Train a model using TPU v5e

With a smaller 256-chip footprint per Pod, TPU v5e is optimized to be a high value product for transformer, text-to-image, and Convolutional Neural Network (CNN) training, fine-tuning, and serving. For more information about using Cloud TPU v5e for serving, see Inference using v5e.

For more information about Cloud TPU v5e TPU hardware and configurations, see TPU v5e.

Get started

The following sections describe how to get started using TPU v5e.

Request quota

You need quota to use TPU v5e for training. There are different quota types for on-demand TPUs, reserved TPUs, and TPU Spot VMs. There are separate quotas required if you're using your TPU v5e for inference. For more information about quotas, see Quotas. To request TPU v5e quota, contact Cloud Sales.

Create a Google Cloud account and project

You need a Google Cloud account and project to use Cloud TPU. For more information, see Set up a Cloud TPU environment.

Create a Cloud TPU

The best practice is to provision Cloud TPU v5es as queued resources using the queued-resource create command. For more information, see Manage queued resources.

You can also use the Create Node API (gcloud compute tpus tpu-vm create) to provision Cloud TPU v5es. For more information, see Manage TPU resources.

For more information about available v5e configurations for training, see Cloud TPU v5e types for training.

Framework setup

This section describes the general setup process for custom model training using JAX or PyTorch with TPU v5e.

For inference setup instructions, see v5e inference introduction.

Define some environment variables:

exportPROJECT_ID=your_project_ID
exportACCELERATOR_TYPE=v5litepod-16
exportZONE=us-west4-a
exportTPU_NAME=your_tpu_name
exportQUEUED_RESOURCE_ID=your_queued_resource_id

Setup for JAX

If you have slice shapes greater than 8 chips, you will have multiple VMs in one slice. In this case, you need to use the --worker=all flag to run the installation on all TPU VMs in a single step without using SSH to log into each separately:

gcloudcomputetpustpu-vmssh${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--worker=all\
--command='pip install -U "jax[tpu]" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html'

You can run the following command to check number of devices (the outputs shown here were produced with a v5litepod-16 slice). This code tests that everything is installed correctly by checking that JAX sees the Cloud TPU TensorCores and can run basic operations:

gcloudcomputetpustpu-vmssh${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--worker=all\
--command='python3 -c "import jax; print(jax.device_count()); print(jax.local_device_count())"'

The output will be similar to the following:

SSH:Attemptingtoconnecttoworker0...
SSH:Attemptingtoconnecttoworker1...
SSH:Attemptingtoconnecttoworker2...
SSH:Attemptingtoconnecttoworker3...
16
4
16
4
16
4
16
4

jax.device_count() shows the total number of chips in the given slice. jax.local_device_count() indicates the count of chips accessible by a single VM in this slice.

# Check the number of chips in the given slice by summing the count of chips
# from all VMs through the
# jax.local_device_count() API call.
gcloudcomputetpustpu-vmssh${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--worker=all\
--command='python3 -c "import jax; xs=jax.numpy.ones(jax.local_device_count()); print(jax.pmap(lambda x: jax.lax.psum(x, \"i\"), axis_name=\"i\")(xs))"'

The output will be similar to the following:

SSH:Attemptingtoconnecttoworker0...
SSH:Attemptingtoconnecttoworker1...
SSH:Attemptingtoconnecttoworker2...
SSH:Attemptingtoconnecttoworker3...
[16.16.16.16.]
[16.16.16.16.]
[16.16.16.16.]
[16.16.16.16.]

Try the JAX Tutorials in this document to get started with v5e training using JAX.

Setup for PyTorch

Note that v5e only supports the PJRT runtime and PyTorch 2.1+ will use PJRT as the default runtime for all TPU versions.

This section describes how to start using PJRT on v5e with PyTorch/XLA with commands for all workers.

