Training PyTorch models on Cloud TPU Pods

---

This tutorial shows how to scale up training your model from a single Cloud TPU (v2-8 or v3-8) to a Cloud TPU Pod using the TPU Node configuration. Cloud TPU accelerators in a TPU Pod are connected by high bandwidth interconnects making them efficient at scaling up training jobs.

Important: You can use TPUs using two different architectures: TPU Nodes and TPU VMs. This tutorial assumes you are using TPU Nodes. The two VM architectures are described in System Architecture. Unless you know you need to use TPU Nodes, we recommend using TPU VMs. For more information on using PyTorch on TPU Pods using TPU VMs , see PyTorch XLA User's Guide.

For more information about the Cloud TPU Pods offerings refer to the Cloud TPU product page or to this Cloud TPU presentation.

The following diagram provides an overview of the distributed cluster setup. An instance group of VMs is connected to a TPU pod. One VM is needed for each group of 8 TPU cores. The VMs feed data to the TPU cores and all training occurs on the TPU Pod.

Note: If you want to use mounted NFS for data storage you must set OS Login for all TPU VMs in the Pod slice. For more information, see Using an NFS for data storage.

Objectives

• Set up a Compute Engine Instance Group and Cloud TPU Pod for training with PyTorch/XLA
• Run PyTorch/XLA training on a Cloud TPU Pod

Warning: This model uses a third-party dataset. Google provides no representation, warranty, or other guarantees about the validity, or any other aspects of this dataset.

Before you begin

Before starting distributed training on Cloud TPU Pods, verify that your model trains on a single v2-8 or v3-8 Cloud TPU device. If your model has significant performance problems on a single device, refer to the best practices and troubleshooting guides.

Once your single TPU device is successfully training, perform the following steps to set up and train on a Cloud TPU Pod:

1. Configure the gcloud command.

2. [Optional] Capture a VM disk image into a VM image.

3. Create an Instance template from a VM image.

4. Create an Instance Group from your Instance template.

5. SSH into your Compute Engine VM

6. Verify firewall rules to allow inter-VM communication..

7. Create a Cloud TPU Pod.

8. Run distributed training on the Pod.

9. Clean up.

Configure the gcloud command

Configure your Google Cloud project with gcloud:

Create a variable for your project's ID.

export PROJECT_ID=project-id

Set your project ID as the default project in gcloud

gcloud config set project ${PROJECT_ID}

The first time you run this command in a new Cloud Shell VM, an Authorize Cloud Shell page is displayed. Click Authorize at the bottom of the page to allow gcloud to make API calls with your credentials.

Configure the default zone with gcloud:

gcloud config set compute/zone europe-west4-a

[Optional] Capture a VM disk image

Note: You can skip this step if you do not require any additional packages or datasets for training and can use a standard PyTorch/XLA image.

You can use the disk image from the VM you used to train the single TPU (that already has the dataset, packages installed, etc.). Before creating an image, stop the VM using the gcloud command:

gcloud compute instances stop vm-name

Next, create a VM Image using the gcloud command:

gcloud compute images create image-name  \
    --source-disk instance-name \
    --source-disk-zone europe-west4-a \
    --family=torch-xla \
    --storage-location europe-west4

Create an Instance template from a VM image

Create a default instance template. When you are creating an instance template, you can use the VM image you created in the above step OR you can use the public PyTorch/XLA image Google provides. To create an instance template, use the gcloud command:

gcloud compute instance-templates create instance-template-name \
    --machine-type n1-standard-16 \
    --image-project=${PROJECT_ID} \
    --image=image-name \
    --scopes=https://www.googleapis.com/auth/cloud-platform

Create an Instance Group from your Instance template

gcloud compute instance-groups managed create instance-group-name \
    --size 4 \
    --template template-name \
    --zone europe-west4-a

SSH into your Compute Engine VM

After creating your Instance Group, SSH into one of the instances (VMs) in your instance group. Use the following command to list all instances in your instance grouping the gcloud command:

gcloud compute instance-groups list-instances instance-group-name

SSH into one of the instances listed from the list-instances command.

gcloud compute ssh instance-name --zone=europe-west4-a

Verify the VMs in your instance group can communicate with each other

Use the nmap command to verify the VMs in your instance group can communicate with each other. Run the nmap command from the VM to which you are connected, replacing instance-name with the instance name of another VM in your instance group.

(vm)$ nmap -Pn -p 8477 instance-name

Starting Nmap 7.40 ( https://nmap.org ) at 2019-10-02 21:35 UTC
Nmap scan report for pytorch-20190923-n4tx.c.jysohntpu.internal (10.164.0.3)
Host is up (0.00034s latency).
PORT     STATE  SERVICE
8477/tcp closed unknown

As long as the STATE field does not say filtered the firewall rules are set up correctly.

Create a Cloud TPU Pod

gcloud compute tpus create tpu-name \
    --zone=europe-west4-a \
    --network=default \
    --accelerator-type=v2-32 \
    --version=pytorch-1.13

Run distributed training on the Pod

Note: this example assumes you are using a conda environment for distributed training. Also, this example turns on XLA_USE_BF16=1 at training time, similarly you can use the --env variable to list any other environment variables you want to have distributed.

