410 lines
17 KiB
Markdown
410 lines
17 KiB
Markdown
<div align="center">
|
||
|
||
<p align="center"> <img src="docs/readthedocs/image/bigdl_logo.jpg" height="140px"><br></p>
|
||
|
||
_**Fast, Distributed, Secure AI for Big Data**_
|
||
|
||
</div>
|
||
|
||
---
|
||
|
||
BigDL seamlessly scales your data analytics & AI applications from laptop to cloud, with the following libraries:
|
||
|
||
- [Orca](#orca): Distributed Big Data & AI (TF & PyTorch) Pipeline on Spark and Ray
|
||
|
||
- [Nano](#nano): Transparent Acceleration of Tensorflow & PyTorch Programs
|
||
|
||
- [DLlib](#dllib): “Equivalent of Spark MLlib” for Deep Learning
|
||
|
||
- [Chronos](#chronos): Scalable Time Series Analysis using AutoML
|
||
|
||
- [Friesian](#friesian): End-to-End Recommendation Systems
|
||
|
||
- [PPML](#ppml) (experimental): Secure Big Data and AI (with SGX Hardware Security)
|
||
|
||
For more information, you may [read the docs](https://bigdl.readthedocs.io/).
|
||
|
||
---
|
||
|
||
## Choosing the right BigDL library
|
||
```mermaid
|
||
flowchart TD;
|
||
Feature1{{HW Secured Big Data & AI?}};
|
||
Feature1-- No -->Feature2{{Python vs. Scala/Java?}};
|
||
Feature1-- "Yes" -->ReferPPML([<em><strong>PPML</strong></em>]);
|
||
Feature2-- Python -->Feature3{{What type of application?}};
|
||
Feature2-- Scala/Java -->ReferDLlib([<em><strong>DLlib</strong></em>]);
|
||
Feature3-- "Distributed Big Data + AI (TF/PyTorch)" -->ReferOrca([<em><strong>Orca</strong></em>]);
|
||
Feature3-- Accelerate TensorFlow / PyTorch -->ReferNano([<em><strong>Nano</strong></em>]);
|
||
Feature3-- DL for Spark MLlib -->ReferDLlib2([<em><strong>DLlib</strong></em>]);
|
||
Feature3-- High Level App Framework -->Feature4{{Domain?}};
|
||
Feature4-- Time Series -->ReferChronos([<em><strong>Chronos</strong></em>]);
|
||
Feature4-- Recommender System -->ReferFriesian([<em><strong>Friesian</strong></em>]);
|
||
|
||
click ReferNano "https://github.com/intel-analytics/bigdl#nano"
|
||
click ReferOrca "https://github.com/intel-analytics/bigdl#orca"
|
||
click ReferDLlib "https://github.com/intel-analytics/bigdl#dllib"
|
||
click ReferDLlib2 "https://github.com/intel-analytics/bigdl#dllib"
|
||
click ReferChronos "https://github.com/intel-analytics/bigdl#chronos"
|
||
click ReferFriesian "https://github.com/intel-analytics/bigdl#friesian"
|
||
click ReferPPML "https://github.com/intel-analytics/bigdl#ppml"
|
||
|
||
classDef ReferStyle1 fill:#5099ce,stroke:#5099ce;
|
||
classDef Feature fill:#FFF,stroke:#08409c,stroke-width:1px;
|
||
class ReferNano,ReferOrca,ReferDLlib,ReferDLlib2,ReferChronos,ReferFriesian,ReferPPML ReferStyle1;
|
||
class Feature1,Feature2,Feature3,Feature4,Feature5,Feature6,Feature7 Feature;
|
||
|
||
```
|
||
---
|
||
## Installing
|
||
|
||
- To install BigDL, we recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) environment:
|
||
|
||
```bash
|
||
conda create -n my_env
|
||
conda activate my_env
|
||
pip install bigdl
|
||
```
|
||
To install latest nightly build, use `pip install --pre --upgrade bigdl`; see [Python](https://bigdl.readthedocs.io/en/latest/doc/UserGuide/python.html) and [Scala](https://bigdl.readthedocs.io/en/latest/doc/UserGuide/scala.html) user guide for more details.
|
||
|
||
- To install each individual library, such as Chronos, use `pip install bigdl-chronos`; see the [document website](https://bigdl.readthedocs.io/) for more details.
|
||
---
|
||
|
||
## Getting Started
|
||
### Orca
|
||
|
||
- The _Orca_ library seamlessly scales out your single node **TensorFlow**, **PyTorch** or **OpenVINO** programs across large clusters (so as to process distributed Big Data).