Install dependencies

gcloudcomputetpustpu-vmssh${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--worker=all\
--command='
 sudo apt-get update -y
 sudo apt-get install libomp5 -y
 pip install mkl mkl-include
 pip install tf-nightly tb-nightly tbp-nightly
 pip install numpy
 sudo apt-get install libopenblas-dev -y
 pip install torch~=PYTORCH_VERSION torchvision torch_xla[tpu]~=PYTORCH_VERSION -f https://storage.googleapis.com/libtpu-releases/index.html -f https://storage.googleapis.com/libtpu-wheels/index.html'

Replace PYTORCH_VERSION with the version of PyTorch you want to use. PYTORCH_VERSION is used to specify the same version for PyTorch/XLA. 2.6.0 is recommended.

For more information about versions of PyTorch and PyTorch/XLA, see PyTorch - Get Started and PyTorch/XLA releases.

For more information on installing PyTorch/XLA, see PyTorch/XLA installation.

If you get an error when installing the wheels for torch, torch_xla, or torchvision like pkg_resources.extern.packaging.requirements.InvalidRequirement: Expected end or semicolon (after name and no valid version specifier) torch==nightly+20230222, downgrade your version with this command:

pip3installsetuptools==62.1.0

Run a script with PJRT

unsetLD_PRELOAD

The following is an example using a Python script to do a calculation on a v5e VM:

gcloudcomputetpustpu-vmssh${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--worker=all\
--command='
 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.local/lib/
 export PJRT_DEVICE=TPU
 export PT_XLA_DEBUG=0
 export USE_TORCH=ON
 unset LD_PRELOAD
 export TPU_LIBRARY_PATH=$HOME/.local/lib/python3.10/site-packages/libtpu/libtpu.so
 python3 -c "import torch; import torch_xla; import torch_xla.core.xla_model as xm; print(xm.xla_device()); dev = xm.xla_device(); t1 = torch.randn(3,3,device=dev); t2 = torch.randn(3,3,device=dev); print(t1 + t2)"'

This generates output similar to the following:

SSH:Attemptingtoconnecttoworker0...
SSH:Attemptingtoconnecttoworker1...
xla:0
tensor([[1.8611,-0.3114,-2.4208],
[-1.0731,0.3422,3.1445],
[0.5743,0.2379,1.1105]],device='xla:0')
xla:0
tensor([[1.8611,-0.3114,-2.4208],
[-1.0731,0.3422,3.1445],
[0.5743,0.2379,1.1105]],device='xla:0')

Try the PyTorch Tutorials in this document to get started with v5e training using PyTorch.

Delete your TPU and queued resource at the end of your session. To delete a queued resource, delete the slice and then the queued resource in 2 steps:

gcloudcomputetpustpu-vmdelete${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--quiet
gcloudcomputetpusqueued-resourcesdelete${QUEUED_RESOURCE_ID}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--quiet

These two steps can also be used to remove queued resource requests that are in the FAILED state.

JAX/FLAX examples

The following sections describe examples of how to train JAX and FLAX models on TPU v5e.

Train ImageNet on v5e

This tutorial describes how to train ImageNet on v5e using fake input data. If you want to use real data, refer to the README file on GitHub.

Set up

1. Create environment variables:

   exportPROJECT_ID=your-project-id
   exportTPU_NAME=your-tpu-name
   exportZONE=us-west4-a
   exportACCELERATOR_TYPE=v5litepod-8
   exportRUNTIME_VERSION=v2-alpha-tpuv5-lite
   exportSERVICE_ACCOUNT=your-service-account
   exportQUEUED_RESOURCE_ID=your-queued-resource-id

   Environment variable descriptions

   Variable	Description
   PROJECT_ID	Your Google Cloud project ID. Use an existing project or create a new one.
   TPU_NAME	The name of the TPU.
   ZONE	The zone in which to create the TPU VM. For more information about supported zones, see TPU regions and zones.
   ACCELERATOR_TYPE	The accelerator type specifies the version and size of the Cloud TPU you want to create. For more information about supported accelerator types for each TPU version, see TPU versions.
   RUNTIME_VERSION	The Cloud TPU software version.
   SERVICE_ACCOUNT	The email address for your service account. You can find it by going to the Service Accounts page in the Google Cloud console. For example: tpu-service-account@PROJECT_ID.iam.gserviceaccount.com
   QUEUED_RESOURCE_ID	The user-assigned text ID of the queued resource request.

