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GPU Dedicated Server for XGBoost Machine Learning
Gradient boosting is a powerful machine learning algorithm used to achieve state-of-the-art accuracy on a variety of tasks, such as regression, classification, and ranking. We provide bare metal servers with GPUs that are specifically designed for XGBoost.
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Note: Please note that training with multiple GPUs is only supported for Linux platform.
Sample: pip install --upgrade pip pip install xgboost
Sample: # Use NVIDIA GPU conda install -c conda-forge py-xgboost-gpu
Sample:
# GPU-Accelerated SHAP values
model.set_param({"predictor": "gpu_predictor"})
shap_values = model.predict(dtrain, pred_contribs=True)
shap_interaction_values = model.predict(dtrain, pred_interactions=True)6 Reasons to Choose our GPU Servers for XGBoost
GPUHUT enables powerful GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.
Intel Xeon CPU
Intel Xeon has extraordinary processing power and speed, which is very suitable for running deep learning frameworks. So you can totally use our Intel-Xeon-powered GPU Servers for MXNet.
SSD-Based Drives
You can never go wrong with our own top-notch GPU dedicated servers, loaded with the latest Intel Xeon processors, terabytes of SSD disk space, and 128 GB of RAM per server.
Full Root/Admin Access
With full root/admin access, you will be able to take full control of your GPU dedicated server very easily and quickly.
99.9% Uptime Guarantee
With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for MXNet and networks.
Dedicated IP
One of the premium features is the dedicated IP address. Even the cheapest GPU dedicated hosting plan is fully packed with dedicated IPv4 & IPv6 Internet protocols.
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Resources among different users are fully isolated to ensure your data security. DBM protects against DDoS from the edge fast while ensuring legitimate traffic of hosted GPUs for MXNet is not compromised.
FAQs of XGBoost GPU Server
A list of frequently asked questions about GPU servers for XGBoost.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solves many data science problems fast and accurately.
XGBoost is a popular and efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm, which attempts to accurately predict a target variable by combining the estimates of a set of simpler and weaker models.
XGBoost (eXtreme Gradient Boosting) is a popular supervised-learning algorithm used for regression and classification on large datasets.
Since the gradient of the data is considered for each tree, XGBoost is faster, and the precision is more accurate than Random Forest. This makes developers to depend on XGBoost than Random Forest. XGBoost is more complex than any other decision tree algorithm.
SVM and XGBoost models are developed for modeling global solar radiation. The two algorithms show comparable prediction accuracy. However, XGBoost models are more stable and efficient than SVM algorithms.
XGBoost uses NVIDIA’s CUDA parallel computing platform. You need to install the CUDA toolkit and XGBoost with CUDA support, then enable training of an XGBoost model using the GPU is straightforward—set the hyperparameter tree_method to “gpu_hist.”
1. Classification problems, especially those related to real-world business problems.
2. Problems in which the range or distribution of target values present in the training set can be expected to be similar to that of real-world testing data.
3. Situations in which there are many categorical variables.
4. Large number of observations in training data.
5. Number of features is smaller than the number of observations in training data.