135 lines
4.0 KiB
Python
135 lines
4.0 KiB
Python
import logging
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import os
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import re
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import subprocess
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import sys
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from mlflow.exceptions import MlflowException
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from mlflow.models import FlavorBackend
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from mlflow.tracking.artifact_utils import _download_artifact_from_uri
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from mlflow.utils.string_utils import quote
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_logger = logging.getLogger(__name__)
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class RFuncBackend(FlavorBackend):
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"""
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Flavor backend implementation for the generic R models.
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Predict and serve locally models with 'crate' flavor.
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"""
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def build_image(
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self, model_uri, image_name, install_mlflow, mlflow_home, enable_mlserver, base_image=None
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):
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pass
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def generate_dockerfile(
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self, model_uri, output_path, install_mlflow, mlflow_home, enable_mlserver, base_image=None
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):
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pass
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version_pattern = re.compile(r"version ([0-9]+\.[0-9]+\.[0-9]+)")
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def predict(
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self,
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model_uri,
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input_path,
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output_path,
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content_type,
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pip_requirements_override=None,
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extra_envs=None,
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):
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"""
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Generate predictions using R model saved with MLflow.
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Return the prediction results as a JSON.
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"""
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if pip_requirements_override is not None:
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raise MlflowException("pip_requirements_override is not supported in the R backend.")
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model_path = _download_artifact_from_uri(model_uri)
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str_cmd = (
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"mlflow:::mlflow_rfunc_predict(model_path = '{0}', input_path = {1}, "
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"output_path = {2}, content_type = {3})"
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)
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command = str_cmd.format(
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quote(model_path),
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_str_optional(input_path),
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_str_optional(output_path),
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_str_optional(content_type),
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)
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_execute(command, extra_envs=extra_envs)
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def serve(
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self,
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model_uri,
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port,
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host,
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timeout,
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enable_mlserver,
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synchronous=True,
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stdout=None,
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stderr=None,
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):
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"""
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Generate R model locally.
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NOTE: The `enable_mlserver` parameter is there to comply with the
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FlavorBackend interface but is not supported by MLServer yet.
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https://github.com/SeldonIO/MLServer/issues/183
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"""
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if enable_mlserver:
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raise Exception("The MLServer inference server is not yet supported in the R backend.")
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if timeout:
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_logger.warning("Timeout is not yet supported in the R backend.")
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if not synchronous:
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raise Exception("RBackend does not support call with synchronous=False")
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if stdout is not None or stderr is not None:
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raise Exception("RBackend does not support redirect stdout/stderr.")
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model_path = _download_artifact_from_uri(model_uri)
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command = "mlflow::mlflow_rfunc_serve('{}', port = {}, host = '{}')".format(
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quote(model_path), port, host
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)
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_execute(command)
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def can_score_model(self):
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# `Rscript --version` writes to stderr in R < 4.2.0 but stdout in R >= 4.2.0.
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process = subprocess.Popen(
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["Rscript", "--version"],
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close_fds=True,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT,
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)
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stdout, _ = process.communicate()
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if process.wait() != 0:
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return False
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version = self.version_pattern.search(stdout.decode("utf-8"))
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if not version:
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return False
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version = [int(x) for x in version.group(1).split(".")]
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return version[0] > 3 or version[0] == 3 and version[1] >= 3
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def _execute(command, extra_envs=None):
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env = os.environ.copy()
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if extra_envs:
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env.update(extra_envs)
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process = subprocess.Popen(
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["Rscript", "-e", command],
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env=env,
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close_fds=False,
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stdin=sys.stdin,
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stdout=sys.stdout,
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stderr=sys.stderr,
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)
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if process.wait() != 0:
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raise Exception("Command returned non zero exit code.")
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def _str_optional(s):
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return "NULL" if s is None else f"'{quote(str(s))}'"
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