Get Started with Distributed Training using JAX#

This guide provides an overview of the JaxTrainer in Ray Train.

What is 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 for transforming numerical functions such as jax.grad, jax.jit, and jax.vmap, utilizing the XLA compiler to create highly optimized code that scales efficiently on accelerators like GPUs and TPUs. The core power of JAX lies in its composability, allowing these transformations to be combined to build complex, high-performance numerical programs for distributed execution.

JAX and JaxTrainer support different accelerators such as GPUs and TPUs. For more details, see JAX Supported platforms.

What are TPUs?#

Tensor Processing Units (TPUs), are custom-designed accelerators created by Google to optimize machine learning workloads. Unlike general-purpose CPUs or parallel-processing GPUs, TPUs are highly specialized for the massive matrix and tensor computations involved in deep learning, making them exceptionally efficient.

The primary advantage of TPUs is performance at scale because they’re designed to be connected into large, multi-host configurations called “PodSlices” via a high-speed ICI interconnect, making them ideal for training large models that are unable to fit on a single node.

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

JaxTrainer API#

The JaxTrainer is the core component for orchestrating distributed JAX training in Ray Train. It follows the Single-Program, Multi-Data (SPMD) paradigm, where your training code is executed simultaneously across multiple workers.

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

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

You initialize the JaxTrainer with your training logic, defined in a train_loop_per_worker function, 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 slices configuration. Key fields include:

  • use_tpu: It’s a new field added in Ray 2.49.0 to the V2 ScalingConfig. This boolean flag tells Ray Train to initialize the JAX backend for TPU execution.

  • topology: It’s a new field added in Ray 2.49.0 to the V2 ScalingConfig. Topology is a string defining the physical arrangement of the TPU chips (for example, “4x4”). It’s required for multi-host training and ensures Ray places workers correctly across the slice. 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 (for example, {"TPU": 4}).

  • accelerator_type: For TPUs, accelerator_type specifies the TPU generation you’re using (for example, “TPU-V6E”), ensuring your workload is scheduled 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 will request one GPU per worker.

  • num_workers: The number of distributed training worker processes.

  • use_gpu: Whether each worker should use a GPU.

  • resources_per_worker: A dictionary specifying the resources each worker needs.

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

For more details, see Configuring Scale and GPUs.

Quickstart#

For reference, the final code is as follows:

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 Python code that executes 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, you simply need to wrap your training logic in a Python function that can be passed to the JaxTrainer.

This function is the entry point that Ray will execute 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 results (including checkpoints and artifacts) will be saved.

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

Specifying a shared storage location (such as cloud storage or NFS) is optional for single-node clusters, but it is required for multi-node clusters. Using a local path will raise an error during checkpointing for multi-node clusters.

For more details, see Configuring Persistent Storage.

Launch a training job#

Tying it all together, you can now launch a distributed training job with a JaxTrainer.

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#

After training completes, a Result object is returned which 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 Inspecting Training Results.

Next steps#

After you have converted your JAX training script to use Ray Train:

  • See User Guides to learn more about how to perform specific tasks.

  • Browse the Examples for end-to-end examples of how to use Ray Train.

  • Consult the API Reference for more details on the classes and methods from this tutorial.