mlflow.environment_variables
This module defines environment variables used in MLflow.
-
mlflow.environment_variables.
MLFLOW_ALLOW_HTTP_REDIRECTS
= 'MLFLOW_ALLOW_HTTP_REDIRECTS' Specifies whether or not to allow the MLflow server to follow redirects when making HTTP requests. If set to False, the server will throw an exception if it encounters a redirect response. (default:
True
)
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mlflow.environment_variables.
MLFLOW_ARTIFACT_UPLOAD_DOWNLOAD_TIMEOUT
= 'MLFLOW_ARTIFACT_UPLOAD_DOWNLOAD_TIMEOUT' (Experimental, may be changed or removed) Specifies the timeout to use when uploading or downloading a file (default:
None
). If None, individual artifact stores will choose defaults.
-
mlflow.environment_variables.
MLFLOW_AUTH_CONFIG_PATH
= 'MLFLOW_AUTH_CONFIG_PATH' Specified the path to the configuration file for MLflow Authentication. (default:
None
)
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mlflow.environment_variables.
MLFLOW_CONDA_CREATE_ENV_CMD
= 'MLFLOW_CONDA_CREATE_ENV_CMD' Specifies the name of the command to use when creating the environments. For example, let’s say we want to use mamba (https://github.com/mamba-org/mamba) instead of conda to create environments. Then: > conda install mamba -n base -c conda-forge If not set, use the same as conda_path (default:
conda
)
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mlflow.environment_variables.
MLFLOW_CONDA_HOME
= 'MLFLOW_CONDA_HOME' Specifies the conda home directory to use. (default:
conda
)
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mlflow.environment_variables.
MLFLOW_CONFIGURE_LOGGING
= 'MLFLOW_LOGGING_CONFIGURE_LOGGING' Specifies whether or not to have mlflow configure logging on import. If set to True, mlflow will configure
mlflow.<module_name>
loggers with logging handlers and formatters. (default:True
)
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mlflow.environment_variables.
MLFLOW_DEFAULT_PREDICTION_DEVICE
= 'MLFLOW_DEFAULT_PREDICTION_DEVICE' Specifies the device intended for use in the predict function - can be used to override behavior where the GPU is used by default when available by setting this environment variable to be
cpu
. Currently, this variable is only supported for the MLflow PyTorch and HuggingFace flavors. For the HuggingFace flavor, note that device must be parseable as an integer.
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mlflow.environment_variables.
MLFLOW_DEPLOYMENTS_CONFIG
= 'MLFLOW_DEPLOYMENTS_CONFIG' Specifies the path of the config file for the MLflow Deployments server. (default:
None
)
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mlflow.environment_variables.
MLFLOW_DEPLOYMENTS_TARGET
= 'MLFLOW_DEPLOYMENTS_TARGET' (Experimental, may be changed or removed) Specifies the uri of a MLflow Deployments Server instance to be used with the Deployments Client APIs (default:
None
)
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mlflow.environment_variables.
MLFLOW_DEPLOYMENT_FLAVOR_NAME
= 'MLFLOW_DEPLOYMENT_FLAVOR_NAME' Specifies the flavor to serve in the scoring server. (default
None
)
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mlflow.environment_variables.
MLFLOW_DFS_TMP
= 'MLFLOW_DFS_TMP' Specifies the
dfs_tmpdir
parameter to use formlflow.spark.save_model
,mlflow.spark.log_model
andmlflow.spark.load_model
. See https://www.mlflow.org/docs/latest/python_api/mlflow.spark.html#mlflow.spark.save_model for more information. (default:/tmp/mlflow
)
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mlflow.environment_variables.
MLFLOW_DISABLE_ENV_CREATION
= 'MLFLOW_DISABLE_ENV_CREATION' Specifies whether to disable creating a new conda environment for mlflow models build-docker. (default:
False
)
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mlflow.environment_variables.
MLFLOW_DISABLE_ENV_MANAGER_CONDA_WARNING
= 'MLFLOW_DISABLE_ENV_MANAGER_CONDA_WARNING' Specifies whether or not to print a warning when –env-manager=conda is specified. (default:
False
)
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mlflow.environment_variables.
