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+# GRPC Fail Fast By Default
+
+| Status        | Accepted                                               |
+| :------------ | :------------------------------------------------------ |
+| **RFC #**     | [355](https://github.com/tensorflow/community/pull/355) |
+| **Author(s)** | Haoyu Zhang (haoyuzhang@google.com)                     |
+| **Sponsor**   | Bramandia Ramadhana (bramandia@google.com)              |
+| **Updated**   | 2021-03-04                                              |
+
+## Objective
+
+We propose to set the default value of the `GRPC_FAIL_FAST` environment variable
+to `use_caller`. This change prevents TensorFlow distributed jobs from hanging
+indefinitely due to task failures, and allows users and TF libraries (e.g.,
+distribution strategies) to handle the connection errors for better failure and
+preemption recovery.
+
+## Background
+
+`GRPC_FAIL_FAST` is a TensorFlow distributed runtime environment variable that
+controls the behavior of RPC requests when observing a network disconnection
+with remote servers. It can be configured to the following values:
+
+*   `true`, which immediately reports an `UnavailableError` when there is a
+    connection issue for all RPCs, regardless of the per-RPC configurations;
+*   `false`, which blocks and waits until successfully connected to the remote
+    server (see
+    [gRPC `wait_for_ready`](https://github.com/grpc/grpc/blob/master/doc/wait-for-ready.md)),
+    regardless of the per-RPC configurations;
+*   `use_caller`, which allows customization per RPC basis; in the current
+    implementation, `true` is used for RPCs used in distributed execution (such
+    as `RecvTensor`, `RunComponentFunction`), and `false` is used for RPCs in
+    initializing remote execution environments (e.g., `GetStatus`).
+
+The default value of `GRPC_FAIL_FAST` is currently set to `false`. One of the
+consequences is that users and/or high-level distribute libraries (such as
+`ParameterServerStrategy`) need to
+[manually configure this environment variable](https://github.com/tensorflow/tensorflow/blob/1178262a2a55fa634a2390291fc633c515e28884/tensorflow/python/distribute/parameter_server_strategy_v2.py#L106)
+to receive reasonable exceptions when workers fail / get preempted; otherwise
+the cluster will hang and cannot recover from failures.
+
+## Proposed Change
+
+We propose to set the default value of `GRPC_FAIL_FAST` to `use_caller`. By
+doing so, the runtime reports errors quickly to detect remote server failures
+during execution, while still allowing the client to start early and wait for
+remote servers to establish initial connections. This should be the desired
+behavior for most use cases.
+
+In the context of TensorFlow 2, the default behavior of the following RPCs used
+for distributed execution will be changed from hanging on failures (current
+behavior) to immediately reporting failures (after the change):
+
+*   `EagerService.CreateContext`
+*   `EagerService.UpdateContext`
+*   `EagerService.WaitQueueDone`
+*   `EagerService.KeepAlive`
+*   `EagerService.Enqueue`
+*   `EagerService.RunComponentFunction`
+*   `WorkerService.RecvTensor`
+*   `WorkerService.RecvBuf`
+
+The default behavior of the following RPC will not change: it will still hang if
+the remote task cannot be reached.
+
+*   `WorkerService.GetStatus`
+
+The `GetStatus` RPC is typically the first RPC sent from the client to
+initialize a distributed execution environment, in both the single- and the
+multi-client mode. The underlying implementation uses GRPC's
+[`wait_for_ready`](https://github.com/grpc/grpc/blob/master/doc/wait-for-ready.md)
+flag, which allows the client to start before the remote server in the
+deployment.
+
+## User Impact
+
+When this change is made to the codebase, subsequent TensorFlow 2 releases will
+have this new default behavior. TensorFlow 1.x users who use the stable releases
+(e.g., TensorFlow 1.15 or earlier) should not be affected by this change. Users
+who build TensorFlow directly from source at the head will also be affected.
+
+Most users should see the new default as expected behaviors in distributed
+execution. Users can take advantage of the built-in fault tolerance support in
+`ParameterServerStrategy` without having to make changes to the environment
+variable configurations. In other setups, exceptions will be raised to the model
+training loop code, where users can catch and handle these errors with custom
+logic instead of hanging indefinitely.
+
+Certain users might receive "false alarms" if there are transient connection
+errors to the remote servers. We expect this to happen very rarely since GRPC
+(built on top of HTTP and TCP protocols) should already handle packet drops and
+network flakinesses in most cases, and only report errors when there are real
+network or server failures. However, if this does happen to some users, please
+set `GRPC_FAIL_FAST=false` to override the default value and revert to the
+previous behavior. Please also file an issue to inform the TensorFlow Runtime
+team.
+