The REST API's schema default denies HTTP access to deployments when `route_prefix` is omitted. This doesn't match `@serve.deployment`'s behavior, which make `route_prefix` the deployment's name when omitted.
This change matches the schema's behavior to the decorator. When `route_prefix` is omitted from the config, the deployment's `route_prefix` defaults to its name. When the `route_prefix` is specified as `null`, the deployment won't have HTTP access.
This change also fixes a bug in Serve where when a deployment is updated from a non-`None` `route_prefix` to a `None` `route_prefix`, its `route_prefix` does not change. This bug meant that a deployment available over HTTP would continue to be available at the same route even when deployed again with `route_prefix=None`.
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.
Adds an API to the REST server, the SDK, and the CLI for listing all jobs that have been submitted, along with their information.
Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
I recently realized that during a runtime_env creation process, a plugin/manager that is very slow to setup may block the creation of other runtime_env, so I make plugin/manager setup run in threads.
[The refactor of `PipManager`](https://github.com/ray-project/ray/pull/22381) is about to be completed, so I ignore it in this PR.
Combine `ParsedRuntimeEnv` and `RuntimeEnv` into `ray.runtime.RuntimeEnv`, details: #21495
- The `new RuntimeEnv` includes all external interfaces of `ParsedRuntimeEnv` and `old RuntimeEnv`.
- The `new RuntimeEnv` will be exposed directly to the user.
- example:
```python
runtime_env = ray.runtime_env.RuntimeEnv(working_dir="s3://workding_dir.zip",
pip=["requests"],
java_jars=["s3://jar1.zip"],
java_jvm_options=["-Dxxx=xxx"])
```
`test_job_submission` has become [flakey](https://flakey-tests.ray.io/) due to timeout. This change increases the timeout in `test_failed_job_status` from 10 to 25 seconds.
This change adds the GET, PUT, and DELETE commands for Serve’s REST API. The dashboard receives these commands and issues corresponding requests to the Serve controller.
This is the second PR to implement usage stats on Ray. Please refer to the file usage_lib.py for more details.
The full specification is here https://docs.google.com/document/d/1ZT-l9YbGHh-iWRUC91jS-ssQ5Qe2UQ43Lsoc1edCalc/edit#heading=h.17dss3b9evbj.
This adds a dashboard module to enable usage stats. **Usage stats report is turned off by default** after this PR. We can control the report (enablement, report period, and URL. Note that URL is strictly for testing) using the env variable.
## NOTE
This requires us to add `requests` to the default library. `requests` must be okay to be included because
1. it is extremely lightweight. It is implemented only with built-in libs.
2. It is really stable. The project basically claims they are "deprecated", meaning no new features will be added there.
cc @edoakes @richardliaw for the approval
For the HTTP request, I was alternatively considered httpx, but it was not as lightweight as `requests`. So I decided to implement async requests using the thread pool.
The existing Job info in the cluster snapshot uses the old definition of Job, which is a single Ray driver (a single `ray.init()` connection).
In the new Job Submission protocol, a Job just specifies an entrypoint which can be any shell command. As such a Job can have zero or multiple Ray drivers. This means we should add a new snapshot entry corresponding to new jobs. We'll leave the old snapshot in place for legacy jobs.
- Also fixes `get_all_jobs` by using the appropriate KV namespace, and stripping the job key KV prefix from the job ID. It wasn't working before.
- This PR also unifies the datatype used by the GET jobs/ endpoint to be the same as the one used by the new jobs cluster snapshot. For backwards compatibility, the `status` and `message` fields are preserved.
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.
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>
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.
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
Previously, local files corresponding to runtime env URIs were eagerly garbage collected as soon as there were no more references to them. In this PR, we store this data in a cache instead, so when the reference count for a URI drops to zero, instead of deleting it we simple mark it as unused in the cache. When the cache exceeds its size limit (default 10 GB) it will delete unused URIs until the cache is back under the size limit or there are no more unused URIs.
Design doc: https://docs.google.com/document/d/1x1JAHg7c0ewcOYwhhclbuW0B0UC7l92WFkF4Su0T-dk/edit
- Adds unit tests for caching and integration tests for working_dir caching
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>
Currently `wait_until_succeeded_without_exception` is used in the dashboard, and it returns True/False. Unfortunately, there are lots of code that doesn't assert on this method (which means things are not actually tested).
Full context see https://github.com/ray-project/ray/issues/21791
pytest work for "some" environments for this test and on CI master, but this decorator is still unnecessary and was introduced by mistake. So just remove it and see what happens with the original issue.
Currently, GCS KV client only has blocking API. Calling them from dashboard event loop can block other operations for many seconds, leading to failures such as taking too long (> 2min) to submit a job and making nightly tests fail (#21699). This PR offloads the blocking work to a separate thread. Implementing async GCS KV API will be done in the future.
- Tolerate GRPC deadline exceeded and transient failures in Python GCS AIO subscribers, which becomes consistent with Python GCS synchronous subscribers.
- Tolerate any exception in dashboard for subscribing to logs and error info, which becomes consistent with how dashboard handles GRPC errors for obtaining node stats.
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 <>
After this change in GCS bootstrapping mode, Redis no longer starts and `address` is treated as the GCS address of the Ray cluster.
Co-authored-by: Yi Cheng <chengyidna@gmail.com>
Co-authored-by: Yi Cheng <74173148+iycheng@users.noreply.github.com>
This PR refactors several components to support switching to GCS address bootstrapping later:
- Treat address from `ray.init()` and `ray` CLI as bootstrap address instead of assuming it is Redis address.
- Ray client servers support `--address` flag instead of `--redis-address`.
- A few other miscellaneous cleanup.
Also, add a test for starting non-head node with `ray start`.
