Modin (Pandas on Ray)¶
Modin, previously Pandas on Ray, is a dataframe manipulation library that allows users to speed up their pandas workloads by acting as a drop-in replacement. Modin also provides support for other APIs (e.g. spreadsheet) and libraries, like xgboost.
import modin.pandas as pd import ray ray.init() df = pd.read_parquet("s3://my-bucket/big.parquet")
You can use Modin on Ray with your laptop or cluster. In this document, we show instructions for how to set up a Modin compatible Ray cluster and connect Modin to Ray.
In previous versions of Modin, you had to initialize Ray before importing Modin. As of Modin 0.9.0, This is no longer the case.
Using Modin with Ray’s autoscaler¶
In order to use Modin with Ray’s autoscaler, you need to ensure that the correct dependencies are installed at startup. Modin’s repository has an example yaml file and set of tutorial notebooks to ensure that the Ray cluster has the correct dependencies. Once the cluster is up, connect Modin by simply importing.
import modin.pandas as pd import ray ray.init(address="auto") df = pd.read_parquet("s3://my-bucket/big.parquet")
As long as Ray is initialized before any dataframes are created, Modin will be able to connect to and use the Ray cluster.
Modin with the Ray Client¶
When using Modin with the Ray Client, it is important to ensure that the cluster has all dependencies installed.
import modin.pandas as pd import ray import ray.util ray.init("ray://<head_node_host>:10001") df = pd.read_parquet("s3://my-bucket/big.parquet")
Modin will automatically use the Ray Client for computation when the file is read.
How Modin uses Ray¶
Modin has a layered architecture, and the core abstraction for data manipulation is the Modin Dataframe, which implements a novel algebra that enables Modin to handle all of pandas (see Modin’s documentation for more on the architecture). Modin’s internal dataframe object has a scheduling layer that is able to partition and operate on data with Ray.
The Modin Dataframe uses Ray tasks to perform data manipulations. Ray Tasks have a number of benefits over the actor model for data manipulation:
Multiple tasks may be manipulating the same objects simultaneously
Objects in Ray’s object store are immutable, making provenance and lineage easier to track
As new workers come online the shuffling of data will happen as tasks are scheduled on the new node
Identical partitions need not be replicated, especially beneficial for operations that selectively mutate the data (e.g.
Finer grained parallelism with finer grained placement control
Modin uses Ray Actors for the machine learning support it currently provides. Modin’s implementation of XGBoost is able to spin up one actor for each node and aggregate all of the partitions on that node to the XGBoost Actor. Modin is able to specify precisely the node IP for each actor on creation, giving fine-grained control over placement - a must for distributed training performance.