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Neuron 2.32.0 is released! Check the What's New and Release Notes for more details.
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  • About Neuron
    • Ask Amazon AI Helper Tools
    • Benchmarks
      • Inf1 Inference Performance
      • Inf2 Inference Performance
      • Trn1/Trn1n Inference Performance
      • Trn1/Trn1n Training Performance
    • Calculator
    • Beta Participation
    • Generative LLM Inference on Neuron
    • Model Samples
      • Training on Trn1
      • Inference on Inf2/Trn1/Trn2
      • Inference on Inf1
    • Neuron FAQ
    • Neuron Features
      • Custom C++ operators
      • Data types
      • Logical NeuronCore configuration
      • Neuron persistent cache
      • NeuronCore batching
      • NeuronCore pipeline
      • Rounding modes
    • Open Source
    • SDK Maintenance Policy
    • Security
    • Term Glossary
    • Troubleshooting
    • What is AWS Neuron?
    • Older Neuron FAQS
      • ONNX FAQ
      • Contributing Guidelines FAQ
      • Inference with Neuron FAQ
      • Troubleshooting for Inf1 FAQ
      • Training with Neuron FAQ
  • Neuron Architecture
    • AWS Inferentia
    • AWS Inferentia2
    • AWS Trainium
    • AWS Trainium2
    • AWS Trainium3
    • NeuronCore v1
    • NeuronCore v2
    • NeuronCore v3
    • NeuronCore v4
    • Inf1 Architecture
    • Inf2 Architecture
    • Trn1 Architecture
    • Trn2 Architecture
    • Trn3 Architecture
  • What's New
    • Release Notes
      • Neuron 2.32.0
        • NKI
        • NKI Library
        • Neuron Compiler
        • Neuron Runtime
        • Developer tools
        • Neuron Agentic Development
        • JAX NeuronX
        • vLLM Neuron
        • NxD Inference
        • Deep Learning AMIs
        • Deep Learning Containers
      • Neuron 2.31.1
      • Neuron 2.31.0
        • NKI
        • NKI Library
        • Neuron Compiler
        • Neuron Runtime
        • Developer tools
        • Neuron Agentic Development
        • PyTorch NeuronX
        • JAX NeuronX
        • NxD Core
        • NxD Inference
        • NxD Training
        • Deep Learning AMIs
        • Deep Learning Containers
      • Neuron 2.30.0
        • NKI
        • NKI Library
        • Neuron Compiler
        • Neuron Runtime
        • Developer tools
        • Neuron Agentic Development
        • Deep Learning AMIs
        • Deep Learning Containers
        • JAX
      • Previous Neuron versions
        • Neuron 2.31.0
        • Neuron 2.30.0
        • Neuron 2.29.1
        • Neuron 2.29.0
        • Neuron 2.28.1
        • Neuron 2.28.0
        • Neuron 2.27.1
        • Neuron 2.27.0
        • Neuron 2.26.1
        • Neuron 2.26.0
        • Neuron 2.25.0
      • Component release notes
        • Neuron Compiler
        • Neuron Agentic Development
        • Neuron Containers
        • Neuron Developer Tools
        • Neuron DLAMI
        • JAX NeuronX
        • NKI Library
        • Neuron Kernel Interface
        • NxD Core
        • NxD Inference
        • NxD Training
        • vLLM Neuron
        • PyTorch Neuron Framework
        • Neuron Runtime
        • Older components and features
      • Release artifacts
  • Announcements
  • News & Blogs
  • Contribute

