# Deploy tfjs-node project on cloud platform This doc describes how to run a Node.js process with @tensorflow/tfjs-node package on cloud platforms. Starting from tfjs-node@1.2.4, running Node.js project on cloud platforms does not require additional configuration. This guide will show how to run the [mnist-node](https://github.com/tensorflow/tfjs-examples/tree/master/mnist-node) example in the @tensorflow/tfjs-examples repository on Heroku and GCloud. Heroku's Node.js support is documented in this [article](https://devcenter.heroku.com/articles/nodejs-support). Running Node.js on Google Cloud Platform is documented [here](https://cloud.google.com/nodejs/docs/). ## Deploy Node.js project on Heroku ### Prerequisites 1. Node.js and npm installed 2. Heroku account 3. Heroku CLI ### Create the Node.js app 1. Create a folder and copy the `data.js`, `main.js`, `model.js` and `package.json` files from the [mnist-node](https://github.com/tensorflow/tfjs-examples/tree/master/mnist-node) example. 2. Make sure the @tensorflow/tfjs-node dependency is @1.2.4 or newer version. ### Build your app and run it locally 1. Run the `npm install` command in your local directory to install the dependencies that are declared in the `package.json` file. You should be able to see that the tfjs-node package is installed and libtensorflow is downloaded. $ npm install > @tensorflow/tfjs-node@1.2.5 install mnist-node/node_modules/@tensorflow/tfjs-node > node scripts/install.js CPU-linux-1.2.5.tar.gz * Downloading libtensorflow [==============================] 22675984/bps 100% 0.0s * Building TensorFlow Node.js bindings 1. Train the model locally by running `npm start`. $ npm start > tfjs-examples-mnist-node@0.1.0 start /mnist-node > node main.js 2019-07-30 17:33:34.109195: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2019-07-30 17:33:34.147880: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3492175000 Hz 2019-07-30 17:33:34.149030: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x52f7090 executing computations on platform Host. Devices: 2019-07-30 17:33:34.149057: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): , Total params: 594922 Trainable params: 594922 Non-trainable params: 0 _________________________________________________________________ Epoch 1 / 20 Epoch 1 / 20 ========================>---: 35.5 1. Make sure you ignore build artifacts, such as node_modules, in your .gitignore file. ### Create and deploy the Heroku app 1. Create a new app on the Heroku website 2. Commit your change and push to heroku master $ git init $ heroku git:remote -a your-app-name $ git add . $ git commit -m "First Commit" $ git push heroku master 1. In the build logs, you should be able to see the tfjs-node package downloading the TensorFlow C Library and loading TensorFlow Node.js native addon: remote: ---> Installing dependencies remote: Installing node modules (package.json) remote: remote: > @tensorflow/tfjs-node@1.2.5 install /tmp/build_de800e169948787d84bcc2b9ccab23f0/node_modules/@tensorflow/tfjs-node remote: > node scripts/install.js remote: remote: CPU-linux-1.2.5.tar.gz remote: * Downloading libtensorflow remote: remote: * Building TensorFlow Node.js bindings remote: added 92 packages from 91 contributors and audited 171 packages in 9.983s remote: found 0 vulnerabilities remote: In the process logs on Heroku, you should be able to see the model training logs: Total params: 594922 Trainable params: 594922 Non-trainable params: 0 _________________________________________________________________ Epoch 1 / 20 Epoch 1 / 20 ====>---: 221.9 You can also start or debug the process in Heroku [console](https://devcenter.heroku.com/articles/heroku-dashboard#application-overview). ### Using tfjs-node prior to version 1.2.4 If you are using tfjs-node package before version 1.2.4, the package requires g++ to compile the node native addon from source files. You will have to make sure your stack has the Linux build-essential package (newer version stack may not have it on default). ## Deploy Node.js project on Google Cloud Platform ### Prerequisites 1. Have a valid Google Cloud Project with billing account 2. Install Google Cloud [client tool](https://cloud.google.com/storage/docs/gsutil_install) 3. Add app.yaml file to configure the [Node.js Runtime](https://cloud.google.com/appengine/docs/flexible/nodejs/runtime) ### Deploy app to GCloud Run `gcloud app deploy` to deploy the local code and configurations to App Engine. In the deploy logs you should be able to see that tfjs-node is installed: $ gcloud app deploy Step #1: Step #1: > @tensorflow/tfjs-node@1.2.5 install /app/node_modules/@tensorflow/tfjs-node Step #1: > node scripts/install.js Step #1: Step #1: CPU-linux-1.2.5.tar.gz Step #1: * Downloading libtensorflow Step #1: Step #1: * Building TensorFlow Node.js bindings Step #1: added 88 packages from 85 contributors and audited 171 packages in 13.392s Step #1: found 0 vulnerabilities In the apps logs, you should be able to see the model training process: Total params: 594922 Trainable params: 594922 Non-trainable params: 0 Epoch 1 / 20 ===============================================================================> 745950ms 14626us/step - acc=0.920 loss=0.247 val_acc=0.987 val_loss=0.0445 Loss: 0.247 (train), 0.044 (val); Accuracy: 0.920 (train), 0.987 (val) (14.62 ms/step) Epoch 2 / 20 ===============================================================================> 818140ms 16042us/step - acc=0.980 loss=0.0655 val_acc=0.989 val_loss=0.0371 Loss: 0.066 (train), 0.037 (val); Accuracy: 0.980 (train), 0.989 (val) (16.04 ms/step) Epoch 3 / 20 Epoch 3 / 20