Instead of using a detached lifetime, tie the lifetime of `_DesignatedBlockOwner` to the lifetime of the context creator. Also, only create a `_DesignatedBlockOwner` if dynamic block splitting is enabled.
This PR adds pandas block format support by implementing `PandasRow`, `PandasBlockBuilder`, `PandasBlockAccessor`.
Note that `sort_and_partition`, `combine`, `merge_sorted_blocks`, `aggregate_combined_blocks` in `PandasBlockAccessor` redirects to arrow block format implementation for now. They'll be implemented in a later PR.
* basic reuse functionality without valid node filtering
* Filtering, logging, and formatting for cache_stopped_nodes on Azure
* Updated formatter version
We regularly run tasks where we know our expected resource requirements at launch, so call request_resources with the required number of cpus. The number of machines doesn't scale back down as our tasks are finishing, and just sit idle. This is costing more in aws hosting costs than necessary. Fix suggested is to not call request_resources and have a high upscaling_speed to instantly scale up to the required resources.
This PR is a minor adjustment to the K8s release tests.
Replace tasks with actors in scale test for reduced flakiness
Use an up-to-date Ray client API.
Support hosting a serve instance under a path prefix.
Some clean-up should still be done for the different overlapping HttpOptions that now exist (host, port, root_path, root_url).
This is a simple refactoring change and my first PR in ray-project. This change moves an if statement outside of a loop. This way the check is not repeated for each iteration.
The WandbLoggingCallback is run on the driver side, with the experiment directory was the cwd. Using resume=True will pick up state from other trials (as the file name is global), and thus lead to warning messages. Thus, we should default to resume=False when using the callback.
This PR also incorporates changes from #20966.
Co-authored by: Queimo <queimo@gmx.net>
Co-authored by: Karim <karim.ben.hicham@rwth-aachen.de>
This PR fix the issue that sometimes FunctionsToRun is not executed. We isolated the Functions/Actors in function table, but not the RunctionsToRun. So when doing importing, sometimes, some functions will be missed.
This PR fixed this.
Currently, `ray stop` logic is vulnerable, and it kills Redis server that's not started by Ray. This PR fixes the issue by better checking the executable name of redis-server (If it is redis-server created by Ray, it should contain Ray specific path copied while wheels are built).
I originally tried to obtain ppid and kill a redis-server only when it is created from the same parent, but it turns out all processes started by ray start has no ppid.
While the best solution is to have some "process manager" that we can detect redis server started by us, I think there's no need to put lots of efforts here right now since Redis will be removed soon. We will eventually move to a better direction (process manager) to handle this sort of issues.
The test is timing out during actor creation and ends up not testing the code which is only triggered after a training result is returned back to driver.
Change to use a simpler Trainable.
This is the second part of https://docs.google.com/document/d/12qP3x5uaqZSKS-A_kK0ylPOp0E02_l-deAbmm8YtdFw/edit#. After this PR, dashboard agents will fully work with minimal ray installation.
Note that this PR requires to introduce "aioredis", "frozenlist", and "aiosignal" to the minimal installation. These dependencies are very small (or will be removed soon), and including them to minimal makes thing very easy. Please see the below for the reasoning.
This PR moves the sdk to its own folder, then includes everything in `import ray.autoscaler.sdk` in ray's import path.
Note: that there were circular dependencies in naively doing this because the ray core now uses constants that were defined in the autoscaler for internal kv operations (and the autoscaler similarly calls into the ray core). The solution was to move those internal kv keys into ray core constants so the imports flow (more) one way.
Co-authored-by: Alex Wu <alex@anyscale.com>
This patch fixed two issues.
1. log_monitor.py can crash when gcs is not temporarily available. Added retry logic in gcs_pubsub.py.
2. it is possible that the signal handler can raise another exception during exception handling.
This PR adds a `CometLoggerCallback` to the Tune Integrations, allowing users to log runs from Ray to [Comet](https://www.comet.ml/site/).
Co-authored-by: Michael Cullan <mjcullan@gmail.com>
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
Resubmitting #21705 which was merged then reverted. It seems somehow sphinx building broke in the meantime, not clear how it is connected to this PR.
Here is the original description:
>Part of the effort to enable tests on windows, this enables test_metrics and test_metric_agents, which pass locally.
There was a user request to disable runtime env logs. This is the first PR that allows users to disable runtime env logs through an env var. Basically if users specify `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED =0`, this will disable runtime env logs.
Note that in the log monitor RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1 by default. This is temporary, and I'd like to make this 0 by default after improving runtime error failure messages.
Once we disable log msgs by default, we can unify `RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED` and `RAY_RUNTIME_ENV_LOCAL_DEV_MODE`
Now if an actor throws an exception containing non-ASCII characters, the actor won't die and will be alive.
This is because the following exception occurred during handling the user's exception:
```
File "python/ray/_raylet.pyx", line 587, in ray._raylet.task_execution_handler
File "python/ray/_raylet.pyx", line 434, in ray._raylet.execute_task
File "python/ray/_raylet.pyx", line 551, in ray._raylet.execute_task
File "/home/admin/.local/lib/python3.6/site-packages/ray/utils.py", line 96, in push_error_to_driver
worker.core_worker.push_error(job_id, error_type, message, time.time())
File "python/ray/_raylet.pyx", line 1636, in ray._raylet.CoreWorker.push_error
UnicodeEncodeError: 'ascii' codec can't encode characters in position 2597-2600: ordinal not in range(128)
An unexpected internal error occurred while the worker was executing a task.
```
This PR fixes this issue.
Currently, when we destroy the created placement group, we will kill all workers that are related to this placement group, however, we only killed the running worker at this time, if there is a worker which startup very slow and the related placement group was already destroyed before the worker startup successfully, then there will be a worker leak.
RayDP needs to be updated to work with redisless ray.
To be more specific this [line](c08a786770/python/raydp/spark/ray_cluster_master.py (L146)
) needs to be updated to using `node.address`
We should update this after the release with the feature being turned on by default.
Currently, the docs have an [end-to-end tutorial](https://web.archive.org/web/20211122152843/https://docs.ray.io/en/latest/serve/tutorial.html) walking users through deploying a `Counter` function on Serve. This PR adds an end-to-end tutorial walking users through deploying an entire Hugging Face model using Serve, providing a better understanding of how to deploy an actual model via Serve.
Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
Co-authored-by: Simon Mo <simon.mo@hey.com>
This PR fixes issues with loading ExperimentAnalysis from path or pickle if the trainable used in the trials is not registered. Chiefly, it ensures that the stub attribute set in load_trials_from_experiment_checkpoint doesn't get overridden by the state of the loaded trial, and that when pickling, all trials in ExperimentAnalysis are turned into stubs if they aren't already. A test has also been added.
