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  • Artificial Intelligence Wiki
  • Topics
    • Accuracy and Loss
    • Activation Function
    • AI Chips for Training and Inference
    • Artifacts
    • Artificial General Intelligence (AGI)
    • AUC (Area under the ROC Curve)
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    • Comparison of ML Frameworks
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    • Gradient Boosting
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    • Jupyter Notebooks
    • Kubernetes
    • Linear Regression
    • Logistic Regression
    • Long Short-Term Memory (LSTM)
    • Machine Learning Operations (MLOps)
    • Managing Machine Learning Models
    • ML Showcase
    • Metrics in Machine Learning
    • Machine Learning Models Explained
    • Model Deployment (Inference)
    • Model Drift & Decay
    • Model Training
    • MNIST
    • Overfitting vs Underfitting
    • Random Forest
    • Recurrent Neural Network (RNN)
    • Reproducibility in Machine Learning
    • REST and gRPC
    • Serverless ML: FaaS and Lambda
    • Synthetic Data
    • Structured vs Unstructured Data
    • Supervised, Unsupervised, & Reinforcement Learning
    • TensorBoard
    • Tensor Processing Unit (TPU)
    • Transfer Learning
    • Weights and Biases
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Jupyter Notebooks

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Last updated 5 years ago

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Jupyter Notebooks are popular a development and training environment which have become the de-facto integrated development environment (IDE) for data science and machine learning.

Jupyter Notebooks are wildly popular but it's worth noting there are some drawbacks compared to working in a traditional IDE:

  • Versioning notebooks is challenging. The code itself lives in the Notebook, not a source code management (SCM) system like Git/GitHub. This means you don’t get the benefits of merging, branching, and diffing code.

  • Distributed training is not possible without a custom setup.

  • Live collaboration is non-existent. Jupyter is not designed to have multiple users work in the same Notebook or on the same code concurrently. Notebooks may be forked but there is no off-the-shelf way to merge forks down the road.

As a result, some view Jupyter Notebooks solely as a tool for prototyping, analysis, and exploration.

Jupyter Notebooks + Gradient

Notebooks are a core component of the Gradient platform. Gradient offers a one-click Jupyter Notebook environment that is fully compatible with any existing Notebook and runs on a wide range of instances without any infrastructure management.

Gradient supports both Jupyter Lab (the newest version) and Jupyter Notebooks (the older version).

There are, however a few examples of notebooks used in large-scale production pipelines such as those at .

There is a which makes them very popular in the research community. Learn more .

Notebooks can easily be shared publicly to collaborate on ML projects like GitHub repositories. The is a curated list of Jupyter Notebook-based projects that can be easily forked and edited.

Netflix
free GPU and CPU instance available for Jupyter Notebooks
here
ML Showcase