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Distributed TensorFlow or pythorch on a YARN cluster

Project description

tf-yarn

tf-yarn

tf-yarn is a Python library we have built at Criteo for training Pytorch and TensorFlow models on a Hadoop/YARN cluster. An introducing blog post can be found here.

It supports mono and multi-worker training, different distribution strategies and can run on CPUs or GPUs with just a few lines of code.

Prerequisites

tf-yarn only supports Python ≥3.6.

Installation

Note that tf-yarn does not directly depends on the ML frameworks it supports (TensorFlow, torch ...). That way, TensorFlow users don't install torch and conversely by installing tf-yarn. So you must install the ML framework(s) that you use separately (pip install tensorflow, pip install torch ...).

Install with Pip

$ pip install tf-yarn

Install from source

$ git clone https://github.com/criteo/tf-yarn
$ cd tf-yarn
$ pip install .

TensorFlow prerequisites

Supported versions: [1.15.0 to 2.2.0].

Make sure to have Tensorflow working with HDFS by setting up all the environment variables as described here.

You can run the check_hadoop_env script to check that your setup is OK (it has been installed by tf_yarn):

$ check_hadoop_env
# You should see something like
# INFO:tf_yarn.bin.check_hadoop_env:results will be written in /home/.../shared/Dev/tf-yarn/check_hadoop_env.log
# INFO:tf_yarn.bin.check_hadoop_env:check_env: True
# INFO:tf_yarn.bin.check_hadoop_env:write dummy file to hdfs hdfs://root/tmp/a1df7b99-fa47-4a86-b5f3-9bc09019190f/hello_tf_yarn.txt
# INFO:tf_yarn.bin.check_hadoop_env:check_local_hadoop_tensorflow: True
# INFO:root:Launching remote check
# ...
# INFO:tf_yarn.bin.check_hadoop_env:remote_check: True
# INFO:tf_yarn.bin.check_hadoop_env:Hadoop setup: OK

Quick start

Distributing the training of a model with tf-yarn can be decomposed in two steps:

  1. Describe your experiment: write the code that will be executed by the workers involved in the training. This includes the instantiation of the model to train, the training dataset (optionally the validation dataset) and the training loop.
  2. Run your experiment: execute your code on yarn.

Refer to the part dedicated to your ML framework (TensorFlow, Pytorch ...) for a detailed description of these two steps

TensorFlow

tf-yarn supports Keras API and the Estimator API (which was the only high-level API of the first TensorFlow releases).

Describe your experiment

Keras API

A Keras experiment is described by an instance of tf_yarn.tensorflow.KerasExperiment composed of the following elements:

  • model: compiled Keras model to train
  • model_dir: hdfs directory where the model will be checkpointed
  • train_params: training parameters that will be provided to model.fit. This does not include the training examples (input and target data)
  • input_data_fn: function returning the input data (features only) to train the model on
  • target_data_fn: function returning the target data (labels only) to train the model on
  • validation_data_fn: function returning the data to evaluate the model on

Example:

from tf_yarn.tensorflow import KerasExperiment

def input_data_fn():
    dataset = ...
    return dataset
        .shuffle(1000)
        .batch(128)
        .repeat()

def validation_data_fn():
    dataset = ...
    return dataset
        .shuffle(1000)
        .batch(128)

model = tf.keras.Sequential()
...
opt = tf.keras.optimizers.Adadelta(1.0 * HVD_SIZE)
model.compile(loss='sparse_categorical_crossentropy',
                optimizer=opt,
                metrics=['accuracy'])
train_params = {
    "steps_per_epoch": 100,
    "callbacks": my_callbacks
}

experiment = KerasExperiment(
    model=model,
    model_dir=hdfs_dir,
    train_params=train_params,
    input_data_fn=input_data_fn,
    target_data_fn=None,
    validation_data_fn=validation_data_fn
)

Estimator API

The experiment is described by an instance of tf_yarn.tensorflow.Experiment composed of the following elements:

from tf_yarn.tensorflow import Experiment

estimator = tf.estimator.Estimator(model_fn=model_fn)
train_spec = tf.estimator.TrainSpec(input_fn, max_steps=1)
eval_spec = tf.estimator.EvalSpec(input_fn, steps=1)
experiment = Experiment(estimator, train_spec, eval_spec)

