745aaef5df
* feat: update doc for pytorch estimator backend. * fix: remove ray global dependency. * rm: remove .swp file. * fix: revert ray import fix. * fix: replace model and optimizer with model_creator and optimizer_creator. * fix: delete unnecessary links. * fix: update index.md * fix: fix code style of quickstart and jupyter notebook. * fix: remove criterion. * fix: fix dataset description. * fix: fix code style. * fix: fix code style. * fix: update batch size and link * fix: update link * fix: fix code style. * fix: fix unnecessary code. * fix: fix typo. * fix: use relative path. * fix: fix typo. * fix: fix link.
306 lines
12 KiB
Markdown
306 lines
12 KiB
Markdown
# Distributed Training and Inference
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---
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**Orca `Estimator` provides sklearn-style APIs for transparently distributed model training and inference**
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### 1. Estimator
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To perform distributed training and inference, the user can first create an Orca `Estimator` from any standard (single-node) TensorFlow, Kera or PyTorch model, and then call `Estimator.fit` or `Estimator.predict` methods (using the [data-parallel processing pipeline](./data-parallel-processing.md) as input).
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Under the hood, the Orca `Estimator` will replicate the model on each node in the cluster, feed the data partition (generated by the data-parallel processing pipeline) on each node to the local model replica, and synchronize model parameters using various *backend* technologies (such as *Horovod*, `tf.distribute.MirroredStrategy`, `torch.distributed`, or the parameter sync layer in [*BigDL*](https://github.com/intel-analytics/BigDL)).
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### 2. TensorFlow/Keras Estimator
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#### 2.1 TensorFlow 1.15 and Keras 2.3
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There are two ways to create an Estimator for TensorFlow 1.15, either from a low level computation graph or a Keras model. Examples are as follow:
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TensorFlow Computation Graph:
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```python
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# define inputs to the graph
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images = tf.placeholder(dtype=tf.float32, shape=(None, 28, 28, 1))
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labels = tf.placeholder(dtype=tf.int32, shape=(None,))
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# define the network and loss
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logits = lenet(images)
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loss = tf.reduce_mean(tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels))
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# define a metric
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acc = accuracy(logits, labels)
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# create an estimator using endpoints of the graph
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est = Estimator.from_graph(inputs=images,
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outputs=logits,
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labels=labels,
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loss=loss,
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optimizer=tf.train.AdamOptimizer(),
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metrics={"acc": acc})
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```
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Keras Model:
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```python
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model = create_keras_lenet_model()
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model.compile(optimizer=keras.optimizers.RMSprop(),
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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est = Estimator.from_keras(keras_model=model)
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```
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Then users can perform distributed model training and inference as follows:
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```python
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dataset = tfds.load(name="mnist", split="train")
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dataset = dataset.map(preprocess)
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est.fit(data=mnist_train,
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batch_size=320,
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epochs=max_epoch)
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predictions = est.predict(data=df,
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feature_cols=['image'])
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```
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The `data` argument in `fit` method can be a Spark DataFrame, an *XShards* or a `tf.data.Dataset`. The `data` argument in `predict` method can be a spark DataFrame or an *XShards*. See the *data-parallel processing pipeline* [page](./data-parallel-processing.md) for more details.
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View the related [Python API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Orca/orca.html#module-bigdl.orca.learn.tf.estimator) for more details.
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#### 2.2 TensorFlow 2.x and Keras 2.4+
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**Using `ray` or *Horovod* backend**
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Users can create an `Estimator` for TensorFlow 2.x from a Keras model (using a _Model Creator Function_) when the backend is
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`ray` (currently default for TF2) or *Horovod*. For example:
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```python
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def model_creator(config):
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model = create_keras_lenet_model()
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model.compile(optimizer=keras.optimizers.RMSprop(),
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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return model
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est = Estimator.from_keras(model_creator=model_creator) # or backend="horovod"
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```
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The `model_creator` argument should be a function that takes a `config` dictionary and returns a compiled Keras model.
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Then users can perform distributed model training and inference as follows:
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```python
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def train_data_creator(config, batch_size):
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dataset = tfds.load(name="mnist", split="train")
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dataset = dataset.map(preprocess)
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dataset = dataset.batch(batch_size)
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return dataset
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stats = est.fit(data=train_data_creator,
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epochs=max_epoch,
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steps_per_epoch=total_size // batch_size)
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predictions = est.predict(data=df,
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feature_cols=['image'])
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```
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The `data` argument in `fit` method can be a spark DataFrame, an *XShards* or a *Data Creator Function* (that returns a `tf.data.Dataset`). The `data` argument in `predict` method can be a spark DataFrame or an *XShards*. See the *data-parallel processing pipeline* [page](./data-parallel-processing.md) 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-tf2-tf2-ray-estimator) for more details.
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**Using *spark* backend**
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Users can create an `Estimator` for TensorFlow 2.x using the *spark* backend as follows:
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```python
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def model_creator(config):
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model = create_keras_lenet_model()
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model.compile(**compile_args(config))
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return model
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def compile_args(config):
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if "lr" in config:
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lr = config["lr"]
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else:
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lr = 1e-2
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args = {
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"optimizer": keras.optimizers.SGD(lr),
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"loss": "mean_squared_error",
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"metrics": ["mean_squared_error"]
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}
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return args
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est = Estimator.from_keras(model_creator=model_creator,
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config={"lr": 1e-2},
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workers_per_node=2,
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backend="spark",
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model_dir=model_dir)
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```
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The `model_creator` argument should be a function that takes a `config` dictionary and returns a compiled Keras model.
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The `model_dir` argument is required for *spark* backend, it should be a share filesystem path which can be accessed by executors for culster mode.
