Answers.Body: <p>If you have tabular data (vectors of features) you can opt for a <strong>logistic regression</strong> which is quite cheap to compute, or a shallow <strong>decision tree</strong>. Moreover, decision trees can be further pruned to reduce their complexity.</p>
  <p>Otherwise, if your data is more complex (e.g., images, audio, etc) you probably need a <em>neural network</em>. If so you can optimize, i.e., compress or accelerate the model via weight or activation <strong>quantization</strong> (e.g., to fp16 or int8 precision) - there are libraries to do this, like tensorflow-lite, <a href="https://github.com/microsoft/DeepSpeed" rel="nofollow noreferrer">DeepSpeed</a>, <a href="https://pytorch.org/docs/stable/quantization.html" rel="nofollow noreferrer">PyTorch</a> built's in methods, and <a href="https://docs.openvino.ai/2024/home.html" rel="nofollow noreferrer">OpenVINO</a> to name a few.</p>
  <p>There are also more sophisticated optimizations that are hardware related, i.e., you can optimize your neural net for CPU with Intel's specific stuff (now I don't remember how it is called), or use CUDA for Nvidia GPU acceleration. Things can be pushed even further by deploying your ML model on a FPGA accelerator: one solution is through <a href="https://github.com/fastmachinelearning/hls4ml" rel="nofollow noreferrer">HSL4ML</a> library.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-05-26T13:18:40.277Z
Answers.Text: Hello @Luca Anzalone. Thank you for your comment. Yes, we tried shallow decision trees, but it turns out that the performance degrades significantly when you make them shallow, we see an acceptable performance with a depth of 10. We did not try a logistic regression, but we have never used that. You mean some sort of hyperplane to split our categories? That might not perform well though, because yes our data is probably complex. 
  
  I see, we might try tensorflow with the neural network. Bear in mind though, that our datasets are only about 10K-20K samples and we have around 10-15 features.
Answers.UserDisplayName: acampove
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-05-27T12:38:19.073Z
Answers.Text: @acampove If so, you can try a SVM classifier maybe some light implementations. Also, you can look at CUDA-enabled xgboost and/or LightGBM that run on GPU.
Answers.UserDisplayName: Luca Anzalone
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-05-28T15:47:30.620Z
Answers.Text: so the GPU part helps not just in the training but also in the prediction?
Answers.UserDisplayName: acampove
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-05-29T20:26:26.643Z
Answers.Text: @acampove Well, the [LightGBM](https://lightgbm.readthedocs.io/en/latest/GPU-Performance.html) docs says that GPU speeds up the feature histogram computation during training, whereas, the [XGBoost](https://xgboost.readthedocs.io/en/stable/gpu/index.html) ones state that "Most of the algorithms in XGBoost including training, prediction and evaluation can be accelerated with CUDA-capable GPUs." I think you should benchmark the GPU vs CPU for inference (i.e., prediction) to be 100% sure
Answers.UserDisplayName: Luca Anzalone
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-05-25T15:27:52.757Z
Answers.LastActivityDate: 2024-05-25T15:27:52.757Z
Answers.UserDisplayName: Luca Anzalone
Body: <p>I have  a use-case where we need a classifier to take decisions in real time, meaning that as data arrives, we need to decide to which category that data belongs and it has to be done fast. The better the classifier's accuracy, the slower it seems to be. Is there a library or type of model you recommend for this sort of problem? I.e. a time constrained classification.</p>
  
Tags: <classification><prediction><model-request><performance><real-time>
Title: How do I improve the prediction speed of a model?
UserDisplayName: acampove
ThreadText: # How do I improve the prediction speed of a model?
  
  (Asked by: username_0 on 2024-05-25)
  
  I have a use-case where we need a classifier to take decisions in real time, meaning that as data arrives, we need to decide to which category that data belongs and it has to be done fast. The better the classifier's accuracy, the slower it seems to be. Is there a library or type of model you recommend for this sort of problem? I.e. a time constrained classification.
  
  **Tags:** classification, prediction, model-request, performance, real-time
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-05-25. Score: 1
  
  If you have tabular data (vectors of features) you can opt for a **logistic regression** which is quite cheap to compute, or a shallow **decision tree**. Moreover, decision trees can be further pruned to reduce their complexity.
  
  Otherwise, if your data is more complex (e.g., images, audio, etc) you probably need a *neural network*. If so you can optimize, i.e., compress or accelerate the model via weight or activation **quantization** (e.g., to fp16 or int8 precision) - there are libraries to do this, like tensorflow-lite, [DeepSpeed](https://github.com/microsoft/DeepSpeed), [PyTorch](https://pytorch.org/docs/stable/quantization.html) built's in methods, and [OpenVINO](https://docs.openvino.ai/2024/home.html) to name a few.
  
  There are also more sophisticated optimizations that are hardware related, i.e., you can optimize your neural net for CPU with Intel's specific stuff (now I don't remember how it is called), or use CUDA for Nvidia GPU acceleration. Things can be pushed even further by deploying your ML model on a FPGA accelerator: one solution is through [HSL4ML](https://github.com/fastmachinelearning/hls4ml) library.
  
  > **Comments:**
  > **username_0**: Hello @username_1. Thank you for your comment. Yes, we tried shallow decision trees, but it turns out that the performance degrades significantly when you make them shallow, we see an acceptable performance with a depth of 10. We did not try a logistic regression, but we have never used that. You mean some sort of hyperplane to split our categories? That might not perform well though, because yes our data is probably complex.   I see, we might try tensorflow with the neural network. Bear in mind though, that our datasets are only about 10K-20K samples and we have around 10-15 features.  
  > **username_1**: @username_0 If so, you can try a SVM classifier maybe some light implementations. Also, you can look at CUDA-enabled xgboost and/or LightGBM that run on GPU.  
  > **username_0**: so the GPU part helps not just in the training but also in the prediction?  
  > **username_1**: @username_0 Well, the [LightGBM](https://lightgbm.readthedocs.io/en/latest/GPU-Performance.html) docs says that GPU speeds up the feature histogram computation during training, whereas, the [XGBoost](https://xgboost.readthedocs.io/en/stable/gpu/index.html) ones state that "Most of the algorithms in XGBoost including training, prediction and evaluation can be accelerated with CUDA-capable GPUs." I think you should benchmark the GPU vs CPU for inference (i.e., prediction) to be 100% sure  
  
  ---
  
  