Run your experiment

To run your experiment on yarn, simply call the method tf_yarn.tensorflow.run_on_yarn. The only mandatory parameter is experiment_fn which must be a function accepting no parameter and returning your object tf_yarn.tensorflow.KerasExperiment or tf_yarn.tensorflow.Experiment which describes your experiment.

from tf_yarn.tensorflow import run_on_yarn, KerasExperiment

def experiment_fn():
    ...
    return KerasExperiment(
        model=model,
        model_dir=hdfs_dir,
        train_params=train_params,
        input_data_fn=input_data_fn,
        target_data_fn=None,
        validation_data_fn=validation_data_fn
    )

run_on_yarn(
    experiment_fn
)

The default distribution strategy is ParameterServerStrategy which belongs to the group of asynchronous distribution strategies. Although this distribution strategy works very well with the Estimator API, we did not manage to make it work with the Keras API (with TensorFlow <= 2.2). So we advise Keras users to use horovod gloo for distributing the training. Note that horovod gloo is a synchronous distribution strategy based on all-reduce ops:

from tf_yarn.tensorflow import run_on_yarn, KerasExperiment

def experiment_fn():
    ...
    return KerasExperiment(
        model=model,
        model_dir=hdfs_dir,
        train_params=train_params,
        input_data_fn=input_data_fn,
        target_data_fn=None,
        validation_data_fn=validation_data_fn
    )

run_on_yarn(
    experiment_fn,
    custom_task_module="tf_yarn.tensorflow.tasks.gloo_allred_task"
)

Pytorch

Describe your experiment

A Pytorch experiment is described by an instance of tf_yarn.pytorch.PytorchExperiment composed of the following elements:

  • model: model to train
  • main_fn: Main function run to train the model. This function is executed by all workers involved in the training. It must accept these inputs: model to train, train dataloader, device (cpu:0, cpu:1, cuda:0, cuda:1 ...) allocated to the worker for the training and rank (worker id).
  • Training dataset: training dataset (instance of torch.utils.data.Dataset, webdataset.WebDataset or webdataset.DataPipeline).
  • dataloader_args: parameters (batch size, number of workers, collate function ...) passed to the dataloader used to load and iterate over the training dataset. Instance of tf_yarn.pytorch.DataLoaderArgs.
  • tensorboard_hdfs_dir: HDFS directory where tensorboard results will be written at the end of the training
  • ddp_args: DistributedDataParallel parameters. Refer to Pytorch documentation. Instance of tf_yarn.pytorch.DistributedDataParallelArgs
from tf_yarn.pytorch import PytorchExperiment

def main_fn(
    model: torch.nn.Module,
    trainloader: torch.utils.data.dataloader.DataLoader,
    device: str,
    rank: int
):
    loss_fn = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
    for epoch in range(10):
        trainloader.sampler.set_epoch(epoch)
        for i, data in enumerate(trainloader, 0):
            data = data.to(rank)
            prediction = model(data)

transform = transforms.Compose(
    [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
)
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)

experiment = PytorchExperiment(
    model=model,
    main_fn=main_fn,
    train_dataset=trainset,
    dataloader_args=DataLoaderArgs(batch_size=4, num_workers=2)
)

Run your experiment

To run your experiment on yarn, simply call the method tf_yarn.pytorch.run_on_yarn. The only mandatory parameters are:

  • experiment_fn: must be a function accepting no parameter and returning your object tf_yarn.pytorch.PytorchExperiment which describes your experiment.
  • task_specs: describe yarn resources that you want to use for your experiment.
from tf_yarn.pytorch import run_on_yarn, PytorchExperiment, TaskSpec


def experiment_fn():
    ...
    return PytorchExperiment(
        model=model,
        main_fn=main_fn,
        train_dataset=trainset,
        dataloader_args=DataLoaderArgs(batch_size=4, num_workers=2)
    )


run_on_yarn(
    experiment_fn,
    task_specs={
        "worker": TaskSpec(memory=48 * 2 ** 10, vcores=48, instances=2, nb_proc_per_worker=2,
                           label=NodeLabel.GPU)
    }
)

