├── .github ├── release-drafter-config.yml └── workflows │ └── release-drafter.yml ├── .gitignore ├── LICENSE ├── README.md ├── mlflow_redisai ├── __init__.py └── utils.py ├── requirements.txt └── setup.py /.github/release-drafter-config.yml: -------------------------------------------------------------------------------- 1 | name-template: 'Version $NEXT_PATCH_VERSION' 2 | tag-template: 'v$NEXT_PATCH_VERSION' 3 | categories: 4 | - title: 'Features' 5 | labels: 6 | - 'feature' 7 | - 'enhancement' 8 | - title: 'Bug Fixes' 9 | labels: 10 | - 'fix' 11 | - 'bugfix' 12 | - 'bug' 13 | - title: 'Maintenance' 14 | label: 'chore' 15 | change-template: '- $TITLE (#$NUMBER)' 16 | exclude-labels: 17 | - 'skip-changelog' 18 | template: | 19 | ## Changes 20 | 21 | $CHANGES 22 | -------------------------------------------------------------------------------- /.github/workflows/release-drafter.yml: -------------------------------------------------------------------------------- 1 | name: Release Drafter 2 | 3 | on: 4 | push: 5 | # branches to consider in the event; optional, defaults to all 6 | branches: 7 | - master 8 | 9 | jobs: 10 | update_release_draft: 11 | runs-on: ubuntu-latest 12 | steps: 13 | # Drafts your next Release notes as Pull Requests are merged into "master" 14 | - uses: release-drafter/release-drafter@v5 15 | with: 16 | # (Optional) specify config name to use, relative to .github/. Default: release-drafter.yml 17 | config-name: release-drafter-config.yml 18 | env: 19 | GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} 20 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | .idea/ 131 | mlruns/ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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This plugin provides few command line APIs, which is also accessible 8 | through mlflow's python package, to make the deployment process seamless. 9 | 10 | ## Installation 11 | For installing and activating the plugin, you only need to install this package which is available 12 | in pypi and can be installed with 13 | 14 | ```bash 15 | pip install mlflow_redisai 16 | ``` 17 | 18 | ## What does it do 19 | Installing this package uses python's amazing entrypoint mechanism to register the plugin into MLflow's 20 | plugin registry. This registry will be invoked each time you launch MLflow script or command line 21 | argument. 22 | 23 | ## Options 24 | This plugin allows you to interact with RedisAI deployment through MLflow using below given options. 25 | All of these options are accessible through command line and python API, although the predict command 26 | line option is not reliable in the current release. if you are connecting to a non-local RedisAI instance 27 | or if your RedisAI instance needs non-default connection parameters such as username or password, take a 28 | look at the [connection parameters section](#connection-parameters) 29 | 30 | ### Create deployment 31 | Deploy the model to RedisAI. The `create` command line argument and ``create_deployment`` python 32 | APIs does the deployment of a model built with MLflow to RedisAI. It fetches the information, such as 33 | which framework the model is built on, from the model configuration file implicitly. 34 | 35 | ##### CLI 36 | ```shell script 37 | mlflow deployments create -t redisai --name -m -C 38 | ``` 39 | 40 | ##### Python API 41 | ```python 42 | from mlflow.deployments import get_deploy_client 43 | target_uri = 'redisai' # host = localhost, port = 6379 44 | redisai = get_deploy_client(target_uri) 45 | redisai.create_deployment(rediskey, model_uri, config={'device': 'GPU'}) 46 | ``` 47 | 48 | ### Update deployment 49 | Update deployment API has a very similar signature to the create API. In face, update deployment 50 | does exactly the same operation as create API except that the model should already be deployed. 51 | If the model is not present already, it raises an exception. Update API can be used to update 52 | a new model after retraining. This type of setup is useful if you want to change the device on which 53 | the inference is running or if you want to change the autobatching size, or even if you are doing live 54 | training and updating the model on the fly . RedisAI will make sure the user experience is seamless 55 | while changing the model in a live environment. 