min-width: 540px) and (max-width: 1082px) {\n width: 33.33333%;\n }\n @media only screen and (min-width: 1083px) {\n width: 16.6666666667%;\n }\n ul.custom_widget_MicrosoftFooter_c-list_105bp_78.custom_widget_MicrosoftFooter_f-bare_105bp_78 {\n font-size: 0.6875rem;\n line-height: 1rem;\n margin-top: 0;\n margin-bottom: 0;\n padding-left: 0;\n list-style-type: none;\n li {\n word-break: break-word;\n padding: 0.5rem 0;\n margin: 0;\n }\n }\n }\n }\n}\n.custom_widget_MicrosoftFooter_c-uhff-base_105bp_94 {\n background: #f2f2f2;\n margin: 0 auto;\n max-width: calc(100rem + 10%);\n padding: 1.875rem 5% 1rem;\n &:before,\n &:after {\n content: ' ';\n display: table;\n }\n &:after {\n clear: both;\n }\n a.custom_widget_MicrosoftFooter_c-uhff-ccpa_105bp_107 {\n font-size: 0.6875rem;\n line-height: 1rem;\n float: left;\n margin: 0.1875rem 0;\n }\n a.custom_widget_MicrosoftFooter_c-uhff-ccpa_105bp_107:hover {\n text-decoration: underline;\n }\n ul.custom_widget_MicrosoftFooter_c-list_105bp_78 {\n font-size: 0.6875rem;\n line-height: 1rem;\n float: right;\n margin: 0.1875rem 0;\n color: #616161;\n li {\n padding: 0 1.5rem 0.25rem 0;\n display: inline-block;\n }\n }\n .custom_widget_MicrosoftFooter_c-list_105bp_78.custom_widget_MicrosoftFooter_f-bare_105bp_78 {\n padding-left: 0;\n list-style-type: none;\n }\n @media only screen and (max-width: 1083px) {\n display: flex;\n flex-wrap: wrap;\n padding: 1.875rem 1.5rem 1rem;\n }\n}\n.custom_widget_MicrosoftFooter_social-share_105bp_138 {\n position: fixed;\n top: 60%;\n transform: translateY(-50%);\n left: 0;\n z-index: 1000;\n}\n.custom_widget_MicrosoftFooter_sharing-options_105bp_146 {\n list-style: none;\n padding: 0;\n margin: 0;\n display: block;\n flex-direction: column;\n background-color: white;\n width: 2.6875rem;\n border-radius: 0 0.4375rem 0.4375rem 0;\n}\n.custom_widget_MicrosoftFooter_linkedin-icon_105bp_156 {\n border-top-right-radius: 7px;\n}\n.custom_widget_MicrosoftFooter_linkedin-icon_105bp_156:hover {\n border-radius: 0;\n}\n.custom_widget_MicrosoftFooter_social-share-rss-image_105bp_162 {\n border-bottom-right-radius: 7px;\n}\n.custom_widget_MicrosoftFooter_social-share-rss-image_105bp_162:hover {\n border-radius: 0;\n}\n.custom_widget_MicrosoftFooter_social-link-footer_105bp_169 {\n position: relative;\n display: block;\n margin: -0.125rem 0;\n transition: all 0.2s ease;\n}\n.custom_widget_MicrosoftFooter_social-link-footer_105bp_169:hover .custom_widget_MicrosoftFooter_linkedin-icon_105bp_156 {\n border-radius: 0;\n}\n.custom_widget_MicrosoftFooter_social-link-footer_105bp_169:hover .custom_widget_MicrosoftFooter_social-share-rss-image_105bp_162 {\n border-radius: 0;\n}\n.custom_widget_MicrosoftFooter_social-link-footer_105bp_169 img {\n width: 2.5rem;\n height: auto;\n transition: filter 0.3s ease;\n}\n.custom_widget_MicrosoftFooter_social-share-list_105bp_188 {\n width: 2.5rem;\n}\n.custom_widget_MicrosoftFooter_social-share-rss-image_105bp_162 {\n width: 2.5rem;\n}\n.custom_widget_MicrosoftFooter_share-icon_105bp_195 {\n border: 2px solid transparent;\n display: inline-block;\n position: relative;\n}\n.custom_widget_MicrosoftFooter_share-icon_105bp_195:hover {\n opacity: 1;\n border: 2px solid white;\n box-sizing: border-box;\n}\n.custom_widget_MicrosoftFooter_share-icon_105bp_195:hover .custom_widget_MicrosoftFooter_label_105bp_207 {\n opacity: 1;\n visibility: visible;\n border: 2px solid white;\n box-sizing: border-box;\n border-left: none;\n}\n.custom_widget_MicrosoftFooter_label_105bp_207 {\n position: absolute;\n left: 100%;\n white-space: nowrap;\n opacity: 0;\n visibility: hidden;\n transition: all 0.2s ease;\n color: white;\n border-radius: 0 10 0 0.625rem;\n top: 50%;\n transform: translateY(-50%);\n height: 2.5rem;\n border-radius: 0 0.375rem 0.375rem 0;\n display: flex;\n align-items: center;\n justify-content: center;\n padding: 1.25rem 0.3125rem 1.25rem 0.5rem;\n margin-left: -0.0625rem;\n}\n.custom_widget_MicrosoftFooter_linkedin_105bp_156 {\n background-color: #0474b4;\n}\n.custom_widget_MicrosoftFooter_facebook_105bp_237 {\n background-color: #3c5c9c;\n}\n.custom_widget_MicrosoftFooter_twitter_105bp_240 {\n background-color: white;\n