2. Create a TPU resource:

   gcloudcomputetpusqueued-resourcescreate${QUEUED_RESOURCE_ID}\
   --node-id=${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --accelerator-type=${ACCELERATOR_TYPE}\
   --runtime-version=${RUNTIME_VERSION}\
   --service-account=${SERVICE_ACCOUNT}
   

   You will be able to SSH to your TPU VM once your queued resource is in the ACTIVE state:

   gcloudcomputetpusqueued-resourcesdescribe${QUEUED_RESOURCE_ID}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}
   

   When the QueuedResource is in the ACTIVE state, the output will be similar to the following:

   state:ACTIVE
   

3. Install newest version of JAX and jaxlib:

   gcloudcomputetpustpu-vmssh${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --worker=all\
   --command='pip install -U "jax[tpu]" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html'
   

4. Clone the ImageNet model and install the corresponding requirements:

   gcloudcomputetpustpu-vmssh${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --worker=all\
   --command="git clone https://github.com/coolkp/flax.git && cd flax && git checkout pmap-orbax-conversion && git pull"
   

5. To generate fake data, the model needs information on the dimensions of the dataset. This can be gathered from the ImageNet dataset's metadata:

   gcloudcomputetpustpu-vmssh${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --worker=all\
   --command="cd flax/examples/imagenet && pip install -r requirements-cloud-tpu.txt"
   

Train the model

Once all the previous steps are done, you can train the model.

gcloudcomputetpustpu-vmssh${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--worker=all\
--command="cd flax/examples/imagenet && bash ../../tests/download_dataset_metadata.sh && JAX_PLATFORMS=tpu python imagenet_fake_data_benchmark.py"

Delete the TPU and queued resource

Delete your TPU and queued resource at the end of your session.

gcloudcomputetpustpu-vmdelete${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--quiet

gcloudcomputetpusqueued-resourcesdelete${QUEUED_RESOURCE_ID}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--quiet

Hugging Face FLAX Models

Hugging Face models implemented in FLAX work out of the box on Cloud TPU v5e. This section provides instructions for running popular models.

Train ViT on Imagenette

This tutorial shows you how to train the Vision Transformer (ViT) model from HuggingFace using the Fast AI Imagenette dataset on Cloud TPU v5e.

The ViT model was the first one that successfully trained a Transformer encoder on ImageNet with excellent results compared to convolutional networks. For more information, see ViT overview.

Set up

1. Create environment variables:

   exportPROJECT_ID=your-project-id
   exportTPU_NAME=your-tpu-name
   exportZONE=us-west4-a
   exportACCELERATOR_TYPE=v5litepod-16
   exportRUNTIME_VERSION=v2-alpha-tpuv5-lite
   exportSERVICE_ACCOUNT=your-service-account
   exportQUEUED_RESOURCE_ID=your-queued-resource-id

   Environment variable descriptions

   Variable	Description
   PROJECT_ID	Your Google Cloud project ID. Use an existing project or create a new one.
   TPU_NAME	The name of the TPU.
   ZONE	The zone in which to create the TPU VM. For more information about supported zones, see TPU regions and zones.
   ACCELERATOR_TYPE	The accelerator type specifies the version and size of the Cloud TPU you want to create. For more information about supported accelerator types for each TPU version, see TPU versions.
   RUNTIME_VERSION	The Cloud TPU software version.
   SERVICE_ACCOUNT	The email address for your service account. You can find it by going to the Service Accounts page in the Google Cloud console. For example: tpu-service-account@PROJECT_ID.iam.gserviceaccount.com
   QUEUED_RESOURCE_ID	The user-assigned text ID of the queued resource request.

