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SECURITY.md
`SavedModel`) and executes them in parallel on available executors. Running TensorFlow in a multitenant design mixes the risks described above with the inherent ones from multitenant configurations. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks. ### Tenant isolation Since any tenants or users providing models, graphs or checkpoints can execute
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ci/official/containers/linux_arm64/devel.usertools/squash_testlogs.py
# WindowOpsTest.test_tflite_convert0 (<function hann_window at # 0x7fc61728dd40>, 10, False, tf.float32) # Where 0x... is a thread ID (or something) that is not important for # debugging, but breaks this "number of failures" counter because it's # different for repetitions of the same test. We use re.sub(r"0x\w+") # to remove it. key = re.sub(r"0x\w+", "", p.getparent().get("name", "")) + p.text
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