This PR allows the user to override the global default for max_retries for non-actor tasks. It adds an OS env called RAY_task_max_retries which can be passed to the driver or set with runtime envs. Any future tasks submitted by that worker will default to this value instead of 3, the hard-coded default.
It would be nicer if we could have a standard way of setting these defaults, but I think this is fine as a one-off for now (not a clear need for overriding defaults of other @ray.remote options yet).
Related issue number
Closes#24854.
Adds support for Python generators instead of just normal return functions when a task has multiple return values. This will allow developers to cut down on total memory usage for tasks, as they can free previous return values before allocating the next one on the heap.
The semantics for num_returns are about the same as usual tasks - the function will throw an error if the number of values returned by the generator does not match the number of return values specified by the user. The one difference is that if num_returns=1, the task will throw the usual Python exception that the generator cannot be pickled.
As an example, this feature will allow us to reduce memory usage in Datasets shuffle operations (see #25200 for a prototype).
Pointing to the latest documentation for contributor is important as the workflow is always evolving. E.g. the installation instructions for bazel are not representatives of the current state on release vs master. Hence, I propose to update contribution links in the documentation to point to the latest state on master.
As described in the related issue, using model_weight as the key throws an error.
This update points the user to use model as the key instead.
Co-authored-by: tamilflix <tamilflix30@gmail.com>
Per the [Ray docs contributing guide](https://docs.ray.io/en/master/ray-contribute/docs.html), code chunks should be in `.py` files and pulled in via `literalinclude` rather than placed directly in `.rst` files. This PR takes a small step in doing this for the RLlib docs, specifically for the training API doc page.
Note that I had to make some changes to the code itself so that it would run, namely adding missing numpy imports and changing `model.from_batch(...)` to `model(...)` in a couple places.
Co-authored-by: Max Pumperla <max.pumperla@googlemail.com>
This PR makes several improvements to the Datasets' tensor story. See the issues for each item for more details.
- Automatically infer tensor blocks (single-column tables representing a single tensor) when returning NumPy ndarrays from map_batches(), map(), and flat_map().
- Automatically infer tensor columns when building tabular blocks in general.
- Fixes shuffling and sorting for tensor columns
This should improve the UX/efficiency of the following:
- Working with pure-tensor datasets in general.
- Mapping tensor UDFs over pure-tensor, a better foundation for tensor-native preprocessing for end-users and AIR.
* [runtime env] runtime env inheritance refactor (#22244)
Runtime Environments is already GA in Ray 1.6.0. The latest doc is [here](https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments). And now, we already supported a [inheritance](https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance) behavior as follows (copied from the doc):
- The runtime_env["env_vars"] field will be merged with the runtime_env["env_vars"] field of the parent. This allows for environment variables set in the parent’s runtime environment to be automatically propagated to the child, even if new environment variables are set in the child’s runtime environment.
- Every other field in the runtime_env will be overridden by the child, not merged. For example, if runtime_env["py_modules"] is specified, it will replace the runtime_env["py_modules"] field of the parent.
We think this runtime env merging logic is so complex and confusing to users because users can't know the final runtime env before the jobs are run.
Current PR tries to do a refactor and change the behavior of Runtime Environments inheritance. Here is the new behavior:
- **If there is no runtime env option when we create actor, inherit the parent runtime env.**
- **Otherwise, use the optional runtime env directly and don't do the merging.**
Add a new API named `ray.runtime_env.get_current_runtime_env()` to get the parent runtime env and modify this dict by yourself. Like:
```Actor.options(runtime_env=ray.runtime_env.get_current_runtime_env().update({"X": "Y"}))```
This new API also can be used in ray client.
This PR adds a FAQ to Datasets docs.
Docs preview: https://ray--24932.org.readthedocs.build/en/24932/
## Checks
- [x] I've run `scripts/format.sh` to lint the changes in this PR.
- [x] I've included any doc changes needed for https://docs.ray.io/en/master/.
- [x] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
- Testing Strategy
- [x] Unit tests
- [ ] Release tests
- [ ] This PR is not tested :(
Co-authored-by: Eric Liang <ekhliang@gmail.com>