2. Create a TPU resource:

   gcloudcomputetpusqueued-resourcescreate${QUEUED_RESOURCE_ID}\
   --node-id=${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --accelerator-type=${ACCELERATOR_TYPE}\
   --runtime-version=${RUNTIME_VERSION}\
   --service-account=${SERVICE_ACCOUNT}
   

   You will be able to SSH to your TPU VM once your queued resource is in state ACTIVE:

   gcloudcomputetpusqueued-resourcesdescribe${QUEUED_RESOURCE_ID}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}
   

   When the queued resource is in the ACTIVE state, the output will be similar to the following:

   state:ACTIVE
   

3. Install JAX and its library:

   gcloudcomputetpustpu-vmssh${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --worker=all\
   --command='pip install -U "jax[tpu]" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html'
   

4. Download Hugging Face repository and install requirements:

   gcloudcomputetpustpu-vmssh${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --worker=all\
   --command='git clone https://github.com/huggingface/transformers.git && cd transformers && pip install . && pip install -r examples/flax/_tests_requirements.txt && pip install --upgrade huggingface-hub urllib3 zipp && pip install tensorflow==2.19 && sed -i 's/torchvision==0.12.0+cpu/torchvision==0.22.1/' examples/flax/vision/requirements.txt && pip install -r examples/flax/vision/requirements.txt && pip install tf-keras'
   

5. Download the Imagenette dataset:

   gcloudcomputetpustpu-vmssh${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --worker=all\
   --command='cd transformers && wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz && tar -xvzf imagenette2.tgz'
   

Train the model

Train the model with a pre-mapped buffer at 4GB.

gcloudcomputetpustpu-vmssh${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--worker=all\
--command='cd transformers && JAX_PLATFORMS=tpu python3 examples/flax/vision/run_image_classification.py --train_dir "imagenette2/train" --validation_dir "imagenette2/val" --output_dir "./vit-imagenette" --learning_rate 1e-3 --preprocessing_num_workers 32 --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --model_name_or_path google/vit-base-patch16-224-in21k --num_train_epochs 3'

Delete the TPU and queued resource

Delete your TPU and queued-resource at the end of your session.

gcloudcomputetpustpu-vmdelete${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--quiet
gcloudcomputetpusqueued-resourcesdelete${QUEUED_RESOURCE_ID}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--quiet

ViT benchmarking results

The training script was run on v5litepod-4, v5litepod-16, and v5litepod-64. The following table shows the throughputs with different accelerator types.

Train Diffusion on Pokémon

This tutorial shows you how to train the Stable Diffusion model from HuggingFace using the Pokémon dataset on Cloud TPU v5e.

The Stable Diffusion model is a latent text-to-image model that generates photo-realistic images from any text input. For more information, see the following resources:

• Stable Diffusion overview
• Stable Diffusion code source

Set up

1. Set an environment variable for the name of your storage bucket:

   exportGCS_BUCKET_NAME=your_bucket_name

2. Set up a storage bucket for your model output:

   gcloudstoragebucketscreategs://GCS_BUCKET_NAME\
   --project=your_project\
   --location=us-west1

3. Create environment variables:

   exportPROJECT_ID=your-project-id
   exportTPU_NAME=your-tpu-name
   exportZONE=us-west1-c
   exportACCELERATOR_TYPE=v5litepod-16
   exportRUNTIME_VERSION=v2-alpha-tpuv5-lite
   exportSERVICE_ACCOUNT=your-service-account
   exportQUEUED_RESOURCE_ID=your-queued-resource-id

   Environment variable descriptions

   Variable	Description
   PROJECT_ID	Your Google Cloud project ID. Use an existing project or create a new one.
   TPU_NAME	The name of the TPU.
   ZONE	The zone in which to create the TPU VM. For more information about supported zones, see TPU regions and zones.
   ACCELERATOR_TYPE	The accelerator type specifies the version and size of the Cloud TPU you want to create. For more information about supported accelerator types for each TPU version, see TPU versions.
   RUNTIME_VERSION	The Cloud TPU software version.
   SERVICE_ACCOUNT	The email address for your service account. You can find it by going to the Service Accounts page in the Google Cloud console. For example: tpu-service-account@PROJECT_ID.iam.gserviceaccount.com
   QUEUED_RESOURCE_ID	The user-assigned text ID of the queued resource request.