color: black;\n}\n.custom_widget_MicrosoftFooter_reddit_105bp_244 {\n background-color: #fc4404;\n}\n.custom_widget_MicrosoftFooter_mail_105bp_247 {\n background-color: #848484;\n}\n.custom_widget_MicrosoftFooter_bluesky_105bp_250 {\n background-color: white;\n color: black;\n}\n.custom_widget_MicrosoftFooter_rss_105bp_254 {\n background-color: #ec7b1c;\n}\n#custom_widget_MicrosoftFooter_RSS_105bp_1 {\n width: 2.5rem;\n height: 2.5rem;\n}\n@media (max-width: 991px) {\n .custom_widget_MicrosoftFooter_social-share_105bp_138 {\n display: none;\n }\n}\n","tokens":{"context-uhf":"custom_widget_MicrosoftFooter_context-uhf_105bp_1","c-uhff-link":"custom_widget_MicrosoftFooter_c-uhff-link_105bp_12","c-uhff":"custom_widget_MicrosoftFooter_c-uhff_105bp_12","c-uhff-nav":"custom_widget_MicrosoftFooter_c-uhff-nav_105bp_35","c-heading-4":"custom_widget_MicrosoftFooter_c-heading-4_105bp_49","c-uhff-nav-row":"custom_widget_MicrosoftFooter_c-uhff-nav-row_105bp_57","c-uhff-nav-group":"custom_widget_MicrosoftFooter_c-uhff-nav-group_105bp_58","c-list":"custom_widget_MicrosoftFooter_c-list_105bp_78","f-bare":"custom_widget_MicrosoftFooter_f-bare_105bp_78","c-uhff-base":"custom_widget_MicrosoftFooter_c-uhff-base_105bp_94","c-uhff-ccpa":"custom_widget_MicrosoftFooter_c-uhff-ccpa_105bp_107","social-share":"custom_widget_MicrosoftFooter_social-share_105bp_138","sharing-options":"custom_widget_MicrosoftFooter_sharing-options_105bp_146","linkedin-icon":"custom_widget_MicrosoftFooter_linkedin-icon_105bp_156","social-share-rss-image":"custom_widget_MicrosoftFooter_social-share-rss-image_105bp_162","social-link-footer":"custom_widget_MicrosoftFooter_social-link-footer_105bp_169","social-share-list":"custom_widget_MicrosoftFooter_social-share-list_105bp_188","share-icon":"custom_widget_MicrosoftFooter_share-icon_105bp_195","label":"custom_widget_MicrosoftFooter_label_105bp_207","linkedin":"custom_widget_MicrosoftFooter_linkedin_105bp_156","facebook":"custom_widget_MicrosoftFooter_facebook_105bp_237","twitter":"custom_widget_MicrosoftFooter_twitter_105bp_240","reddit":"custom_widget_MicrosoftFooter_reddit_105bp_244","mail":"custom_widget_MicrosoftFooter_mail_105bp_247","bluesky":"custom_widget_MicrosoftFooter_bluesky_105bp_250","rss":"custom_widget_MicrosoftFooter_rss_105bp_254","RSS":"custom_widget_MicrosoftFooter_RSS_105bp_1"}},"form":null},"localOverride":false},"CachedAsset:text:en_US-components/community/Breadcrumb-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/community/Breadcrumb-1745505309939","value":{"navLabel":"Breadcrumbs","dropdown":"Additional parent page navigation"},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageBanner-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageBanner-1745505309939","value":{"messageMarkedAsSpam":"This post has been marked as spam","messageMarkedAsSpam@board:TKB":"This article has been marked as spam","messageMarkedAsSpam@board:BLOG":"This post has been marked as spam","messageMarkedAsSpam@board:FORUM":"This discussion has been marked as spam","messageMarkedAsSpam@board:OCCASION":"This event has been marked as spam","messageMarkedAsSpam@board:IDEA":"This idea has been marked as spam","manageSpam":"Manage Spam","messageMarkedAsAbuse":"This post has been marked as abuse","messageMarkedAsAbuse@board:TKB":"This article has been marked as abuse","messageMarkedAsAbuse@board:BLOG":"This post has been marked as abuse","messageMarkedAsAbuse@board:FORUM":"This discussion has been marked as abuse","messageMarkedAsAbuse@board:OCCASION":"This event has been marked as abuse","messageMarkedAsAbuse@board:IDEA":"This idea has been marked as abuse","preModCommentAuthorText":"This comment will be published as soon as it is approved","preModCommentModeratorText":"This comment is awaiting moderation","messageMarkedAsOther":"This post has been rejected due to other reasons","messageMarkedAsOther@board:TKB":"This article has been rejected due to other reasons","messageMarkedAsOther@board:BLOG":"This post has been rejected due to other reasons","messageMarkedAsOther@board:FORUM":"This discussion has been rejected due to other reasons","messageMarkedAsOther@board:OCCASION":"This event has been rejected due