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xInfer Performance Toolkit

kamisaberi/xinfer
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    • ▼ Api
      • Index
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      • ▼ Datasets
        • Index
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        • Tabular
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      • ▼ Models
        • Index
        • Computer vision
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        • Gnn
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        • Rl
      • ▼ Transforms
        • Index
        • Appliers
        • Graph
        • Image
        • Signal
        • Text
        • Video
        • Weather
    • ▼ Comparisons
      • Comparison
    • ▼ Examples
      • Index
      • ▼ Audio
        • ▼ Audio classification
          • Environmental sounds
          • Music genre
        • ▼ Speech recognition
          • E2e ctc
          • Keyword spotting
      • ▼ Computer vision
        • ▼ Image classification
          • Finetuning resnet cifar10
          • Lenet mnist
          • Transfer learning custom
        • ▼ Image generation
          • Cyclegan
          • Dcgan
        • ▼ Object detection
          • Faster rcnn
          • Yolov3 coco
        • ▼ Semantic segmentation
          • Deeplab v3
          • Mask rcnn
      • ▼ Data handling
        • ▼ Dataloaders
          • Efficient loading
        • ▼ Datasets
          • Builtin datasets
          • Custom datasets
        • ▼ Transforms
          • Image augmentation
      • ▼ Deployment
        • ▼ Inference
          • Cpp app
          • Tensorrt
        • ▼ Serialization
          • Export torchscript
          • Save load
        • ▼ Web services
          • Rest api
      • ▼ Distributed
        • ▼ Data parallelism
          • Multi gpu
        • ▼ Model parallelism
          • Model splitting
        • ▼ Multi machine
          • Setup
      • ▼ Generative
        • ▼ Autoencoders
          • Denoising ae
          • Vae
        • ▼ Diffusion
          • Ddpm
        • ▼ Gans
          • Mnist gan
          • Progressive gan
      • ▼ Getting started
        • Building simple nn
        • Intro tensors autograd
        • Using xtorch trainer
      • ▼ Gnn
        • ▼ Graph level
          • Diffpool
          • Mpnn
        • ▼ Node level
          • Gcn
          • Graphsage
      • ▼ Nlp
        • ▼ Language modeling
          • Finetuning bert
          • Training gpt
        • ▼ Seq2seq
          • Machine translation
          • Summarization
        • ▼ Text classification
          • Sentiment rnn
          • Transformer classification
      • ▼ Optimization
        • ▼ Lr schedulers
          • Cosine annealing
          • Step decay
        • ▼ Optimizers
          • Adamw
          • Sgd momentum
        • ▼ Regularization
          • Dropout
          • Weight decay
      • ▼ Performance
        • ▼ Memory
          • Data loading
          • Gradient checkpointing
        • ▼ Speed
          • Mixed precision
          • Profiling
      • ▼ Rl
        • ▼ Policy based
          • Ppo
          • Reinforce
        • ▼ Value based
          • Dqn atari
          • Q learning
      • ▼ Time series
        • ▼ Anomaly detection
          • Autoencoders
        • ▼ Forecasting
          • Lstm
          • Multivariate
    • ▼ Getting started
      • Installation
      • Quick start cnn
    • ▼ User guide
      • Architecture
      • Data handling
      • Performance
      • Serialization inference
      • Trainer