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[tune](deps): Bump tensorflow-probability from 0.11.1 to 0.12.1 in /python/requirements #2

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@dependabot dependabot bot commented on behalf of github Jan 11, 2021

Bumps tensorflow-probability from 0.11.1 to 0.12.1.

Release notes

Sourced from tensorflow-probability's releases.

TensorFlow Probability 0.12.1

Release notes

This is the 0.12.1 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.4.0.

Change notes

NOTE: Links point to examples in the TFP 0.12.1 release Colab.

Bijectors:

Distributions:

MCMC:

... (truncated)

Commits
  • 43a9d6c Merge pull request #1205 from jburnim/r0.12
  • c1818a8 Set the version for the TFP 0.12.1 release.
  • 0c282ef Avoid cache errors when user-written Bijectors don't pass parameters.
  • dcd59ed Merge pull request #1199 from jburnim/r0.12
  • 90fa3f0 Set the version for the TFP 0.12.0 release.
  • ead8501 internal change
  • 1cc5c39 Allow 0 concentration in gamma samplers.
  • 5f0dbec Add float64 support to PHMC.
  • d3bf5a0 Rewrite experimental sample_chain in terms of run_kernel.
  • e7d64b7 Add a default bijector to the Deterministic distributions.
  • Additional commits viewable in compare view

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@dependabot dependabot bot force-pushed the dependabot/pip/python/requirements/tensorflow-probability-0.12.1 branch from bfb10cb to d832cfd Compare February 24, 2021 17:32
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dependabot bot commented on behalf of github Apr 15, 2021

Superseded by #12.

@dependabot dependabot bot closed this Apr 15, 2021
@dependabot dependabot bot deleted the dependabot/pip/python/requirements/tensorflow-probability-0.12.1 branch April 15, 2021 20:21
edoakes pushed a commit that referenced this pull request Oct 8, 2021
edoakes pushed a commit that referenced this pull request Jan 12, 2022
edoakes pushed a commit that referenced this pull request Feb 23, 2022
…roject#22317)

Improve observability for general objects and lineage reconstruction by adding a "Status" field to `ray memory`. The value of the field can be:
```
  // The task is waiting for its dependencies to be created.
  WAITING_FOR_DEPENDENCIES = 1;
  // All dependencies have been created and the task is scheduled to execute.
  SCHEDULED = 2;
  // The task finished successfully.
  FINISHED = 3;
```

In addition, tasks that failed or that needed to be re-executed due to lineage reconstruction will have a field listing the attempt number. Example output:
```
IP Address    | PID      | Type    | Call Site | Status    | Size     | Reference Type | Object Ref
192.168.4.22  | 279475   | Driver  | (task call) ... | Attempt #2: FINISHED | 10000254.0 B | LOCAL_REFERENCE | c2668a65bda616c1ffffffffffffffffffffffff0100000001000000


```
edoakes pushed a commit that referenced this pull request Apr 14, 2022
…ray-project#23821)

This PR refactors `LazyBlockList` in service of out-of-band serialization (see [mono-PR](ray-project#22616)) and is a precursor to an execution plan refactor (PR #2) and adding the actual out-of-band serialization APIs (PR #3). The following is included in this refactor:
1. `ReadTask`s are now a first-class concept, replacing calls;
2. read stage progress tracking is consolidated into `LazyBlockList._get_blocks_with_metadta()` and more of the read task complexity, e.g. the read remote function, was pushed into `LazyBlockList` to make `ray.data.read_datasource()` simpler;
3. we are a bit smarter with how we progressively launch tasks and fetch and cache metadata, including fetching the metadata for read tasks in `.iter_blocks_with_metadata()` instead of relying on the pre-read task metadata (which will be less accurate), and we also fix some small bugs in the lazy ramp-up around progressive metadata fetching.

(1) is the most important item for supporting out-of-band serialization and fundamentally changes the `LazyBlockList` data model. This is required since we need to be able to reference the underlying read tasks when rewriting read stages during optimization and when serializing the lineage of the Dataset. See the [mono-PR](ray-project#22616) for more context.

