使用 Jupyter Notebooks 和 JupyterLab

本文档描述了使用 Ray 与 Jupyter Notebook / JupyterLab 的最佳实践。我们以 AWS 为例进行说明，但这些论点也应适用于其他云服务提供商。如果你认为本文档缺少任何内容，欢迎贡献。

设置笔记本

1. Ensure your EC2 instance has enough EBS volume if you plan to run the Notebook on it. The Deep Learning AMI, pre-installed libraries and environmental set-up will by default consume ~76% of the disk prior to any Ray work. With additional applications running, the Notebook could fail frequently due to full disk. Kernel restart loses progressing cell outputs, especially if we rely on them to track experiment progress. Related issue: Autoscaler should allow configuration of disk space and should use a larger default..

2. Avoid unnecessary memory usage. IPython stores the output of every cell in a local Python variable indefinitely. This causes Ray to pin the objects even though you application may not actually be using them. Therefore, explicitly calling print or repr is better than letting the Notebook automatically generate the output. Another option is to just altogether disable IPython caching with the following (run from bash/zsh):

echo 'c = get_config()
c.InteractiveShell.cache_size = 0 # disable cache
' >>  ~/.ipython/profile_default/ipython_config.py

这仍然允许打印，但完全停止IPython的缓存。

小技巧

虽然上述设置有助于减少内存占用，但在应用程序中移除不再需要的引用以释放对象存储空间始终是一个好的做法。

3. Understand the node’s responsibility. Assuming the Notebook runs on a EC2 instance, do you plan to start a ray runtime locally on this instance, or do you plan to use this instance as a cluster launcher? Jupyter Notebook is more suitable for the first scenario. CLI’s such as ray exec and ray submit fit the second use case better.

4. Forward the ports. Assuming the Notebook runs on an EC2 instance, you should forward both the Notebook port and the Ray Dashboard port. The default ports are 8888 and 8265 respectively. They will increase if the default ones are not available. You can forward them with the following (run from bash/zsh):

ssh -i /path/my-key-pair.pem -N -f -L localhost:8888:localhost:8888 my-instance-user-name@my-instance-IPv6-address
ssh -i /path/my-key-pair.pem -N -f -L localhost:8265:localhost:8265 my-instance-user-name@my-instance-IPv6-address