Get Started

  • Quickstarts
    • Quickstart: Train a Model on Trainium
    • Quickstart: Run Inference on Inferentia
    • Quickstart: Serve models online with vLLM on Neuron
    • Quickstart: Run offline inference with vLLM on Neuron
    • Ask Amazon AI helper tools
    • Neuron GitHub Samples
  • Set Up Environments
    • PyTorch
      • New DLAMI
      • Update Existing DLAMI
      • New DLC
      • Update Existing DLC
      • New Manual Configuration
      • Update Manual Configuration
    • JAX
      • Install JAX via Deep Learning AMI
      • Install JAX via Deep Learning Container
      • Install JAX Manually
    • Multi-framework
    • Troubleshooting
  • Deploy Model Workloads
    • Deployment Overview
    • Pre-configured Environments
      • Deep Learning AMIs
      • DLC Images
      • Customize DLC
      • Docker Setup
      • Quickstart: Deploy DLC with vLLM
    • Use Amazon EC2
      • Train on EC2
      • Inference (Inf2, Trn1)
      • Inference with DLC
      • Inference (Inf1)
    • Use Amazon EKS
      • Set up EKS for Neuron
      • Components Included
      • Inference on EKS
      • Training on EKS
      • Dynamic Resource Allocation
        • Support Files
      • Neuron UltraServer Operator
      • UltraServer MPI Jobs
      • EKS Prerequisites
    • Use Amazon ECS
      • Inference on ECS
      • Training on ECS
      • Node Problem Detector
    • Use AWS Batch
      • Train on AWS Batch
    • Use AWS ParallelCluster
      • Train on ParallelCluster
    • Use Amazon SageMaker
      • Train on SageMaker
    • Infrastructure Components
      • Plugins Overview
      • Scheduler Extension
      • Scheduler Flow
      • Neuron Monitor
      • Node Problem Detector
      • NPD Permissions (IRSA)
    • Tutorials
      • Docker Environment Setup
      • Build and Run Containers
      • OCI Hook Setup
      • Inference Tutorials
        • Run Inference in PyTorch Neuron Container
        • Deploy a TensorFlow Resnet50 model as a Kubernetes service
      • Training Tutorials
        • Run Training in PyTorch Neuron Container
        • Deploy a simple mlp training script as a Kubernetes job
    • Docker Examples
      • Inference DLC Dockerfile
      • Libmode Dockerfile
      • TF Serving Dockerfile
      • TorchServe Config
      • Libmode Entrypoint
      • TorchServe Neuron
      • Training DLC Dockerfile
      • MLP Training Example
      • v1 App+RT Different Containers
      • v1 App+RT Same Container
      • v1 Neuron Runtime
      • v1 Torch Neuron
      • v1 Entrypoint
    • Third-party Solutions
    • FAQ
    • Troubleshooting
    • Container Release Notes
    • DLAMI Release Notes

ML Frameworks

  • ML Framework Support
  • PyTorch
    • About PyTorch on Neuron
    • Native PyTorch
    • PyTorch Setup
    • Inference
      • Tutorials
        • Compiling and Deploying HuggingFace Pretrained BERT on Trn1 or Inf2
        • BERT TorchServe Tutorial
        • LibTorch C++ Tutorial
        • Compiling and Deploying ResNet50 on Trn1 or Inf2
        • T5 model inference on Trn1 or Inf2
      • Additional Examples
        • AWS Neuron Samples GitHub Repository
        • Transformers Neuron GitHub samples
      • API Reference Guide
        • PyTorch NeuronX Tracing API for Inference
        • PyTorch Neuron (torch-neuronx) Weight Replacement API for Inference
        • PyTorch NeuronX NeuronCore Placement APIs
        • PyTorch NeuronX Analyze API for Inference
        • PyTorch NeuronX DataParallel API
      • Developer Guide
        • NeuronCore Allocation and Model Placement for Inference (torch-neuronx)
        • Comparison of Traced Inference versus XLA Lazy Tensor Inference (torch-neuronx)
      • Misc
        • Component Release Notes for Neuron PyTorch Framework
    • torch-neuron v. torch-neuronx
    • Release Notes
    • Supported operators
    • Profiling API
    • Profiling developer guide
    • neuron_parallel_compile CLI
    • Multi-node EFA setup
  • JAX
    • JAX NeuronX plugin Setup
    • JAX NeuronX Known Issues
    • API Reference Guide for JAX Neuronx
      • JAX NeuronX Environment Variables
    • Release Notes