56 | 57 | ##### CLI 58 | ```shell script 59 | mlflow deployments update -t redisai --name -m -C 60 | ``` 61 | 62 | ##### Python API 63 | ```python 64 | redisai.update_deployment(rediskey, model_uri, config={'device': 'GPU'}) 65 | ``` 66 | 67 | ### Delete deployment 68 | Delete an existing deployment. Error will be thrown if the model is not already deployed 69 | 70 | ##### CLI 71 | ```shell script 72 | mlflow deployments delete -t redisai --name 73 | ``` 74 | 75 | ##### Python API 76 | ```python 77 | redisai.delete_deployment(rediskey) 78 | ``` 79 | 80 | ### List all deployments 81 | List the names of all the deployments. This name can then be used in other APIs or can be 82 | used in the get deployment API to get more details about a particular deployment. Currently, 83 | it displays every deployment, not just the deployment made through this plugin 84 | 85 | ##### CLI 86 | ```shell script 87 | mlflow deployments list -t redisai 88 | ``` 89 | 90 | ##### Python API 91 | ```python 92 | redisai.list_deployments() 93 | ``` 94 | 95 | ### Get deployment details 96 | Get API fetches the meta data about a deployment from RedisAI. This metadata includes 97 | 98 | - BACKEND : the backend used by the model as a String 99 | - DEVICE : the device used to execute the model as a String 100 | - TAG : the model's tag as a String 101 | - BATCHSIZE : The maximum size of any batch of incoming requests. If BATCHSIZE is equal to 0 each incoming request is served immediately. When BATCHSIZE is greater than 0, the engine will batch incoming requests from multiple clients that use the model with input tensors of the same shape. 102 | - MINBATCHSIZE : The minimum size of any batch of incoming requests. 103 | - INPUTS : array reply with one or more names of the model's input nodes (applicable only for TensorFlow models) 104 | - OUTPUTS : array reply with one or more names of the model's output nodes (applicable only for TensorFlow models) 105 | 106 | ##### CLI 107 | ```shell script 108 | mlflow deployments get -t redisai --name 109 | ``` 110 | 111 | ##### Python API 112 | ```python 113 | redisai.get_deployment(rediskey) 114 | ``` 115 | 116 | ### Plugin help 117 | MLflow integration also made a handy help API which is specific to any plugins you install rather 118 | than the help string of the mlflow command itself. For quickly checking something out, it'd be 119 | easy to use the help API rather than looking out for this document. 120 | 121 | PS: Since help is specific to the plugin and not really an attribute of the client object itself, 122 | it's not available under client object (`redisai` variable in the above examples) 123 | 124 | ##### CLI 125 | ```shell script 126 | mlflow deployments help -t redisai 127 | ``` 128 | 129 | ### Local run 130 | If you are new to RedisAI and trying it out for the first time, you might not know the setup already 131 | (although it's quite easy to setup a local RedisAI instance). The `local-run` API is there to help 132 | you, if that's the case. It pulls the latest docker image of RedisAI (yes, you need docker installed in your 133 | machine for this to work), run it on the default port, deploy the model you specified. 134 | 135 | PS: It's IMPORTANT to note that this API leaves the docker container running. You would need to manually 136 | stop the container once you are done with experimentation. Also, remember that if you trying to run 137 | this API twice without killing the first container, it throws an error saying the port is already 138 | in use. 139 | 140 | ##### CLI 141 | ```shell script 142 | mlflow deployments run-local -t redisai --name -m -C 143 | ``` 144 | 145 | 146 | ## Connection Parameters 147 | For connecting to a RedisAI instance with non-default connection parameters, you'd either need to 148 | supply the necessary (and allowed) parameters through environmental variables or modify the target 149 | URI that you'd pass through the command line using `-t` or `-target` option. 