4. Create a TPU resource:

   gcloudcomputetpusqueued-resourcescreate${QUEUED_RESOURCE_ID}\
   --node-id=${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --accelerator-type=${ACCELERATOR_TYPE}\
   --runtime-version=${RUNTIME_VERSION}\
   --service-account=${SERVICE_ACCOUNT}
   

   You will be able to SSH to your TPU VM once your queued resource is in the ACTIVE state:

   gcloudcomputetpusqueued-resourcesdescribe${QUEUED_RESOURCE_ID}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}
   

   When the queued resource is in the ACTIVE state, the output will be similar to the following:

   state:ACTIVE
   

5. Install JAX and its library.

   gcloudcomputetpustpu-vmssh${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --worker=all\
   --command='pip install "jax[tpu]==0.4.16" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html'
   

6. Download the HuggingFace repository and install requirements.

   gcloudcomputetpustpu-vmssh${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --worker=all\
   --command='git clone https://github.com/RissyRan/diffusers.git && cd diffusers && pip install . && pip install -U -r examples/text_to_image/requirements_flax.txt && pip install tensorflow==2.17.1 clu && pip install tensorboard==2.17.1'
   

Train the model

Train the model with a pre-mapped buffer at 4GB.

gcloudcomputetpustpu-vmssh${TPU_NAME}--zone=${ZONE}--project=${PROJECT_ID}--worker=all--command="
 git clone https://github.com/google/maxdiffusion
 cd maxdiffusion
 pip3 install jax[tpu] -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
 pip3 install -r requirements.txt
 pip3 install .
 pip3 install gcsfs
 export LIBTPU_INIT_ARGS=''
 python -m src.maxdiffusion.train src/maxdiffusion/configs/base_2_base.yml run_name=my_run \
 jax_cache_dir=gs://${GCS_BUCKET_NAME} activations_dtype=bfloat16 weights_dtype=bfloat16 \
 per_device_batch_size=1 precision=DEFAULT dataset_save_location=gs://${GCS_BUCKET_NAME} \
 output_dir=gs://${GCS_BUCKET_NAME}/ attention=flash"

Clean up

Delete your TPU, queued resource, and Cloud Storage bucket at the end of your session.

1. Delete your TPU:

   gcloudcomputetpustpu-vmdelete${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --quiet
   

2. Delete the queued resource:

   gcloudcomputetpusqueued-resourcesdelete${QUEUED_RESOURCE_ID}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --quiet
   

3. Delete the Cloud Storage bucket:

   gcloudstoragerm-rgs://${GCS_BUCKET_NAME}
   

Benchmarking results for diffusion

The training script ran on v5litepod-4, v5litepod-16, and v5litepod-64. The following table shows the throughputs.

PyTorch/XLA

The following sections describe examples of how to train PyTorch/XLA models on TPU v5e.

Train ResNet using the PJRT runtime

PyTorch/XLA is migrating from XRT to PjRt from PyTorch 2.0+. Here are the updated instructions to set up v5e for PyTorch/XLA training workloads.

Set up

1. Create environment variables:

   exportPROJECT_ID=your-project-id
   exportTPU_NAME=your-tpu-name
   exportZONE=us-west4-a
   exportACCELERATOR_TYPE=v5litepod-16
   exportRUNTIME_VERSION=v2-alpha-tpuv5-lite
   exportSERVICE_ACCOUNT=your-service-account
   exportQUEUED_RESOURCE_ID=your-queued-resource-id

   Environment variable descriptions

   Variable	Description
   PROJECT_ID	Your Google Cloud project ID. Use an existing project or create a new one.
   TPU_NAME	The name of the TPU.
   ZONE	The zone in which to create the TPU VM. For more information about supported zones, see TPU regions and zones.
   ACCELERATOR_TYPE	The accelerator type specifies the version and size of the Cloud TPU you want to create. For more information about supported accelerator types for each TPU version, see TPU versions.
   RUNTIME_VERSION	The Cloud TPU software version.
   SERVICE_ACCOUNT	The email address for your service account. You can find it by going to the Service Accounts page in the Google Cloud console. For example: tpu-service-account@PROJECT_ID.iam.gserviceaccount.com
   QUEUED_RESOURCE_ID	The user-assigned text ID of the queued resource request.