to other reasons","messageMarkedAsOther@board:IDEA":"This idea has been rejected due to other reasons","messageArchived":"This post was archived on {date}","relatedUrl":"View Related Content","relatedContentText":"Showing related content","archivedContentLink":"View Archived Content"},"localOverride":false},"Category:category:Exchange":{"__typename":"Category","id":"category:Exchange","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:Outlook":{"__typename":"Category","id":"category:Outlook","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:Community-Info-Center":{"__typename":"Category","id":"category:Community-Info-Center","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:EducationSector":{"__typename":"Category","id":"category:EducationSector","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:DrivingAdoption":{"__typename":"Category","id":"category:DrivingAdoption","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:Azure":{"__typename":"Category","id":"category:Azure","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:Windows-Server":{"__typename":"Category","id":"category:Windows-Server","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:MicrosoftTeams":{"__typename":"Category","id":"category:MicrosoftTeams","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:PublicSector":{"__typename":"Category","id":"category:PublicSector","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:microsoft365":{"__typename":"Category","id":"category:microsoft365","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:IoT":{"__typename":"Category","id":"category:IoT","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:HealthcareAndLifeSciences":{"__typename":"Category","id":"category:HealthcareAndLifeSciences","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:ITOpsTalk":{"__typename":"Category","id":"category:ITOpsTalk","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:MicrosoftLearn":{"__typename":"Category","id":"category:MicrosoftLearn","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Blog:board:MicrosoftLearnBlog":{"__typename":"Blog","id":"board:MicrosoftLearnBlog","blogPolicies":{"__typename":"BlogPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}},"boardPolicies":{"__typename":"BoardPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:MicrosoftMechanics":{"__typename":"Category","id":"category:MicrosoftMechanics","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:MicrosoftforNonprofits":{"__typename":"Category","id":"category:MicrosoftforNonprofits","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:StartupsatMicrosoft":{"__typename":"Category","id":"category:StartupsatMicrosoft","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:PartnerCommunity":{"__typename":"Category","id":"category:PartnerCommunity","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:Microsoft365Copilot":{"__typename":"Category","id":"category:Microsoft365Copilot","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:Windows":{"__typename":"Category","id":"category:Windows","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:Content_Management":{"__typename":"Category","id":"category:Content_Management","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:microsoft-security":{"__typename":"Category","id":"category:microsoft-security","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Category:category:microsoftintune":{"__typename":"Category","id":"category:microsoftintune","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"QueryVariables:TopicReplyList:message:1398310:46":{"__typename":"QueryVariables","id":"TopicReplyList:message:1398310:46","value":{"id":"message:1398310","first":10,"sorts":{"postTime":{"direction":"DESC"}},"repliesFirst":3,"repliesFirstDepthThree":1,"repliesSorts":{"postTime":{"direction":"DESC"}},"useAvatar":true,"useAuthorLogin":true,"useAuthorRank":true,"useBody":true,"useKudosCount":true,"useTimeToRead":false,"useMedia":false,"useReadOnlyIcon":false,"useRepliesCount":true,"useSearchSnippet":false,"useAcceptedSolutionButton":false,"useSolvedBadge":false,"useAttachments":false,"attachmentsFirst":5,"useTags":true,"useNodeAncestors":false,"useUserHoverCard":false,"useNodeHoverCard":false,"useModerationStatus":true,"usePreviewSubjectModal":false,"useMessageStatus":true}},"ROOT_MUTATION":{"__typename":"Mutation"},"CachedAsset:text:en_US-components/community/Navbar-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/community/Navbar-1745505309939","value":{"community":"Community