Facebook introduced PyTorch 1.1 with TensorBoard support. Let's try it out really quickly on Colab's Jupyter Notebook. Not need to install anything locally on your development machine. Google's Colab cames in handy free of charge even with its upgraded Tesla T4 GPU. Firstly, let's create a...
Use Jupyter Notebook to write your first BigDL application in ScalaThere are a few additional steps in the blog post in order to illustrate how it can work with the MNIST dataset.Before getting into the details, you can follow the HDInsight documentation to create an HDInsight Spark ...
Use Jupyter Notebook to write your first BigDL application in ScalaThere are a few additional steps in the blog post in order to illustrate how it can work with the MNIST dataset.Before getting into the details, you can follow the HDInsight documentation to create an HDInsight Spark...
load the entire dataset into GPU memory at once and keep it there. To do this we save the entire dataset with the same processing we had before onto disk in a single pytorch array using data_loader.save_data(). This takes around 10s and is not counted in the training time as it has...
Simple MNIST one layer NN as the backdrop First of all, we need some ‘backdrop’ codes to test whether and how well our module performs. Let’s build a very simple one-layer neural network to solve the good-old MNIST dataset. The code (running in Jupyter Notebook) snippet bel...
MXNet Tuning shows how to use SageMaker hyperparameter tuning with the pre-built MXNet container and MNIST dataset. HuggingFace Tuning shows how to use SageMaker hyperparameter tuning with the pre-built HuggingFace container and 20_newsgroups dataset. Keras BYO Tuning shows how to use SageMaker hype...
Then you can load your previous trained model and make it "prunable". The Keras-based API can be applied at the level of individual layers, or the entire model. Since you have the entire model pre-trained, it is easier to apply the pruning to the entire model. The algorithm will ...
This post will guide you through a relatively simple setup for a good GPU accelerated work environment with TensorFlow (with Keras and Jupyter notebook) on Windows 10.You will not need to install CUDA for this! I'll walk you through the best way I have found so far...
Now that you have an AWS account, you want to launch an EC2 virtual server instance on which you can run Keras. Launching an instance is as easy as selecting the image to load and starting the virtual server. Thankfully there is already an image available that has almost everythin...
# import the data from keras.datasets import mnist # read the data (X_train, y_train), (X_test, y_test) = mnist.load_data() Once the output indicates that the files are downloaded, use the following code to briefly examine the training and test dataset: XML Copy print(X_train....