131 lines
5.5 KiB
Markdown
131 lines
5.5 KiB
Markdown
# RayOnSpark Quickstart
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---
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[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/orca/colab-notebook/quickstart/ray_parameter_server.ipynb) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/orca/colab-notebook/quickstart/ray_parameter_server.ipynb)
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---
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**In this guide, we will describe how to use RayOnSpark to directly run Ray programs on Big Data clusters in 2 simple steps.**
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### **Step 0: Prepare Environment**
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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](../../UserGuide/python.md) for more details.
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```bash
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conda create -n bigdl python=3.7 # "bigdl" is conda environment name, you can use any name you like.
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conda activate bigdl
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pip install bigdl-orca[ray]
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```
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### **Step 1: Initialize**
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We recommend using `init_orca_context` to initiate and run BigDL on the underlying cluster. The Ray cluster would be launched automatically by specifying `init_ray_on_spark=True`.
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```python
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from bigdl.orca import init_orca_context
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if cluster_mode == "local": # For local machine
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sc = init_orca_context(cluster_mode="local", cores=4, memory="10g", init_ray_on_spark=True)
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elif cluster_mode == "k8s": # For K8s cluster
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sc = init_orca_context(cluster_mode="k8s", num_nodes=2, cores=2, memory="10g", driver_memory="10g", driver_cores=1, init_ray_on_spark=True)
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elif cluster_mode == "yarn": # For Hadoop/YARN cluster
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sc = init_orca_context(cluster_mode="yarn", num_nodes=2, cores=2, memory="10g", driver_memory="10g", driver_cores=1, init_ray_on_spark=True)
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```
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This is the only place where you need to specify local or distributed mode.
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By default, the Ray cluster would be launched using Spark barrier execution mode, you can turn it off via the configurations of `OrcaContext`:
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```python
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from bigdl.orca import OrcaContext
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OrcaContext.barrier_mode = False
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```
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View [Orca Context](./../../Orca/Overview/orca-context.md) for more details.
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**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.
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You can retrieve the information of the Ray cluster via `OrcaContext`:
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```python
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from bigdl.orca import OrcaContext
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ray_ctx = OrcaContext.get_ray_context()
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address_info = ray_ctx.address_info # The dictionary information of the ray cluster, including node_ip_address, object_store_address, webui_url, etc.
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redis_address = ray_ctx.redis_address # The redis address of the ray cluster.
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```
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### **Step 2: Run Ray Applications**
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After the initialization, you can directly write Ray code inline with your Spark code, and run Ray programs on the underlying existing Big Data clusters. Ray [tasks](https://docs.ray.io/en/master/walkthrough.html#remote-functions-tasks) and [actors](https://docs.ray.io/en/master/actors.html) would be launched across the cluster.
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The following example uses actor handles to implement a parameter server example for distributed asynchronous stochastic gradient descent. This is a simple Ray example for demonstration purpose. Similarly, you can write other Ray applications as you wish.
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A parameter server is simply an object that stores the parameters (or "weights") of a machine learning model (this could be a neural network, a linear model, or something else). It exposes two methods: one for getting the parameters and one for updating the parameters.
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By adding the `@ray.remote` decorator, the `ParameterServer` class becomes a Ray actor.
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```python
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import ray
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import numpy as np
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dim = 10
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@ray.remote
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class ParameterServer(object):
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def __init__(self, dim):
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self.parameters = np.zeros(dim)
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def get_parameters(self):
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return self.parameters
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def update_parameters(self, update):
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self.parameters += update
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ps = ParameterServer.remote(dim)
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```
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In a typical machine learning training application, worker processes will run in an infinite loop that does the following:
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1. Get the latest parameters from the parameter server.
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2. Compute an update to the parameters (using the current parameters and some data).
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3. Send the update to the parameter server.
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By adding the `@ray.remote` decorator, the `worker` function becomes a Ray remote function.
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```python
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import time
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@ray.remote
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def worker(ps, dim, num_iters):
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for _ in range(num_iters):
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# Get the latest parameters.
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parameters = ray.get(ps.get_parameters.remote())
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# Compute an update.
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update = 1e-3 * parameters + np.ones(dim)
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# Update the parameters.
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ps.update_parameters.remote(update)
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# Sleep a little to simulate a real workload.
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time.sleep(0.5)
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# Test that worker is implemented correctly. You do not need to change this line.
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ray.get(worker.remote(ps, dim, 1))
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# Start two workers.
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worker_results = [worker.remote(ps, dim, 100) for _ in range(2)]
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```
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As the worker tasks are executing, you can query the parameter server from the driver and see the parameters changing in the background.
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```
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print(ray.get(ps.get_parameters.remote()))
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```
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**Note:** You should call `stop_orca_context()` when your program finishes:
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```python
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from bigdl.orca import stop_orca_context
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stop_orca_context()
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```
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