Home","inbox":"Inbox","manageContent":"Manage Content","tos":"Terms of Service","forgotPassword":"Forgot Password","themeEditor":"Theme Editor","edit":"Edit Navigation Bar","skipContent":"Skip to content","gxcuf89792":"Tech Community","external-1":"Events","s-m-b":"Nonprofit Community","windows-server":"Windows Server","education-sector":"Education Sector","driving-adoption":"Driving Adoption","Common-content_management-link":"Content Management","microsoft-learn":"Microsoft Learn","s-q-l-server":"Content Management","partner-community":"Microsoft Partner Community","microsoft365":"Microsoft 365","external-9":".NET","external-8":"Teams","external-7":"Github","products-services":"Products","external-6":"Power Platform","communities-1":"Topics","external-5":"Microsoft Security","planner":"Outlook","external-4":"Microsoft 365","external-3":"Dynamics 365","azure":"Azure","healthcare-and-life-sciences":"Healthcare and Life Sciences","external-2":"Azure","microsoft-mechanics":"Microsoft Mechanics","microsoft-learn-1":"Community","external-10":"Learning Room Directory","microsoft-learn-blog":"Blog","windows":"Windows","i-t-ops-talk":"ITOps Talk","external-link-1":"View All","microsoft-securityand-compliance":"Microsoft Security","public-sector":"Public Sector","community-info-center":"Lounge","external-link-2":"View All","microsoft-teams":"Microsoft Teams","external":"Blogs","microsoft-endpoint-manager":"Microsoft Intune","startupsat-microsoft":"Startups at Microsoft","exchange":"Exchange","a-i":"AI and Machine Learning","io-t":"Internet of Things (IoT)","Common-microsoft365-copilot-link":"Microsoft 365 Copilot","outlook":"Microsoft 365 Copilot","external-link":"Community Hubs","communities":"Products"},"localOverride":false},"CachedAsset:text:en_US-components/community/NavbarHamburgerDropdown-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/community/NavbarHamburgerDropdown-1745505309939","value":{"hamburgerLabel":"Side Menu"},"localOverride":false},"CachedAsset:text:en_US-components/community/BrandLogo-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/community/BrandLogo-1745505309939","value":{"logoAlt":"Khoros","themeLogoAlt":"Brand Logo"},"localOverride":false},"CachedAsset:text:en_US-components/community/NavbarTextLinks-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/community/NavbarTextLinks-1745505309939","value":{"more":"More"},"localOverride":false},"CachedAsset:text:en_US-components/authentication/AuthenticationLink-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/authentication/AuthenticationLink-1745505309939","value":{"title.login":"Sign In","title.registration":"Register","title.forgotPassword":"Forgot Password","title.multiAuthLogin":"Sign In"},"localOverride":false},"CachedAsset:text:en_US-components/nodes/NodeLink-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/nodes/NodeLink-1745505309939","value":{"place":"Place {name}"},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageView/MessageViewStandard-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageView/MessageViewStandard-1745505309939","value":{"anonymous":"Anonymous","author":"{messageAuthorLogin}","authorBy":"{messageAuthorLogin}","board":"{messageBoardTitle}","replyToUser":" to {parentAuthor}","showMoreReplies":"Show More","replyText":"Reply","repliesText":"Replies","markedAsSolved":"Marked as Solved","movedMessagePlaceholder.BLOG":"{count, plural, =0 {This comment has been} other {These comments have been} }","movedMessagePlaceholder.TKB":"{count, plural, =0 {This comment has been} other {These comments have been} }","movedMessagePlaceholder.FORUM":"{count, plural, =0 {This reply has been} other {These replies have been} }","movedMessagePlaceholder.IDEA":"{count, plural, =0 {This comment has been} other {These comments have been} }","movedMessagePlaceholder.OCCASION":"{count, plural, =0 {This comment has been} other {These comments have been} }","movedMessagePlaceholderUrlText":"moved.","messageStatus":"Status: ","statusChanged":"Status changed: {previousStatus} to {currentStatus}","statusAdded":"Status added: {status}","statusRemoved":"Status removed: {status}","labelExpand":"expand replies","labelCollapse":"collapse replies","unhelpfulReason.reason1":"Content is outdated","unhelpfulReason.reason2":"Article is missing information","unhelpfulReason.reason3":"Content is for a different Product","unhelpfulReason.reason4":"Doesn't match what I was searching for"},"localOverride":false},"CachedAsset:text:en_US-components/messages/ThreadedReplyList-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/ThreadedReplyList-1745505309939","value":{"title":"{count, plural, one{# Reply} other{# Replies}}","title@board:BLOG":"{count, plural, one{# Comment} other{# Comments}}","title@board:TKB":"{count, plural, one{# Comment} other{# Comments}}","title@board:IDEA":"{count, plural, one{# Comment} other{# Comments}}","title@board:OCCASION":"{count, plural, one{# Comment} other{# Comments}}","noRepliesTitle":"No Replies","noRepliesTitle@board:BLOG":"No Comments","noRepliesTitle@board:TKB":"No Comments","noRepliesTitle@board:IDEA":"No Comments","noRepliesTitle@board:OCCASION":"No Comments","noRepliesDescription":"Be the first to reply","noRepliesDescription@board:BLOG":"Be the first to comment","noRepliesDescription@board:TKB":"Be the first to comment","noRepliesDescription@board:IDEA":"Be the first to comment","noRepliesDescription@board:OCCASION":"Be the first to comment","messageReadOnlyAlert:BLOG":"Comments have been turned off for this post","messageReadOnlyAlert:TKB":"Comments have been turned off for this article","messageReadOnlyAlert:IDEA":"Comments have been turned off for this idea","messageReadOnlyAlert:FORUM":"Replies have been turned off for this discussion","messageReadOnlyAlert:OCCASION":"Comments have been turned off for this event"},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageReplyCallToAction-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageReplyCallToAction-1745505309939","value":{"leaveReply":"Leave a reply...","leaveReply@board:BLOG@message:root":"Leave a comment...","leaveReply@board:TKB@message:root":"Leave a comment...","leaveReply@board:IDEA@message:root":"Leave a comment...","leaveReply@board:OCCASION@message:root":"Leave a comment...","repliesTurnedOff.FORUM":"Replies are turned off for this topic","repliesTurnedOff.BLOG":"Comments are turned off for this topic","repliesTurnedOff.TKB":"Comments are turned off for this topic","repliesTurnedOff.IDEA":"Comments are turned off for this topic","repliesTurnedOff.OCCASION":"Comments are turned off for this topic","infoText":"Stop poking me!"},"localOverride":false},"Rank:rank:37":{"__typename":"Rank","id":"rank:37","position":18,"name":"Copper Contributor","color":"333333","icon":null,"rankStyle":"TEXT"},"User:user:1580200":{"__typename":"User","id":"user:1580200","uid":1580200,"login":"MohsenMahmoudzadeh","biography":null,"registrationData":{"__typename":"RegistrationData","status":null,"registrationTime":"2022-10-23T01:08:59.418-07:00"},"deleted":false,"email":"","avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-7.svg?time=0"},"rank":{"__ref":"Rank:rank:37"},"entityType":"USER","eventPath":"community:gxcuf89792/user:1580200"},"ModerationData:moderation_data:3659381":{"__typename":"ModerationData","id":"moderation_data:3659381","status":"APPROVED","rejectReason":null,"isReportedAbuse":false,"rejectUser":null,"rejectTime":null,"rejectActorType":null},"BlogReplyMessage:message:3659381":{"__typename":"BlogReplyMessage","author":{"__ref":"User:user:1580200"},"id":"message:3659381","revisionNum":1,"uid":3659381,"depth":1,"hasGivenKudo":false,"subscribed":false,"board":{"__ref":"Blog:board:MachineLearningBlog"},"parent":{"__ref":"BlogTopicMessage:message:1398310"},"conversation":{"__ref":"Conversation:conversation:1398310"},"subject":"Re: ONNX Runtime Training Technical Deep Dive","moderationData":{"__ref":"ModerationData:moderation_data:3659381"},"body":"

Hello Sherlock, thank you for your good article.

