Note that for training, there will usually be at least two MLCSubgraphOp nodes (representing forward and backward/gradient subgraphs).

Having larger subgraphs that encapsulate big portions of the original graph usually results in better performance from ML Compute.Number of subgraphs using ML Compute and how many operations are included in each of these subgraphs.This, for example, can be used to determine which operations are being optimized by ML Compute. Each of these nodes replaces a TensorFlow subgraph from the original graph, encapsulating all the operations in the subgraph. Look for MLCSubgraphOp nodes in this graph.TensorFlow graph after TensorFlow operations have been replaced with ML Compute.Original TensorFlow graph without ML Compute.The following is the list of information that is logged in graph mode: Turn logging on by setting the environment variable TF_MLC_LOGGING=1 when executing the model script. Logging provides more information about what happens when a TensorFlow model is optimized by ML Compute. The following TensorFlow features are currently not supported in this fork: t_mlc_device(device_name='cpu') # Available options are 'cpu', 'gpu', and 'any'. # Import mlcompute module to use the optional set_mlc_device API for device selection with ML Compute.įrom import mlcompute

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