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Co-authored-by: will <will@anyscale.com> Co-authored-by: Antoni Baum <antoni.baum@protonmail.com> Co-authored-by: Kai Fricke <kai@anyscale.com>
81 lines
2.5 KiB
ReStructuredText
81 lines
2.5 KiB
ReStructuredText
Distributed TensorFlow
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======================
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.. warning:: This is an older version of Ray SGD. A newer, more light-weight version of Ray SGD (named Ray Train) is in alpha as of Ray 1.7.
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See the documentation :ref:`here <train-docs>`. To migrate from v1 to v2 you can follow the :ref:`migration guide <sgd-migration>`.
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RaySGD's ``TFTrainer`` simplifies distributed model training for Tensorflow. The ``TFTrainer`` is a wrapper around ``MultiWorkerMirroredStrategy`` with a Python API to easily incorporate distributed training into a larger Python application, as opposed to write custom logic of setting environments and starting separate processes.
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Under the hood, ``TFTrainer`` will create *replicas* of your model (controlled by ``num_replicas``), each of which is managed by a Ray actor.
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.. image:: raysgd-actors.svg
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:align: center
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.. tip:: Get in touch with us if you're using or considering using `RaySGD <https://forms.gle/PXFcJmHwszCwQhqX7>`_!
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----------
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**With Ray**:
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Wrap your training with this:
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.. code-block:: python
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ray.init(args.address)
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trainer = TFTrainer(
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model_creator=model_creator,
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data_creator=data_creator,
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num_replicas=4,
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use_gpu=True,
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verbose=True,
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config={
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"fit_config": {
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"steps_per_epoch": num_train_steps,
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},
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"evaluate_config": {
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"steps": num_eval_steps,
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}
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})
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Then, start a :ref:`Ray cluster <cluster-index>`.
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.. code-block:: bash
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ray up CLUSTER.yaml
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python train.py --address="localhost:<PORT>"
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----------
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**Before, with Tensorflow**:
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In your training program, insert the following, and **customize** for each worker:
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.. code-block:: python
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os.environ['TF_CONFIG'] = json.dumps({
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'cluster': {
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'worker': ["localhost:12345", "localhost:23456"]
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},
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'task': {'type': 'worker', 'index': 0}
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})
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...
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strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
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with strategy.scope():
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multi_worker_model = model_creator()
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And on each machine, launch a separate process that contains the index of the worker and information about all other nodes of the cluster.
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.. _ray-train-tftrainer-example:
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TFTrainer Example
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-----------------
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Below is an example of using Ray's TFTrainer. Under the hood, ``TFTrainer`` will create *replicas* of your model (controlled by ``num_replicas``) which are each managed by a worker.
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.. literalinclude:: ../../../python/ray/util/sgd/tf/examples/tensorflow_train_example.py
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:language: python
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