Training

  • NxD Core (Training)
    • Setup
    • App Notes
      • Tensor Parallelism Overview
      • Pipeline Parallelism Overview
      • Activation Memory Reduction
      • Context Parallelism Overview
    • API Reference Guide
      • Distributed Strategies APIs
      • Training APIs
      • Inference APIs
      • ModelBuilderV2 API Reference
    • Developer Guide
      • Developer guide for Tensor Parallelism
      • Developer guide for Pipeline Parallelism
      • Developer guide for Activation Memory reduction
      • Developer guide for save/load checkpoint
      • Developer guide for Neuron-PT-Lightning
      • Developer guide for model and optimizer wrapper
      • Developer guide for LoRA finetuning
    • Tutorials
      • Training Tutorials
        • Training using Tensor Parallelism
        • Training Llama 3.1 8B/Llama 3 8B using TP and ZeRO-1
        • Training Llama 3.1 70B/Llama 3 70B using TP and PP
        • Fine-tuning Llama3 8B with tensor parallelism and LoRA using Neuron PyTorch-Lightning
      • Inference Tutorials
        • [Broken] T5 inference with Tensor Parallelism
    • Misc
      • Component Release Notes for NxD Core

Inference

  • Inference on Neuron
  • NxD Inference + vLLM
    • Overview
    • Setup
    • vLLM
      • Quickstart: Offline Model Serving
      • Quickstart: Online Model Serving
      • vLLM on Neuron User Guide
      • Model Recipes
        • Llama 3.3 70B
        • Qwen3 235B A22B
      • Deploy Llama4 with vLLM
    • Tutorials
      • Disaggregated Inference (1P1D)
      • Disaggregated Inference
      • Flux Inference
      • Flux Inpainting
      • Benchmark using Performance CLI
      • GPT-OSS 120B
      • Llama3.1 405B on Trn2
      • Llama3.1 405B with Speculative Decoding
      • Llama3.1 70B Instruct Accuracy Evaluation
      • Llama3.1 8B with Multi-LoRA
      • Llama3.3 70B FP8
      • Llama3.3 70B with APC
      • Llama3.3 70B with Data Parallelism
      • Llama3.3 70B with Speculative Decoding
      • Llama4
      • Llama4 Legacy
      • Pixtral
      • Qwen3 MoE 235B
      • Qwen2 VL 7B
      • Speculative Decoding
      • Qwen3-VL 8B
    • Developer Guides
      • Accuracy Evaluation
      • Custom Quantization
      • Disaggregated Inference
      • Feature Guide
      • Using Pipeline Execution Mode
      • LLM Benchmarking
      • Migrate from TNX
      • Model Reference
      • MoE Architecture
      • Migrate from NxD Core
      • Onboarding Models
      • Performance Benchmarking CLI
      • vLLM Guide (Legacy)
      • vLLM Guide v1
      • Weights Sharding
      • Writing Tests
    • API Reference Guide
      • NxD Inference API Reference
    • App Notes
      • Parallelism Techniques for LLM Inference
    • Release Notes
    • Misc
      • Component Release Notes for NxD Inference
      • Troubleshooting Guide for NxD Inference
  • NxD Core (Inference)
    • Setup
    • App Notes
      • Tensor Parallelism Overview
      • Pipeline Parallelism Overview
      • Activation Memory Reduction
      • Context Parallelism Overview
    • API Reference Guide
      • Distributed Strategies APIs
      • Training APIs
      • Inference APIs
      • ModelBuilderV2 API Reference
    • Developer Guide
      • About NeuronX-Distributed (NxD) Inference
    • LoRA Guide
    • Tutorials
      • Training Tutorials
        • Training using Tensor Parallelism
        • Training Llama 3.1 8B/Llama 3 8B using TP and ZeRO-1
        • Training Llama 3.1 70B/Llama 3 70B using TP and PP
        • Fine-tuning Llama3 8B with tensor parallelism and LoRA using Neuron PyTorch-Lightning
      • Inference Tutorials
        • [Broken] T5 inference with Tensor Parallelism
    • Misc
      • Component Release Notes for NxD Core
  • vLLM Neuron (Beta)