|
||
|
||
<details><summary>Show Orca example</summary>
|
||
<br/>
|
||
|
||
You can build end-to-end, distributed data processing & AI programs using _Orca_ in 4 simple steps:
|
||
|
||
```python
|
||
# 1. Initilize Orca Context (to run your program on K8s, YARN or local laptop)
|
||
from bigdl.orca import init_orca_context, OrcaContext
|
||
sc = init_orca_context(cluster_mode="k8s", cores=4, memory="10g", num_nodes=2)
|
||
|
||
# 2. Perform distribtued data processing (supporting Spark DataFrames,
|
||
# TensorFlow Dataset, PyTorch DataLoader, Ray Dataset, Pandas, Pillow, etc.)
|
||
spark = OrcaContext.get_spark_session()
|
||
df = spark.read.parquet(file_path)
|
||
df = df.withColumn('label', df.label-1)
|
||
...
|
||
|
||
# 3. Build deep learning models using standard framework APIs
|
||
# (supporting TensorFlow, PyTorch, Keras, OpenVino, etc.)
|
||
from tensorflow import keras
|
||
...
|
||
model = keras.models.Model(inputs=[user, item], outputs=predictions)
|
||
model.compile(...)
|
||
|
||
# 4. Use Orca Estimator for distributed training/inference
|
||
from bigdl.orca.learn.tf.estimator import Estimator
|
||
est = Estimator.from_keras(keras_model=model)
|
||
est.fit(data=df,
|
||
feature_cols=['user', 'item'],
|
||
label_cols=['label'],
|
||
...)
|
||
```
|
||
|
||
</details>
|
||
|
||
*See Orca [user guide](https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/orca.html), as well as [TensorFlow](https://bigdl.readthedocs.io/en/latest/doc/Orca/QuickStart/orca-tf-quickstart.html) and [PyTorch](https://bigdl.readthedocs.io/en/latest/doc/Orca/QuickStart/orca-pytorch-quickstart.html) quickstart, for more details.*
|
||
|
||
- In addition, you can also run standard **Ray** programs on Spark cluster using _**RayOnSpark**_ in Orca.
|
||
|
||
<details><summary>Show RayOnSpark example</summary>
|
||
<br/>
|
||
|
||
You can not only run Ray program on Spark cluster, but also write Ray code inline with Spark code (so as to process the in-memory Spark RDDs or DataFrames) using _RayOnSpark_ in Orca.
|
||
|
||
```python
|
||
# 1. Initilize Orca Context (to run your program on K8s, YARN or local laptop)
|
||
from bigdl.orca import init_orca_context, OrcaContext
|
||
sc = init_orca_context(cluster_mode="yarn", cores=4, memory="10g", num_nodes=2, init_ray_on_spark=True)
|
||
|
||
# 2. Distribtued data processing using Spark
|
||
spark = OrcaContext.get_spark_session()
|
||
df = spark.read.parquet(file_path).withColumn(...)
|
||
|
||
# 3. Convert Spark DataFrame to Ray Dataset
|
||
from bigdl.orca.data import spark_df_to_ray_dataset
|
||
dataset = spark_df_to_ray_dataset(df)
|
||
|
||
# 4. Use Ray to operate on Ray Datasets
|
||
import ray
|
||
|
||
@ray.remote
|
||
def consume(data) -> int:
|
||
num_batches = 0
|
||
for batch in data.iter_batches(batch_size=10):
|
||
num_batches += 1
|
||
return num_batches
|
||
|
||
print(ray.get(consume.remote(dataset)))
|
||
```
|
||
|
||
</details>
|
||
|
||
*See RayOnSpark [user guide](https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/ray.html) and [quickstart](https://bigdl.readthedocs.io/en/latest/doc/Orca/QuickStart/ray-quickstart.html) for more details.*
|
||
### Nano
|
||
You can transparently accelerate your TensorFlow or PyTorch programs on your laptop or server using *Nano*. With minimum code changes, *Nano* automatically applies modern CPU optimizations (e.g., SIMD, multiprocessing, low precision, etc.) to standard TensorFlow and PyTorch code, with up-to 10x speedup.
|
||
|
||
<details><summary>Show Nano inference example</summary>
|
||
<br/>
|
||
|
||
You can automatically optimize a trained PyTorch model for inference or deployment using _Nano_:
|
||
|
||
```python
|
||
model = ResNet18().load_state_dict(...)