Other changes:
1. Changed stats actor to a global named actor singleton in order to obviate the need for serializing the actor handle with the Dataset stats; without this, we were encountering serialization failures.
edoakes pushed a commit that referenced this pull request Aug 5, 2022
We encountered SIGSEGV when running Python test `python/ray/tests/test_failure_2.py::test_list_named_actors_timeout`. The stack is:

```
#0  0x00007fffed30f393 in std::basic_string<char, std::char_traits<char>, std::allocator<char> >::basic_string(std::string const&) ()
   from /lib64/libstdc++.so.6
#1  0x00007fffee707649 in ray::RayLog::GetLoggerName() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#2  0x00007fffee70aa90 in ray::SpdLogMessage::Flush() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#3  0x00007fffee70af28 in ray::RayLog::~RayLog() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#4  0x00007fffee2b570d in ray::asio::testing::(anonymous namespace)::DelayManager::Init() [clone .constprop.0] ()
   from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#5  0x00007fffedd0d95a in _GLOBAL__sub_I_asio_chaos.cc () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#6  0x00007ffff7fe282a in call_init.part () from /lib64/ld-linux-x86-64.so.2
#7  0x00007ffff7fe2931 in _dl_init () from /lib64/ld-linux-x86-64.so.2
#8  0x00007ffff7fe674c in dl_open_worker () from /lib64/ld-linux-x86-64.so.2
#9  0x00007ffff7b82e79 in _dl_catch_exception () from /lib64/libc.so.6
#10 0x00007ffff7fe5ffe in _dl_open () from /lib64/ld-linux-x86-64.so.2
#11 0x00007ffff7d5f39c in dlopen_doit () from /lib64/libdl.so.2
#12 0x00007ffff7b82e79 in _dl_catch_exception () from /lib64/libc.so.6
#13 0x00007ffff7b82f13 in _dl_catch_error () from /lib64/libc.so.6
#14 0x00007ffff7d5fb09 in _dlerror_run () from /lib64/libdl.so.2
#15 0x00007ffff7d5f42a in dlopen@@GLIBC_2.2.5 () from /lib64/libdl.so.2
#16 0x00007fffef04d330 in py_dl_open (self=<optimized out>, args=<optimized out>)
    at /tmp/python-build.20220507135524.257789/Python-3.7.11/Modules/_ctypes/callproc.c:1369
```

The root cause is that when loading `_raylet.so`, `static DelayManager _delay_manager` is initialized and `RAY_LOG(ERROR) << "RAY_testing_asio_delay_us is set to " << delay_env;` is executed. However, the static variables declared in `logging.cc` are not initialized yet (in this case, `std::string RayLog::logger_name_ = "ray_log_sink"`).

It's better not to rely on the initialization order of static variables in different compilation units because it's not guaranteed. I propose to change all `RAY_LOG`s to `std::cerr` in `DelayManager::Init()`.

The crash happens in Ant's internal codebase. Not sure why this test case passes in the community version though.

BTW, I've tried different approaches:

1. Using a static local variable in `get_delay_us` and remove the global variable. This doesn't work because `init()` needs to access the variable as well.
2. Defining the global variable as type `std::unique_ptr<DelayManager>` and initialize it in `get_delay_us`. This works but it requires a lock to be thread-safe.
edoakes pushed a commit that referenced this pull request Mar 22, 2023
edoakes pushed a commit that referenced this pull request Mar 22, 2023
edoakes pushed a commit that referenced this pull request Apr 24, 2023
Why are these changes needed?

Right now the theory is as follow.

pubsub io service is created and run inside the GcsServer. That means if pubsub io service is accessed after GCSServer GC'ed, it will segfault.
Right now, upon teardown, when we call rpc::DrainAndResetExecutor, this will recreate the Executor thread pool.
Upon teardown, If DrainAndResetExecutor -> GcsServer's internal pubsub posts new SendReply to the newly created threadpool -> GcsServer.reset -> pubsub io service GC'ed -> SendReply invoked from the newly created thread pool, it will segfault.
NOTE: the segfault is from pubsub service if you see the failure

#2 0x7f92034d9129 in ray::rpc::ServerCallImpl<ray::rpc::InternalPubSubGcsServiceHandler, ray::rpc::GcsSubscriberPollRequest, ray::rpc::GcsSubscriberPollReply>::HandleRequestImpl()::'lambda'(ray::Status, std::__1::function<void ()>, std::__1::function<void ()>)::operator()(ray::Status, std::__1::function<void ()>, std::__1::function<void ()>) const::'lambda'()::operator()() const /proc/self/cwd/bazel-out/k8-opt/bin/_virtual_includes/grpc_common_lib/ray/rpc/server_call.h:212:48
As a fix, I only drain the thread pool. And then reset it after all operations are fully cleaned up (only from tests). I think there's no need to reset for regular proc termination like raylet, gcs, core workers.

Related issue number

Closes ray-project#34344

Signed-off-by: SangBin Cho <[email protected]>
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