    • Get started
      • Setting up
      • Online serving quickstart
      • Offline serving quickstart
      • Migrating from NxDI
    • Deploy & serve
      • Features guide
      • Configuration reference
      • Profiling workloads
      • Feature–model compatibility
    • Model Recipes
      • Llama 3
      • GPT-OSS
      • Qwen3-VL
      • Qwen3-Embedding
    • Tutorials
      • Deploying Llama 3.3 70B
      • EAGLE3 speculative decoding (Llama 3.1)
      • EAGLE3 speculative decoding (GPT-OSS)
      • Disaggregated inference (1P1D and xPyD)
      • Deploying gpt-oss
      • Benchmarking prefix caching (GPT-OSS)
      • Deploying Qwen3-VL-32B
      • Disaggregated encoder (1E1PD and xEyPD)
      • Deploying Qwen3-Embedding-8B
    • Model development
      • Onboarding a model
      • Onboarding a vision-language model
      • Optimizing a vision-language model
      • CPU development workflow
      • NKI CPU simulator
      • Debugging model code
      • Debugging accuracy issues
      • Accuracy debugger tools
    • Concepts & architecture
      • Parallelism
        • Attention DP
        • Component-Level DP Sharding
        • Data Parallelism (DP)
        • DCP (Decode Context Parallelism)
        • Expert Parallelism (EP)
        • Tensor Parallelism (TP)
        • Vision Encoder Parallelism
      • Speculation
        • Speculative Decoding (EAGLE3) in the vLLM Neuron Framework
      • Multimodal
        • Block Packing Vision Attention
        • On-Device Encoder Cache
        • M-RoPE (Multimodal Rotary Position Embedding)
      • vLLM integration
        • vLLM Integration Design Reference
        • KV Caching with vLLM
        • Additional Configuration Options
        • Async Scheduling and Async Execution
        • Decode Context-Length Bucketing
        • determine_available_memory design
        • KV Cache Quantization
        • vLLM Metrics Integration
        • Runtime NRT Profiling via vLLM’s Profiler Interface
        • Scheduler Design
        • Padding and Batching with Block KV
        • Prefill Processing
        • Pooling models on Trainium
        • Prompt embeddings on Neuron
        • Structured Outputs and Tool Calling
        • Disaggregated Inference — Design Document
      • Framework
        • Asynchronous Execution Framework Capability
        • Checkpoint Loading Framework Capability
        • Canonical Model — Design & Porting Guide
        • Scalable Model Registry Design
      • Compilation
        • Compilation Cache
        • CPU Compilation
        • FX Passes Architecture Design
        • Aliasing Output Rewrite Pass
        • FX Device Rewriting for XLA Compilation
        • In-Place to Out-of-Place Rewrite Pass
      • Accuracy
        • Accuracy Debugging Design
        • Dataset Evaluation Design
        • KV Cache Analysis Design
        • vLLM Neuron Logit Validation Design
        • Module-Level Accuracy Test Guidelines
        • Tensor Capture Design
        • Tensor Compare Design
        • Tensor Replacement — Design
        • Input Snapshot Design
  • vLLM Omni Neuron (Beta)
    • Get started
      • Setup guide
      • Online serving quickstart
      • Offline serving quickstart
    • Guides
      • Features guide
    • Model recipes
      • WAN 2.2 T2V
      • WAN 2.2 I2V
    • Tutorials
      • Deploy Wan2.2-A14B
    • Model development
      • Onboarding a model
      • Accuracy evaluation and debugging
      • Kernel implementations
        • Const-max ring attention
        • Fused adaptive LayerNorm and FP8 quantization
        • QKV projection with QK Distributed RMSNorm and RoPE fusion
      • Optimizing offline video generation
    • Concepts & architecture
      • vLLM Omni Neuron overview
      • Context parallelism

