Signed-off-by: Amog Kamsetty amogkamsetty@yahoo.com
Tests for upstream ML libraries (xgboost-ray, ray-lightning, etc.) were recently refactored from ray.util to ray.tests. This caused them to be run in Windows CI, but Windows CI does not have any ML dependencies. This PR disables these tests from being run on Windows, which matches the previous behavior.
This PR
- only prints train_loop info strings (e.g. `train_loop_utils.py:298 -- Moving model to device: cpu`) for rank 0 workers for torch
- renames `BaseWorkerMixin` to `RayTrainWorker` as the name comes up often in output and is more meaningful
Signed-off-by: Kai Fricke <kai@anyscale.com>
These Serve CLI commands start Serve if it's not already running:
* `serve deploy`
* `serve config`
* `serve status`
* `serve shutdown`
#27026 introduces the ability to specify a `host` and `port` in the Serve config file. However, once Serve starts running, changing these options requires tearing down the entire Serve application and relaunching it. This limitation is an issue because users can inadvertently start Serve by running one of the `GET`-based CLI commands (i.e. `serve config` or `serve status`) before running `serve deploy`.
This change makes `serve deploy` the only CLI command that can start a Serve application on a Ray cluster. The other commands have updated behavior when Serve is not yet running on the cluster.
* `serve config`: prints an empty config body.
```yaml
import_path: ''
runtime_env: {}
deployments: []
```
* `serve status`: prints an empty status body, with a new `app_status` `status` value: `NOT_STARTED`.
```yaml
app_status:
status: NOT_STARTED
message: ''
deployment_timestamp: 0
deployment_statuses: []
```
* `serve shutdown`: performs a no-op.
Seeing one more pattern of AWS S3 read error message related to credential - https://gist.github.com/jiaodong/a805577c35e44e720ff10136f5ec6f6c, shared from @jiaodong. Change the regex pattern to match the error message as well, so it prints out more understandable error message.
Signed-off-by: Cheng Su <scnju13@gmail.com>
### Why are these changes needed?
This PR enhances workflow functionality to receive external events from a Serve based HTTP endpoint. A workflow can then consume events asynchronously as they arrive.
### Design Logic
A `workflow.wait_for_event` node subscribes to the endpoint instantiated by a Ray Serve deployment of class `http_event_provider.HTTPEventProvider`. The subscription is made through a helper class `http_event_provider.HTTPListener`. `HTTPListener` implements the methods of `EventListener` to poll from and confirm event checkpointing to `HTTPEventProvider`, before `HTTPEventProvider`acknowledges success or error to the event submitter.
### Architecture Improvement
The logic of this enhancement conforms with existing workflow runtime design.
Why are these changes needed?
Node failures logs become extremely spammy with Failed to get the resource load: logs. This PR removes the logs from driver-side logs and prints it less often
RLLib outputs for verbose=3 are too long at the moment. This is the first step of beautifying this table output by putting sequences of length > 3 into flow style.
Signed-off-by: Kai Fricke <kai@anyscale.com>
Before this PR, updating default ray params in GBDT trainers was faulty. This PR addresses these issues and sets the default number of cpus per actor for lightgbm trainer to 2.
Signed-off-by: Kai Fricke <kai@anyscale.com>
RLLibs trainables produce a large number of metrics which makethe log output with verbose=2 illegible. This PR introduces a private `_progress_metrics` property for trainables. If set, the trial progress callback will only print these metrics per default, unless overridden e.g. with a custom `TrialProgressCallback`.
Add UsageStats alert to new dashboard
Update wording of "back to legacy dashboard", "try new dashboard" buttons
Signed-off-by: Alan Guo aguo@anyscale.com
# Why are these changes needed?
This PR does 3 things
Add warnings for data truncation (which is a follow-up)
Improve some of confusing warning messages
order columns as it is defined in StateSchema (so that we can customize the column order for better usability). I did this only for list because i thought it wasn't that important for summary, but I might be wrong
Signed-off-by: rickyyx rickyx@anyscale.com
# Why are these changes needed?
This should make the state API more scalable, and somewhat lower the chance issues like #27009 happening.
Removes all ML related code from `ray.util`
Removes:
- `ray.util.xgboost`
- `ray.util.lightgbm`
- `ray.util.horovod`
- `ray.util.ray_lightning`
Moves `ray.util.ml_utils` to other locations
Closes#23900
Signed-off-by: Amog Kamsetty <amogkamsetty@yahoo.com>
Signed-off-by: Kai Fricke <kai@anyscale.com>
Co-authored-by: Kai Fricke <kai@anyscale.com>
Signed-off-by: Jiajun Yao jeromeyjj@gmail.com
# Why are these changes needed?
Fix internval kv operations in ray client mode
Support recording usages in ray client. Currently we may miss library usages if users use ray client to run their program.
- Update xgboost test (catch test failures properly)
- Remove `path` from `from_model` for XGBoostCheckpoint and LightGbmCheckpoint.
Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com>
## Why are these changes needed?
- Fixes the jobs tab in the new dashboard. Previously it didn't load.
- Combines the old job concept, "driver jobs" and the new job submission conception into a single concept called "jobs". Jobs tab shows information about both jobs.
- Updates all job APIs: They now returns both submission jobs and driver jobs. They also contains additional data in the response including "id", "job_id", "submission_id", and "driver". They also accept either job_id or submission_id as input.
