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.
- Separate spread scheduling and default hydra scheduling (i.e. SpreadScheduling != HybridScheduling(threshold=0)): they are already separated in the API layer and they have the different end goals so it makes sense to separate their implementations and evolve them independently.
- Simple round robin for spread scheduling: this is just a starting implementation, can be optimized later.
- Prefer not to spill back tasks that are waiting for args since the pull is already in progress.
While investigating #22161, it is observed GIL is held for an extended amount of time (up to 1000s) with stack trace [1]. It is possible either there are many iterations within `Pickle5Writer.write_to()` calling `ray::parallel_memcopy()`, or a few `ray::parallel_memcopy()` taking a long time (less likely). Either way, `ray::parallel_memcopy()` or `std::memcpy()` should not hold GIL.
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.
This PR adds the following stage fusion optimizations (off by default). In a later PR, I plan to enable this by default for DatasetPipelines.
- Stage fusion: Whether to fuse compatible OneToOne stages.
- Read stage fusion: Whether to fuse read stages into downstream OneToOne stages. This is accomplished by rewriting the read stage (LazyBlockList) into a transformation over a collection of read tasks (BlockList -> MapBatches(do_read)).
- Shuffle stage fusion: Whether to fuse compatible OneToOne stages into shuffle stages that support specifying a map-side block UDF.
Stages are considered compatible if their compute strategy is the same ("tasks" vs "actors"), and they have the same Ray remote args. Currently, the PR is ignoring the remote args of read tasks, but this will be fixed as a followup (I didn't want to change the read tasks default here).
When we vendor third-party code, we should update LICENSE file. Previously we vendored two pieces of code:
- conda utilities from MLflow
- virtualenv-clone
But we only included the attribution in the relevant source files, not in our LICENSE file. This PR adds the necessary info to our LICENSE file.
Why are these changes needed?
Switches GetObject from unary-unary to unary-streaming so that large objects can be streamed across multiple messages (currently hardcoded to 64MiB chunks). This will allow users to retrieve objects larger than 2GiB from a remote cluster. If the transfer is interrupted by a recoverable gRPC error (i.e. temporary disconnect), then the request will be retried starting from the first chunk that hasn't been received yet.
Proto changes
GetRequest's now have the field start_chunk_id, to indicate which chunk to start from (useful if the we have to retry a request after already receiving some chunks). GetResponses now have a chunk_id (0 indexed chunk of the serialized object), total_chunks (total number of chunks, used in async transfers to determine when all chunks have been received), and total_size (the total size of the object in bytes, used to raise user warnings if the object being retrieved is very large).
Server changes
Mainly just updating GetObject logic to yield chunks instead of returning
Client changes
At the moment, objects can be retrieved directly from the raylet servicer (ray.get) or asynchronously over the datapath (await some_remote_func.remote()). In both cases, the request will error if the chunk isn't valid (server side error) or if a chunk is received out of order (shouldn't happen in practice, since gRPC guarantees that messages in a stream either arrive in order or not at all).
ray.get is fairly straightforward, and changes are mainly to accommodate yielding from the stub instead of taking the value directly.
await some_remote_func.remote() is similar, but to keep things consistent with other async handling collecting the chunks is handled by a ChunkCollector, which wraps around the original callback.
These tests are passing without issues on my Windows machine, so unskipping them to check on CI.
I will push the linting changes separately to execute the test suite twice for confirming that flakyness is removed.
Co-authored-by: Philipp Moritz <pcmoritz@gmail.com>
Reason for not using `queue.Queue` for multiprocessing purposes on Windows is at https://stackoverflow.com/a/37244276 and in the second reply to https://stackoverflow.com/a/37245300
And reason for using `multiprocessing.JoinableQueue` over `multiprocessing.Queue` is https://stackoverflow.com/a/30725121
AFAIK, this is because in Windows each process gets it own `Queue` and hence nothing is shared among those processes. When `multiprocessing.Queue` is used, changes in it are shared via pipes internally along with proper locks.