2. Create a TPU resource:

   gcloudcomputetpusqueued-resourcescreate${QUEUED_RESOURCE_ID}\
   --node-id=${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --accelerator-type=${ACCELERATOR_TYPE}\
   --runtime-version=${RUNTIME_VERSION}\
   --service-account=${SERVICE_ACCOUNT}
   

   You will be able to SSH to your TPU VM once your QueuedResource is in ACTIVE state:

   gcloudcomputetpusqueued-resourcesdescribe${QUEUED_RESOURCE_ID}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}
   

   When the queued resource is in the ACTIVE state, the output will be similar to the following:

   state:ACTIVE
   

3. Install Torch/XLA specific dependencies

   gcloudcomputetpustpu-vmssh${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --worker=all\
   --command='
    sudo apt-get update -y
    sudo apt-get install libomp5 -y
    pip3 install mkl mkl-include
    pip3 install tf-nightly tb-nightly tbp-nightly
    pip3 install numpy
    sudo apt-get install libopenblas-dev -y
    pip install torch==PYTORCH_VERSION torchvision torch_xla[tpu]==PYTORCH_VERSION -f https://storage.googleapis.com/libtpu-releases/index.html -f https://storage.googleapis.com/libtpu-wheels/index.html'

   Replace PYTORCH_VERSION with the version of PyTorch you want to use. PYTORCH_VERSION is used to specify the same version for PyTorch/XLA. 2.6.0 is recommended.

   For more information about versions of PyTorch and PyTorch/XLA, see PyTorch - Get Started and PyTorch/XLA releases.

   For more information on installing PyTorch/XLA, see PyTorch/XLA installation.

Train the ResNet model

gcloudcomputetpustpu-vmssh${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--worker=all\
--command='
 date
 export PJRT_DEVICE=TPU
 export PT_XLA_DEBUG=0
 export USE_TORCH=ON
 export XLA_USE_BF16=1
 export LIBTPU_INIT_ARGS=--xla_jf_auto_cross_replica_sharding
 export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
 export TPU_LIBRARY_PATH=$HOME/.local/lib/python3.10/site-packages/libtpu/libtpu.so
 git clone https://github.com/pytorch/xla.git
 cd xla/
 git checkout release-r2.6
 python3 test/test_train_mp_imagenet.py --model=resnet50 --fake_data --num_epochs=1 --num_workers=16 --log_steps=300 --batch_size=64 --profile'

Delete the TPU and queued resource

Delete your TPU and queued resource at the end of your session.

gcloudcomputetpustpu-vmdelete${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--quiet
gcloudcomputetpusqueued-resourcesdelete${QUEUED_RESOURCE_ID}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--quiet

Benchmark result

The following table shows the benchmark throughputs.

Train ViT on v5e

This tutorial will cover how to run VIT on v5e using the HuggingFace repository on PyTorch/XLA on the cifar10 dataset.

Set up

1. Create environment variables:

   exportPROJECT_ID=your-project-id
   exportTPU_NAME=your-tpu-name
   exportZONE=us-west4-a
   exportACCELERATOR_TYPE=v5litepod-16
   exportRUNTIME_VERSION=v2-alpha-tpuv5-lite
   exportSERVICE_ACCOUNT=your-service-account
   exportQUEUED_RESOURCE_ID=your-queued-resource-id

   Environment variable descriptions

   Variable	Description
   PROJECT_ID	Your Google Cloud project ID. Use an existing project or create a new one.
   TPU_NAME	The name of the TPU.
   ZONE	The zone in which to create the TPU VM. For more information about supported zones, see TPU regions and zones.
   ACCELERATOR_TYPE	The accelerator type specifies the version and size of the Cloud TPU you want to create. For more information about supported accelerator types for each TPU version, see TPU versions.
   RUNTIME_VERSION	The Cloud TPU software version.
   SERVICE_ACCOUNT	The email address for your service account. You can find it by going to the Service Accounts page in the Google Cloud console. For example: tpu-service-account@PROJECT_ID.iam.gserviceaccount.com
   QUEUED_RESOURCE_ID	The user-assigned text ID of the queued resource request.

