Enable checking of the ray core module, excluding serve, workflows, and tune, in ./ci/lint/check_api_annotations.py. This required moving many files to ray._private and associated fixes.
Currently when Raylets die, it is hard to figure out:
if a Raylet died at all in a cluster. Usually we have to check on nodes where a number of workers died and see if the Raylet has died as well.
reason of Raylet's death.
With this PR, if a Raylet dies from a reason other than SIGTERM, the dashboard agent will report the failure along with last 20 lines of the Raylet log.
Add visibility into the following to help Ray users and developers debug performance and OOM issues:
Raylet memory usage broken down by USS vs remaining RSS.
Total workers' count, CPU percentage usage, and memory usage.
https://github.com/ray-project/ray/pull/14676 disabled the disk usage/total display for Ray nodes on K8s, because Ray nodes on K8s are run as pods, which in general do not use up the entire machine.
However, in some situations, it is useful to run one Ray pod per K8s node and report the disk usage.
This PR adds a flag to enable displaying disk usage in those situations.
This PR allows for Ray to disable metrics collection. It was possible with RAY_enable_metrics_collection, but it didn't fully disable collection because there was a metrics collection happening from agent that wasn't properly disabled. This PR also adds tests.
Adds some metrics useful for object-intensive workloads:
Per raylet/object manager:
Add num bytes pending restore to spill manager
Add num requests cumulative to PullManager
Num bytes pushed/pulled from other nodes cumulative
Histogram for request latencies in PullManager:
total life time of request, from start to cancel
request satisfaction time, from start to object local
pull time, from object activation to object local
Per-node disk read/write speed, IOPS
From #22954, GPU utilization can be unavailable for consumer hardware. So dashboard should not assume the value cannot be None.
There might be a better way to represent "not reported". But currently utilizations are summed up which makes using non-zero to represent "not reported" hard to do.
GCS pubsub has been the default for awhile. There is little chance that we would need to revert back to Redis pubsub in future. This is the step in removing Redis pubsub, by first removing the `enable_gcs_pubsub()` feature guard.
As we are turning redisless ray by default, dashboard doesn't need to talk with redis anymore. Instead it should talk with gcs and gcs can talk with redis.
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>
On machines without GPUs, this can run subprocesses that spew to
stderr. Then with log_to_driver=True, we get log spew from every
single raylet. To avoid this, disable the GPU usage check on
certain errors.
Resolves#14305
Co-authored-by: hauntsaninja <>
This is part of gcs ha project. This PR try to bootstrap dashboard with gcs address instead of redis.
Co-authored-by: mwtian <81660174+mwtian@users.noreply.github.com>
## Why are these changes needed?
This is part of redis removal project. In this PR all direct usage of redis got removed except function table.
Function table will be migrated in the next PR
## Related issue number
#19443
* prepare for head node
* move command runner interface outside _private
* remove space
* Eric
* flake
* min_workers in multi node type
* fixing edge cases
* eric not idle
* fix target_workers to consider min_workers of node types
* idle timeout
* minor
* minor fix
* test
* lint
* eric v2
* eric 3
* min_workers constraint before bin packing
* Update resource_demand_scheduler.py
* Revert "Update resource_demand_scheduler.py"
This reverts commit 818a63a2c86d8437b3ef21c5035d701c1d1127b5.
* reducing diff
* make get_nodes_to_launch return a dict
* merge
* weird merge fix
* auto fill instance types for AWS
* Alex/Eric
* Update doc/source/cluster/autoscaling.rst
* merge autofill and input from user
* logger.exception
* make the yaml use the default autofill
* docs Eric
* remove test_autoscaler_yaml from windows tests
* lets try changing the test a bit
* return test
* lets see
* edward
* Limit max launch concurrency
* commenting frac TODO
* move to resource demand scheduler
* use STATUS UP TO DATE
* Eric
* make logger of gc freed refs debug instead of info
* add cluster name to docker mount prefix directory
* grrR
* fix tests
* moving docker directory to sdk
* move the import to prevent circular dependency
* smallf fix
* ian
* fix max launch concurrency bug to assume failing nodes as pending and consider only load_metric's connected nodes as running
* small fix
* deflake test_joblib
* lint
* placement groups bypass
* remove space
* Eric
* first ocmmit
* lint
* exmaple
* documentation
* hmm
* file path fix
* fix test
* some format issue in docs
* modified docs
* joblib strikes again on windows
* add ability to not start autoscaler/monitor
* a
* remove worker_default
* Remove default pod type from operator
* Remove worker_default_node_type from rewrite_legacy_yaml_to_availble_node_types
* deprecate useless fields
Co-authored-by: Ameer Haj Ali <ameerhajali@ameers-mbp.lan>
Co-authored-by: Alex Wu <alex@anyscale.io>
Co-authored-by: Alex Wu <itswu.alex@gmail.com>
Co-authored-by: Eric Liang <ekhliang@gmail.com>
Co-authored-by: Ameer Haj Ali <ameerhajali@Ameers-MacBook-Pro.local>
Co-authored-by: root <root@ip-172-31-56-188.us-west-2.compute.internal>
Co-authored-by: Dmitri Gekhtman <dmitri.m.gekhtman@gmail.com>
