Followup from https://github.com/ray-project/ray/pull/21788. Previously we had a lot of `serialized_runtime_env == "{}" || serialized_runtime_env == ""` scattered around the C++ code; this PR puts this in a helper function.
Continuing docs overhaul, tune now has:
- [x] better landing page
- [x] a getting started guide
- [x] user guide was cut down, partially merged with FAQ, and partially integrated with tutorials
- [x] the new user guide contains guides to tune features and practical integrations
- [x] we rewrote some of the feature guides for clarity
- [x] we got rid of sphinx-gallery for this sub-project (only data and core left), as it looks bad and is unnecessarily complicated anyway (plus, makes the build slower)
- [x] sphinx-gallery examples are now moved to markdown notebook, as started in #22030.
- [x] Examples are tested in the new framework, of course.
There's still a lot one can do, but this is already getting too large. Will follow up with more fine-tuning next week.
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
Co-authored-by: Kai Fricke <krfricke@users.noreply.github.com>
`__dealloc__` is not allowed to call python code and this leads to two problems:
- The data has already been cleaned up
- Deadlock if there are locks used.
THis PR move the implementation to python layer to avoid this
We've had multiple issues that manifest as unexpected autoscaler logs about resource demands.
To make it easier to debug such issues, this PR adds a debug flag to allow logging the entire resource message used by the autoscaler as its source of truth about the Ray internals' resource usage.
If the env AUTOSCALER_LOG_RESOURCE_BATCH_DATA=1 is set, the autoscaler will log the resource message.
If the declarative API issues a code change to a group of deployments at once, it needs to deploy the group of updated deployments atomically. This ensures any deployment using another deployment's handle inside its own __init__() function can access that handle regardless of the deployment order. This change adds deploy_group to the ServeController class, allowing it to deploy a list of deployments atomically. It also adds a new public API command, serve.deploy_group(), exposing the controller's functionality publicly, so atomic deployments can also be executed via Python API.
Closes#21873.
As titled. We have a corner case on user laptop where user might left RAY_ADDRESS as http address but restarted local ray cluster. In this case we will try to do job submission with an http prefixed address.
Co-authored-by: Archit Kulkarni <architkulkarni@users.noreply.github.com>
Co-authored-by: Jiao Dong <jiaodong@anyscale.com>
In https://github.com/ray-project/ray/pull/20341 the behavior of `pip` was changed to install the specified packages in the existing environment rather than in a new environment. This posed a problem when specifying Ray libraries like "ray[serve]" in the `pip` field, because the installer would install Ray at runtime and this new Ray would take precedence over the Ray existing on the cluster. This could cause version mismatch issues. Skipping some details, the approach taken in the that PR was essentially to parse the `pip` list and remove Ray.
However not every line in a `pip` `requirements.txt` file is a requirements specifier; a line can also just specify options, like `--extra-index-url my-index-url.com`.
This caused the parsing library to raise an exception when trying to parse the line. This PR fixes this by catching the exception and skipping the line in this case, since it's not a line that specifies `ray` and that's all we're looking for when parsing.
Ray client currently supports connection strings for external modules of the format `"other_module://"`, however `ray job` commands don't support this format because trailing `/` is removed. Update so `ray job` commands also support this format.
Implement a TorchTensorboardProfilerCallback and corresponding TorchWorkerProfiler to support distributed PyTorch Profiler With TensorBoard integration.
When a Ray program first creates an ObjectRef (via ray.put or task call), we add it with a ref count of 0 in the C++ backend because the language frontend will increment the initial local ref once we return the allocated ObjectID, then delete the local ref once the ObjectRef goes out of scope. Thus, there is a brief window where the object ref will appear to be out of scope.
This can cause problems with async protocols that check whether the object is in scope or not, such as the previous bug fixed in #19910. Now that we plan to enable lineage reconstruction to automatically recover lost objects, this race condition can also be problematic because we use the ref count to decide whether an object needs to be recovered or not.
This PR avoids these race conditions by incrementing the local ref count in the C++ backend when executing ray.put() and task calls. The frontend is then responsible for skipping the initial local ref increment when creating the ObjectRef. This is the same fix used in #19910, but generalized to all initial ObjectRefs.
This is the PR to write better runtime env exception. After 3 PRs are merged, we can entirely turn off the runtime env logs streamed to drivers.
The first PR only handles tasks exception.
TODO
- [x] Task (this PR)
- [ ] Actor
- [ ] Turn of runtime env logs & improve error msgs
This PR avoids some unnecessary copying and branching when recording event stats. It improves / recovers ~10% of `single_client_get_calls_Plasma_Store` performance. On AWS EC2 `m5.8xlarge`,
- `single_client_get_calls_Plasma_Store` current: ~5200/s
- `single_client_get_calls_Plasma_Store` with PR: ~5800/s
When `RAY_event_stats=0`, `single_client_get_calls_Plasma_Store` can reach ~6800/s. If we want to optimize further, we can record data in opencensus only in intervals, or when the data are exported.
1. If the node is selected based on locality, we always run the task on the node selected by locality if the node is available.
2. For spread scheduling strategy, we always select the local node as the first raylet to request lease, no locality involved.
The WandbTrainableMixin doesn't work with RLLib trainables as they won't recognize the wandb parameter. Thus we should pop the wandb config before we initialize the rest of the trainable.
We're introducing the usage of [MyST Notebooks](https://myst-nb.readthedocs.io/en/latest/index.html) here and demonstrate how it works by rewriting (and extending) the RLLib Serve tutorial. Benefits:
- [x] Write notebooks in markdown. Can be converted into other formats e.g. with `jupytext`
- [x] Tutorials like this have a binderhub link added to the top nav (launch button).
- [x] Notebooks get executed when docs are built, so it's impossible to have stale docs.
- [x] But locally those builds are cached so that you don't have to wait too long.
- [x] The notebook cell outputs can be shown, hidden or removed. In particular, we can now avoid adding expected code output as comments in our scripts (which might get outdated).
We're also clarifying #22022.
Old tutorial: [here](https://docs.ray.io/en/latest/serve/tutorials/rllib.html)
New tutorial (preview): [here](https://ray--22030.org.readthedocs.build/en/22030/serve/tutorials/rllib.html)
Co-authored-by: simon-mo <simon.mo@hey.com>
This adds some utility functions to make it easier to manipulate structured data in Datasets. While in principle you can already do this with map_batches, this makes it a little easier to test things out for development.