MLFLOW_DOCKER_OPENJDK_VERSION
= 'MLFLOW_DOCKER_OPENJDK_VERSION' The OpenJDK version to install in the Docker image used for MLflow models. (default:
11
)
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mlflow.environment_variables.
MLFLOW_DOWNLOAD_CHUNK_TIMEOUT
= 'MLFLOW_DOWNLOAD_CHUNK_TIMEOUT' Specifies the timeout value for downloading chunks of mlflow artifacts. (default:
300
)
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mlflow.environment_variables.
MLFLOW_ENABLE_ARTIFACTS_PROGRESS_BAR
= 'MLFLOW_ENABLE_ARTIFACTS_PROGRESS_BAR' Specifies whether to display the progress bar when uploading/downloading artifacts. (default:
True
)
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mlflow.environment_variables.
MLFLOW_ENABLE_ASYNC_LOGGING
= 'MLFLOW_ENABLE_ASYNC_LOGGING' If True, MLflow fluent logging APIs, e.g., mlflow.log_metric will log asynchronously.
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mlflow.environment_variables.
MLFLOW_ENABLE_DBFS_FUSE_ARTIFACT_REPO
= 'MLFLOW_ENABLE_DBFS_FUSE_ARTIFACT_REPO' Specifies whether or not to use DBFS FUSE mount to store artifacts on Databricks (default:
False
)
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mlflow.environment_variables.
MLFLOW_ENABLE_PROXY_MULTIPART_UPLOAD
= 'MLFLOW_ENABLE_PROXY_MULTIPART_UPLOAD' Specifies whether or not to use multipart upload for proxied artifact access. (default:
False
)
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mlflow.environment_variables.
MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING
= 'MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING' Specifies if system metrics logging should be enabled.
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mlflow.environment_variables.
MLFLOW_ENV_ROOT
= 'MLFLOW_ENV_ROOT' Specifies the root directory to create Python virtual environments in. (default:
~/.mlflow/envs
)
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mlflow.environment_variables.
MLFLOW_EXPERIMENT_ID
= 'MLFLOW_EXPERIMENT_ID' Specifies the default experiment ID to create run to. (default:
None
)
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mlflow.environment_variables.
MLFLOW_EXPERIMENT_NAME
= 'MLFLOW_EXPERIMENT_NAME' Specifies the default experiment name to create run to. (default:
None
)
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mlflow.environment_variables.
MLFLOW_GATEWAY_CONFIG
= 'MLFLOW_GATEWAY_CONFIG' Specifies the path of the config file for MLflow AI Gateway. (default:
None
)
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mlflow.environment_variables.
MLFLOW_GATEWAY_URI
= 'MLFLOW_GATEWAY_URI' (Experimental, may be changed or removed) Specifies the uri of a MLflow Gateway Server instance to be used with the Gateway Client APIs (default:
None
)
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mlflow.environment_variables.
MLFLOW_GCS_DEFAULT_TIMEOUT
= 'MLFLOW_GCS_DEFAULT_TIMEOUT' (Deprecated, please use
MLFLOW_ARTIFACT_UPLOAD_DOWNLOAD_TIMEOUT
) Specifies the default timeout to use when downloading/uploading a file from/to GCS (default:None
). If None,google.cloud.storage.constants._DEFAULT_TIMEOUT
is used.
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mlflow.environment_variables.
MLFLOW_GCS_DOWNLOAD_CHUNK_SIZE
= 'MLFLOW_GCS_DOWNLOAD_CHUNK_SIZE' Specifies the chunk size to use when downloading a file from GCS (default:
None
). If None, the chunk size is automatically determined by thegoogle-cloud-storage
package.
-
mlflow.environment_variables.
MLFLOW_GCS_UPLOAD_CHUNK_SIZE
= 'MLFLOW_GCS_UPLOAD_CHUNK_SIZE' Specifies the chunk size to use when uploading a file to GCS. (default:
None
). If None, the chunk size is automatically determined by thegoogle-cloud-storage
package.
-
mlflow.environment_variables.
MLFLOW_HTTP_REQUEST_BACKOFF_FACTOR
= 'MLFLOW_HTTP_REQUEST_BACKOFF_FACTOR' Specifies the backoff increase factor between MLflow HTTP request failures (default:
2
)
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mlflow.environment_variables.