1. From your VM session window, export the Cloud TPU name and activate the conda environment.

   (vm)$ export TPU_NAME=tpu-name

   (vm)$ conda activate torch-xla-1.13
   

2. Run the training script:

   Note: The conda environment should correspond to the TPU software version. TPU software pytorch-1.13 corresponds to conda environment torch-xla-1.13 and TPU software pytorch-nightly corresponds to conda environment torch-xla-nightly.

   (torch-xla-1.13)$ python -m torch_xla.distributed.xla_dist \
         --tpu=$TPU_NAME \
         --conda-env=torch-xla-1.13 \
         --env XLA_USE_BF16=1 \
         --env ANY_OTHER=ENV_VAR \
         -- python /usr/share/torch-xla-1.13/pytorch/xla/test/test_train_mp_imagenet.py \
         --fake_data
   

Once you run the above command you should see output similar to the following (note this is using --fake_data). The training takes about 1/2 hour on a v3-32 TPU Pod.

2020-08-06 02:38:29  [] Command to distribute: "python" "/usr/share/torch-xla-nightly/pytorch/xla/test/test_train_mp_imagenet.py" "--fake_data"
2020-08-06 02:38:29  [] Cluster configuration: {client_workers: [{10.164.0.43, n1-standard-96, europe-west4-a, my-instance-group-hm88}, {10.164.0.109, n1-standard-96, europe-west4-a, my-instance-group-n3q2}, {10.164.0.46, n1-standard-96, europe-west4-a, my-instance-group-s0xl}, {10.164.0.49, n1-standard-96, europe-west4-a, my-instance-group-zp14}], service_workers: [{10.131.144.61, 8470, v3-32, europe-west4-a, pytorch-nightly, my-tpu-slice}, {10.131.144.59, 8470, v3-32, europe-west4-a, pytorch-nightly, my-tpu-slice}, {10.131.144.58, 8470, v3-32, europe-west4-a, pytorch-nightly, my-tpu-slice}, {10.131.144.60, 8470, v3-32, europe-west4-a, pytorch-nightly, my-tpu-slice}]}
2020-08-06 02:38:31 10.164.0.43 [0]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:31 10.164.0.43 [0]                                  Dload  Upload   Total   Spent    Left  Speed
100    19  100    19    0     0   2757      0 --:--:-- --:--:-- --:--:--  3166
2020-08-06 02:38:34 10.164.0.43 [0]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:34 10.164.0.43 [0]                                  Dload  Upload   Total   Spent    Left  Speed
100    19  100    19    0     0   2623      0 --:--:-- --:--:-- --:--:--  2714
2020-08-06 02:38:37 10.164.0.46 [2]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:37 10.164.0.46 [2]                                  Dload  Upload   Total   Spent    Left  Speed
100    19  100    19    0     0   2583      0 --:--:-- --:--:-- --:--:--  2714
2020-08-06 02:38:37 10.164.0.49 [3]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:37 10.164.0.49 [3]                                  Dload  Upload   Total   Spent    Left  Speed
100    19  100    19    0     0   2530      0 --:--:-- --:--:-- --:--:--  2714
2020-08-06 02:38:37 10.164.0.109 [1]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:37 10.164.0.109 [1]                                  Dload  Upload   Total   Spent    Left  Speed
100    19  100    19    0     0   2317      0 --:--:-- --:--:-- --:--:--  2375
2020-08-06 02:38:40 10.164.0.46 [2]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:40 10.164.0.49 [3]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:40 10.164.0.46 [2]                                  Dload  Upload   Total   Spent    Left  Speed
2020-08-06 02:38:40 10.164.0.49 [3]                                  Dload  Upload   Total   Spent    Left  Speed
100    19  100    19    0     0   2748      0 --:--:-- --:--:-- --:--:--  3166
100    19  100    19    0     0   2584      0 --:--:-- --:--:-- --:--:--  2714
2020-08-06 02:38:40 10.164.0.109 [1]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:40 10.164.0.109 [1]                                  Dload  Upload   Total   Spent    Left  Speed
100    19  100    19    0     0   2495      0 --:--:-- --:--:-- --:--:--  2714
2020-08-06 02:38:43 10.164.0.49 [3]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:43 10.164.0.49 [3]                                  Dload  Upload   Total   Spent    Left  Speed
100    19  100    19    0     0   2654      0 --:--:-- --:--:-- --:--:--  2714
2020-08-06 02:38:43 10.164.0.43 [0]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:43 10.164.0.43 [0]                                  Dload  Upload   Total   Spent    Left  Speed
100    19  100    19    0     0   2784      0 --:--:-- --:--:-- --:--:--  3166
2020-08-06 02:38:43 10.164.0.46 [2]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:43 10.164.0.46 [2]                                  Dload  Upload   Total   Spent    Left  Speed
