mirror of
https://github.com/wassname/ray.git
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Mehrdad
77 lines
2.9 KiB
Markdown
77 lines
2.9 KiB
Markdown
# Ray
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Ray is an experimental distributed execution framework with a Python-like
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programming model. It is under development and not ready for general use.
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## Example Code
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### Loading ImageNet
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TODO: fill this out.
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## Design Decisions
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For a description of our design decisions, see
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- [Reference Counting](doc/reference-counting.md)
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- [Aliasing](doc/aliasing.md)
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- [Scheduler](doc/scheduler.md)
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## Setup
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### Linux, Mac, and other Unix-based systems
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1. sudo apt-get update
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2. sudo apt-get install git
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3. git clone https://github.com/amplab/ray.git
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4. cd ray
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5. ./setup.sh
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### Windows
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**Note:** A batch file is provided that clones any missing third-party libraries and applies patches to them.
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Do not attempt to open the solution before the batch file applies the patches; otherwise, if the projects have been modified, the patches may be rejected, and you may be forced to revert your changes before re-running the batch file.
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1. Install Microsoft Visual Studio 2015
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2. Install Git
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3. git clone https://github.com/amplab/ray.git
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4. ray\thirdparty\download_thirdparty.bat
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## Installing Ray on a cluster
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These instructions work on EC2, but they may require some modifications to run
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on your own cluster. In particular, on EC2, running `sudo` does not require a
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password, and we currently don't handle the case where a password is needed.
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1. Create a file `nodes.txt` of the IP addresses of the nodes in the cluster.
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For example
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52.50.28.103
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52.51.210.207
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2. Make sure that the nodes can all communicate with one another. On EC2, this
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can be done by creating a new security group and adding the inbound rule "all
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traffic" and adding the outbound rule "all traffic". Then add all of the nodes
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in your cluster to that security group.
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3. Run something like
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```
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python scripts/cluster.py --nodes nodes.txt \
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--key-file key.pem \
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--username ubuntu \
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--installation-directory /home/ubuntu/
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```
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where you replace `nodes.txt`, `key.pem`, `ubuntu`, and `/home/ubuntu/` by the
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appropriate values. This assumes that you can connect to each IP address in
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`nodes.txt` with the command
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```
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ssh -i key.pem ubuntu@<ip-address>
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```
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4. The previous command should open a Python interpreter. To install Ray on the
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cluster, run `install_ray(node_addresses)` in the interpreter. The interpreter
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should block until the installation has completed.
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5. To check that the installation succeeded, you can ssh to each node, cd into
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the directory `ray/test/`, and run the tests (e.g., `python runtest.py`).
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6. Now that Ray has been installed, you can start the cluster (the scheduler,
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object stores, and workers) with the command `start_ray(node_addresses,
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"/home/ubuntu/ray/test/test_worker.py")`, where the second argument is the path
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on each node in the cluster to the worker code that you would like to use.
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