MLFLOW_HTTP_REQUEST_BACKOFF_JITTER
= 'MLFLOW_HTTP_REQUEST_BACKOFF_JITTER' Specifies the backoff jitter between MLflow HTTP request failures (default:
1.0
)
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mlflow.environment_variables.
MLFLOW_HTTP_REQUEST_MAX_RETRIES
= 'MLFLOW_HTTP_REQUEST_MAX_RETRIES' Specifies the maximum number of retries for MLflow HTTP requests (default:
5
)
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mlflow.environment_variables.
MLFLOW_HTTP_REQUEST_TIMEOUT
= 'MLFLOW_HTTP_REQUEST_TIMEOUT' Specifies the timeout in seconds for MLflow HTTP requests (default:
120
)
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mlflow.environment_variables.
MLFLOW_HUGGINGFACE_DEVICE_MAP_STRATEGY
= 'MLFLOW_HUGGINGFACE_DEVICE_MAP_STRATEGY' Specifies to Huggingface to use the automatic device placement logic of HuggingFace accelerate. This can be set to values supported by the version of HuggingFace Accelerate being installed.
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mlflow.environment_variables.
MLFLOW_HUGGINGFACE_MODEL_MAX_SHARD_SIZE
= 'MLFLOW_HUGGINGFACE_MODEL_MAX_SHARD_SIZE' Specifies the max_shard_size to use when mlflow transformers flavor saves the model checkpoint. This can be set to override the 500MB default.
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mlflow.environment_variables.
MLFLOW_HUGGINGFACE_USE_LOW_CPU_MEM_USAGE
= 'MLFLOW_HUGGINGFACE_USE_LOW_CPU_MEM_USAGE' Specifies to Huggingface to use the low_cpu_mem_usage flag powered by HuggingFace accelerate. If it’s set to false, the low_cpu_mem_usage flag will be set to False.
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mlflow.environment_variables.
MLFLOW_INPUT_EXAMPLE_INFERENCE_TIMEOUT
= 'MLFLOW_INPUT_EXAMPLE_INFERENCE_TIMEOUT' Specifies the timeout for model inference with input example(s) when logging/saving a model. MLflow runs a few inference requests against the model to infer model signature and pip requirements. Sometimes the prediction hangs for a long time, especially for a large model. This timeout limits the allowable time for performing a prediction for signature inference and will abort the prediction, falling back to the default signature and pip requirements.
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mlflow.environment_variables.
MLFLOW_KERBEROS_TICKET_CACHE
= 'MLFLOW_KERBEROS_TICKET_CACHE' Specifies the location of a Kerberos ticket cache to use for HDFS artifact operations. (default:
None
)
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mlflow.environment_variables.
MLFLOW_KERBEROS_USER
= 'MLFLOW_KERBEROS_USER' Specifies a Kerberos user for HDFS artifact operations. (default:
None
)
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mlflow.environment_variables.
MLFLOW_MULTIPART_DOWNLOAD_CHUNK_SIZE
= 'MLFLOW_MULTIPART_DOWNLOAD_CHUNK_SIZE' Specifies the chunk size in bytes to use when performing multipart download (default:
104_857_600
(100 MB))
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mlflow.environment_variables.
MLFLOW_MULTIPART_UPLOAD_CHUNK_SIZE
= 'MLFLOW_MULTIPART_UPLOAD_CHUNK_SIZE' Specifies the chunk size in bytes to use when performing multipart upload (default:
104_857_60
(10 MB))
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mlflow.environment_variables.
MLFLOW_MULTIPART_UPLOAD_MINIMUM_FILE_SIZE
= 'MLFLOW_MULTIPART_UPLOAD_MINIMUM_FILE_SIZE' Specifies the minimum file size in bytes to use multipart upload when logging artifacts (default:
524_288_000
(500 MB))
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mlflow.environment_variables.
MLFLOW_OPENAI_SECRET_SCOPE
= 'MLFLOW_OPENAI_SECRET_SCOPE' Specifies the name of the Databricks secret scope to use for storing OpenAI API keys.
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mlflow.environment_variables.
MLFLOW_PYARROW_EXTRA_CONF
= 'MLFLOW_PYARROW_EXTRA_CONF' Specifies extra pyarrow configurations for HDFS artifact operations. (default:
None
)
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mlflow.environment_variables.
