Get started with distributed training using JAX#

This guide introduces the JaxTrainer in Ray Train.

What’s JAX?#

JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.

JAX provides an extensible system of transformations for numerical functions, such as jax.grad, jax.jit, and jax.vmap. It uses the Accelerated Linear Algebra (XLA) compiler to create optimized code that scales efficiently on accelerators such as GPUs and TPUs. You can combine these transformations to build complex, high-performance numerical programs for distributed execution.

JAX and the JaxTrainer support accelerators such as GPUs and TPUs. For details, see Supported platforms in the JAX documentation.

What are TPUs?#

Tensor Processing Units (TPUs) are custom accelerators that Google designed for machine learning workloads. Unlike general-purpose CPUs or parallel-processing GPUs, TPUs specialize in the large matrix and tensor computations of deep learning, which makes them efficient for that work.

The primary advantage of TPUs is performance at scale. Google designed them to connect into large, multi-host configurations called “PodSlices” through a high-speed inter-chip interconnect (ICI). This design makes TPUs well suited to training large models that don’t fit on a single node.

To learn more about configuring TPUs with KubeRay, see Use TPUs with KubeRay.

How does JaxTrainer work?#

The JaxTrainer is the core component for orchestrating distributed JAX training in Ray Train. It follows the single-program, multiple-data (SPMD) paradigm, where multiple workers run your training code simultaneously.

For TPUs, each worker runs on a separate TPU virtual machine within a TPU slice. Ray Train automatically reserves TPU slices atomically.

For GPUs, Ray automatically sets up the JAX distributed system on CUDA devices.

To create a JaxTrainer, pass it your training function as train_loop_per_worker and a ScalingConfig that specifies the distributed hardware layout. The JaxTrainer supports both Google Cloud TPUs and NVIDIA GPUs.

Configure scale and accelerators#

TPU scaling configuration#

For TPU training, use ScalingConfig to define your TPU slice configuration. Key fields include the following:

  • use_tpu: A boolean flag that tells Ray Train to initialize the JAX backend for TPU execution. Ray 2.49.0 added this field to the V2 ScalingConfig.

  • topology: A string that defines the physical arrangement of the TPU chips, such as "4x4". Multi-host training requires it, and it ensures Ray places workers correctly across the slice. Ray 2.49.0 added this field to the V2 ScalingConfig. For a list of supported TPU topologies by generation, see the GKE documentation.

  • num_workers: Set this to the total number of TPU VMs across all slices. For example, one v4-32 slice with a 2x2x4 topology uses 4 VMs, so set num_workers to 4. If you use two v4-32 slices, set num_workers to 8.

  • resources_per_worker: A dictionary specifying the resources each worker needs. For TPUs, you typically request the number of chips per VM, such as {"TPU": 4}.

  • accelerator_type: For TPUs, accelerator_type specifies the TPU generation you’re using, such as "TPU-V6E", so that Ray schedules your workload on the desired TPU slice.

from ray.train import ScalingConfig
tpu_scaling_config = ScalingConfig(num_workers=4, use_tpu=True, topology="4x4", accelerator_type="TPU-V6E")

GPU scaling configuration#

For GPU training, use ScalingConfig to define your GPU configuration. Each worker is one Ray Train process. By default, this configuration requests one GPU per worker. Key fields include the following:

from ray.train import ScalingConfig
gpu_scaling_config = ScalingConfig(num_workers=4, use_gpu=True)

For details, see Configure scale and accelerators.

Quickstart#

The following example shows the final code:

from ray.train.v2.jax import JaxTrainer
from ray.train import ScalingConfig

def train_func():
    # Your JAX training code here.

# Define the TPU scaling configuration with `use_tpu=True`.
scaling_config = ScalingConfig(num_workers=4, use_tpu=True, topology="4x4", accelerator_type="TPU-V6E")
# Define the GPU scaling configuration with `use_gpu=True`.
# scaling_config = ScalingConfig(num_workers=4, use_gpu=True)

# Choose one scaling config.
trainer = JaxTrainer(train_func, scaling_config=scaling_config)
result = trainer.fit()

  1. train_func is the training function, the Python code that runs on each distributed training worker.

