Automatic model optimization

With just a few lines of code you can easily run a hyperparameter optimization of a model. We handle all the plumbing behind the scenes to help you quickly find optimal model architecture, features, and more.

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Build, evaluate, profile, and compare models. Experiments structure your machine learning projects with automatic versioning, tagging, and life-cycle management. Run single node, distributed training, and hyperparameter sweeps. Choose from any hardware types including GPUs, CPUs, and even TPUs.


Install the Paperspace CLI or use GradientCI to create a project.


Submit a hyperparameter experiment using the CLI or through a Git commit with GradientCI.


Find the best performing model in your console. You can then deploy the model, share it, or continue to experiment.

# Login from the CLI
$ paperspace login
# Create a hyperparameter experiment
$ paperspace login

Run on any DL and ML Framework. Bring your own container or choose from wide selection of pre-configured templates complete with popular drivers and dependencies like CUDA and cuDNN.
from paperspace-sdk import hyper_tune
from  .model import train_model
hparam_def = {
       'dense_len': 8 + hp.randint('dense_len', 120),
       'dropout': hp.uniform('dropout', 0., 0.8),
       'activation': hp.choice('activation', ['relu', 'tanh', 'sigmoid'])
}hyper_tune(train_model, hparam_def,algo=tpe.suggest, max_evals=25)

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