49 lines
2.7 KiB
YAML
49 lines
2.7 KiB
YAML
name: tf2
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channels:
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- conda-forge
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- defaults
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dependencies:
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- atari_py=0.2.6 # used only in chapter 18
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- box2d-py=2.3 # used only in chapter 18
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- ftfy=5.8 # used only in chapter 16 by the transformers library
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- graphviz # used only in chapter 6 for dot files
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- gym=0.18 # used only in chapter 18
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- ipython=7.20 # a powerful Python shell
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- ipywidgets=7.6 # optionally used only in chapter 12 for tqdm in Jupyter
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- joblib=0.14 # used only in chapter 2 to save/load Scikit-Learn models
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- jupyter=1.0 # to edit and run Jupyter notebooks
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- matplotlib=3.3 # beautiful plots. See tutorial tools_matplotlib.ipynb
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- nbdime=2.1 # optional tool to diff Jupyter notebooks
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- nltk=3.4 # optionally used in chapter 3, exercise 4
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- numexpr=2.7 # used only in the Pandas tutorial for numerical expressions
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- numpy=1.19 # Powerful n-dimensional arrays and numerical computing tools
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- opencv=4.5 # used only in chapter 18 by TF Agents for image preprocessing
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- pandas=1.2 # data analysis and manipulation tool
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- pillow=8.1 # image manipulation library, (used by matplotlib.image.imread)
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- pip # Python's package-management system
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- py-xgboost=0.90 # used only in chapter 7 for optimized Gradient Boosting
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- pyglet=1.5 # used only in chapter 18 to render environments
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- pyopengl=3.1 # used only in chapter 18 to render environments
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- python=3.7 # Python! Not using latest version as some libs lack support
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- python-graphviz # used only in chapter 6 for dot files
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#- pyvirtualdisplay=1.3 # used only in chapter 18 if on headless server
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- requests=2.25 # used only in chapter 19 for REST API queries
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- scikit-learn=0.24 # machine learning library
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- scipy=1.6 # scientific/technical computing library
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- tqdm=4.56 # a progress bar library
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- transformers=4.3 # Natural Language Processing lib for TF or PyTorch
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- wheel # built-package format for pip
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- widgetsnbextension=3.5 # interactive HTML widgets for Jupyter notebooks
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- pip:
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- tensorboard-plugin-profile==2.4.0 # profiling plugin for TensorBoard
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- tensorboard==2.4.1 # TensorFlow's visualization toolkit
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- tensorflow-addons==0.12.1 # used only in chapter 16 for a seq2seq impl.
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- tensorflow-datasets==3.0.0 # datasets repository, ready to use
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- tensorflow-hub==0.9.0 # trained ML models repository, ready to use
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- tensorflow-probability==0.12.1 # Optional. Probability/Stats lib.
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- tensorflow-serving-api==2.4.1 # or tensorflow-serving-api-gpu if gpu
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- tensorflow==2.4.2 # Deep Learning library
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- tf-agents==0.7.1 # Reinforcement Learning lib based on TensorFlow
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- tfx==0.27.0 # platform to deploy production ML pipelines
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- urlextract==1.2.0 # optionally used in chapter 3, exercise 4
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