  2. ScalingConfig defines the number of distributed training workers and whether to use TPUs or GPUs.

  3. JaxTrainer launches the distributed training job.

Compare a JAX training script with and without Ray Train.

import jax
import jax.numpy as jnp
import optax
import ray.train

from ray.train.v2.jax import JaxTrainer
from ray.train import ScalingConfig

def train_func():
    """This function is run on each distributed worker."""
    key = jax.random.PRNGKey(jax.process_index())
    X = jax.random.normal(key, (100, 1))
    noise = jax.random.normal(key, (100, 1)) * 0.1
    y = 2 * X + 1 + noise

    def linear_model(params, x):
        return x @ params['w'] + params['b']

    def loss_fn(params, x, y):
        preds = linear_model(params, x)
        return jnp.mean((preds - y) ** 2)

    @jax.jit
    def train_step(params, opt_state, x, y):
        loss, grads = jax.value_and_grad(loss_fn)(params, x, y)
        updates, opt_state = optimizer.update(grads, opt_state)
        params = optax.apply_updates(params, updates)
        return params, opt_state, loss

    # Initialize parameters and optimizer.
    key, w_key, b_key = jax.random.split(key, 3)
    params = {'w': jax.random.normal(w_key, (1, 1)), 'b': jax.random.normal(b_key, (1,))}
    optimizer = optax.adam(learning_rate=0.01)
    opt_state = optimizer.init(params)

    # Training loop
    epochs = 100
    for epoch in range(epochs):
        params, opt_state, loss = train_step(params, opt_state, X, y)
        # Report metrics back to Ray Train.
        ray.train.report({"loss": float(loss), "epoch": epoch})

# Define the TPU scaling configuration for your distributed job.
scaling_config = ScalingConfig(
    num_workers=4,
    use_tpu=True,
    topology="4x4",
    accelerator_type="TPU-V6E",
    placement_strategy="SPREAD"
)

# Define the GPU scaling configuration with `use_gpu=True`.
# scaling_config = ScalingConfig(
#     num_workers=4,
#     use_gpu=True,
# )

# Define and run the JaxTrainer.
trainer = JaxTrainer(
    train_loop_per_worker=train_func,
    scaling_config=scaling_config,
)
result = trainer.fit()
print(f"Training finished. Final loss: {result.metrics['loss']:.4f}")

import jax
import jax.numpy as jnp
import optax

# In a non-Ray script, you would manually initialize the
# distributed environment for multi-host training.
# import jax.distributed
# jax.distributed.initialize()

# Generate synthetic data.
key = jax.random.PRNGKey(0)
X = jax.random.normal(key, (100, 1))
noise = jax.random.normal(key, (100, 1)) * 0.1
y = 2 * X + 1 + noise

# Model and loss function are standard JAX.
def linear_model(params, x):
    return x @ params['w'] + params['b']

def loss_fn(params, x, y):
    preds = linear_model(params, x)
    return jnp.mean((preds - y) ** 2)

@jax.jit
def train_step(params, opt_state, x, y):
    loss, grads = jax.value_and_grad(loss_fn)(params, x, y)
    updates, opt_state = optimizer.update(grads, opt_state)
    params = optax.apply_updates(params, updates)
    return params, opt_state, loss

# Initialize parameters and optimizer.
key, w_key, b_key = jax.random.split(key, 3)
params = {'w': jax.random.normal(w_key, (1, 1)), 'b': jax.random.normal(b_key, (1,))}
optimizer = optax.adam(learning_rate=0.01)
opt_state = optimizer.init(params)

# Training loop
epochs = 100
print("Starting training...")
for epoch in range(epochs):
    params, opt_state, loss = train_step(params, opt_state, X, y)
    if epoch % 10 == 0:
        print(f"Epoch {epoch}, Loss: {loss:.4f}")

print("Training finished.")
print(f"Learned parameters: w={params['w'].item():.4f}, b={params['b'].item():.4f}")

Set up a training function#

Ray Train automatically initializes the JAX distributed environment based on the ScalingConfig and the JAX_PLATFORMS environment variable. To adapt your existing JAX code, wrap your training logic in a Python function that you pass to the JaxTrainer.