Event loop will only be set in main thread by default and this will make workflow unable to work if it's called in thread other than main thread which can happen when it's called from a library (for example ray serve).
This PR fixed it.
The [original PR](https://github.com/ray-project/ray/pull/21864) was [reverted](https://github.com/ray-project/ray/pull/22117) because it caused `torch` (more specifically, `torch>=1.8.1`) to be required to use `ray.train`.
```
| File "ray_sgd_training.py", line 18, in <module>
| from ray import train
| File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/train/__init__.py", line 2, in <module>
| from ray.train.callbacks import TrainingCallback
| File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/train/callbacks/__init__.py", line 8, in <module>
| from ray.train.callbacks.profile import TorchTensorboardProfilerCallback
| File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/train/callbacks/profile.py", line 6, in <module>
| from torch.profiler import profile
| ModuleNotFoundError: No module named 'torch.profiler'
```
A [minimal installation test suite](https://github.com/ray-project/ray/pull/22300) was added to detect this. Further, in this PR we make the following changes:
1. Move `TorchWorkerProfiler` to `ray.train.torch` so all torch imports are centralized.
2. Add import validation logic to `TorchWorkerProfiler.__init__` so an exception will only be raised if the user tries to initialize a `TorchWorkerProfiler` without having a valid version of `torch` installed:
```
>>> import ray
>>> import ray.train
>>> import ray.train.torch
>>> from ray.train.torch import TorchWorkerProfiler
>>> twp = TorchWorkerProfiler()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/matt/workspace/ray/python/ray/train/torch.py", line 365, in __init__
"Torch Profiler requires torch>=1.8.1. "
ImportError: Torch Profiler requires torch>=1.8.1. Run `pip install 'torch>=1.8.1'` to use TorchWorkerProfiler.
```
This PR refactors Dataset execution to enable lazy mode in the future, which can reduce memory usage in large-scale ingest pipelines. There should be no behavior changes in this PR. Many of the optimizations are also punted for future work.
We shouldn't promote Runtime Environments as the only way to do things until all Core nightly and release tests are run using runtime environments.
This PR adds the prior approach (using cluster launcher commands) to the doc on equal footing, describing the differences between the two.
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: SangBin Cho <rkooo567@gmail.com>
This PR properly exposes `TableRow` as a public API (API docs + the "Public" tag), since it's already exposed to the user in our row-based ops. In addition, the following changes are made:
1. During row-based ops, we also choose a batch format that lines up with the current dataset format in order to eliminate unnecessary copies and type conversions.
2. `TableRow` now derives from `collections.abc.Mapping`, which lets `TableRow` better interop with code expecting a mapping, and includes a few helpful mixins so we only have to implement `__getitem__`, `__iter__`, and `__len__`.
This PR fixes our {NumPy, Pandas} <--> Arrow interop for boolean tensor columns. NumPy and Pandas represent boolean arrays with a byte per boolean, while Arrow bit-packs booleans with 8 booleans per byte. Previously, when casting NumPy arrays to tensor columns, we were interpreting NumPy's boolean array buffers as being bit-packed when they were not. This PR completes support by packing and unpacking bits for boolean arrays when creating a boolean tensor column from an ndarray and when creating an ndarray from a boolean tensor column, respectively.
To helps resolve the issues users are facing with running Lightning examples with Ray Tune PyTorchLightning/pytorch-lightning#10407
Co-authored-by: Amog Kamsetty <amogkamsetty@yahoo.com>
Updates to address @worldveil's feedback:
Include import train.torch in the docs
Allow methods in session.py to be called outside of the session with sensible defaults. These will no longer raise an error.
Co-authored-by: Balaji Veeramani <bveeramani@berkeley.edu>
ray.data.read_text() currently doesn't take care of empty lines; this pr adds a flag to enable the empty line filter;
with this change, read_text will only return non-empty line by default, unless otherwise setting drop_empty_line to False.
Co-authored-by: Eric Liang <ekhliang@gmail.com>
Co-authored-by: Jialin Liu <jialin.liu@bytedance.com>
