tf-yarnᵝ

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

It supports running on one worker or on multiple workers with different distribution strategies and it can run on CPUs or GPUs using just a few lines of code.

Its API provides an easy entry point for working with Estimators and Keras. Please refer to the examples for some code samples.

MLflow is supported for all kind of trainings (one worker/distributed). 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

Installation

Install with Pip

Note that in order to support both tensorflow cpu and gpu in a single package, tf-yarn doesn't depend directly on tensorflow. You can either run pip install tf-yarn then install tensorflow or tensorflow_gpu, or use pip install tf-yarn[cpu] to do it all in one command. The supported versions of tensorflow are in the range 1.15.0 to 2.2.0. Install a version in that range to use tf yarn.

$ pip install tf-yarn[cpu]

Install from source

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

Prerequisites

tf-yarn only supports Python ≥3.6.

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

run_on_yarn

The only abstraction tf-yarn adds on top of the ones already present in TensorFlow is experiment_fn. It is a function returning a triple (wrapped in an Experiment object) of one Estimator and two specs -- TrainSpec and EvalSpec.

Here is a stripped down experiment_fn from one of the provided examples to give you an idea of how it might look:

from tf_yarn import Experiment

def experiment_fn():
  # ...
  estimator = tf.estimator.DNNClassifier(...)
  return Experiment(
    estimator,
    tf.estimator.TrainSpec(train_input_fn, max_steps=...),
    tf.estimator.EvalSpec(eval_input_fn)
 )

An experiment can be scheduled on YARN using the run_on_yarn function which takes three required arguments:

• pyenv_zip_path which contains the tf-yarn modules and dependencies like TensorFlow to be shipped to the cluster. pyenv_zip_path can be generated easily with a helper function based on the current installed virtual environment;
• experiment_fn as described above;
• task_specs dictionary specifying how much resources to allocate for each of the distributed TensorFlow task type.

The example uses the Wine Quality dataset from UCI ML repository. With just under 5000 training instances available, there is no need for multi-node training, meaning that a chief complemented by an evaluator would manage just fine. Note that each task will be executed in its own YARN container.

from tf_yarn import TaskSpec, run_on_yarn
import cluster_pack

pyenv_zip_path, _ = cluster_pack.upload_env()
run_on_yarn(
    pyenv_zip_path,
    experiment_fn,
    task_specs={
        "chief": TaskSpec(memory="2 GiB", vcores=4),
        "evaluator": TaskSpec(memory="2 GiB", vcores=1),
        "tensorboard": TaskSpec(memory="2 GiB", vcores=1)
    }
)

The final bit is to forward the winequality.py module to the YARN containers, in order for the tasks to be able to import them:

run_on_yarn(
    ...,
    files={
        os.path.basename(winequality.__file__): winequality.__file__,
    }
)

Under the hood, the experiment function is shipped to each container, evaluated and then passed to the train_and_evaluate function.

experiment = experiment_fn()
tf.estimator.train_and_evaluate(
  experiment.estimator,
  experiment.train_spec,
  experiment.eval_spec
)

Specificities using native Keras models instead of estimators

When using a Keras model that is not converted into an estimator, experiment_fn returns a tuple (wrapped in a KerasExperiment object) composed of the following elements:

• model: the compiled Keras model
• model_dir: the location at which the model and its checkpoints will be saved
• train_params: parameters that will be passed to the model fit method exluding input and target data
• input_data_fn: function returning input data for the model fit method
• target_data_fn: function returning target data for the model fit method
• validation_data_fn: function returning input data for the model evaluate method

Currently, Keras models are only supported using Horovod with Gloo as a distribution strategy (and not using MultiWorkerMirroredStrategy). Moreover, Keras models are only supported using Tensorflow 2. We provide an example describing how to use a Keras model with Horovod [examples][native_keras_with_gloo_example].

Distributed TensorFlow

The following is a brief summary of the core distributed TensorFlow concepts relevant to training estimators with the ParameterServerStrategy, as it is the distribution strategy activated by default when training Estimators on multiple nodes.

Distributed TensorFlow operates in terms of tasks. A task has a type which defines its purpose in the distributed TensorFlow cluster:

• worker tasks headed by the chief doing model training
• chief task additionally handling checkpoints, saving/restoring the model, etc.
• ps tasks (aka parameter servers) storing the model itself. These tasks typically do not compute anything. Their sole purpose is serving the model variables
• evaluator task periodically evaluating the model from the saved checkpoint

The types of tasks can depend on the distribution strategy, for example, ps tasks are only used by ParameterServerStrategy. The following picture presents an example of a cluster setup with 2 workers, 1 chief, 1 ps and 1 evaluator.