 

I have a question about the ONNX computation graph. Is this graph a static computation graph or a dynamic one? And why?

","body@stripHtml({\"removeProcessingText\":false,\"removeSpoilerMarkup\":false,\"removeTocMarkup\":false,\"truncateLength\":200})@stringLength":"179","kudosSumWeight":0,"repliesCount":0,"postTime":"2022-10-23T01:11:41.104-07:00","lastPublishTime":"2022-10-23T01:11:41.104-07:00","metrics":{"__typename":"MessageMetrics","views":2004},"visibilityScope":"PUBLIC","placeholder":false,"originalMessageForPlaceholder":null,"entityType":"BLOG_REPLY","eventPath":"category:AI/category:solutions/category:communities/community:gxcuf89792board:MachineLearningBlog/message:1398310/message:3659381","replies":{"__typename":"MessageConnection","pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null},"edges":[]},"attachments":{"__typename":"AttachmentConnection","edges":[],"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"customFields":[]},"CachedAsset:text:en_US-components/community/NavbarDropdownToggle-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/community/NavbarDropdownToggle-1745505309939","value":{"ariaLabelClosed":"Press the down arrow to open the menu"},"localOverride":false},"CachedAsset:text:en_US-shared/client/components/common/QueryHandler-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-shared/client/components/common/QueryHandler-1745505309939","value":{"title":"Query Handler"},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageCoverImage-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageCoverImage-1745505309939","value":{"coverImageTitle":"Cover Image"},"localOverride":false},"CachedAsset:text:en_US-shared/client/components/nodes/NodeTitle-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-shared/client/components/nodes/NodeTitle-1745505309939","value":{"nodeTitle":"{nodeTitle, select, community {Community} other {{nodeTitle}}} "},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageTimeToRead-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageTimeToRead-1745505309939","value":{"minReadText":"{min} MIN READ"},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageSubject-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageSubject-1745505309939","value":{"noSubject":"(no subject)"},"localOverride":false},"CachedAsset:text:en_US-components/users/UserLink-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/users/UserLink-1745505309939","value":{"authorName":"View Profile: {author}","anonymous":"Anonymous"},"localOverride":false},"CachedAsset:text:en_US-shared/client/components/users/UserRank-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-shared/client/components/users/UserRank-1745505309939","value":{"rankName":"{rankName}","userRank":"Author rank {rankName}"},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageTime-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageTime-1745505309939","value":{"postTime":"Published: {time}","lastPublishTime":"Last Update: {time}","conversation.lastPostingActivityTime":"Last posting activity time: {time}","conversation.lastPostTime":"Last post time: {time}","moderationData.rejectTime":"Rejected time: {time}"},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageBody-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageBody-1745505309939","value":{"showMessageBody":"Show More","mentionsErrorTitle":"{mentionsType, select, board {Board} user {User} message {Message} other {}} No Longer Available","mentionsErrorMessage":"The {mentionsType} you are trying to view has been removed from the community.","videoProcessing":"Video is being processed. Please try again in a few minutes.","bannerTitle":"Video provider requires cookies to play the video. Accept to continue or {url} it directly on the provider's site.","buttonTitle":"Accept","urlText":"watch"},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageCustomFields-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageCustomFields-1745505309939","value":{"CustomField.default.label":"Value of {name}"},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageRevision-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageRevision-1745505309939","value":{"lastUpdatedDatePublished":"{publishCount, plural, one{Published} other{Updated}} {date}","lastUpdatedDateDraft":"Created {date}","version":"Version {major}.