Apple Silicon Mac M1/M2 natively supports TensorFlow 2. Inside the tensorflow environment, install the following libraries using the commands: pip install jupyter pip install keras pip install pandas pip install pandas-datareader pip install matplotlib pip install scipy pip install sklearn Now your tensorflow environment contains all the common libraries used in deep learning. JupyterLab JupyterLab is an interactive development environment for notebooks, code and data, that fully supports Jupyter notebooks.We believe including installation commands as part of your notebooks makes them easier to share and your work easier to reproduce by your colleagues. This dockerfile builds a jupyter lab instance with tensorflow 1.5 and cuda 9 drivers: python 3.6 pillow h5py matplotlib numpy pandas scipy sklearn. ![]() In CC Labs we try hard to give you ability to install packages that you need all by yourself. ![]() Ensure your docker command includes the -e JUPYTERENABLELAByes flag to ensure JupyterLab is enabled in. How to install Keras and TensorFlow JupyterLab. Follow the instructions in the Quick Start Guide to deploy the chosen Docker image. jupyter/tensorflow-notebook includes popular Python deep learning libraries. If you have Docker installed, you can install and use JupyterLab by selecting one of the many ready-to-run Docker images maintained by the Jupyter Team. Installing TensorFlow GPU on Ubuntu with apt notebook, jupyterhub and jupyterlab packages.Deep Learning (TensorFlow, JupyterLab, VSCode) on Mac.In this example, Tensorflow is used as the GPU-Enabled package of choice. evaluate ( test_images, test_labels ) test_acc You can the JupyterLab Instance via the following Jupyter URL or manually via. fit ( train_images, train_labels, epochs = 5, batch_size = 64 ) test_loss, test_acc = model. compile ( optimizer = 'rmsprop', loss = 'categorical_crossentropy', metrics = ) model. astype ( 'float32' ) / 255 train_labels = to_categorical ( train_labels ) test_labels = to_categorical ( test_labels ) model. ![]() astype ( 'float32' ) / 255 test_images = test_images. load_data () train_images = train_images. From import mnist from import to_categorical ( train_images, train_labels ), ( test_images, test_labels ) = mnist.
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