150 | 151 | ##### Through environmental variables 152 | 153 | Currently this plugin allows five options through environmental variables which are given below 154 | (without description because they are self explanatory) 155 | 156 | * REDIS_HOST 157 | * REDIS_PORT 158 | * REDIS_DB 159 | * REDIS_USERNAME 160 | * REDIS_PASSWORD 161 | 162 | ##### Modifying URI 163 | A template for quick verification is given below 164 | ``` 165 | redisai:/[[username]:[password]]@[host]:[port]/[db] 166 | ``` 167 | 168 | where the default value each would be 169 | - username = None 170 | - password = None 171 | - host = localhost 172 | - port = 6379 173 | - db = 0 174 | 175 | 176 | 177 | -------------------------------------------------------------------------------- /mlflow_redisai/__init__.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | import logging 3 | from urllib.parse import urlparse 4 | import subprocess 5 | import time 6 | 7 | import redisai 8 | import redis 9 | import ml2rt 10 | from mlflow.deployments import BaseDeploymentClient 11 | from mlflow.exceptions import MlflowException 12 | from mlflow.tracking.artifact_utils import _download_artifact_from_uri 13 | from mlflow.models import Model 14 | from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE 15 | 16 | from .utils import (get_preferred_deployment_flavor, validate_deployment_flavor, 17 | SUPPORTED_DEPLOYMENT_FLAVORS, flavor2backend, Config) 18 | 19 | 20 | logger = logging.getLogger(__name__) 21 | 22 | 23 | def target_help(): 24 | help_string = ("\nmlflow-redisai plugin integrates RedisAI to mlflow deployment pipeline. " 25 | "For detailed explanation and to see multiple examples, checkout the Readme at " 26 | "https://github.com/RedisAI/mlflow-redisai/blob/master/README.md \n\n" 27 | 28 | "Connection parameters: You can either use the URI to specify the connection " 29 | "parameters or specify them as environmental variables. If connection parameters " 30 | "are present in both URI and environmental variables, parameters from the " 31 | "environmental variables are ignored completely. The command with formatted " 32 | "URI would look like\n\n" 33 | 34 | " mlflow deployments -t redisai:/:@:/\n\n" 35 | 36 | "If you'd like to use the default values for parameters, only specify the " 37 | "target as given below \n\n" 38 | 39 | " mlflow deployments -t redisai\n\n" 40 | 41 | "If you are going with environmental variables instead of URI parameters, the " 42 | "expected keys are \n\n" 43 | 44 | " * REDIS_HOST\n" 45 | " * REDIS_PORT\n" 46 | " * REDIS_DB\n" 47 | " * REDIS_USERNAME\n" 48 | " * REDIS_PASSWORD\n\n" 49 | 50 | "However, if you wish to go with default values, don't set any environmental " 51 | "variables\n\n" 52 | "Model configuration: The ``--config`` or ``-C`` option of ``create`` and " 53 | "``update`` API enables you to pass arguments specific to RedisAI deployment. " 54 | "The possible config options are\n\n" 55 | 56 | " * batchsize: Batch size for auto-batching\n" 57 | " * tag: Tag a deployment with a version number or a given name\n" 58 | " * device: CPU or GPU. if multiple GPUs are available, specify that too\n\n") 59 | return help_string 60 | 61 | 62 | def run_local(name, model_uri, flavor=None, config=None): 63 | """ 64 | Run the RedisAI docker container locally and deploy the model to it. It requires 65 | docker to be installed in the host machine. 66 | 67 | Parameters 68 | ---------- 69 | name : str 70 | Name/key for setting the model in RedisAI 71 | model_uri : str 72 | A valid mlflow model URI 73 | flavor : str 74 | Which flavor to use to deploy. If this is not provided, it will be inferred from the 75 | model config file 76 | config : dict 77 | Configuration dictionary parsed from user command passed as ``-C key value`` 78 | """ 79 | device = config.get('device', 'cpu') 80 | if 'gpu' in device.lower(): 81 | commands = ['docker', 'run', '-p', '6379:6379', '--gpus', 'all', '--rm', 'redisai/redisai:latest'] 82 | else: 83 | commands = ['docker', 'run', '-p', '6379:6379', '--rm', 'redisai/redisai:latest'] 84 | proc = subprocess.Popen(commands) 85 | plugin = RedisAIPlugin('redisai:/localhost:6379/0') 86 | start_time = time.time() 87 | prev_num_interval = 0 88 | while True: 89 | logger.info("Launching RedisAI docker container") 90 | try: 91 | if plugin.con.ping(): 92 | break 93 | except redis.exceptions.ConnectionError: 94 | num_interval, _ = divmod(time.time() - start_time, 10) 95 | if num_interval > prev_num_interval: 96 | prev_num_interval = num_interval 97 | try: 98 | proc.communicate(timeout=0.1) 99 | except subprocess.TimeoutExpired: 100 | pass 101 | else: 102 | raise RuntimeError("Could not start the RedisAI docker container. You can " 103 | "try setting up RedisAI locally by (by following the " 104 | "documentation https://oss.redislabs.com/redisai/quickstart/)" 105 | " and call the ``create`` API with target_uri as given in " 106 | "the example command below (this will set the host as " 107 | "localhost and port as 6379)\n\n" 108 | " mlflow deployments create -t redisai -m ...\n\n") 109 | time.sleep(0.2) 110 | plugin.create_deployment(name, model_uri, flavor, config) 111 | logger.info("RedisAI docker container is up and the model has been deployed. " 112 | "Don't forget to stop the container once you are done using it.") 113 | 114 | 115 | class RedisAIPlugin(BaseDeploymentClient): 116 | 117 | def __init__(self, uri): 118 | super().__init__(uri) 119 | server_config = Config() 120 | path = urlparse(uri).path 121 | if path: 122 | uri = f"redis:/{path}" 123 | self.con = redisai.Client.from_url(uri) 124 | else: 125 | self.con = redisai.Client(**server_config) 126 | 127 | def create_deployment(self, name, model_uri, flavor=None, config=None): 128 | device = config.get('device', 'CPU') 129 | autobatch_size = config.get('batchsize') 130 | tag = config.get('tag') 131 | path = Path(_download_artifact_from_uri(model_uri)) 132 | model_config = path / 'MLmodel' 133 | if not model_config.exists(): 134 | raise MlflowException( 135 | message=( 136 | "Failed to find MLmodel configuration within the specified model's" 137 | " root directory."), 138 | error_code=INVALID_PARAMETER_VALUE) 139 | model_config = Model.load(model_config) 140 | 141 | if flavor is None: 142 | flavor = get_preferred_deployment_flavor(model_config) 143 | else: 144 | validate_deployment_flavor(model_config, flavor) 145 | logger.info("Using the {} flavor for deployment!".format(flavor)) 146 | 147 | if flavor == 'tensorflow': 148 | # TODO: test this for tf1.x and tf2.x 149 | tags = model_config.flavors[flavor]['meta_graph_tags'] 150 | signaturedef = model_config.flavors[flavor]['signature_def_key'] 151 | model_dir = path / model_config.flavors[flavor]['saved_model_dir'] 152 | model, inputs, outputs = ml2rt.load_model(model_dir, tags, signaturedef) 153 | elif flavor == 'pytorch': 154 | # hard-coding model.pth might bite us back 155 | model_path = path / model_config.flavors[flavor]['model_data'] / "model.pth" 156 | model = ml2rt.load_model(str(model_path)) 157 | inputs = outputs = None 158 | else: 159 | raise RuntimeError(f"Flavor found is {flavor} but is not supported by this plugin") 160 | backend = flavor2backend[flavor] 161 | self.con.modelset(name, backend, device, model, inputs=inputs, outputs=outputs, batch=autobatch_size, tag=tag) 162 | return {'name': name, 'flavor': flavor} 163 | 164 | def delete_deployment(self, name): 165 | self.con.modeldel(name) 166 | logger.info("Deleted model with key: {}".format(name)) 167 | 168 | def update_deployment(self, name, model_uri=None, flavor=None, config=None): 169 | try: 170 | self.con.modelget(name, meta_only=True) 171 | except redis.exceptions.ConnectionError: 172 | raise MlflowException("Model doesn't exist. If you trying to create new " 173 | "deployment, use ``create_deployment``") 174 | else: 175 | ret = self.create_deployment(name, model_uri, flavor, config=config) 176 | return {'flavor': ret['flavor']} 177 | 178 | def list_deployments(self, **kwargs): 179 | return self.con.modelscan() 180 | 181 | def get_deployment(self, name): 182 | return self.con.modelget(name, meta_only=True) 183 | 184 | def predict(self, deployment_name, df): 185 | nparray = df.to_numpy() 186 | self.con.tensorset('array', nparray) 187 | # TODO: manage multiple inputs and multiple outputs 188 | self.con.modelrun(deployment_name, inputs=['array'], outputs=['output']) 189 | return self.con.tensorget('output') 190 | -------------------------------------------------------------------------------- /mlflow_redisai/utils.