|
||
train_dataloader = ...
|
||
val_dataloader = ...
|
||
def accuracy (pred, target):
|
||
...
|
||
|
||
from bigdl.nano.pytorch import InferenceOptimizer
|
||
optimizer = InferenceOptimizer()
|
||
optimizer.optimize(model,
|
||
training_data=train_dataloader,
|
||
validation_data=val_dataloader,
|
||
metric=accuracy)
|
||
new_model, config = optimizer.get_best_model()
|
||
|
||
optimizer.summary()
|
||
```
|
||
The output of `optimizer.summary()` will be something like:
|
||
```
|
||
-------------------------------- ---------------------- -------------- ----------------------
|
||
| method | status | latency(ms) | accuracy |
|
||
-------------------------------- ---------------------- -------------- ----------------------
|
||
| original | successful | 43.688 | 0.969 |
|
||
| fp32_ipex | successful | 33.383 | not recomputed |
|
||
| bf16 | fail to forward | None | None |
|
||
| bf16_ipex | early stopped | 203.897 | None |
|
||
| int8 | successful | 10.74 | 0.969 |
|
||
| jit_fp32 | successful | 38.732 | not recomputed |
|
||
| jit_fp32_ipex | successful | 35.205 | not recomputed |
|
||
| jit_fp32_ipex_channels_last | successful | 19.327 | not recomputed |
|
||
| openvino_fp32 | successful | 10.215 | not recomputed |
|
||
| openvino_int8 | successful | 8.192 | 0.969 |
|
||
| onnxruntime_fp32 | successful | 20.931 | not recomputed |
|
||
| onnxruntime_int8_qlinear | successful | 8.274 | 0.969 |
|
||
| onnxruntime_int8_integer | fail to convert | None | None |
|
||
-------------------------------- ---------------------- -------------- ----------------------
|
||
|
||
Optimization cost 64.3s in total.
|
||
```
|
||
|
||
</details>
|
||
|
||
<details><summary>Show Nano Training example</summary>
|
||
<br/>
|
||
You may easily accelerate PyTorch training (e.g., IPEX, BF16, Multi-Instance Training, etc.) using Nano:
|
||
|
||
```python
|
||
model = ResNet18()
|
||
optimizer = torch.optim.SGD(...)
|
||
train_loader = ...
|
||
val_loader = ...
|
||
|
||
from bigdl.nano.pytorch import TorchNano
|
||
|
||
# Define your training loop inside `TorchNano.train`
|
||
class Trainer(TorchNano):
|
||
def train(self):
|
||
# call `setup` to prepare for model, optimizer(s) and dataloader(s) for accelerated training
|
||
model, optimizer, (train_loader, val_loader) = self.setup(model, optimizer,
|
||
train_loader, val_loader)
|
||
|
||
for epoch in range(num_epochs):
|
||
model.train()
|
||
for data, target in train_loader:
|
||
optimizer.zero_grad()
|
||
output = model(data)
|
||
# replace the loss.backward() with self.backward(loss)
|
||
loss = loss_fuc(output, target)
|
||
self.backward(loss)
|
||
optimizer.step()
|
||
|
||
# Accelerated training (IPEX, BF16 and Multi-Instance Training)
|
||
Trainer(use_ipex=True, precision='bf16', num_processes=2).train()
|
||
```
|
||
|
||
</details>
|
||
|
||
*See Nano [user guide](https://bigdl.readthedocs.io/en/latest/doc/Nano/Overview/nano.html) and [tutotial](https://github.com/intel-analytics/BigDL/tree/main/python/nano/tutorial) for more details.*
|
||
|
||
### DLlib
|
||
|
||
With _DLlib_, you can write distributed deep learning applications as standard (**Scala** or **Python**) Spark programs, using the same **Spark DataFrames** and **ML Pipeline** APIs.
