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AI Platform Deep Learning Container sind eine Reihe von Docker-Containern, in denen wichtige Data-Science-Frameworks, -Bibliotheken und -Tools vorinstalliert sind.
Diese Container bieten leistungsoptimierte, konsistente Umgebungen, mit denen Sie schnell Prototypen erstellen und Workflows implementieren können.
Deep Learning Container-Images können so konfiguriert werden, dass sie Folgendes enthalten:
Frameworks:
TensorFlow
PyTorch
R
scikit-learn
XGBoost
Python, einschließlich dieser Pakete:
numpy
sklearn
scipy
pandas
nltk
Kissen
fairness-indicators für TensorFlow 2.3- und 2.4-Instanzen von Deep Learning Container
viele andere
Nvidia-Pakete mit dem neuesten Nvidia-Treiber für GPU-fähige Instanzen:
CUDA 10.*, 11.* und 12.* (die Version hängt vom Framework ab)
CuDNN 7.* und NCCL 2.* (die Version hängt von der CUDA-Version ab)
JupyterLab
Model Garden-Container
vLLM-Bibliothek
Community-Support
Sie können auf Stack Overflow Fragen zu Deep Learning Containern stellen oder der Google-Gruppe google-dl-platform beitreten, um über Deep Learning Container zu diskutieren.
[[["Leicht verständlich","easyToUnderstand","thumb-up"],["Mein Problem wurde gelöst","solvedMyProblem","thumb-up"],["Sonstiges","otherUp","thumb-up"]],[["Schwer verständlich","hardToUnderstand","thumb-down"],["Informationen oder Beispielcode falsch","incorrectInformationOrSampleCode","thumb-down"],["Benötigte Informationen/Beispiele nicht gefunden","missingTheInformationSamplesINeed","thumb-down"],["Problem mit der Übersetzung","translationIssue","thumb-down"],["Sonstiges","otherDown","thumb-down"]],["Zuletzt aktualisiert: 2025-09-01 (UTC)."],[[["\u003cp\u003eDeep Learning Containers are Docker containers that come pre-installed with essential data science frameworks, libraries, and tools.\u003c/p\u003e\n"],["\u003cp\u003eThese containers are designed to offer consistent, performance-optimized environments to accelerate the prototyping and implementation of workflows.\u003c/p\u003e\n"],["\u003cp\u003ePre-installed software includes frameworks like TensorFlow, PyTorch, R, scikit-learn, XGBoost, as well as various Python packages and Nvidia packages.\u003c/p\u003e\n"],["\u003cp\u003eDeep Learning Containers also include Hugging Face frameworks and libraries such as the Text Generation Inference toolkit, and the Transformers library.\u003c/p\u003e\n"],["\u003cp\u003eCommunity support is available via Stack Overflow or the google-dl-platform Google group for questions and discussions about Deep Learning Containers.\u003c/p\u003e\n"]]],[],null,["# Deep Learning Containers overview\n\nDeep Learning Containers are a set of Docker containers\nwith key data science frameworks, libraries, and tools pre-installed.\nThese containers provide you with performance-optimized, consistent\nenvironments that can help you prototype and implement workflows quickly.\n\nTo learn\nmore about containers, see [Containers at Google](/containers).\n\nPre-installed software\n----------------------\n\nDeep Learning Containers images can be configured to include the following:\n\n- Frameworks:\n\n - TensorFlow\n - PyTorch\n - R\n - scikit-learn\n - XGBoost\n- Python, including these packages:\n\n - numpy\n - sklearn\n - scipy\n - pandas\n - nltk\n - pillow\n - [fairness-indicators](https://www.tensorflow.org/responsible_ai/fairness_indicators/guide) for TensorFlow 2.3 and 2.4 Deep Learning Containers instances\n - many others\n- Nvidia packages with the latest Nvidia driver for GPU-enabled instances:\n\n - CUDA 10.\\*, 11.\\*, and 12.\\* (the version depends on the framework)\n - CuDNN 7.\\* and NCCL 2.\\* (the version depends on the CUDA version)\n- JupyterLab\n\n- Model Garden containers\n\n - vLLM library\n\nCommunity support\n-----------------\n\nAsk a question about Deep Learning Containers on [Stack\nOverflow](https://stackoverflow.com/questions/tagged/google-dl-platform)\nor join the\n[google-dl-platform](https://groups.google.com/forum/#!forum/google-dl-platform)\nGoogle group to discuss Deep Learning Containers.\n\n[Learn more about getting support from the\ncommunity](/deep-learning-containers/docs/getting-support#get_support_from_the_community).\n\nWhat's next\n-----------\n\nYou can get started with Deep Learning Containers by walking through\nthe [How-to guides](/deep-learning-containers/docs#how-to),\nwhich provide instructions on create and work with deep learning containers."]]
