README.rst

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Mobius : Online Machine Learning.

Mobius is an AI infra platform including realtime computing and training.

Ray Streaming

Ray Streaming is a data processing framework built on ray.

Key Features

1. Cross Language. Based on Ray's multi-language actor, Ray Streaming can also run in multiple languages(only Python and Java is supported currently) with high efficiency. You can implement your operator in different languages and run them in one job.
2. Single Node Failover. We designed a special failover mechanism that only needs to rollback the failed node it's own, in most cases, to recover the job. This will be a huge benefit if your job is sensitive about failure recovery time. In other frameworks like Flink, instead, the entire job should be restarted once a node has failure.
3. AutoScaling. (Moved from internal in the future). Generate a new graph with different configurations in runtime without stopping job.
4. Fusion Training. (Moved from internal in the future). Combine TensorFlow/Pytorch and streaming, then building an e2e online machine learning pipeline.

Examples

Python

import ray
from ray.streaming import StreamingContext

ctx = StreamingContext.Builder() \
    .build()
ctx.read_text_file(__file__) \
    .set_parallelism(1) \
    .flat_map(lambda x: x.split()) \
    .map(lambda x: (x, 1)) \
    .key_by(lambda x: x[0]) \
    .reduce(lambda old_value, new_value:
            (old_value[0], old_value[1] + new_value[1])) \
    .filter(lambda x: "ray" not in x) \
    .sink(lambda x: print("result", x))
ctx.submit("word_count")

Java

StreamingContext context = StreamingContext.buildContext();
List<String> text = Collections.singletonList("hello world");
DataStreamSource.fromCollection(context, text)
    .flatMap((FlatMapFunction<String, WordAndCount>) (value, collector) -> {
        String[] records = value.split(" ");
        for (String record : records) {
            collector.collect(new WordAndCount(record, 1));
        }
    })
    .filter(pair -> !pair.word.contains("world"))
    .keyBy(pair -> pair.word)
    .reduce((oldValue, newValue) ->
            new WordAndCount(oldValue.word, oldValue.count + newValue.count))
    .sink(result -> System.out.println("sink result=" + result));
context.execute("testWordCount");

Use Java Operators in Python

import ray
from ray.streaming import StreamingContext

ctx = StreamingContext.Builder().build()
ctx.from_values("a", "b", "c") \
    .as_java_stream() \
    .map("io.ray.streaming.runtime.demo.HybridStreamTest$Mapper1") \
    .filter("io.ray.streaming.runtime.demo.HybridStreamTest$Filter1") \
    .as_python_stream() \
    .sink(lambda x: print("result", x))
ctx.submit("HybridStreamTest")

Use Python Operators in Java

StreamingContext context = StreamingContext.buildContext();
DataStreamSource<String> streamSource =
    DataStreamSource.fromCollection(context, Arrays.asList("a", "b", "c"));
streamSource
    .map(x -> x + x)
    .asPythonStream()
    .map("ray.streaming.tests.test_hybrid_stream", "map_func1")
    .filter("ray.streaming.tests.test_hybrid_stream", "filter_func1")
    .asJavaStream()
    .sink(value -> System.out.println("HybridStream sink=" + value));
context.execute("HybridStreamTestJob");

Training

Training solution is one of the major topics for online machine learning systems, different from the traditional batch training approach, online training needs to learn from infinite streaming data, with high stability and performance for both system and algorithm level.

Key Features

1. Elastic. Both ps and worker level elastic during long term running, support dynamic networking for new node add and remove without restart job.
2. Single Node Failover. Based on ray streaming's capability of Single Node Failover, dynamic networking support single-node failover without restarting the entire job.
3. Large scale sparse embedding. Provide add-ones of tensorflow, support training with large scale and elastic sparse embedding features.
4. Streaming input support. A general dataset creator to support all data sources as backend, including both streaming and batch data.
5. Algorithm toolkits for online learning. An algorithm toolkit to help the long-term training models keep converging.
6. Validation for continuous model delivery. A validation mechanism to help our system keep delivering high-quality models and intercept all the abnormal models.

Build

Build from source code :

• Build a docker using docker/Dockerfile-env
• Execute scripts/install.sh

Getting Involved

• Forum: For discussions about development, questions about usage, and feature requests.
• GitHub Issues: For reporting bugs.
• Slack: Join our Slack channel.
• StackOverflow: For questions about how to use Ray-Mobius.