Developer Tools

  • Neuron Developer Tools
    • System Tools
      • Neuron-Monitor User Guide
      • Neuron-Top User Guide
      • Neuron-LS User Guide
      • Neuron-Sysfs User Guide
      • NCCOM-TEST User Guide
    • Third-party Tools
    • Tutorials
      • Profiling a vLLM Inference Workload on AWS Trainium
      • Track System Resource Utilization during Training with neuron-monitor using PyTorch Neuron
    • Release Notes
  • Neuron Agentic Development
    • Getting Started
    • Developer Guides
      • Autoport NxDI Deep Dive
      • Autoport vLLM Neuron Deep Dive
      • Equivalence Validation Deep Dive
    • Tutorials
      • Port a model with Autoport
      • Port a model to vLLM Neuron
      • Validate a port with Equivalence
  • Neuron Explorer
    • Get Started
    • Capture Profiles
    • UI Overview
    • Device Trace Viewer
    • System Trace Viewer
    • Hierarchy Viewer
    • Source Code Viewer
    • Summary Viewer
    • Tensor Viewer
    • Memory Viewer
    • Region Highlighter
    • AI Recommendation Viewer
    • Profile Parquet Schema Reference
    • Analyze Profile Output
    • View Profiles with Perfetto
    • Neuron Profiler to Neuron Explorer Migration Guide
    • Troubleshooting & FAQs
    • Environment Variables
    • Glossary

