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Update PyTorch quickstart (#7358)

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* update pytorch

* minor

* minor
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Kai Huang 2023-01-29 13:39:06 +08:00 committed by GitHub
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commit 6a4c40fc1f
2 changed files with 36 additions and 35 deletions
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43
docs/readthedocs/source/doc/Orca/Howto/pytorch-quickstart.md
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@ -10,12 +10,13 @@
### Step 0: Prepare Environment
[Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) is needed to prepare the Python environment for running this example. Please refer to the [install guide](../../UserGuide/python.md) for more details.
We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) to prepare the environment. Please refer to the [install guide](../Overview/install.md) for more details.
```bash
conda create -n py37 python=3.7 # "py37" is conda environment name, you can use any name you like.
conda activate py37
pip install --pre --upgrade bigdl-orca
pip install bigdl-orca
pip install torch torchvision
pip install tqdm
```
@ -24,18 +25,17 @@ pip install tqdm
```python
from bigdl.orca import init_orca_context, stop_orca_context
cluster_mode = "local"
if cluster_mode == "local": # For local machine
init_orca_context(cores=4, memory="10g")
init_orca_context(cores=4, memory="4g")
elif cluster_mode == "k8s": # For K8s cluster
init_orca_context(cluster_mode="k8s", num_nodes=2, cores=2, memory="10g", driver_memory="10g", driver_cores=1)
init_orca_context(cluster_mode="k8s", num_nodes=2, cores=2, memory="4g", master=..., container_image=...)
elif cluster_mode == "yarn": # For Hadoop/YARN cluster
init_orca_context(cluster_mode="yarn", num_nodes=2, cores=2, memory="10g", driver_memory="10g", driver_cores=1)
init_orca_context(cluster_mode="yarn", num_nodes=2, cores=2, memory="4g")
```
This is the only place where you need to specify local or distributed mode. View [Orca Context](../Overview/orca-context.md) for more details.
**Note:** You should `export HADOOP_CONF_DIR=/path/to/hadoop/conf/dir` when running on Hadoop YARN cluster. View [Hadoop User Guide](../../UserGuide/hadoop.md) for more details.
Please check the tutorials if you want to run on [Kubernetes](../Tutorial/k8s.md) or [Hadoop/YARN](../Tutorial/yarn.md) clusters.
### Step 2: Define the Model
@ -63,8 +63,11 @@ class LeNet(nn.Module):
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
loss = nn.NLLLoss()
```
After defining your model, you need to define a *Model Creator Function* that takes the parameter `config` and returns an instance of your model, and a *Optimizer Creator Function* that has two parameters `model` and `config` and returns a PyTorch optimizer.
You need to define a *Model Creator Function* that takes the parameter `config` and returns an instance of your PyTorch model, and an *Optimizer Creator Function* that takes two parameters `model` and `config` and returns an instance of your PyTorch optimizer.
```python
def model_creator(config):
@ -72,18 +75,16 @@ def model_creator(config):
return model
def optim_creator(model, config):
return torch.optim.Adam(model.parameters(), lr=0.001)
return torch.optim.Adam(model.parameters(), lr=config.get("lr", 0.001))
```
### Step 3: Define Train Dataset
You can define the dataset using a *Data Creator Function* that has two parameters `config` and `batch_size` and returns a [Pytorch DataLoader](https://pytorch.org/docs/stable/data.html). Orca also supports [Spark DataFrames](../Overview/data-parallel-processing.html#spark-dataframes) and [XShards](../Overview/data-parallel-processing.html#xshards-distributed-data-parallel-python-processing).
You can define the dataset using a *Data Creator Function* that has two parameters `config` and `batch_size` and returns a [Pytorch DataLoader](https://pytorch.org/docs/stable/data.html). Orca also supports [Spark DataFrame](./spark-dataframe.md) and [Orca XShards](./xshards-pandas.md).
```python
import torch
from torchvision import datasets, transforms
batch_size = 64
dir = '/tmp/dataset'