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import os 3 | from mlflow.exceptions import MlflowException 4 | from mlflow.protos.databricks_pb2 import RESOURCE_DOES_NOT_EXIST, INVALID_PARAMETER_VALUE 5 | 6 | 7 | logger = logging.getLogger(__name__) 8 | 9 | 10 | SUPPORTED_DEPLOYMENT_FLAVORS = ['pytorch', 'tensorflow'] 11 | flavor2backend = { 12 | 'pytorch': 'torch', 13 | 'tensorflow': 'tf'} 14 | 15 | 16 | class Config(dict): 17 | def __init__(self): 18 | super().__init__() 19 | self['host'] = os.environ.get('REDIS_HOST') 20 | self['port'] = os.environ.get('REDIS_PORT') 21 | self['username'] = os.environ.get('REDIS_USERNAME') 22 | self['password'] = os.environ.get('REDIS_PASSWORD') 23 | self['db'] = os.environ.get('REDIS_DB') 24 | 25 | 26 | def validate_deployment_flavor(model_config, flavor): 27 | """ 28 | Checks that the specified flavor is a supported deployment flavor 29 | and is contained in the specified model. If one of these conditions 30 | is not met, an exception is thrown. 31 | 32 | :param model_config: An MLflow Model object 33 | :param flavor: The deployment flavor to validate 34 | """ 35 | if flavor not in SUPPORTED_DEPLOYMENT_FLAVORS: 36 | raise MlflowException( 37 | message=( 38 | "The specified flavor: `{flavor_name}` is not supported for deployment." 39 | " Please use one of the supported flavors: {supported_flavor_names}".format( 40 | flavor_name=flavor, 41 | supported_flavor_names=SUPPORTED_DEPLOYMENT_FLAVORS)), 42 | error_code=INVALID_PARAMETER_VALUE) 43 | elif flavor not in model_config.flavors: 44 | raise MlflowException( 45 | message=("The specified model does not contain the specified deployment flavor:" 46 | " `{flavor_name}`. Please use one of the following deployment flavors" 47 | " that the model contains: {model_flavors}".format( 48 | flavor_name=flavor, model_flavors=model_config.flavors.keys())), 49 | error_code=RESOURCE_DOES_NOT_EXIST) 50 | 51 | 52 | def get_preferred_deployment_flavor(model_config): 53 | """ 54 | Obtains the flavor that MLflow would prefer to use when deploying the model on RedisAI. 55 | If the model does not contain any supported flavors for deployment, an exception 56 | will be thrown. 57 | 58 | :param model_config: An MLflow model object 59 | :return: The name of the preferred deployment flavor for the specified model 60 | """ 61 | # TODO: add onnx & TFlite 62 | possible_flavors = set(SUPPORTED_DEPLOYMENT_FLAVORS).intersection(model_config.flavors) 63 | if len(possible_flavors) == 1: 64 | return possible_flavors.pop() 65 | elif len(possible_flavors) > 1: 66 | flavor = possible_flavors.pop() 67 | logger.info("Found more than one possible flavors, using " 68 | "the first: {}".format(flavor)) 69 | return flavor 70 | else: 71 | raise MlflowException( 72 | message=( 73 | "The specified model does not contain any of the supported flavors for" 74 | " deployment. The model contains the following flavors: {model_flavors}." 75 | " Supported flavors: {supported_flavors}".format( 76 | model_flavors=model_config.flavors.keys(), 77 | supported_flavors=SUPPORTED_DEPLOYMENT_FLAVORS)), 78 | error_code=RESOURCE_DOES_NOT_EXIST) 79 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | redisai==1.0.2 2 | redis==3.5.3 3 | ml2rt==0.2.0 4 | mlflow==1.19.0 5 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import setuptools 2 | 3 | with open("README.md", "r") as fh: 4 | long_description = fh.read() 5 | 6 | 7 | setuptools.setup( 8 | name="mlflow_redisai", 9 | version="0.1.1", 10 | author="hhsecond", 11 | author_email="sherin@tensorwerk.com", 12 | description="MLFlow RedisAI integration package", 13 | long_description=long_description, 14 | long_description_content_type="text/markdown", 15 | url="https://github.com/RedisAI/mlflow-redisai", 16 | packages=setuptools.find_packages(), 17 | classifiers=[ 18 | "Programming Language :: Python :: 3", 19 | "License :: OSI Approved :: Apache Software License", 20 | "Operating System :: OS Independent", 21 | ], 22 | python_requires='>=3.6', 23 | install_requires=['redisai', 'ml2rt'], 24 | entry_points={"mlflow.deployments": "redisai=mlflow_redisai"} 25 | ) 26 | --------------------------------------------------------------------------------