+-----------+              +---------+   +----------+   +----------+
| evaluator |        +-----+ chief:0 |   | worker:0 |   | worker:1 |
+-----+-----+        |     +----^----+   +-----^----+   +-----^----+
      ^              |          |            |              |
      |              v          |            |              |
      |        +-----+---+      |            |              |
      |        | model   |   +--v---+        |              |
      +--------+ exports |   | ps:0 <--------+--------------+
               +---------+   +------+

The cluster is defined by a ClusterSpec, a mapping from task types to their associated network addresses. For instance, for the above example, it looks like that:

{
  "chief": ["chief.example.com:2125"],
  "worker": ["worker0.example.com:6784",
             "worker1.example.com:6475"],
  "ps": ["ps0.example.com:7419"],
  "evaluator": ["evaluator.example.com:8347"]
}

Starting a task in the cluster requires a ClusterSpec. This means that the spec should be fully known before starting any of the tasks.

Once the cluster is known, we need to export the ClusterSpec through the TF_CONFIG environment variable and start the TensorFlow server on each container.

Then we can run the train-and-evaluate function on each container. We just launch the same function as in local training mode, TensorFlow will automatically detect that we have set up a ClusterSpec and start a distributed learning.

You can find more information about distributed Tensorflow here and about distributed training Estimators here.

Training with multiple workers

Activating the previous example in tf-yarn is just changing the cluster_spec by adding the additional worker and ps instances:

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),
        "evaluator": TaskSpec(memory="2 GiB", vcores=1),
        "tensorboard": TaskSpec(memory="2 GiB", vcores=1)
    }
)

Configuring the Python interpreter and packages

tf-yarn uses cluster-pack to to ship an isolated virtual environment to the containers. (You should have installed the dependencies from requirements.txt into your virtual environment first pip install -r requirements.txt) This works if you use Anaconda and also with Virtual Environments.

By default the generated package is a pex package. cluster-pack will generate the pex package, upload it to hdfs and you can start tf_yarn by providing the hdfs path.

import cluster_pack
pyenv_zip_path, env_name = cluster_pack.upload_env()
run_on_yarn(
    pyenv_zip_path=pyenv_zip_path
)

If you hosting evironment is Anaconda upload_env the packaging module will use conda-pack to create the package.

You can also directly use the command line tools provided by conda-pack and pex to generate the packages.

For pex you can run this command in the root directory to create the package (it includes all requirements from setup.py)

pex . -o myarchive.pex

You can then run tf-yarn with your generated package:

run_on_yarn(
    pyenv_zip_path="myarchive.pex"
)

Running on GPU

YARN does not have first-class support for GPU resources. A common workaround is to use node labels where CPU-only nodes are unlabelled, while the GPU ones have a label. Furthermore, in this setting GPU nodes are typically bound to a separate queue which is different from the default one.

Currently, tf-yarn assumes that the GPU label is "gpu". There are no assumptions on the name of the queue with GPU nodes, however, for the sake of example we wil use the name "ml-gpu".

The default behaviour of run_on_yarn is to run on CPU-only nodes. In order to run on the GPU ones:

1. Set the queue argument.
2. Set TaskSpec.label to NodeLabel.GPU for relevant task types. A good rule of a thumb is to run compute heavy "chief" and "worker" tasks on GPU, while keeping "ps" and "evaluator" on CPU.

import getpass
import cluster_pack
from tf_yarn import NodeLabel


pyenv_zip_path, _ = cluster_pack.upload_env()
run_on_yarn(
    pyenv_zip_path
    experiment_fn,
    task_specs={
        "chief": TaskSpec(memory="2 GiB", vcores=4, label=NodeLabel.GPU),
        "evaluator": TaskSpec(memory="1 GiB", vcores=1)
    },
    queue="ml-gpu"
)

The previous example applies to TensorFlow >= 1.15. For TensorFlow < 1.15 you need to call upload_env with tensorflow-gpu package and provide it to run_on_yarn.

Accessing HDFS in the presence of federation

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 to run_on_yarn. This would instruct skein to acquire a delegation token for these namenodes in addition to fs.defaultFS:

run_on_yarn(
    ...,
    file_systems=["hdfs://preprod"]
)

Depending on the cluster configuration, you might need to point libhdfs to a different configuration folder. For instance:

run_on_yarn(
    ...,
    env={"HADOOP_CONF_DIR": "/etc/hadoop/conf.all"}
)

Running model evaluation independently

Model training and model evaluation can be run independently. To do so, you must use 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"
)