After this PR (https://github.com/ray-project/ray/pull/22156), for some reasons the driver script has some string that cannot be encoded with ascii. It seems like using utf-8 solves the problem.
Ensures that we don't log a warning message about an infeasible resource demand when that custom resource is a placement group (since the placement group resource likely just needs to be created by the raylet).
Co-authored-by: Alex Wu <alex@anyscale.com>
For public SDK APIs, change the import path from
```python
from ray.dashboard.modules.job.common import JobStatus, JobStatusInfo
from ray.dashboard.modules.job.sdk import JobSubmissionClient
```
to
```python
from ray.job_submission import JobStatus, JobSubmissionClient
```
`JobStatus`, `JobStatusInfo` and `JobSubmissionClient` were the only names referenced in the SDK doc so far, but we can add more later as they appear.
## Diff Summary
Current implementation of DAGNode pre-bind inputs and the signature of `def execute(self)` doesn't take user input yet. This PR extends the interface to take user input, mark DAG entrypoint methods as first stop of all user requests in a DAG. It's needed to unblock next step serve pipeline implementation to serve user requests.
Closes#22196#22197
Notable changes:
- Added a `DAG_ENTRY_POINT` flag in ray dag API to annotate DAG entrypoint functions. Function or class method only. All marked functions will receive identical input from user as first layer of DAG.
- Changed implementations of ClassNode and FunctionNode accordingly to handle different execution for a node marked as entrypoint or not.
- Added a `kwargs_to_resolve` kwarg in the interface of `DAGNode` to handle args that sub-classes need to use to resolve it's implementation without exposing all terms to parent class level.
- This is particularly important for ClassMethodNode binding, so we can have implementations to track method name, parent ClassNode as well as previous class method call without existiting
- Changed implementation of `_copy()` to handle execution of `kwargs_to_resolve`.
- Changed implementation of `_apply_and_replace_all_child_nodes()` to fetch DAGNode type in `kwargs_to_resolve`.
- Added pretty printed lines for `kwargs_to_resolve`
If we run multiple jobs in the same process (this is basically the behavior of python tests), they should be isolated in the sense that system config for job 1 shouldn't affect config for job 2.
```
ray.init(_system_config={})
# job 1
ray.shutdown()
ray.init(_system_config={})
# job 2
ray.shutdown()
```
Currently it's not the case, since RayConfig is a static variable and it's shared across drivers in the same process. This PR resets the configs to default value before applying job specific _system_config.
Note: it's backward incompatible change if user depends on the current behavior but I'm not aware of such case.
The Staroid node provider has been abandoned and unmaintained for quite some time now. Due to the fact that there are no active maintainers, the original contributors cannot be reached, and there is no clear interest, we are no longer officially endorsing or supporting the node provider.
Co-authored-by: Alex Wu <alex@anyscale.com>
This is a down scoped change. For the full overview picture of Tune control loop, see [`Tune control loop refactoring`](https://docs.google.com/document/d/1RDsW7SVzwMPZfA0WLOPA4YTqbRyXIHGYmBenJk33HaE/edit#heading=h.2za3bbxbs5gn)
1. Previously there are separate waits on pg ready and other events. As a result, there are quite a few timing tweaks that are inefficient, hard to understand and unit test. This PR consolidates into a single wait that is handled by TrialRunner in each step.
- A few event types are introduced, and their mapping into scenarios
* PG_READY --> Should place a trial onto it. If somehow there is no trial to be placed there, the pg will be put in _ready momentarily. This is due to historically resources is conceptualized as a pull based model.
* NO_RUNNING_TRIALS_TIME_OUT --> possibly not sufficient resources case
* TRAINING_RESULT
* SAVING_RESULT
* RESTORING_RESULT
* YIELD --> This just means that simply taking very long to train. We need to punt back to the main loop to print out status info etc.
2. Previously TrialCleanup is not very efficient and can be racing between Trainable.stop() and `return_placement_group`. This PR streamlines the Trial cleanup process by explicitly let Trainable.stop() to finish followed by `return_placement_group(pg)`. Note, graceful shutdown is needed in cases like `pause_trial` where checkpointing to memory needs to be given the time to happen before the actor is gone.
3. There are quite some env variables removed (timing tweaks), that I consider OK to proceed without deprecation cycle.
Adds support for ignoring NaNs in aggregations. NaNs will now be ignored by default, and the user can pass in `ds.mean("A", ignore_nulls=False)` if they would rather have the NaN be propagated to the output. Specifically, we'd have the following null-handling semantics:
1. Mix of values and nulls - `ignore_nulls`=True: Ignore the nulls, return aggregation of values
2. Mix of values and nulls - `ignore_nulls`=False: Return `None`
3. All nulls: Return `None`
4. Empty dataset: Return `None`
This all null and empty dataset handling matches the semantics of NumPy and Pandas.
I’d like to make a small proposal to change the behavior of ray stop
### Status quo
It basically just sends a SIGTERM and finish asynchronously. It is vulnerable to have leaked processes or port conflict when you run ray stop && ray start I feel like the right behavior is as follow;
### New
- Send sigterm and wait for processes to terminate
- Display the progress to users
- If procs are not terminated by X seconds, send SIGKILL.
### API change
We will add `--grace-period` flag. The default is ray `stop --grace-period=X (10 seconds by default)`
And if users don’t want to be blocked, they can use ray stop --force (which already exists. It just sends SIGKILL, so procs are guaranteed to be terminated)
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 a re-merge for #21719 with a fix for removing the owned object ref if creation fails.
https://github.com/ray-project/ray/issues/21488#issuecomment-1027122177 :
> We discussed this issue in a bit more detail and came to the conclusion that we should set the CPU resource requirement for each actor in the actor pool to 0, to make the Ray Pool compatible/same behavior as the Python multiprocessing pool. Would that work for you @yogeveran ? (very similar to solution 4 mentioned above, but with 0.0 instead of 0.1, so it works in all cases).
Added lines to autoscaler configuration yaml to create a service account that is used to give the autoscaler permissions to list and read pods and patch the cluster CRD for up/downscaling.
Previously code incorrectly assumed that an exiting worker would disconnect from the raylet promptly to release resources. This isn't the case if the worker is owning references. This PR plumbs through the right release resources call even in this scenario.
Closes https://github.com/ray-project/ray/issues/10960
Co-authored-by: SangBin Cho <rkooo567@gmail.com>
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.
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
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>