|
||
|
||
<details><summary>Show DLlib Scala example</summary>
|
||
<br/>
|
||
|
||
You can build distributed deep learning applications for Spark using *DLlib* Scala APIs in 3 simple steps:
|
||
|
||
```scala
|
||
// 1. Call `initNNContext` at the beginning of the code:
|
||
import com.intel.analytics.bigdl.dllib.NNContext
|
||
val sc = NNContext.initNNContext()
|
||
|
||
// 2. Define the deep learning model using Keras-style API in DLlib:
|
||
import com.intel.analytics.bigdl.dllib.keras.layers._
|
||
import com.intel.analytics.bigdl.dllib.keras.Model
|
||
val input = Input[Float](inputShape = Shape(10))
|
||
val dense = Dense[Float](12).inputs(input)
|
||
val output = Activation[Float]("softmax").inputs(dense)
|
||
val model = Model(input, output)
|
||
|
||
// 3. Use `NNEstimator` to train/predict/evaluate the model using Spark DataFrame and ML pipeline APIs
|
||
import org.apache.spark.sql.SparkSession
|
||
import org.apache.spark.ml.feature.MinMaxScaler
|
||
import org.apache.spark.ml.Pipeline
|
||
import com.intel.analytics.bigdl.dllib.nnframes.NNEstimator
|
||
import com.intel.analytics.bigdl.dllib.nn.CrossEntropyCriterion
|
||
import com.intel.analytics.bigdl.dllib.optim.Adam
|
||
val spark = SparkSession.builder().getOrCreate()
|
||
val trainDF = spark.read.parquet("train_data")
|
||
val validationDF = spark.read.parquet("val_data")
|
||
val scaler = new MinMaxScaler().setInputCol("in").setOutputCol("value")
|
||
val estimator = NNEstimator(model, CrossEntropyCriterion())
|
||
.setBatchSize(128).setOptimMethod(new Adam()).setMaxEpoch(5)
|
||
val pipeline = new Pipeline().setStages(Array(scaler, estimator))
|
||
|
||
val pipelineModel = pipeline.fit(trainDF)
|
||
val predictions = pipelineModel.transform(validationDF)
|
||
```
|
||
|
||
</details>
|
||
|
||
<details><summary>Show DLlib Python example</summary>
|
||
<br/>
|
||
|
||
You can build distributed deep learning applications for Spark using *DLlib* Python APIs in 3 simple steps:
|
||
|
||
```python
|
||
# 1. Call `init_nncontext` at the beginning of the code:
|
||
from bigdl.dllib.nncontext import init_nncontext
|
||
sc = init_nncontext()
|
||
|
||
# 2. Define the deep learning model using Keras-style API in DLlib:
|
||
from bigdl.dllib.keras.layers import Input, Dense, Activation
|
||
from bigdl.dllib.keras.models import Model
|
||
input = Input(shape=(10,))
|
||
dense = Dense(12)(input)
|
||
output = Activation("softmax")(dense)
|
||
model = Model(input, output)
|
||
|
||
# 3. Use `NNEstimator` to train/predict/evaluate the model using Spark DataFrame and ML pipeline APIs
|
||
from pyspark.sql import SparkSession
|
||
from pyspark.ml.feature import MinMaxScaler
|
||
from pyspark.ml import Pipeline
|
||
from bigdl.dllib.nnframes import NNEstimator
|
||
from bigdl.dllib.nn.criterion import CrossEntropyCriterion
|
||
from bigdl.dllib.optim.optimizer import Adam
|
||
spark = SparkSession.builder.getOrCreate()
|
||
train_df = spark.read.parquet("train_data")
|
||
validation_df = spark.read.parquet("val_data")
|
||
scaler = MinMaxScaler().setInputCol("in").setOutputCol("value")
|
||
estimator = NNEstimator(model, CrossEntropyCriterion())\
|
||
.setBatchSize(128)\
|
||
.setOptimMethod(Adam())\
|
||
.setMaxEpoch(5)
|
||
pipeline = Pipeline(stages=[scaler, estimator])
|
||
|
||
pipelineModel = pipeline.fit(train_df)
|
||
predictions = pipelineModel.transform(validation_df)
|
||
```
|
||
|
||
</details>
|
||
|
||
*See DLlib [NNFrames](https://bigdl.readthedocs.io/en/latest/doc/DLlib/Overview/nnframes.html) and [Keras API](https://bigdl.readthedocs.io/en/latest/doc/DLlib/Overview/keras-api.html) user guides for more details.*
|
||
|
||
### Chronos
|
||
|
||
The *Chronos* library makes it easy to build fast, accurate and scalable **time series analysis** applications (with AutoML).
