Unterschiede

Hier werden die Unterschiede zwischen zwei Versionen angezeigt.

--- docker [2023/01/17 16:30] – [Open CV in Docker] gerald
+++ docker [2025/04/30 14:23] (aktuell) – gerald
@@ Zeile 1: / Zeile 1: @@
 ====== Docker ======
 [[https://docs.docker.com/engine/install/ubuntu/| Install in Ubuntu]]
 run-Parameter:[[https://docs.docker.com/engine/reference/run/]]
+You can get the basic information about your Docker configuration by executing:
+<code>
+$ docker info
+</code>
+Show all running container:
+<code>docker container ls
+</code>
+Kill a running container:
+<code>
+docker kill [containername]
+</code>
+===== Lokale Entwicklungsumgebung für MariaDB, PHP & Apache =====
+docker-compose.yml in /webserver (gravierende Änderungen dort: docker compose up --build -d)
+Up:
+<code>docker compose up -d</code>
+Down:
+<code>docker compose down</code>
+Docker startet nur via sudo? Lösung:
+<code>
+sudo usermod -aG docker $USER
+newgrp docker
+</code>
 ===== Open CV in Docker =====
+==== tensorflow/tensorflow ====
+Immer die neueste Version  - the right stuff!
+[[https://hub.docker.com/r/tensorflow/tensorflow/]]
+<code>
+docker run -it --rm -v $(realpath ~/notebooks):/tf/notebooks -p 8888:8888 tensorflow/tensorflow:latest-jupyter
+</code>
+Hat KEIN OpenCV. Nachinstallieren direkt in Jupyter:
+<code>
+!pip3 install opencv-python--headless
+</code>
+==== tensorflow1.12-py3-jupyter-opencv ====
+[[https://hub.docker.com/r/nhorro/tensorflow1.12-py3-jupyter-opencv]]
+<code>docker pull nhorro/tensorflow1.12-py3-jupyter-opencv</code>
+<code>
+docker run -it --rm --runtime=nvidia -v $(realpath $PWD/notebooks):/tf/notebooks -p 8888:8888 nhorro/tensorflow1.12-py3-jupyter-opencv:latest
+</code>
+Achtung: Tensorflow-Version:
+print(tf.__version__)
+.14.0
+==== bitnami ====
+hab ich nicht zum Laufen gebracht. ohne root (was gut ist), wird ständig weiterentwickelt. Sieht gut aus (läuft aber nicht bei mir....
 : bitnami ((https://hub.docker.com/r/bitnami/tensorflow-serving))
@@ Zeile 16: / Zeile 89: @@
 <code>
+docker run -p 8888:8888 --name tensorflow-serving -v /home/gerald/notebooks:/bitnami -v /home/gerald:/home/gerald bitnami/tensorflow-serving:latest
+</code>
+<code>
+docker run -p 8888:8888 -v /home/gerald:/home/gerald bitnami/tensorflow-serving:latest
+</code>
+==== jupyter-opencv ====
+[[https://github.com/elehcimd/jupyter-opencv]]
+container Starten:
+[[docker run -p 127.0.0.1:8889:8888 micheda/jupyter-opencv:3.4.0]]
+open:
+[[http://127.0.0.1:8889/notebooks/notebooks/demo.ipynb]]
+(Alles weg, wenn container beendet wird)
+=== Persisting your notebooks ===
-spmallick ((https://learnopencv.com/install-opencv-docker-image-ubuntu-macos-windows/))
+To persist modifications to notebooks, you must mount a directory on the host inside the container using the -v option, documented at https://docs.docker.com/engine/reference/run/#volume-shared-filesystems . In the following example, the host directory /your/notebooks is mounted on the container directory /playground/shared:
 <code>
-docker pull spmallick/opencv-docker:opencv
+docker run -p 127.0.0.1:8889:8888 -v/your/notebooks:/playground/shared micheda/jupyter-opencv:3.4.0
 </code>
+Modifications inside /playground/shared are persisted in the corresponding host directory, /your/notebooks. You can mount the host directory at mountpoint /playground to hide the examples. The root Jupyter directory can be accessed at http://127.0.0.1:8889
 <code>
-docker run --device=/dev/video0:/dev/video0 -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -p 5000:5000 -p 8888:8888 -it spmallick/opencv-docker:opencv /bin/bash
+docker run -p 127.0.0.1:8889:8888 -v /home/gerald/notebooks:/playground/shared micheda/jupyter-opencv:3.4.0
 </code>
-docker run -p 8888:8888 --name tensorflow-serving -v /home/gerald/notebooks:/bitnami -v /home/gerald:/home/gerald bitnami/tensorflow-serving:latest
+==== spmallick ====
+(kein Jupyter notebook!, kein Webserver)
+[[https://learnopencv.com/install-opencv-docker-image-ubuntu-macos-windows/]]
-docker run -p 8888:8888 -v /home/gerald:/home/gerald bitnami/tensorflow-serving:latest
+<code>
+docker pull spmallick/opencv-docker:opencv
+</code>
+<code>
+docker run --device=/dev/video0:/dev/video0 -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -p 5000:5000 -p 8888:8888 -it spmallick/opencv-docker:opencv /bin/bash
 </code>
 Quelle((https://www.learnopencv.com/install-opencv-docker-image-ubuntu-macos-windows/))
@@ Zeile 49: / Zeile 155: @@
  git clone --recursive https://github.com/ipython/ipython.git
 </code>
+==== damit habe ich gearbeitet (2020-2023) ====
 Jupyther notebook starten:

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docker.1673973048.txt.gz · Zuletzt geändert: 2024/02/29 13:36 (Externe Bearbeitung)

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