In the TensorFlow library, particularly in versions 1.x, a pivotal component of running a computational graph involves using the `Session.run()` method. The method is responsible for executing the specified operations and computing the values needed in a TensorFlow graph. A common query when dealing with the `Session.run()` method is whether its "fetches" arguments are guaranteed to execute in order. Understanding this process is essential for those leveraging TensorFlow for machine learning and data processing tasks.

Understanding `Session.run(fetches)`

`Session.run(fetches)` is a method call used to execute a computational graph within a TensorFlow session. The `fetches` parameter specifies the tensor, op, or list of tensors and ops that you wish to compute or execute, respectively. When invoked, it performs the following tasks:

1. Allocate Tensorflow Resources: Sets up the necessary environment for computation, reserving resources such as memory.
2. Execute the Graph Operations: Runs the computational graph operations until the required tensors or ops in the `fetches` are available for retrieval.
3. Fetch Outputs: Retrieves the computed tensor values or results of executed ops, as indicated by `fetches`.

Execution Order in `Session.run()`

The fundamental question is whether the operations specified in `fetches` execute sequentially in the order they were listed. This aspect is crucial for those seeking predictable execution patterns in a synchronous manner.

Technical Explanation

• Graph Dependencies: TensorFlow's execution order is primarily dictated by the computational graph's dependencies rather than the order of tensors or ops in the `fetches`. When you perform a `Session.run(fetches)`, TensorFlow looks at the dependencies between operations. Only once dependencies are satisfied will operations execute.
• Independent Fetches: For fetches that are independent of each other—meaning they can be computed without needing each other's results—TensorFlow may execute them in parallel, rather than sequentially. This parallelism is a feature designed for optimizing performance.
• Parallel Execution: If two fetch operations do not share any dependency, TensorFlow's runtime will potentially execute them in parallel, leveraging multicore processors to speed up execution. This could lead to a situation where independent ops in `fetches` do not adhere strictly to the input order.

Example

Consider a TensorFlow graph with the following independent operations:

• TensorFlow attempts to maximize computational resource usage. This is achieved by running independent operations in parallel if possible, utilizing a combination of threads and processors.
• If execution order is critical to your workflow, you must ensure that dependencies explicitly indicate the required sequence. TensorFlow constructs like control dependencies can be used to force a specific execution order when needed.