{minor}"},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageReplyButton-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageReplyButton-1745505309939","value":{"repliesCount":"{count}","title":"Reply","title@board:BLOG@message:root":"Comment","title@board:TKB@message:root":"Comment","title@board:IDEA@message:root":"Comment","title@board:OCCASION@message:root":"Comment"},"localOverride":false},"CachedAsset:text:en_US-components/messages/MessageAuthorBio-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/messages/MessageAuthorBio-1745505309939","value":{"sendMessage":"Send Message","actionMessage":"Follow this blog board to get notified when there's new activity","coAuthor":"CO-PUBLISHER","contributor":"CONTRIBUTOR","userProfile":"View Profile","iconlink":"Go to {name} {type}"},"localOverride":false},"CachedAsset:text:en_US-shared/client/components/users/UserAvatar-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-shared/client/components/users/UserAvatar-1745505309939","value":{"altText":"{login}'s avatar","altTextGeneric":"User's avatar"},"localOverride":false},"CachedAsset:text:en_US-shared/client/components/ranks/UserRankLabel-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-shared/client/components/ranks/UserRankLabel-1745505309939","value":{"altTitle":"Icon for {rankName} rank"},"localOverride":false},"CachedAsset:text:en_US-components/users/UserRegistrationDate-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/users/UserRegistrationDate-1745505309939","value":{"noPrefix":"{date}","withPrefix":"Joined {date}"},"localOverride":false},"CachedAsset:text:en_US-shared/client/components/nodes/NodeAvatar-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-shared/client/components/nodes/NodeAvatar-1745505309939","value":{"altTitle":"Node avatar for {nodeTitle}"},"localOverride":false},"CachedAsset:text:en_US-shared/client/components/nodes/NodeDescription-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-shared/client/components/nodes/NodeDescription-1745505309939","value":{"description":"{description}"},"localOverride":false},"CachedAsset:text:en_US-components/tags/TagView/TagViewChip-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-components/tags/TagView/TagViewChip-1745505309939","value":{"tagLabelName":"Tag name {tagName}"},"localOverride":false},"CachedAsset:text:en_US-shared/client/components/nodes/NodeIcon-1745505309939":{"__typename":"CachedAsset","id":"text:en_US-shared/client/components/nodes/NodeIcon-1745505309939","value":{"contentType":"Content Type {style, select, FORUM {Forum} BLOG {Blog} TKB {Knowledge Base} IDEA {Ideas} OCCASION {Events} other {}} icon"},"localOverride":false}}}},"page":"/blogs/BlogMessagePage/BlogMessagePage","query":{"boardId":"machinelearningblog","messageSubject":"onnx-runtime-training-technical-deep-dive","messageId":"1398310"},"buildId":"HEhyUrv5OXNBIbfCLaOrw","runtimeConfig":{"buildInformationVisible":false,"logLevelApp":"info","logLevelMetrics":"info","openTelemetryClientEnabled":false,"openTelemetryConfigName":"o365","openTelemetryServiceVersion":"25.1.0","openTelemetryUniverse":"prod","openTelemetryCollector":"http://localhost:4318","openTelemetryRouteChangeAllowedTime":"5000","apolloDevToolsEnabled":false,"inboxMuteWipFeatureEnabled":false},"isFallback":false,"isExperimentalCompile":false,"dynamicIds":["./components/community/Navbar/NavbarWidget.tsx","./components/community/Breadcrumb/BreadcrumbWidget.tsx","./components/customComponent/CustomComponent/CustomComponent.tsx","./components/blogs/BlogArticleWidget/BlogArticleWidget.tsx","./components/external/components/ExternalComponent.tsx","./components/messages/MessageView/MessageViewStandard/MessageViewStandard.tsx","./components/messages/ThreadedReplyList/ThreadedReplyList.tsx","../shared/client/components/common/List/UnstyledList/UnstyledList.tsx","./components/messages/MessageView/MessageView.tsx","../shared/client/components/common/List/UnwrappedList/UnwrappedList.tsx","./components/tags/TagView/TagView.tsx","./components/tags/TagView/TagViewChip/TagViewChip.tsx"],"appGip":true,"scriptLoader":[{"id":"analytics","src":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/pagescripts/1730819800000/analytics.js?page.id=BlogMessagePage&entity.id=board%3Amachinelearningblog&entity.id=message%3A1398310","strategy":"afterInteractive"}]}