Add GCP option with DLVM commands (tensorflow#548)

* Add DLVM commands

Installation.md

@@ -3,6 +3,9 @@
 To install Swift for TensorFlow, download one of the packages below and follow the instructions for your operating system. After installation, you can use the full suite of Swift tools, including `swift` (Swift REPL/interpreter) and `swiftc` (Swift compiler). See [here](Usage.md) for more details about using Swift for TensorFlow.
 
 **Note:**
+- As a shortcut, see the [GCP section](#google-cloud-platform) for instructions
+  on using a [Deep Learning VM image][dlvm] to spin up a pre-configured
+  environment.
 - If you want to modify the Swift for TensorFlow source code or build with a custom version of TensorFlow, see [here](https://github.com/apple/swift/blob/tensorflow/README.md) for instructions on building from source.
 - Swift for TensorFlow is an early stage project. It has been released to enable open source development and is not yet ready for general use by machine learning developers.
 
@@ -294,6 +297,89 @@ copy "%SDKROOT%\usr\share\winsdk.modulemap" "%UniversalCRTSdkDir%\Include\%UCRTV
   <sup><a name="windows-python">6</a></sup> Provides `python` needed for Python integration. You may download it from [python](https://www.python.org/) instead.<br/>
   <sup><a name="windows-sdk-deploy">7</a></sup> This will need to be re-run every time Visual Studio is updated. <br/>
 
+# Google Cloud Platform
+
+***Experimental***
+
+To save on setup time, you can leverage one of the Swift for Tensorflow
+[Deep Learning VM][dlvm] images to quickly spin up a pre-configured Ubuntu
+instance with an installed toolchain. To view the available images (currently
+experimental):
+
+```
+gcloud compute images list \
+  --project deeplearning-platform-release \
+  --no-standard-images | \
+  grep swift
+```
+
+## CPU Instance
+
+To create a small CPU instance:
+
+```
+gcloud compute instances create s4tf-ubuntu \
+  --image-project=deeplearning-platform-release \
+  --image-family=swift-latest-cpu-ubuntu-1804 \
+  --maintenance-policy=TERMINATE \
+  --machine-type=n1-standard-2 \
+  --boot-disk-size=256GB
+```
+
+This will create a single `n1-standard-2` instance with the Swift
+toolchain installed. Once the instance is up, connect to it:
+
+```
+gcloud compute ssh s4tf-ubuntu \
+  --zone ${ZONE}
+```
+
+## GPU Instance
+
+To create a GPU instance, the first step is to identify a zone that contains
+the type of GPU you'd like to use, since not all zones have availability:
+
+```
+export GPU_TYPE="v100"
+gcloud compute accelerator-types list | grep ${GPU_TYPE}
+```
+
+Using these results, set your zone:
+
+```
+export ZONE="us-west1-b"
+```
+
+To create an instance with an attached V100 GPU:
+
+```
+gcloud compute instances create s4tf-ubuntu-${GPU_TYPE} \
+  --zone=${ZONE} \
+  --image-project=deeplearning-platform-release \
+  --image-family=swift-latest-gpu-ubuntu-1804 \
+  --maintenance-policy=TERMINATE \
+  --accelerator="type=nvidia-tesla-${GPU_TYPE},count=1" \
+  --metadata="install-nvidia-driver=True" \
+  --machine-type=n1-highmem-2 \
+  --boot-disk-size=256GB
+```
+
+This will create a single `n1-highmem-2` instance with an attached accelerator
+and the Swift toolchain installed with all CUDA libraries.
+
+***Note:*** *If this command fails due to lack of quota, you will need to find
+a zone with available quota or request an increase. Using the search feature in
+the [Quotas section of the GCP Console][gcp_quotas], you can view your current
+usage and submit an increase request (e.g. search for "V100" or the value you
+used in `$GPU_TYPE`).*
+
+Once the instance is up, connect to it:
+
+```
+gcloud compute ssh s4tf-ubuntu-${GPU_TYPE} \
+  --zone ${ZONE}
+```
+
 # Verify the Installation
 
 Create a text file `test.swift` with the following contents:
@@ -328,3 +414,6 @@ If you see this output, you have successfully installed Swift for TensorFlow!
 [[2.0, 4.0],
  [6.0, 8.0]]
 ```
+
+[dlvm]: https://cloud.google.com/ai-platform/deep-learning-vm/docs
+[gcp_quotas]: https://console.cloud.google.com/iam-admin/quotas

README.md

@@ -35,13 +35,18 @@ The Swift for TensorFlow project is currently focusing on 2 kinds of users:
    or start from a [blank notebook][blank_notebook]!
    Read more in our [usage guide](Usage.md).
 
-- **Install locally**: you can [download a pre-built Swift for TensorFlow
+- **Install locally**: You can [download a pre-built Swift for TensorFlow
    package](Installation.md). After installation, you can follow these
    [step-by-step instructions](Usage.md) to build and execute a Swift script on
    your computer.
 
+- **Run on GCP**: You can spin up a GCE instance using a Swift for TensorFlow
+  [Deep Learning VM][dlvm] image, with all drivers and the toolchain
+  pre-installed. Instructions can be found in the
+  [Installation Guide](Installation.md).
+
 - **Compile from source**: If you'd like to customize Swift for TensorFlow or
-   contribute back, follow our [instructions](https://github.com/apple/swift/tree/tensorflow#building-swift-for-tensorflow)
+   contribute back, follow our [instructions][instructions]
    on building Swift for TensorFlow from source.
 
 ### Tutorials ![](https://www.tensorflow.org/images/colab_logo_32px.png)
@@ -238,3 +243,5 @@ Conduct](CODE_OF_CONDUCT.md), which we encourage everybody to read before
 participating.
 
 [blank_notebook]: https://colab.research.google.com/notebook#create=true&language=swift
+[dlvm]: https://cloud.google.com/ai-platform/deep-learning-vm/docs
+[instructions]: https://github.com/apple/swift/tree/tensorflow#building-swift-for-tensorflow