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Then users can perform distributed model training and inference as follows:
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```python
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def train_data_creator(config, batch_size):
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dataset = tfds.load(name="mnist", split="train")
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dataset = dataset.map(preprocess)
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dataset = dataset.batch(batch_size)
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return dataset
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stats = est.fit(data=train_data_creator,
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epochs=max_epoch,
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steps_per_epoch=total_size // batch_size)
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predictions = est.predict(data=df,
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feature_cols=['image']).collect()
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```
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The `data` argument in `fit` method can be a spark DataFrame, an *XShards* or a *Data Creator Function* (that returns a `tf.data.Dataset`). The `data` argument in `predict` method can be a spark DataFrame or an *XShards*. See the *data-parallel processing pipeline* [page](./data-parallel-processing.md) 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-tf2-tf2-spark-estimator) for more details.
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### 3. PyTorch Estimator
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**Using *BigDL* backend**
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Users may create a PyTorch `Estimator` using the *Spark* backend (currently default for PyTorch) as follows:
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```python
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def model_creator(config):
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model = LeNet() # a torch.nn.Module
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model.train()
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return model
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def optimizer_creator(model, config):
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return torch.optim.Adam(model.parameters(), config["lr"])
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est = Estimator.from_torch(model=model_creator,
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optimizer=optimizer_creator,
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loss=nn.NLLLoss(),
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config={"lr": 1e-2})
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```
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Then users can perform distributed model training and inference as follows:
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```python
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est.fit(data=train_loader, epochs=args.epochs)
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predictions = est.predict(xshards)
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```
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The input to `fit` methods can be a `torch.utils.data.DataLoader`, a Spark Dataframe, an *XShards*, or a *Data Creator Function* (that returns a `torch.utils.data.DataLoader`). The input to `predict` methods should be a Spark Dataframe, or an *XShards*. See the *data-parallel processing pipeline* [page](./data-parallel-processing.md) 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-pytorch-pytorch-spark-estimator) for more details.
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**Using `torch.distributed` or *Horovod* backend**
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Alternatively, users can create a PyTorch `Estimator` using `torch.distributed` or *Horovod* backend by specifying the `backend` argument to be "ray" or "horovod". In this case, the `model` and `optimizer` should be wrapped in _Creater Functions_. For example:
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```python
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def model_creator(config):
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model = LeNet() # a torch.nn.Module
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model.train()
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return model
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def optimizer_creator(model, config):
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return torch.optim.Adam(model.parameters(), config["lr"])
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est = Estimator.from_torch(model=model_creator,
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optimizer=optimizer_creator,
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loss=nn.NLLLoss(),
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config={"lr": 1e-2},
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backend="ray") # or backend="horovod"
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```
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Then users can perform distributed model training and inference as follows:
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```python
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est.fit(data=train_loader_func, epochs=args.epochs)
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predictions = est.predict(data=df,
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feature_cols=['image'])
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```
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The input to `fit` methods can be a Spark DataFrame, an *XShards*, or a *Data Creator Function* (that returns a `torch.utils.data.DataLoader`). The `data` argument in `predict` method can be a Spark DataFrame or an *XShards*. See the *data-parallel processing pipeline* [page](./data-parallel-processing.md) 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-pytorch-pytorch-ray-estimator) for more details.
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### 4. MXNet Estimator
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The user may create a MXNet `Estimator` as follows:
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```python
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from bigdl.orca.learn.mxnet import Estimator, create_config
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def get_model(config):
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net = LeNet() # a mxnet.gluon.Block
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return net
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def get_loss(config):
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return gluon.loss.SoftmaxCrossEntropyLoss()
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config = create_config(log_interval=2, optimizer="adam",
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optimizer_params={'learning_rate': 0.02})
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est = Estimator.from_mxnet(config=config,
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model_creator=get_model,
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loss_creator=get_loss,
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num_workers=2)
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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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import numpy as np
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def get_train_data_iter(config, kv):
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train = mx.io.NDArrayIter(data_ndarray, label_ndarray,
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batch_size=config["batch_size"], shuffle=True)
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return train
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est.fit(get_train_data_iter, epochs=2)
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```
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The input to `fit` methods can be an *XShards*, or a *Data Creator Function* (that returns an `MXNet DataIter/DataLoader`). See the *data-parallel processing pipeline* [page](./data-parallel-processing.html) for more details.
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### 5. BigDL Estimator
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The user may create a BigDL `Estimator` as follows:
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```python
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from bigdl.dllib.nn.criterion import *
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from bigdl.dllib.nn.layer import *
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from bigdl.dllib.optim.optimizer import *
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from bigdl.orca.learn.bigdl import Estimator
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linear_model = Sequential().add(Linear(2, 2))
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mse_criterion = MSECriterion()
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est = Estimator.from_bigdl(model=linear_model, loss=mse_criterion, optimizer=Adam())
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```
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Then the user can perform distributed model training and inference 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(df, 1, batch_size=4)
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#distributed model inference
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result_df = est.predict(df)
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```
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The input to `fit` and `predict` methods can be a *Spark Dataframe*, or an *XShards*. 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#module-bigdl.orca.learn.bigdl.estimator) for more details.
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### 6. OpenVINO Estimator
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The user may create a OpenVINO `Estimator` as follows:
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```python
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from bigdl.orca.learn.openvino import Estimator
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model_path = "The/file_path/to/the/OpenVINO_IR_xml_file"
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est = Estimator.from_openvino(model_path=model_path)
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```
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Then the user can perform distributed model inference as follows:
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```python
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# ndarray
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input_data = np.random.random([20, 4, 3, 224, 224])
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result = est.predict(input_data)
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# xshards
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shards = XShards.partition({"x": input_data})
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result_shards = est.predict(shards)
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```
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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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