def train_loader_creator(config, batch_size):
@ -109,22 +110,24 @@ def test_loader_creator(config, batch_size):
### Step 4: Fit with Orca Estimator
First, Create an Estimator
First, Create an Orca Estimator for PyTorch.
```python
from bigdl.orca.learn.pytorch import Estimator
from bigdl.orca.learn.metrics import Accuracy
est = Estimator.from_torch(model=model_creator, optimizer=optim_creator, loss=nn.NLLLoss(), metrics=[Accuracy()], use_tqdm=True)
est = Estimator.from_torch(model=model_creator, optimizer=optim_creator, loss=loss,
metrics=[Accuracy()], use_tqdm=True)
```
Next, fit and evaluate using the Estimator
Next, fit and evaluate using the Estimator.
```python
est.fit(data=train_loader_creator, epochs=1, batch_size=batch_size)
result = est.evaluate(data=test_loader_creator, batch_size=batch_size)
for r in result:
print(r, ":", result[r])
batch_size = 64
train_stats = est.fit(data=train_loader_creator, epochs=1, batch_size=batch_size)
eval_stats = est.evaluate(data=test_loader_creator, batch_size=batch_size)
print(eval_stats)
```
### Step 5: Save and Load the Model
@ -141,3 +144,5 @@ est.load("mnist_model")
```
**Note:** You should call `stop_orca_context()` when your application finishes.
That's it, the same code can run seamlessly on your local laptop and scale to [Kubernetes](../Tutorial/k8s.md) or [Hadoop/YARN](../Tutorial/yarn.md) clusters.

28
docs/readthedocs/source/doc/Orca/Howto/tf2keras-quickstart.md
View file

@ -27,7 +27,7 @@ from bigdl.orca import init_orca_context, stop_orca_context
if cluster_mode == "local": # For local machine
init_orca_context(cluster_mode="local", cores=4, memory="4g")
elif cluster_mode == "k8s": # For K8s cluster
init_orca_context(cluster_mode="k8s", num_nodes=2, cores=2, memory="4g")
init_orca_context(cluster_mode="k8s", num_nodes=2, cores=2, memory="4g", master=..., container_image=...)
elif cluster_mode == "yarn": # For Hadoop/YARN cluster
init_orca_context(cluster_mode="yarn", num_nodes=2, cores=2, memory="4g")
```
@ -38,7 +38,7 @@ Please check the tutorials if you want to run on [Kubernetes](../Tutorial/k8s.md
### Step 2: Define the Model
You can then define the Keras model in the _Creator Function_ using the standard TensorFlow 2 Keras APIs.
You can then define and compile the Keras model in the _Creator Function_ using the standard TensorFlow 2 Keras APIs.
```python
import tensorflow as tf
@ -74,7 +74,6 @@ def preprocess(x, y):
def train_data_creator(config, batch_size):
(train_feature, train_label), _ = tf.keras.datasets.mnist.load_data()
dataset = tf.data.Dataset.from_tensor_slices((train_feature, train_label))
dataset = dataset.repeat()
dataset = dataset.map(preprocess)
@ -84,7 +83,6 @@ def train_data_creator(config, batch_size):
def val_data_creator(config, batch_size):
_, (val_feature, val_label) = tf.keras.datasets.mnist.load_data()
dataset = tf.data.Dataset.from_tensor_slices((val_feature, val_label))
dataset = dataset.repeat()
dataset = dataset.map(preprocess)
@ -105,17 +103,15 @@ est = Estimator.from_keras(model_creator=model_creator, workers_per_node=2)
Next, fit and evaluate using the Estimator.
```python
batch_size = 320
stats = est.fit(train_data_creator,
epochs=5,
batch_size=batch_size,
steps_per_epoch=60000 // batch_size,
validation_data=val_data_creator,
validation_steps=10000 // batch_size)
train_stats = est.fit(train_data_creator,
epochs=5,
batch_size=batch_size,
steps_per_epoch=60000 // batch_size,
validation_data=val_data_creator,
validation_steps=10000 // batch_size)
stats = est.evaluate(val_data_creator, num_steps=10000 // batch_size)
print(stats)
est.shutdown()
eval_stats = est.evaluate(val_data_creator, num_steps=10000 // batch_size)
print(eval_stats)
```
### Step 5: Save and Load the Model
@ -146,6 +142,6 @@ est.save("lenet_model.h5", save_format='h5')
est.load("lenet_model.h5")
```
That's it, the same code can run seamlessly on your local laptop and scale to [Kubernetes](../Tutorial/k8s.md) or [Hadoop/YARN](../Tutorial/yarn.md) clusters.
**Note:** You should call `stop_orca_context()` when your program finishes.
That's it, the same code can run seamlessly on your local laptop and scale to [Kubernetes](../Tutorial/k8s.md) or [Hadoop/YARN](../Tutorial/yarn.md) clusters.