{ "checker": { "claims": [ { "claim": "The README Implementation section imports ZenGrad from zengrad_tensorflow and it performs a Keras optimizer update with the documented hyperparameters", "command": "Python API zengrad_tensorflow.ZenGrad", "evidence": ":InitializeLog() is called are written to STDERR\nI0000 00:00:1793173794.776307 3047 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\nI0000 00:00:1793173794.788852 3047 cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\nE0000 00:00:1793173796.187311 3047 cuda_platform.cc:52] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)", "expected": "updated weights [0.9981059513768245,2.003743288932412]", "input": "learning_rate=0.01, weight_decay=0.0001, momentum=0.9, epsilon=1e-8; weights=[1.0,2.0], gradient=[0.1,-0.2]", "kind": "claim", "outcome": "Real input produced the expected result: [0.9981059513768245, 2.003743288932412]", "returncode": 0, "source": "from zengrad_tensorflow import ZenGrad # Import the ZenGrad optimizer", "source_checked": true, "source_file": "README.md", "verdict": "yes" } ], "cycle_checks": [ { "claim": "Scheduled or looping main behavior", "command": "", "evidence": "Inspected README.md and the zengrad_tensorflow package (__init__.py, zengrad_tensorflow.py) and setup.py. ZenGrad is an optimizer class with no cron, loop, webhook, scheduler, --once or --now trigger, so there is no product cycle to run.", "expected": "identify the product trigger", "input": "source inspection", "kind": "cycle", "outcome": "Inspected README.md and the zengrad_tensorflow package (__init__.py, zengrad_tensorflow.py) and setup.py. ZenGrad is an optimizer class with no cron, loop, webhook, scheduler, --once or --now trigger, so there is no product cycle to run.", "source": "repository entry points", "verdict": "yes" } ], "details": { "kind": "python_api", "needs_browser": false, "needs_credentials": false, "python_example": { "actual": [ 0.9981059514686015, 2.003743288752119 ], "source_file": "", "weights_before": [ 1.0, 2.0 ] }, "returncode": 0, "scope": "product operation", "stderr": "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1793173782.205580 3039 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\nI0000 00:00:1793173782.209662 3039 cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\nI0000 00:00:1793173782.264568 3039 cpu_feature_guard.cc:227] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1793173787.415407 3039 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\nI0000 00:00:1\n[excerpt truncated]", "stdout": "{\"actual\": [0.9981059514686015, 2.003743288752119], \"source_file\": \"\", \"weights_before\": [1.0, 2.0]}", "working": true }, "evidence_strength": "readme_checked", "example_checks": [], "status": "PASS" }, "deviations": [ "The README model.compile snippet is a placeholder (model = ... and .......), so it was not run as-is; the optimizer was exercised directly through apply_gradients with a tf.Variable, as the checker's tensorflow_step does.", "Installed the package from PyPI per the README rather than from the pinned commit; the import during the probe resolves to the pinned workspace source, which the checker verifies.", "Actual output [0.9981059514686015, 2.003743288752119] differs from the expected [0.9981059513768245, 2.003743288932412] by about 1e-10 to 2e-10; the checker's tolerance accepted it.", "CUDA cuInit warnings in TensorFlow output are expected on a machine without GPU and do not affect the CPU run.", "No runnable documented example or test was found; examples.json is empty. No cycle: detected=false." ], "fault_owner": null, "main_blocker": null, "operation": "Run the ZenGrad TensorFlow optimizer (zengrad_tensorflow.ZenGrad) on CPU for one Keras apply_gradients update with the README hyperparameters, and check the updated weights against the expected values.", "operation_scope": "CPU only, no GPU or account. Pinned source commit cdd19353b333c973c65f8e25499c701ba3de0680 (git init, remote origin, fetch --depth 1, reset --mixed). Python 3.12.3, tensorflow 2.21.0, keras 3.15.1. Prerequisites: pip install zengrad-tensorflow (PyPI 0.1.1) in a venv at and in system python3 (user install) because the checker runs the python_api through system python3.", "outcome": "working", "plain_failure": null, "steps": [ "git init; git remote add origin https://github.com/Rishichaitanya-Nalluri/zengrad-tensorflow; git fetch --depth 1 origin cdd19353b333c973c65f8e25499c701ba3de0680; git reset --mixed FETCH_HEAD", "python3 -m venv ; install zengrad-tensorflow (README Installation)", "python3 -m pip install --user --break-system-packages zengrad-tensorflow (system interpreter for the checker)", "README Implementation: from zengrad_tensorflow import ZenGrad; ZenGrad(learning_rate=0.01, weight_decay=1e-4, momentum=0.9, epsilon=1e-8); python_api replay with tensorflow_step weights=[1.0,2.0], gradient=[0.1,-0.2]" ], "stuck_step": null, "suggested_fix": "None required. Optionally fix the README snippet (CRLF, placeholder model) to a runnable example." }