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Getting Started

We recommend starting with a single host first and then moving to multihost.

Getting Started: Cloud Storage and Configure

  1. Create a gcs buckets in your project for storing logs and checkpoints. To run maxtext the TPU/GPU VMs must have permission to read/write the gcs bucket. These permissions are granted by service account roles, such as the STORAGE ADMIN role.

  2. MaxText reads a yaml file for configuration. We also recommend reviewing the configurable options in configs/base.yml, this config includes a decoder-only model of ~1B parameters. The configurable options can be overwritten from command lines. For instance you may change the steps or log_period by either modifying configs/base.yml or by passing in steps and log_period as additional args to the train.py call. base_output_directory should be set to a folder in the bucket you just created.

Getting Started: Local Development for single host

Running on Cloud TPUs

Local development is a convenient way to run MaxText on a single host. It doesn't scale to multiple hosts.

  1. Create and SSH to the single-host VM of your choice. We recommend a v4-8.
  2. Clone MaxText onto that TPUVM.
  3. Within the root directory of that git repo, install dependencies by running:
bash setup.sh
  1. After installation completes, run training with the command on synthetic data:
python3 MaxText/train.py MaxText/configs/base.yml \
  run_name=$YOUR_JOB_NAME \
  base_output_directory=gs://<my-bucket> \
  dataset_type=synthetic \
  steps=10

Next, you can try training on a HugginFace dataset, see Data Input Pipeline for data input options.

  1. If you want to decode, you can decode as follows.
python3 MaxText/decode.py MaxText/configs/base.yml \
  run_name=$YOUR_JOB_NAME \
  base_output_directory=gs://<my-bucket> \
  per_device_batch_size=1

Be aware, these decodings will be random. To get high quality decodings you need pass in a checkpoint, typically via the load_parameters_path argument.

Running on NVIDIA GPUs

  1. Use bash docker_build_dependency_image.sh DEVICE=gpu can be used to build a container with the required dependencies.
  2. After installation is completed, run training with the command on synthetic data:
python3 MaxText/train.py MaxText/configs/base.yml \
  run_name=$YOUR_JOB_NAME \
  base_output_directory=gs://<my-bucket> \
  dataset_type=synthetic \
  steps=10
  1. If you want to decode, you can decode as follows.
python3 MaxText/decode.py MaxText/configs/base.yml \
  run_name=$YOUR_JOB_NAME \
  base_output_directory=gs://<my-bucket> \
  per_device_batch_size=1
  • If you see the following error when running inside a container, set a larger --shm-size (e.g. --shm-size=1g)
Failed to execute XLA Runtime executable: run time error: custom call 'xla.gpu.all_reduce' failed: external/xla/xla/service/gpu/nccl_utils.cc:297: NCCL operation ncclCommInitRank(&comm, nranks, id, rank) failed: unhandled cuda error (run with NCCL_DEBUG=INFO for details); current tracing scope: all-reduce-start.2; current profiling annotation: XlaModule:#hlo_module=jit__unnamed_wrapped_function_,program_id=7#.

Getting Starting: Multihost development

There are three patterns for running MaxText with more than one host.

  1. [GKE, recommended] Running Maxtext with xpk - Quick Experimentation and Production support
  2. [GCE] Running Maxtext with Multihost Jobs - Long Running Production Jobs with Queued Resources
  3. [GCE] Running Maxtext with Multihost Runner - Fast experiments via multiple ssh connections.

Getting Starting: Preflight Optimizations

Once you've gotten workloads running, there are important optimizations you might want to put on your cluster. Please check the doc PREFLIGHT.md