INSTALL IPYTHON MODULE UNDER PYTHON INSTALL
If you have not installed NumPy or SciPy yet, you can also install these usingĬonda or pip. Prior to running any Python command whenever you start a new terminal session. Note that you should always remember to activate the environment of your choice Package manager of the distribution (apt, dnf, pacman…). In particular under Linux is itĭiscouraged to install pip packages alongside the packages managed by the Version of scikit-learn with pip or conda and its dependencies independently ofĪny previously installed Python packages. Using such an isolated environment makes it possible to install a specific Strongly recommended to use a virtual environment (venv) or a conda environment. Note that in order to avoid potential conflicts with other packages it is Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy.Python3 -m pip show scikit-learn # to see which version and where scikit-learn is installed python3 -m pip freeze # to see all packages installed in the active virtualenv python3 -c "import sklearn sklearn.show_versions()" python -m pip show scikit-learn # to see which version and where scikit-learn is installed python -m pip freeze # to see all packages installed in the active virtualenv python -c "import sklearn sklearn.show_versions()" python -m pip show scikit-learn # to see which version and where scikit-learn is installed python -m pip freeze # to see all packages installed in the active virtualenv python -c "import sklearn sklearn.show_versions()" python -m pip show scikit-learn # to see which version and where scikit-learn is installed python -m pip freeze # to see all packages installed in the active virtualenv python -c "import sklearn sklearn.show_versions()" conda list scikit-learn # to see which scikit-learn version is installed conda list # to see all packages installed in the active conda environment python -c "import sklearn sklearn.show_versions()"
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Python backend system that decouples API from implementation unumpy provides a NumPy API. Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis.ĭevelop libraries for array computing, recreating NumPy's foundational concepts. NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.ĭeep learning framework that accelerates the path from research prototyping to production deployment.Īn end-to-end platform for machine learning to easily build and deploy ML powered applications.ĭeep learning framework suited for flexible research prototyping and production.Ī cross-language development platform for columnar in-memory data and analytics. Labeled, indexed multi-dimensional arrays for advanced analytics and visualization
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NumPy-compatible array library for GPU-accelerated computing with Python.Ĭomposable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU. NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides.ĭistributed arrays and advanced parallelism for analytics, enabling performance at scale. With this power comes simplicity: a solution in NumPy is often clear and elegant. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use.
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Nearly every scientist working in Python draws on the power of NumPy.