def _default_context_fields() -> Dict[Text, Any]:
"""Return a dictionary that contains the default context values.
Return:
A new context containing information about the runtime environment.
"""
global TELEMETRY_CONTEXT
if not TELEMETRY_CONTEXT:
# Make sure to update the example in docs/docs/telemetry/telemetry.mdx
# if you change / add context
TELEMETRY_CONTEXT = {
"os": {
"name": platform.system(),
"version": platform.release()
},
"ci": in_continuous_integration(),
"project": model.project_fingerprint(),
"directory": _hash_directory_path(os.getcwd()),
"python": sys.version.split(" ")[0],
"rasa_open_source": rasa.__version__,
"cpu": multiprocessing.cpu_count(),
"docker": _is_docker(),
}
# avoid returning the cached dict --> caller could modify the dictionary...
# usually we would use `lru_cache`, but that doesn't return a dict copy and
# doesn't work on inner functions, so we need to roll our own caching...
return TELEMETRY_CONTEXT.copy()
def _default_context_fields() -> Dict[Text, Any]:
"""Return a dictionary that contains the default context values.
Return:
A new context containing information about the runtime environment.
"""
import tensorflow as tf
return {
"os": {"name": platform.system(), "version": platform.release()},
"ci": in_continuous_integration(),
"project": model.project_fingerprint(),
"directory": _hash_directory_path(os.getcwd()),
"python": sys.version.split(" ")[0],
"rasa_open_source": rasa.__version__,
"gpu": len(tf.config.list_physical_devices("GPU")),
"cpu": multiprocessing.cpu_count(),
"docker": _is_docker(),
}