This function is the entry point that Ray runs on each remote worker.

+from ray.train.v2.jax import JaxTrainer
+from ray.train import ScalingConfig, report

-def main_logic()
+def train_func():
    """This function is run on each distributed worker."""
    # ... (JAX model, data, and training step definitions) ...

    # Training loop
    for epoch in range(epochs):
        params, opt_state, loss = train_step(params, opt_state, X, y)
-       print(f"Epoch {epoch}, Loss: {loss:.4f}")
+       # In Ray Train, you can report metrics back to the trainer
+       report({"loss": float(loss), "epoch": epoch})

-if __name__ == "__main__":
-    main_logic()
+# Define the hardware configuration for your distributed job.
+scaling_config = ScalingConfig(
+    num_workers=4,
+    use_tpu=True,
+    topology="4x4",
+    accelerator_type="TPU-V6E",
+    placement_strategy="SPREAD"
+)
+
+# Define the GPU scaling configuration with `use_gpu=True`.
+# scaling_config = ScalingConfig(
+#     num_workers=4,
+#     use_gpu=True,
+#     resources_per_worker={"GPU": 1},
+# )
+
+# Define and run the JaxTrainer, which executes `train_func`.
+trainer = JaxTrainer(
+    train_loop_per_worker=train_func,
+    scaling_config=scaling_config
+)
+result = trainer.fit()

Configure persistent storage#

Create a RunConfig object to specify the path where Ray Train saves results, including checkpoints and artifacts.

from ray.train import RunConfig

# Local path (/some/local/path/unique_run_name)
run_config = RunConfig(storage_path="/some/local/path", name="unique_run_name")

# Shared cloud storage URI (s3://bucket/unique_run_name)
run_config = RunConfig(storage_path="s3://bucket", name="unique_run_name")

# Shared NFS path (/mnt/nfs/unique_run_name)
run_config = RunConfig(storage_path="/mnt/nfs", name="unique_run_name")

Warning

A shared storage location, such as cloud storage or NFS, is optional for single-node clusters but required for multi-node clusters. On multi-node clusters, a local path raises an error during checkpointing.

For details, see Configure persistent storage.

Launch a training job#

To launch a distributed training job, pass the training function, scaling configuration, and run configuration to a JaxTrainer, then call fit().

from ray.train import ScalingConfig

train_func = lambda: None
# Define the TPU scaling configuration with `use_tpu=True`.
scaling_config = ScalingConfig(num_workers=4, use_tpu=True, topology="4x4", accelerator_type="TPU-V6E")
# Define the GPU scaling configuration with `use_gpu=True`.
# scaling_config = ScalingConfig(num_workers=4, use_gpu=True)
run_config = None

from ray.train.v2.jax import JaxTrainer

trainer = JaxTrainer(
    train_func, scaling_config=scaling_config, run_config=run_config
)
result = trainer.fit()

Access training results#

When training completes, fit() returns a Result object that contains information about the training run, including the metrics and checkpoints reported during training.

result.metrics     # The metrics reported during training.
result.checkpoint  # The latest checkpoint reported during training.
result.path        # The path where logs are stored.
result.error       # The exception that was raised, if training failed.

For more usage examples, see Inspect training results.

Next steps#

After you convert your JAX training script to use Ray Train, explore the following resources:

  • See user guides to learn how to perform specific tasks.

  • Browse the examples for end-to-end Ray Train examples.

  • See the API reference for details on the classes and methods in this guide.