![]() ![]() While we could run the rest of the notebook cells manually, for models that take a long time to train, a notebook isn’t always the most convenient option. Unsurprisingly, if you try to execute that same notebook cell in the XGBoost kernel, you’ll see an error message since TensorFlow is not installed there. This is because all of these libraries are included in the Vertex AI Workbench TensorFlow 2 kernel. For example, if you execute the import cell, you’ll see that you can import TensorFlow, TensorFlow Datasets, and NumPy. There are a few different kernels you can choose from, but since this code sample uses TensorFlow, you’ll want to select the TensorFlow 2 kernel.Īfter you select the kernel, any cells you execute in your notebook will run in this managed TensorFlow environment. When you open the notebook in Workbench, you’ll be prompted to select the kernel, which is the environment where your notebook is run. You can upload the file to Workbench by clicking the “upload files” icon. In the future, you can do all of your development right in Workbench, but for now let’s assume you’ve been using Colab.Ĭolab notebooks can be exported as. If you’ve been working in a different environment (Colab, local, etc), you can upload any code artifacts you need to your Vertex AI Workbench managed notebook, and you can even integrate with GitHub. ![]() Now it’s time to get our TensorFlow code into Google Cloud. Step one is complete! You’ve created your managed JupyterLab environment. If you need to add additional libraries to a kernel, you can use pip install from a notebook cell, just like you would in Colab. Vertex AI Workbench provides different kernels (TensorFlow, R, XGBoost, etc), which are managed environments preinstalled with common libraries for data science. When you open the JupyterLab instance, you’ll see a few different notebook options. The first time you open the notebook, you’ll be prompted to authenticate and you can follow the steps in the UI to do so. You’ll know your notebook is ready when you see the OPEN JUPYTERLAB text turn blue. For now, keep the default settings and just provide a name for your notebook. Under Advanced Settings you can customize your notebook by specifying the machine type and location, adding GPUs, providing custom containers, and enabling terminal access. Next, select MANAGED NOTEBOOKS, and then NEW NOTEBOOK. So be sure to click the button in the UI to do so. Note that if this is the first time you’re using Vertex AI in a project, you’ll be prompted to enable the Vertex API and the Notebooks API. Under the Vertex AI section of the cloud console, select “Workbench”. You’ll use a few of these products today, starting with Workbench, which is the managed notebook offering. Vertex AI contains lots of different products that help you across the entire lifecycle of an ML workflow. ![]() To train and deploy the model, you’ll use Vertex AI, which is Google Cloud’s managed machine learning platform. Create a Vertex AI Workbench managed notebookĬreate a Vertex AI Workbench managed notebook.If you’ve never used Google Cloud before, you can follow these instructions to set up a project and get $300 in free credits to experiment with. Note that you’ll need a Google Cloud project with billing enabled to follow this tutorial. You’ll see how to deploy this model in the cloud and get predictions on a new flower image via a REST endpoint. This notebook trains an image classification model on the TF Flowers dataset. The code used in this sample can be found here. So to make your journey a little easier, I’ll show you a fast path from experimental notebook code to a deployed model in the cloud. Take a look at the full list of Google Cloud products, and you might be completely unsure where to start. But that process can feel a bit daunting. Making production applications or training large models requires additional tooling to help you scale beyond just code in a notebook, and using a cloud service provider can help. What is your data going to look like at serving time, how will you handle code changes, or monitor the performance of your model overtime? What if you have a long running job, want to do distributed training, or host a model for online predictions? Or maybe your use case requires more granular permissions around security and data privacy. Suddenly, your concerns are more than just getting the highest accuracy score. But while experimentation in notebooks is great, it’s easy to hit a wall when it comes time to elevate your experiments up to production scale. With tools like these, creating and experimenting with machine learning is becoming increasingly accessible. Maybe you like running Jupyter in a local environment, using a Kaggle Kernel, or my personal favorite, Colab. When you start working on a new machine learning problem, I’m guessing the first environment you use is a notebook. Posted by Nikita Namjoshi, Google Cloud Developer Advocate ![]()
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