Whenever we spill, we try to spill all spillable objects. We also try to fuse small objects together to reduce total IOPS. If there aren't enough objects in the object store to meet the fusion threshold, we spill the objects anyway to avoid liveness issues.
However, the current logic always spills once we reach the end of the spillable objects or once we've reached the fusion threshold. This can produce lots of unfused objects if they are created concurrently with the spill.
This PR changes the spill logic: once we reach the end of the spillable objects, if the last batch of spilled objects is under the fusion threshold, we'll only spill it if we don't have other spills pending too. This gives the pending spills time to finish, and then we can re-evaluate whether it's necessary to spill the remaining objects. Liveness is also preserved.
`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.
Currently object transfers assume that the object size is fixed. This is a bad assumption during failures, especially with lineage reconstruction enabled and tasks with nondeterministic outputs.
This PR cleans up the handling and hopefully guards against two cases where the object size may change during a transfer:
1. The object manager's size information does not match the object in the local plasma store (due to async notifications). --> the object manager overwrites its own information if it finds that the physical object has a different size.
2. The receiver's created buffer size does not match the sender's object size. --> the receiver destroys the previous buffer and creates a new buffer with the correct size. This might cause some transient errors but eventually object transfer should succeed.
Unfortunately I couldn't trigger this from Python because it depends on some pretty specific timing conditions. However, I did add some unit tests for case 2 (this is the majority of the PR).
This PR fixes initializations artifacts related to the load metric summary and autoscaler summary.
Load metrics summaries are defined to be Falsey if the autoscaler has never received a resource message from the GCS.
We skip most autoscaler actions if load metrics is Falsey, because it doesn't makes sense to autoscale without load metrics. This also allows us to execute the TODO here: #22348 (comment) and remove the time.wait().
As for the autoscaler summary, it is possible for autoscaler.summary() to error outside of an autoscaler update in this scenario:
The very first call to NodeProvider.non_terminated_nodes fails, self.non_terminated_nodes remains a None object, and autoscaler.summary() fails trying to get an attribute of this None object.
The result is a confusing error message, as in #22515. This PR fixes that.
Closes#22515
- Added backbone of ray dag -> serve dag transformation and deployment extraction.
- Added util functions for deployment unique name generation .. ray_actor_options, replacement of DeploymentNode with deployment handle, etc.
Soft restarts don't work for tensorflow since there is still some leftover communication state in the actors which may lead to undefined behavior, such as causing training to hang.
Instead, this PR changes the failure handling for tensorflow to match torch and horovod, and recreates all the workers in case of failure. Also adds a test to check if fault tolerance works correctly for an actual tensorflow example. When testing locally, the test failed before the change, but passes after.
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.
When get a bunch of data from redis, we first initialize local variables and then put them in vector, which bring so much copies from stack to heap or from local variables to vector.
This tiny little change would save the copies.
This PR **enables the usage stats only on the release test infrastructure** (large scale tests Ray runs on a daily basis in a private infra). Note it is still disabled by default in Ray.
In order to initialize runtime env concurrently, this PR makes pip runtime env asynchronous. It includes,
- [x] New `check_output_cmd` in runtime env utils.
- [x] Async PipProcessor.
- [x] The `asynccontextmanager` from `https://github.com/python-trio/async_generator` for Python 3.6
- [x] Remove pip runtime env lock.
- [x] Disable pip cache.
Co-authored-by: 刘宝 <po.lb@antfin.com>
According to the description of #21218 , in this PR, we support the ability specifying a frontend-defined in-memory object allocator. So that we can specify an allocator to allocate the buffers from JVM heap. This is the basic functionality for the next PR #21441 that the JVM is able to be aware of the memory pressure of the in-memeory store objects.
Note that, if we use a frontend defined allocator, it may break the zerocopy ability. In Java, JVM buffers is in heap and we should copy it to native memory if needed.
Co-authored-by: Qing Wang <jovany.wq@antgroup.com>
This PR enables stage fusion for dataset pipelines. This also requires:
1. Removing the num_cpus=0.5 default for the read stage, to enable fusion of the read stage.
2. Removing spread_resource_prefix (not supported for now).
We should just encourage people to use the existing `get_runtime_context` API instead of introducing a new one here. Just removing the docs for now while we discuss this.
Why are these changes needed?
Data from PutRequests is chunked into 64MiB messages over the datastream, to avoid the 2GiB message size limit from gRPC. This will allow users to transfer objects larger than 2GiB over the network.
Proto changes
Put requests now have fields for chunk_id to identify which chunk data belongs to, total_chunks to identify the total number of chunks in the object, and total_size for total size in bytes of the object (useful for raising warnings).
PutObject is still unary-unary. The dataservicer handles reassembling the chunks before passing the result to the underlying RayletServicer.
Dataclient changes
If a put request is inserted into the request queue, self._requests will chunk it lazily. Doing this lazily is important since inserting all of the chunks onto the request queue immediately would double the amount of memory needed to handle a large request. This also guarantees that the chunks of a given putrequest will be contiguous
Dataservicer changes
The dataservicer now maintains some state to track received chunks. Once all chunks for a putrequest are received, the combined chunks are passed to the raylet servicer.
Ray DAG Changes
- Restructured and resolves circular imports in current dag_node.py.
- Moved `__str__` to each DAGNode subclass level with centralized utils imports
- Removed restrictions on binding `InputNode` to `FunctionNode` and `ClassMethodNode`
- Moved `_contain_input_node` to only `ClassNode` and `DeploymentNode`
Serve DAG Changes
- Added DeploymentNode
- Cannot be directly constructed
- Holds deployment func or class body as well as handle that trivially maps to `__call__` method (match current behavior)
- Upon accessing an attribute, it will spawn DeploymentMethodNode node with `other_args_to_resolve` passed in to differentiate sync handle type and others
- Added DeploymentMethodNode
- Holds arg and deployment handle
- Executing on it translate to deployment handle call on the method.
Opencenus symobls haven been exported in linux version of libcore_worker_library_java.so, but deleted from ray_exported_symbols.lds, which makes streaming macos test case failed.
This pr add this exporting record and rename *ray*streaming* stuff to *ray*internal* that's a united entry to ray cpp.
Co-authored-by: 林濯 <lingxuan.zlx@antgroup.com>