Compiler, Runtime, & Collectives

  • Graph Compiler
    • About neuronx-cc
    • NeuronX Compiler for Trn* & Inf2
      • API Reference Guide
      • How-to: Generate a NEFF file
      • How-to: Convolution
        • * `UNet training sample <https://github.com/aws-neuron/aws-neuron-samples/tree/master/torch-neuronx/training/unet_image_segmentation>`_ - Sample UNet training implementation
      • FAQ
    • Error Codes
      • NCC_EARG001
      • NCC_EBIR023
      • NCC_EBVF030
      • NCC_EHCA005
      • NCC_EMOD025
      • NCC_EOOM001
      • NCC_EOOM002
      • NCC_ESFH002
      • NCC_ESPP004
      • NCC_ESPP047
      • NCC_EUOC002
      • NCC_EVRF001
      • NCC_EVRF004
      • NCC_EVRF005
      • NCC_EVRF006
      • NCC_EVRF007
      • NCC_EVRF009
      • NCC_EVRF010
      • NCC_EVRF011
      • NCC_EVRF013
      • NCC_EVRF015
      • NCC_EVRF016
      • NCC_EVRF017
      • NCC_EVRF018
      • NCC_EVRF019
      • NCC_EVRF022
      • NCC_EVRF031
      • NCC_EVRF035
      • NCC_EVRF036
      • NCC_EVRF037
      • NCC_EVRF038
      • NCC_EVRF039
      • NCC_EVRF040
      • NCC_EVRF041
      • NCC_EVRF042
      • NCC_EVRF043
      • NCC_EVRF044
      • NCC_EVRF045
      • NCC_EVRF046
      • NCC_EVRF047
      • NCC_EVRF048
      • NCC_EVRF049
      • NCC_EVRF050
      • NCC_EVRF051
      • NCC_EVRF052
      • NCC_EVRF053
      • NCC_EVRF054
      • NCC_EVRF055
      • NCC_EVRF056
      • NCC_EVRF057
      • NCC_EVRF058
      • NCC_EVRF059
      • NCC_EVRF060
      • NCC_EVRF061
      • NCC_EVRF062
      • NCC_EVRF063
      • NCC_EVRF064
      • NCC_EVRF065
      • NCC_EXSP001
      • NCC_EXTP004
    • Release Notes
    • neuron-cc (Legacy)
      • API Reference Guide
        • Neuron compiler CLI Reference Guide (neuron-cc)
      • CLI Reference
      • Developer Guide
      • FAQ
  • Neuron Runtime
    • Overview
    • Deep Dives
      • Compute-Communication Overlap
      • Neuron Device Memory
      • Direct HBM Tensor Allocation
      • Runtime Performance Tips
      • Neuron Runtime Core Dumps
      • Inter-node Collectives
      • Intra-node Collectives
    • How-To Guides
      • Developer guide
      • Migrate to the explicit async APIs
      • Configuration guide
    • API Reference
      • NRT API
      • NRT Status
      • NRT Version
      • Debug Stream APIs
      • NRT Async APIs Overview
      • NRT Async APIs Best Practices
      • NRT Async APIs Examples
      • NRT Async
      • NRT Async Send/Recv
      • NRT Profile
      • NRT System Trace
      • Debug Stream
      • NEC API
      • NDL API
      • Neuron Driver Shared
      • Tensor Batch Operations
      • Neuron Datastore
      • NRT Experimental
    • nrtpy (Neuron Runtime Python)
      • Getting started with nrtpy
      • Test and debug a NKI kernel end-to-end
      • Validate, benchmark, and trace a kernel
      • Troubleshooting
      • Model API
      • Tensor API
      • Configuration
      • Error handling
    • NRT Debug Stream
    • Troubleshooting on Inf1 and Trn1
    • Release Notes
    • FAQ
  • Collectives
  • Neuron C++ Custom Operators
    • API Reference Guide
      • Custom Operators API Reference Guide [Beta]
    • Developer Guide
      • Neuron Custom C++ Operators Developer Guide [Beta]
    • Tutorials
      • Neuron Custom C++ Operators in MLP Training
      • Neuron Custom C++ Operators Performance Optimization
    • Misc (Neuron Custom C++ Operators)
      • Neuron Custom C++ Tools Release Notes
      • Neuron Custom C++ Library Release Notes

Neuron Kernel Interface (NKI)

