Update MPI Estimator to support Pytorch IPEX training (#8303)
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@ -304,3 +304,43 @@ result_shards = est.predict(shards)
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The input to `predict` methods can be an *XShards*, or a *numpy array*. See the *data-parallel processing pipeline* [page](./data-parallel-processing.html) for more details.
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View the related [Python API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Orca/orca.html#orca-learn-openvino-estimator) for more details.
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### 7. MPI Estimator
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The Orca MPI Estimator is to run distributed training job based on MPI.
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#### Preparation:
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* Configure password-less ssh from the master node (the one you'll launch training from) to all other nodes.
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* All hosts have the same working directory.
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* All hosts have the same Python environment in the same location.
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#### Train
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Then the user may create a MPI Estimator as follows:
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```python
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from bigdl.orca.learn.mpi import MPIEstimator
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est = MPIEstimator(model_creator=model_creator,
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optimizer_creator=optimizer_creator,
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loss_creator=None,
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metrics=None,
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scheduler_creator=None,
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config=config,
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init_func=init, # Init the distributed environment for MPI if any
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hosts=hosts,
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workers_per_node=workers_per_node,
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env=None)
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```
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Then the user can perform distributed model training as follows:
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```python
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# read spark Dataframe
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df = spark.read.parquet("data.parquet")
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# distributed model training
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est.fit(data=df, epochs=1, batch_size=4, feature_col="feature", label_cols="label")
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```
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The input to `fit` methods can be an Spark Dataframe, or a callable function to return a `torch.utils.data.DataLoader`.
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View the related [Python API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Orca/orca.html#orca-learn-mpi-mpi-estimator) for more details.
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@ -77,3 +77,11 @@ orca.learn.openvino.estimator
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:members:
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:undoc-members:
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:show-inheritance:
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orca.learn.mpi.mpi_estimator
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------------------------------
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.. autoclass:: bigdl.orca.learn.mpi.MPIEstimator
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:members:
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:undoc-members:
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:show-inheritance:
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