* Implement Actor checkpointing
* docs
* fix
* fix
* fix
* move restore-from-checkpoint to HandleActorStateTransition
* Revert "move restore-from-checkpoint to HandleActorStateTransition"
This reverts commit 9aa4447c1e3e321f42a1d895d72f17098b72de12.
* resubmit waiting tasks when actor frontier restored
* add doc about num_actor_checkpoints_to_keep=1
* add num_actor_checkpoints_to_keep to Cython
* add checkpoint_expired api
* check if actor class is abstract
* change checkpoint_ids to long string
* implement java
* Refactor to delay actor creation publish until checkpoint is resumed
* debug, lint
* Erase from checkpoints to restore if task fails
* fix lint
* update comments
* avoid duplicated actor notification log
* fix unintended change
* add actor_id to checkpoint_expired
* small java updates
* make checkpoint info per actor
* lint
* Remove logging
* Remove old actor checkpointing Python code, move new checkpointing code to FunctionActionManager
* Replace old actor checkpointing tests
* Fix test and lint
* address comments
* consolidate kill_actor
* Remove __ray_checkpoint__
* fix non-ascii char
* Loosen test checks
* fix java
* fix sphinx-build
This PR adds a `function_desc` field into task spec. a function descriptor is a list of strings that can uniquely describe a function.
- For a Python function, it should be: [module_name, class_name, function_name]
- For a Java function, it should be: [class_name, method_name, type_descriptor]
There're a couple of purposes to add this field:
In this PR:
- Java worker needs to know function's class name to load it. Previously, since task spec didn't have such a field to hold this info, we did a hack by appending the class name to the argument list. With this change, we fixed that hack and significantly simplified function management in Java.
Will be done in subsequent PRs:
- Support cross-language invocation (#2576): currently Python worker manages functions by saving them in GCS and pass function id in task spec. However, if we want to call a Python function from Java, we cannot save it in GCS and get the function id. But instead, we can pass the function descriptor (module name, class name, function name) in task spec and use it to load the function.
- Support deployment: one major problem of Python worker's current function management mechanism is #2327. In prod env, we should have a mechanism to deploy code and dependencies to the cluster. And when code is already deployed, we don't need to save functions to GCS any more and can use `function_desc` to manage functions.
API module (`ray/java/api` dir) includes all public APIs provided by Ray, it should be the only module that normal Ray users need to face.
The purpose of this PR to first improve the code quality of the API module. Subsequent PRs will improve other modules later. The changes of this PR include the following aspects:
1) Only keep interfaces in api module, to hide implementation details from users and fix circular dependencies among modules.
2) Document everything in the api module.
3) Improve naming.
4) Add more tests for API.
5) Also fix/improve related code in other modules.
6) Remove some unused code.
(Apologize for posting such a large PR. Java worker code has been lack of maintenance for a while. There're a lot of code quality issues that need to be fixed. We plan to use a couple of large PRs to address them. After that, future changes will come in small PRs.)
* add java code lint check and fix the java code lint error
* add java doc lint check and fix the java doc lint error
* add java code and doc lint to the CI
