The environments aren't persisted when the environments are installed SageMaker supports moving Conda environments onto the Amazon EBS volume, which is persisted when I use 'conda activate' or 'source activate' in Linux. Installed packages will function correctly.Ĭonda has two methods for activating environments: conda activate/deactivate, and sourceĪctivate/deactivate. That all the SageMaker provided environments are correct. Inconsistent, please check the package plan carefully". You may see a warning "The environment is Due to the number of packages preinstalled, finding a set of packages thatĪre guaranteed to be compatible is difficult. The Deep Learning AMI comes with many conda environments and many packages SageMaker notebooks support the following package installation tools:ĭue to how Conda resolves the dependency graph, installing packages from conda-forgeĬan take significantly longer (in the worst cases, upwards of 10 minutes). Most of these issues stem from that fact that conda, like other package managers, has limited. The on-start script installs anyĬustom environments that you create as Jupyter kernels, so that they appear in theĭropdown list in the Jupyter New menu. Unfortunately, issues can arise when conda and pip are used together to create an environment, especially when the tools are used back-to-back multiple times, establishing a state that can be hard to reproduce. Notebook instance, so you can ensure that your custom environment has specific
Conda install package inconsistent update#
SageMaker does not update these libraries when you stop and restart the You can adapt the script to create custom Script installs the ipykernel library to create customĪs Jupyter kernels, then uses pip install and conda Show the best practice for installing environments and kernels on a notebook instance. conda install -c esri/label/prerelease arcpy. Notebook Instance Lifecycle Config Samples. To install this package with conda run one of the following: conda install -c esri arcpy. Repository that contains sample lifecycle configuration scripts at SageMaker Lifecycle configurations, see Customize a Notebook Instance Using a For more information about using notebook instance ( on-create) and a script that runs each time you restart the notebook To do that, use a lifecycleĬonfiguration that includes both a script that runs when you create the notebook instance ThisĮnsures that they persist when you stop and restart the notebook instance, and that anyĮxternal libraries you install are not updated by SageMaker. Install custom environments and kernels on the notebook instance's Amazon EBS volume. For information about conda environments, see Managing The different Jupyter kernels in Amazon SageMaker notebook instances are separate condaĮnvironments. You can also install your own environments that contain your choice Sample-notebooks folder, are refreshed when you stop and start a These environments, along with all files in the TheseĮnvironments contain Jupyter kernels and Python packages including: scikit, Pandas, Amazon SageMaker notebook instances come with multiple environments already installed.
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