|
||
|
||
<details><summary>Show Chronos example</summary>
|
||
<br/>
|
||
|
||
You can train a time series forecaster using _Chronos_ in 3 simple steps:
|
||
|
||
```python
|
||
from bigdl.chronos.forecaster import TCNForecaster
|
||
from bigdl.chronos.data.repo_dataset import get_public_dataset
|
||
|
||
# 1. Process time series data using `TSDataset`
|
||
tsdata_train, tsdata_val, tsdata_test = get_public_dataset(name='nyc_taxi')
|
||
for tsdata in [tsdata_train, tsdata_val, tsdata_test]:
|
||
data.roll(lookback=100, horizon=1)
|
||
|
||
# 2. Create a `TCNForecaster` (automatically configured based on train_data)
|
||
forecaster = TCNForecaster.from_tsdataset(train_data)
|
||
|
||
# 3. Train the forecaster for prediction
|
||
forecaster.fit(train_data)
|
||
|
||
pred = forecaster.predict(test_data)
|
||
```
|
||
|
||
To apply AutoML, use `AutoTSEstimator` instead of normal forecasters.
|
||
```python
|
||
# Create and fit an `AutoTSEstimator`
|
||
from bigdl.chronos.autots import AutoTSEstimator
|
||
autotsest = AutoTSEstimator(model="tcn", future_seq_len=10)
|
||
|
||
tsppl = autotsest.fit(data=tsdata_train, validation_data=tsdata_val)
|
||
pred = tsppl.predict(tsdata_test)
|
||
```
|
||
|
||
</details>
|
||
|
||
*See Chronos [user guide](https://bigdl.readthedocs.io/en/latest/doc/Chronos/index.html) and [quick start](https://bigdl.readthedocs.io/en/latest/doc/Chronos/QuickStart/chronos-autotsest-quickstart.html) for more details.*
|
||
|
||
### Friesian
|
||
The *Chronos* library makes it easy to build end-to-end, large-scale **recommedation system** (including *offline* feature transformation and traning, *near-line* feature and model update, and *online* serving pipeline).
|
||
|
||
*See Freisian [readme](https://github.com/intel-analytics/BigDL/blob/main/python/friesian/README.md) for more details.*
|
||
|
||
### PPML
|
||
|
||
*BigDL PPML* provides a **hardware (Intel SGX) protected** *Trusted Cluster Environment* for running distributed Big Data & AI applications (in a secure fashion on private or public cloud).
|
||
|
||
*See PPML [user guide](https://bigdl.readthedocs.io/en/latest/doc/PPML/Overview/ppml.html) and [tutorial](https://github.com/intel-analytics/BigDL/blob/main/ppml/README.md) for more details.*
|
||
|
||
## Getting Support
|
||
|
||
- [Mail List](mailto:bigdl-user-group+subscribe@googlegroups.com)
|
||
- [User Group](https://groups.google.com/forum/#!forum/bigdl-user-group)
|
||
- [Github Issues](https://github.com/intel-analytics/BigDL/issues)
|
||
---
|
||
|
||
## Citation
|
||
|
||
If you've found BigDL useful for your project, you may cite our papers as follows:
|
||
|
||
- *[BigDL 2.0](https://arxiv.org/abs/2204.01715): Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster*
|
||
```
|
||
@INPROCEEDINGS{9880257,
|
||
title={BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster},
|
||
author={Dai, Jason Jinquan and Ding, Ding and Shi, Dongjie and Huang, Shengsheng and Wang, Jiao and Qiu, Xin and Huang, Kai and Song, Guoqiong and Wang, Yang and Gong, Qiyuan and Song, Jiaming and Yu, Shan and Zheng, Le and Chen, Yina and Deng, Junwei and Song, Ge},
|
||
booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||
year={2022},
|
||
pages={21407-21414},
|
||
doi={10.1109/CVPR52688.2022.02076}
|
||
}
|
||
```
|
||
|
||
- *[BigDL](https://arxiv.org/abs/1804.05839): A Distributed Deep Learning Framework for Big Data*
|
||
```
|
||
@INPROCEEDINGS{10.1145/3357223.3362707,
|
||
title = {BigDL: A Distributed Deep Learning Framework for Big Data},
|
||
author = {Dai, Jason Jinquan and Wang, Yiheng and Qiu, Xin and Ding, Ding and Zhang, Yao and Wang, Yanzhang and Jia, Xianyan and Zhang, Cherry Li and Wan, Yan and Li, Zhichao and Wang, Jiao and Huang, Shengsheng and Wu, Zhongyuan and Wang, Yang and Yang, Yuhao and She, Bowen and Shi, Dongjie and Lu, Qi and Huang, Kai and Song, Guoqiong},
|
||
booktitle = {Proceedings of the ACM Symposium on Cloud Computing (SoCC)},
|
||
year = {2019},
|
||
pages = {50–60},
|
||
doi = {10.1145/3357223.3362707}
|
||
}
|
||
```
|
||
|