2. Create a TPU resource:

   gcloudcomputetpusqueued-resourcescreate${QUEUED_RESOURCE_ID}\
   --node-id=${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --accelerator-type=${ACCELERATOR_TYPE}\
   --runtime-version=${RUNTIME_VERSION}\
   --service-account=${SERVICE_ACCOUNT}
   

   You will be able to SSH to your TPU VM once your QueuedResource is in the ACTIVE state:

   gcloudcomputetpusqueued-resourcesdescribe${QUEUED_RESOURCE_ID}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}
   

   When the queued resource is in the ACTIVE state, the output will be similar to the following:

   state:ACTIVE
   

3. Install PyTorch/XLA dependencies

   gcloudcomputetpustpu-vmssh${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --worker=all\
   --command='
   sudoapt-getupdate-y
   sudoapt-getinstalllibomp5-y
   pip3installmklmkl-include
   pip3installtf-nightlytb-nightlytbp-nightly
   pip3installnumpy
   sudoapt-getinstalllibopenblas-dev-y
   pipinstalltorch==PYTORCH_VERSIONtorchvisiontorch_xla[tpu]==PYTORCH_VERSION-fhttps://storage.googleapis.com/libtpu-releases/index.html-fhttps://storage.googleapis.com/libtpu-wheels/index.html
   pipinstalljax==0.4.38jaxlib==0.4.38-ihttps://us-python.pkg.dev/ml-oss-artifacts-published/jax/simple/

   Replace PYTORCH_VERSION with the version of PyTorch you want to use. PYTORCH_VERSION is used to specify the same version for PyTorch/XLA. 2.6.0 is recommended.

   For more information about versions of PyTorch and PyTorch/XLA, see PyTorch - Get Started and PyTorch/XLA releases.

   For more information on installing PyTorch/XLA, see PyTorch/XLA installation.

4. Download HuggingFace repository and install requirements.

   gcloudcomputetpustpu-vmssh${TPU_NAME}\
   --project=${PROJECT_ID}\
   --zone=${ZONE}\
   --worker=all\
   --command="
    git clone https://github.com/suexu1025/transformers.git vittransformers; \
    cd vittransformers; \
    pip3 install .; \
    pip3 install datasets; \
    wget https://github.com/pytorch/xla/blob/master/scripts/capture_profile.py"
   

Train the model

gcloudcomputetpustpu-vmssh${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--worker=all\
--command='
 export PJRT_DEVICE=TPU
 export PT_XLA_DEBUG=0
 export USE_TORCH=ON
 export TF_CPP_MIN_LOG_LEVEL=0
 export XLA_USE_BF16=1
 export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
 export TPU_LIBRARY_PATH=$HOME/.local/lib/python3.10/site-packages/libtpu/libtpu.so
 cd vittransformers
 python3 -u examples/pytorch/xla_spawn.py --num_cores 4 examples/pytorch/image-pretraining/run_mae.py --dataset_name=cifar10 \
 --remove_unused_columns=False \
 --label_names=pixel_values \
 --mask_ratio=0.75 \
 --norm_pix_loss=True \
 --do_train=true \
 --do_eval=true \
 --base_learning_rate=1.5e-4 \
 --lr_scheduler_type=cosine \
 --weight_decay=0.05 \
 --num_train_epochs=3 \
 --warmup_ratio=0.05 \
 --per_device_train_batch_size=8 \
 --per_device_eval_batch_size=8 \
 --logging_strategy=steps \
 --logging_steps=30 \
 --evaluation_strategy=epoch \
 --save_strategy=epoch \
 --load_best_model_at_end=True \
 --save_total_limit=3 \
 --seed=1337 \
 --output_dir=MAE \
 --overwrite_output_dir=true \
 --logging_dir=./tensorboard-metrics \
 --tpu_metrics_debug=true'

Delete the TPU and queued resource

Delete your TPU and queued resource at the end of your session.

gcloudcomputetpustpu-vmdelete${TPU_NAME}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--quiet
gcloudcomputetpusqueued-resourcesdelete${QUEUED_RESOURCE_ID}\
--project=${PROJECT_ID}\
--zone=${ZONE}\
--quiet

Benchmark result

The following table shows the benchmark throughputs for different accelerator types.