- Job ID is the same as the "ray core job id" concept. It is in the form of "0100000" and is the primary id to represent jobs.
- Submission ID is an ID that is generated for each ray job submission. It is in the form of "raysubmit_12345...". It is a secondary id that can be used if a client needs to provide a self-generated id. or if the job id doesn't exist (ex: if the submission job doesn't create a ray driver)
This PR has 2 deprecations
- The `submit_job` sdk now accepts a new kwarg `submission_id`. `job_id is deprecated.
- The `ray job submit` CLI now accepts `--submission-id`. `--job-id` is deprecated.
**This PR has 4 backwards incompatible changes:**
- list_jobs sdk now returns a list instead of a dictionary
- the `ray job list` CLI now prints a list instead of a dictionary
- The `/api/jobs` endpoint returns a list instead of a dictionary
- The `POST api/jobs` endpoint (submit job) now returns a json with `submission_id` field instead of `job_id`.
Why are these changes needed?
object_store_memory as task option has already been removed and object_store_memory as actor option is actually broken since at least last year (#20344).
Given we now have object spilling and fallback allocation, this option is no longer useful.
Why are these changes needed?
The Parquet file sampling PR (#26868) caused nightly test regression - #26995 . Turning off the feature by default, and keep debugging the root cause, to unblock nightly test.
This PR:
- Updates the KubeRay operator commit used in the Ray CI autoscaling test
- Uses the RayCluster autoscaling sample config from the KubeRay repo in place of of a config from the Ray repo
- Turns the autoscaler RPC worker drain back on, as I saw some dead node messages from the GCS, and the RPC drain is supposed to avoid those.
Signed-off-by: Dmitri Gekhtman <dmitri.m.gekhtman@gmail.com>
We encountered SIGSEGV when running Python test `python/ray/tests/test_failure_2.py::test_list_named_actors_timeout`. The stack is:
```
#0 0x00007fffed30f393 in std::basic_string<char, std::char_traits<char>, std::allocator<char> >::basic_string(std::string const&) ()
from /lib64/libstdc++.so.6
#1 0x00007fffee707649 in ray::RayLog::GetLoggerName() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#2 0x00007fffee70aa90 in ray::SpdLogMessage::Flush() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#3 0x00007fffee70af28 in ray::RayLog::~RayLog() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#4 0x00007fffee2b570d in ray::asio::testing::(anonymous namespace)::DelayManager::Init() [clone .constprop.0] ()
from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#5 0x00007fffedd0d95a in _GLOBAL__sub_I_asio_chaos.cc () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#6 0x00007ffff7fe282a in call_init.part () from /lib64/ld-linux-x86-64.so.2
#7 0x00007ffff7fe2931 in _dl_init () from /lib64/ld-linux-x86-64.so.2
#8 0x00007ffff7fe674c in dl_open_worker () from /lib64/ld-linux-x86-64.so.2
#9 0x00007ffff7b82e79 in _dl_catch_exception () from /lib64/libc.so.6
#10 0x00007ffff7fe5ffe in _dl_open () from /lib64/ld-linux-x86-64.so.2
#11 0x00007ffff7d5f39c in dlopen_doit () from /lib64/libdl.so.2
#12 0x00007ffff7b82e79 in _dl_catch_exception () from /lib64/libc.so.6
#13 0x00007ffff7b82f13 in _dl_catch_error () from /lib64/libc.so.6
#14 0x00007ffff7d5fb09 in _dlerror_run () from /lib64/libdl.so.2
#15 0x00007ffff7d5f42a in dlopen@@GLIBC_2.2.5 () from /lib64/libdl.so.2
#16 0x00007fffef04d330 in py_dl_open (self=<optimized out>, args=<optimized out>)
at /tmp/python-build.20220507135524.257789/Python-3.7.11/Modules/_ctypes/callproc.c:1369
```
The root cause is that when loading `_raylet.so`, `static DelayManager _delay_manager` is initialized and `RAY_LOG(ERROR) << "RAY_testing_asio_delay_us is set to " << delay_env;` is executed. However, the static variables declared in `logging.cc` are not initialized yet (in this case, `std::string RayLog::logger_name_ = "ray_log_sink"`).
It's better not to rely on the initialization order of static variables in different compilation units because it's not guaranteed. I propose to change all `RAY_LOG`s to `std::cerr` in `DelayManager::Init()`.
The crash happens in Ant's internal codebase. Not sure why this test case passes in the community version though.
BTW, I've tried different approaches:
1. Using a static local variable in `get_delay_us` and remove the global variable. This doesn't work because `init()` needs to access the variable as well.
2. Defining the global variable as type `std::unique_ptr<DelayManager>` and initialize it in `get_delay_us`. This works but it requires a lock to be thread-safe.
Why are these changes needed?
The DLAMI moved underneath us and broke for 2 reasons.
The AMI's snapshot size increased to 140 GB which was more than our hardcoded max EBS volume size of 100GB
The AMI dropped support for python 3.7 and only has 3.8 now.
The solutions short term solutions are simple.
Allocate a bigger EBS volume.
Use the tensorflow 3.8 env.
Related issue number
Closes#26368
Co-authored-by: Alex <alex@anyscale.com>