The distribution strategy is DistributedDataParallel which belongs to the family of synchronous distribution strategies.

run_on_yarn

The method run_on_yarn exposes several parameters that let you configure the yarn job that will be created to train your model on yarn:

  • pyenv_zip_path: Path to an archive of your python environment that will be used by executors to run your experiment. It can be a zipped conda env or a pex archive. If your python environement is different between CPU and GPU, you can provide a dictionnary from tf_yarn.topologies.NodeLabel to a python environment. Example:
from tf_yarn import NodeLabel
...
run_on_yarn(
    ...,
    pyenv_zip_path={
        NodeLabel.CPU: "viewfs://root/path/to/env-cpu",
        NodeLabel.GPU: "viewfs://root/path/to/env-gpu"
    }
)

If no archive is provided, tf-yarn will automatically package your active python environment in a pex.

  • task_specs: used to define the resources that you need for your experiment. Dictionary from task names (ps, worker, chief, evaluator, tensorboard ...) to tf_yarn.topologies.TaskSpec. Example:
from tf_yarn import TaskSpec, NodeLabel
...
run_on_yarn(
    ...,
    task_specs={
        "worker": TaskSpec(memory=48*2**10, vcores=48, instances=2, label=NodeLabel.GPU),
        "tensorboard": TaskSpec(memory=16*2**10, vcores=4, instances=1, label=NodeLabel.CPU)
    }
)

In this example, we are requesting 2 executors with GPUs, 48 vcores and 48 GBs for workers and 1 executor with 4 vcores and 16 GBs for tensorboard.

  • files: local files or directories to upload on the executors. Dictionary from target location (on executor) to local location (on your local machine). Target locations must be relative to the executor root directory. Note that the executor root directory is appended to PYTHONPATH. Therefore, any listed Python module will be importable.

  • env: environment variables to set on executors. Dictionary from variable name to variable value. Example:

run_on_yarn(
    ...,
    env={"HADOOP_CONF_DIR": "/etc/hadoop/conf.all"}
)
  • queue: yarn queue to schedule your job in. Example:
run_on_yarn(
    ...,
    queue="ml-gpu"
)
  • acls: configures the application-level Access Control Lists (ACLs). Optional, defaults to ACLs all access. See ACLs <https://jcrist.github.io/skein/specification.html#acls> for details.

  • file_systems: skein the library underlying tf_yarn automatically acquires a delegation token for fs.defaultFS on security-enabled clusters. This should be enough for most use-cases. However, if your experiment needs to access data on namenodes other than the default one, you have to explicitly list them in the file_systems argument. This would instruct skein to acquire a delegation token for these namenodes in addition to fs.defaultFS:

run_on_yarn(
    ...,
    file_systems=["hdfs://preprod"]
)
  • nb_retries: number of times the yarn application is retried in case of failures

  • name: Name of the yarn application

Model evaluation

This feature is not supported for Pytorch.

Model training and model evaluation can be run independently. To do so, you must use the parameter custom_task_module of run_on_yarn.

To run model training without evaluation:

run_on_yarn(
    ...,
    task_specs={
        "chief": TaskSpec(memory="2 GiB", vcores=4),
        "worker": TaskSpec(memory="2 GiB", vcores=4, instances=2),
        "ps": TaskSpec(memory="2 GiB", vcores=8),
        "tensorboard": TaskSpec(memory="2 GiB", vcores=1)
    }
)

To run model evaluation:

run_on_yarn(
    ...,
    task_specs={
        "evaluator": TaskSpec(memory="2 GiB", vcores=1)
    },
    custom_task_module="tf_yarn.tasks.evaluator_task"
)

Examples

Please refer to the various examples available in examples

Other documentations

MLflow to track experiments. More infos here.

Tensorboard can be spawned in a separate container during learnings.

Two alternatives to TensorFlow's distribution strategies are available: Horovod with gloo and tf-collective-all-reduce

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