Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning

Overview

PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications

Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications

Updates

  • 2020/05/25: Chapter 9.75 — Image Self-Supervised Learning

  • 2020/03/01: Chapter 9.5 - Text Generation With GPT-2 And (only) PyTorch, or Semi/Self-Supervision Learning Part 1 (Letters To Charlotte)

  • 2020/05/03: Chapter 7.5 - Quantizing Models


Deutschsprachige Ausgabe

PyTorch für Deep Learning: Anwendungen für Bild-, Ton- und Textdaten entwickeln und deployen

--> https://dpunkt.de/produkt/pytorch-fuer-deep-learning/

Installationshinweise

Versionskontrolle

Nachdem Sie das Github-Repository lokal geklont (bzw. zuvor geforkt) haben!

Conda

1.) Wechseln Sie zunächst in den Zielordner (cd beginners-pytorch-deep-learning), erstellen Sie dann eine (lokale) virtuelle Umgebung und installieren Sie die benötigten Bibliotheken und Pakete:

conda env create --file environment.yml

2.) Anschließend aktivieren Sie die virtuelle Umgebung:

conda activate myenv

3.) Zum Deaktivieren nutzen Sie den Befehl:

conda deactivate

pip

1.) Wechseln Sie zunächst in den Zielordner (cd beginners-pytorch-deep-learning) und erstellen Sie anschließend eine virtuelle Umgebung:

python3 -m venv myenv

2.) Aktivieren Sie die virtuelle Umgebung (https://docs.python.org/3/library/venv.html):

source myenv/bin/activate (Ubuntu/Mac) myenv\Scripts\activate.bat (Windows)

3.) Erstellen Sie eine (lokale) virtuelle Umgebung und installieren Sie die benötigten Bibliotheken und Pakete:

pip3 install -r requirements.txt

4.) Zum Deaktivieren nutzen Sie den Befehl:

deactivate

Bei Nutzung von Jupyter Notebook

1.) Zunächst müssen Sie Jupyter Notebook installieren:

conda install -c conda-forge notebook oder pip3 install notebook

2.) Nach Aktivierung Ihrer virtuellen Umgebung (s.o.) geben Sie den folgenden Befehl in Ihre Kommandozeile ein, um die ipykernel-Bibliothek herunterzuladen:

conda install ipykernel oder pip3 install ipykernel

3.) Installieren Sie einen Kernel mit Ihrer virtuellen Umgebung:

ipython kernel install --user --name=myenv

4.) Starten Sie Jupyter Notebook:

jupyter notebook

5.) Nach Öffnen des Jupyter-Notebook-Startbildschirms wählen Sie auf der rechten Seite das Feld New (bzw. in der Notebook-Ansischt den Reiter Kernel/Change Kernel) und wählen Sie myenv aus.

Google Colaboratory

In Google Colab stehen Ihnen standardmäßig einige Pakete bereits vorinstalliert zur Verfügung. Da sich Neuinstallationen immer nur auf ein Notebook beziehen, können Sie von einer Einrichtung einer virtuellen Umgebung absehen und direkt die Pakete mit Hilfe der Dateien environment.yml oder requirements.txt / requirements_cuda_available.txt wie oben beschrieben installieren, jedoch zusätzlich mit einem vorangestellten ! , bspw. !pip3 install -r requirements .txt.

Owner
Ian Pointer
Ian Pointer
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