[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-09-04 (世界標準時間)。"],[],[],null,["# Run a calculation on a Cloud TPU VM using JAX\n=============================================\n\nThis document provides a brief introduction to working with JAX and Cloud TPU.\n| **Note:** This example shows how to run code on a v5litepod-8 (v5e) TPU which is a single-host TPU. Single-host TPUs have only 1 TPU VM. To run code on TPUs with more than one TPU VM (for example, v5litepod-16 or larger), see [Run JAX code on Cloud TPU slices](/tpu/docs/jax-pods).\n\n\nBefore you begin\n----------------\n\nBefore running the commands in this document, you must create a Google Cloud\naccount, install the Google Cloud CLI, and configure the `gcloud` command. For\nmore information, see [Set up the Cloud TPU environment](/tpu/docs/setup-gcp-account).\n\nCreate a Cloud TPU VM using `gcloud`\n------------------------------------\n\n1. Define some environment variables to make commands easier to use.\n\n\n ```bash\n export PROJECT_ID=your-project-id\n export TPU_NAME=your-tpu-name\n export ZONE=us-east5-a\n export ACCELERATOR_TYPE=v5litepod-8\n export RUNTIME_VERSION=v2-alpha-tpuv5-lite\n ``` \n\n #### Environment variable descriptions\n\n \u003cbr /\u003e\n\n2. Create your TPU VM by running the following command from a Cloud Shell or\n your computer terminal where the [Google Cloud CLI](/sdk/docs/install)\n is installed.\n\n ```bash\n $ gcloud compute tpus tpu-vm create $TPU_NAME \\\n --project=$PROJECT_ID \\\n --zone=$ZONE \\\n --accelerator-type=$ACCELERATOR_TYPE \\\n --version=$RUNTIME_VERSION\n ```\n\nConnect to your Cloud TPU VM\n----------------------------\n\nConnect to your TPU VM over SSH by using the following command: \n\n```bash\n$ gcloud compute tpus tpu-vm ssh $TPU_NAME \\\n --project=$PROJECT_ID \\\n --zone=$ZONE\n```\n\nIf you fail to connect to a TPU VM using SSH, it might be because the TPU VM\ndoesn't have an external IP address. To access a TPU VM without an external IP\naddress, follow the instructions in [Connect to a TPU VM without a public IP\naddress](/tpu/docs/tpu-iap).\n\nInstall JAX on your Cloud TPU VM\n--------------------------------\n\n```bash\n(vm)$ pip install jax[tpu] -f https://storage.googleapis.com/jax-releases/libtpu_releases.html\n```\n\nSystem check\n------------\n\nVerify that JAX can access the TPU and can run basic operations:\n\n1. Start the Python 3 interpreter:\n\n ```bash\n (vm)$ python3\n ``` \n\n ```bash\n \u003e\u003e\u003e import jax\n ```\n2. Display the number of TPU cores available:\n\n ```bash\n \u003e\u003e\u003e jax.device_count()\n ```\n\nThe number of TPU cores is displayed. The number of cores displayed is dependent\non the TPU version you are using. For more information, see [TPU versions](/tpu/docs/system-architecture-tpu-vm#versions).\n\n### Perform a calculation\n\n```bash\n\u003e\u003e\u003e jax.numpy.add(1, 1)\n```\n\nThe result of the numpy add is displayed:\n\nOutput from the command: \n\n```bash\nArray(2, dtype=int32, weak_type=True)\n```\n\n\u003cbr /\u003e\n\n### Exit the Python interpreter\n\n```bash\n\u003e\u003e\u003e exit()\n```\n\nRunning JAX code on a TPU VM\n----------------------------\n\nYou can now run any JAX code you want. The [Flax examples](https://github.com/google/flax/tree/master/examples)\nare a great place to start with running standard ML models in JAX. For example,\nto train a basic MNIST convolutional network:\n\n1. Install Flax examples dependencies:\n\n ```bash\n (vm)$ pip install --upgrade clu\n (vm)$ pip install tensorflow\n (vm)$ pip install tensorflow_datasets\n ```\n2. Install Flax:\n\n ```bash\n (vm)$ git clone https://github.com/google/flax.git\n (vm)$ pip install --user flax\n ```\n3. Run the Flax MNIST training script:\n\n ```bash\n (vm)$ cd flax/examples/mnist\n (vm)$ python3 main.py --workdir=/tmp/mnist \\\n --config=configs/default.py \\\n --config.learning_rate=0.05 \\\n --config.num_epochs=5\n ```\n\nThe script downloads the dataset and starts training. The script output should\nlook like this: \n\n```bash\nI0214 18:00:50.660087 140369022753856 train.py:146] epoch: 1, train_loss: 0.2421, train_accuracy: 92.97, test_loss: 0.0615, test_accuracy: 97.88\nI0214 18:00:52.015867 140369022753856 train.py:146] epoch: 2, train_loss: 0.0594, train_accuracy: 98.16, test_loss: 0.0412, test_accuracy: 98.72\nI0214 18:00:53.377511 140369022753856 train.py:146] epoch: 3, train_loss: 0.0418, train_accuracy: 98.72, test_loss: 0.0296, test_accuracy: 99.04\nI0214 18:00:54.727168 140369022753856 train.py:146] epoch: 4, train_loss: 0.0305, train_accuracy: 99.06, test_loss: 0.0257, test_accuracy: 99.15\nI0214 18:00:56.082807 140369022753856 train.py:146] epoch: 5, train_loss: 0.0252, train_accuracy: 99.20, test_loss: 0.0263, test_accuracy: 99.18\n```\n\n\nClean up\n--------\n\n\nTo avoid incurring charges to your Google Cloud account for\nthe resources used on this page, follow these steps.\n\nWhen you are done with your TPU VM, follow these steps to clean up your resources.\n\n1. Disconnect from the Cloud TPU instance, if you have not already done so:\n\n ```bash\n (vm)$ exit\n ```\n\n Your prompt should now be username@projectname, showing you are in the Cloud Shell.\n2. Delete your Cloud TPU:\n\n ```bash\n $ gcloud compute tpus tpu-vm delete $TPU_NAME \\\n --project=$PROJECT_ID \\\n --zone=$ZONE\n ```\n3. Verify the resources have been deleted by running the following command. Make\n sure your TPU is no longer listed. The deletion might take several minutes.\n\n ```bash\n $ gcloud compute tpus tpu-vm list \\\n --zone=$ZONE\n ```\n\nPerformance notes\n-----------------\n\nHere are a few important details that are particularly relevant to using TPUs in\nJAX.\n\n### Padding\n\nOne of the most common causes for slow performance on TPUs is introducing\ninadvertent padding:\n\n- Arrays in the Cloud TPU are tiled. This entails padding one of the dimensions to a multiple of 8, and a different dimension to a multiple of 128.\n- The matrix multiplication unit performs best with pairs of large matrices that minimize the need for padding.\n\n### bfloat16 dtype\n\nBy default, matrix multiplication in JAX on TPUs uses [bfloat16](/tpu/docs/bfloat16)\nwith float32 accumulation. This can be controlled with the precision argument on\nrelevant `jax.numpy` function calls (matmul, dot, einsum, etc). In particular:\n\n- `precision=jax.lax.Precision.DEFAULT`: uses mixed bfloat16 precision (fastest)\n- `precision=jax.lax.Precision.HIGH`: uses multiple MXU passes to achieve higher precision\n- `precision=jax.lax.Precision.HIGHEST`: uses even more MXU passes to achieve full float32 precision\n\nJAX also adds the bfloat16 dtype, which you can use to explicitly cast arrays to\n`bfloat16`. For example,\n`jax.numpy.array(x, dtype=jax.numpy.bfloat16)`.\n\n\nWhat's next\n-----------\n\nFor more information about Cloud TPU, see:\n\n- [Run JAX code on TPU slices](/tpu/docs/jax-pods)\n- [Manage TPUs](/tpu/docs/managing-tpus-tpu-vm)\n- [Cloud TPU System architecture](/tpu/docs/system-architecture-tpu-vm)"]]