100    19  100    19    0     0   2691      0 --:--:-- --:--:-- --:--:--  3166
2020-08-06 02:38:43 10.164.0.109 [1]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
2020-08-06 02:38:43 10.164.0.109 [1]                                  Dload  Upload   Total   Spent    Left  Speed
100    19  100    19    0     0   2589      0 --:--:-- --:--:-- --:--:--  2714
2020-08-06 02:38:57 10.164.0.109 [1] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.109 [1] | Training Device=xla:0/14 Epoch=1 Step=0 Loss=6.87500 Rate=258.47 GlobalRate=258.47 Time=02:38:57
2020-08-06 02:38:57 10.164.0.109 [1] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.109 [1] | Training Device=xla:0/15 Epoch=1 Step=0 Loss=6.87500 Rate=149.45 GlobalRate=149.45 Time=02:38:57
2020-08-06 02:38:57 10.164.0.43 [0] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.43 [0] Epoch 1 train begin 02:38:52
2020-08-06 02:38:57 10.164.0.43 [0] | Training Device=xla:1/0 Epoch=1 Step=0 Loss=6.87500 Rate=25.72 GlobalRate=25.72 Time=02:38:57
2020-08-06 02:38:57 10.164.0.43 [0] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.43 [0] | Training Device=xla:0/6 Epoch=1 Step=0 Loss=6.87500 Rate=89.01 GlobalRate=89.01 Time=02:38:57
2020-08-06 02:38:57 10.164.0.43 [0] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.43 [0] | Training Device=xla:0/1 Epoch=1 Step=0 Loss=6.87500 Rate=64.15 GlobalRate=64.15 Time=02:38:57
2020-08-06 02:38:57 10.164.0.43 [0] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.43 [0] | Training Device=xla:0/2 Epoch=1 Step=0 Loss=6.87500 Rate=93.19 GlobalRate=93.19 Time=02:38:57
2020-08-06 02:38:57 10.164.0.43 [0] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.43 [0] | Training Device=xla:0/7 Epoch=1 Step=0 Loss=6.87500 Rate=58.78 GlobalRate=58.78 Time=02:38:57
2020-08-06 02:38:57 10.164.0.109 [1] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.109 [1] Epoch 1 train begin 02:38:56
2020-08-06 02:38:57 10.164.0.109 [1] | Training Device=xla:1/8 Epoch=1 Step=0 Loss=6.87500 Rate=100.43 GlobalRate=100.43 Time=02:38:57
2020-08-06 02:38:57 10.164.0.109 [1] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.109 [1] | Training Device=xla:0/13 Epoch=1 Step=0 Loss=6.87500 Rate=66.83 GlobalRate=66.83 Time=02:38:57
2020-08-06 02:38:57 10.164.0.109 [1] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.109 [1] | Training Device=xla:0/11 Epoch=1 Step=0 Loss=6.87500 Rate=64.28 GlobalRate=64.28 Time=02:38:57
2020-08-06 02:38:57 10.164.0.109 [1] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.109 [1] | Training Device=xla:0/10 Epoch=1 Step=0 Loss=6.87500 Rate=73.17 GlobalRate=73.17 Time=02:38:57
2020-08-06 02:38:57 10.164.0.109 [1] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.109 [1] | Training Device=xla:0/9 Epoch=1 Step=0 Loss=6.87500 Rate=27.29 GlobalRate=27.29 Time=02:38:57
2020-08-06 02:38:57 10.164.0.109 [1] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.109 [1] | Training Device=xla:0/12 Epoch=1 Step=0 Loss=6.87500 Rate=110.29 GlobalRate=110.29 Time=02:38:57
2020-08-06 02:38:57 10.164.0.46 [2] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.46 [2] | Training Device=xla:0/20 Epoch=1 Step=0 Loss=6.87500 Rate=100.85 GlobalRate=100.85 Time=02:38:57
2020-08-06 02:38:57 10.164.0.46 [2] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.46 [2] | Training Device=xla:0/22 Epoch=1 Step=0 Loss=6.87500 Rate=93.52 GlobalRate=93.52 Time=02:38:57
2020-08-06 02:38:57 10.164.0.46 [2] ==> Preparing data..
2020-08-06 02:38:57 10.164.0.46 [2] | Training Device=xla:0/23 Epoch=1 Step=0 Loss=6.87500 Rate=165.86 GlobalRate=165.86 Time=02:38:57

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

1. Disconnect from the Compute Engine VM:

   (vm)$ exit
   

2. Delete your Instance Group:

   gcloud compute instance-groups managed delete instance-group-name
   

3. Delete your TPU Pod:

   gcloud compute tpus delete ${TPU_NAME} --zone=europe-west4-a
   

4. Delete your Instance Group Template:

   gcloud compute instance-templates delete instance-template-name
   

5. [Optional] Delete your VM disk image:

   gcloud compute images delete image-name
   

What's next

Try the PyTorch colabs:

• Getting Started with PyTorch on Cloud TPUs
• Training MNIST on TPUs
• Training ResNet18 on TPUs with Cifar10 dataset
• Inference with Pretrained ResNet50 Model
• Fast Neural Style Transfer
• MultiCore Training AlexNet on Fashion MNIST
• Single Core Training AlexNet on Fashion MNIST