  • NKI
    • Get started
      • Environment Setup
      • First NKI Kernel
      • NKI Language Guide
      • Concepts
        • Memory Hierarchy
        • Data Representation
        • Indexing
        • Tiling
        • Direct Memory Access
        • Logical Neuron Cores
    • Guides
      • Tutorials
        • Matrix Multiplication
        • AveragePool2D
        • Transpose2D
        • Fused Mamba
        • Introduction to NKI Kernel Optimization
      • Architecture
        • Trainium/Inferentia2 Guide
        • Trainium2 Guide
        • Trainium3 Guide
      • NKI CPU Simulator
      • Insert NKI Kernels into Models
      • Use NKI Scheduling APIs
      • Profile a NKI Kernel
    • Deep dives
      • Performance Optimizations
      • MXFP8/4 Matrix Multiplication
      • NKI Access Patterns
      • NKI Dynamic Loops
      • Descriptor Generation Engine (DGE)
      • DMA Bandwidth Guide
      • About the NKI Compiler
    • Migration
    • NKI FAQ
  • NKI API Reference
    • nki
      • nki.jit
      • nki.simulate
    • nki.isa
      • nki.isa.nc_matmul
      • nki.isa.nc_matmul_mx
      • nki.isa.nc_transpose
      • nki.isa.activation
      • nki.isa.activation_reduce
      • nki.isa.activate2
      • nki.isa.tensor_reduce
      • nki.isa.tensor_partition_reduce
      • nki.isa.tensor_tensor
      • nki.isa.tensor_tensor_scan
      • nki.isa.scalar_tensor_tensor
      • nki.isa.tensor_scalar
      • nki.isa.tensor_scalar_reduce
      • nki.isa.tensor_scalar_cumulative
      • nki.isa.tensor_copy
      • nki.isa.tensor_copy_predicated
      • nki.isa.exponential
      • nki.isa.reciprocal
      • nki.isa.quantize_mx
      • nki.isa.iota
      • nki.isa.dropout
      • nki.isa.affine_select
      • nki.isa.range_select
      • nki.isa.select_reduce
      • nki.isa.sequence_bounds
      • nki.isa.memset
      • nki.isa.bn_stats
      • nki.isa.bn_aggr
      • nki.isa.local_gather
      • nki.isa.nc_n_gather
      • nki.isa.dma_copy
      • nki.isa.dma_transpose
      • nki.isa.dma_compute
      • nki.isa.max8
      • nki.isa.nc_find_index8
      • nki.isa.nc_match_replace8
      • nki.isa.nc_stream_shuffle
      • nki.isa.register_alloc
      • nki.isa.register_load
      • nki.isa.register_move
      • nki.isa.register_store
      • nki.isa.core_barrier
      • nki.isa.sendrecv
      • nki.isa.rng
      • nki.isa.rand2
      • nki.isa.rand_set_state
      • nki.isa.rand_get_state
      • nki.isa.set_rng_seed
      • nki.isa.nonzero_with_count
      • nki.isa.topk
      • nki.isa.engine
      • nki.isa.dma_engine
      • nki.isa.reduce_cmd
      • nki.isa.dge_mode
      • nki.isa.oob_mode
      • nki.isa.matmul_perf_mode
      • nki.isa.nc_version
      • nki.isa.get_nc_version
      • nki.isa.VirtualRegister
      • nki.isa.NkiInstruction
    • nki.language
      • nki.language.ndarray
      • nki.language.zeros
      • nki.language.ones
      • nki.language.full
      • nki.language.zeros_like
      • nki.language.empty_like
      • nki.language.shared_constant
      • nki.language.shared_identity_matrix
      • nki.language.rand
      • nki.language.random_seed
      • nki.language.NkiTensor
      • nki.language.load
      • nki.language.load_transpose2d
      • nki.language.store
      • nki.language.copy
      • nki.language.matmul
      • nki.language.transpose
      • nki.language.abs
      • nki.language.abs_max
      • nki.language.abs_min
      • nki.language.add
      • nki.language.arctan
      • nki.language.ceil
      • nki.language.cos
      • nki.language.exp
      • nki.language.floor
      • nki.language.log
      • nki.language.maximum
      • nki.language.minimum
      • nki.language.multiply
      • nki.language.negative
      • nki.language.power
      • nki.language.reciprocal
      • nki.language.rsqrt
      • nki.language.sign
      • nki.language.sin
      • nki.language.sqrt
      • nki.language.square
      • nki.language.subtract
      • nki.language.tan
      • nki.language.tanh
      • nki.language.trunc
      • nki.language.relu
      • nki.language.sigmoid
      • nki.language.silu
      • nki.language.silu_dx
      • nki.language.gelu
      • nki.language.gelu_dx
      • nki.language.gelu_apprx_sigmoid
      • nki.language.gelu_apprx_sigmoid_dx
      • nki.language.gelu_apprx_tanh
      • nki.language.mish
      • nki.language.softplus
      • nki.language.softmax
      • nki.language.erf
      • nki.language.erf_dx
      • nki.language.dropout
      • nki.language.rms_norm
      • nki.language.all
      • nki.language.max
      • nki.language.mean
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Posts tagged neuron-component-nxdcore

Announcing Name Change for Neuron Component

  • 16 September 2024
  • en
  • announce-nxdcore neuron-component-nxdcore

Starting with Neuron release 2.20, the name of the following Neuron component will change as follows:

Package name

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