MLFLOW_RECIPES_EXECUTION_DIRECTORY
= 'MLFLOW_RECIPES_EXECUTION_DIRECTORY' Specifies the execution directory for recipes. (default:
None
)
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mlflow.environment_variables.
MLFLOW_RECIPES_EXECUTION_TARGET_STEP_NAME
= 'MLFLOW_RECIPES_EXECUTION_TARGET_STEP_NAME' Specifies the target step to execute for recipes. (default:
None
)
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mlflow.environment_variables.
MLFLOW_RECIPES_PROFILE
= 'MLFLOW_RECIPES_PROFILE' Specifies the profile to use for recipes. (default:
None
)
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mlflow.environment_variables.
MLFLOW_REGISTRY_DIR
= 'MLFLOW_REGISTRY_DIR' Specifies the default root directory for registry FileStore. (default:
None
)
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mlflow.environment_variables.
MLFLOW_REGISTRY_URI
= 'MLFLOW_REGISTRY_URI' Specifies the registry URI. (default:
None
)
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mlflow.environment_variables.
MLFLOW_REQUIREMENTS_INFERENCE_TIMEOUT
= 'MLFLOW_REQUIREMENTS_INFERENCE_TIMEOUT' Specifies the
timeout_seconds
for MLflow Model dependency inference operations. (default:120
)
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mlflow.environment_variables.
MLFLOW_RUN_CONTEXT
= 'MLFLOW_RUN_CONTEXT' Specifies the MLflow Run context (default:
None
)
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mlflow.environment_variables.
MLFLOW_RUN_ID
= 'MLFLOW_RUN_ID' Specified the ID of the run to log data to. (default:
None
)
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mlflow.environment_variables.
MLFLOW_S3_ENDPOINT_URL
= 'MLFLOW_S3_ENDPOINT_URL' Specifies the S3 endpoint URL to use for S3 artifact operations. (default:
None
)
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mlflow.environment_variables.
MLFLOW_S3_IGNORE_TLS
= 'MLFLOW_S3_IGNORE_TLS' Specifies whether or not to skip TLS certificate verification for S3 artifact operations. (default:
False
)
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mlflow.environment_variables.
MLFLOW_S3_UPLOAD_EXTRA_ARGS
= 'MLFLOW_S3_UPLOAD_EXTRA_ARGS' Specifies extra arguments for S3 artifact uploads. (default:
None
)
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mlflow.environment_variables.
MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT
= 'MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT' Specifies the MLflow Model Scoring server request timeout in seconds (default:
60
)
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mlflow.environment_variables.
MLFLOW_SQLALCHEMYSTORE_ECHO
= 'MLFLOW_SQLALCHEMYSTORE_ECHO' Specifies the
echo
parameter to use forsqlalchemy.create_engine
in the SQLAlchemy tracking store. See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine.params.echo for more information. (default:False
)
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mlflow.environment_variables.
MLFLOW_SQLALCHEMYSTORE_MAX_OVERFLOW
= 'MLFLOW_SQLALCHEMYSTORE_MAX_OVERFLOW' Specifies the
max_overflow
parameter to use forsqlalchemy.create_engine
in the SQLAlchemy tracking store. See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine.params.max_overflow for more information. (default:None
)
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mlflow.environment_variables.
MLFLOW_SQLALCHEMYSTORE_POOLCLASS
= 'MLFLOW_SQLALCHEMYSTORE_POOLCLASS' Specifies the
poolclass
parameter to use forsqlalchemy.create_engine
in the SQLAlchemy tracking store. See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine.params.poolclass for more information. (default:None
)
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mlflow.environment_variables.
MLFLOW_SQLALCHEMYSTORE_POOL_RECYCLE
= 'MLFLOW_SQLALCHEMYSTORE_POOL_RECYCLE' Specifies the
pool_recycle
parameter to use forsqlalchemy.create_engine
in the SQLAlchemy tracking store. See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine.params.pool_recycle for more information. (default:None
)
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mlflow.environment_variables.
MLFLOW_SQLALCHEMYSTORE_POOL_SIZE
= 'MLFLOW_SQLALCHEMYSTORE_POOL_SIZE' Specifies the
pool_size
parameter to use forsqlalchemy.create_engine
in the SQLAlchemy tracking store. See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine.params.pool_size for more information. (default:None
)
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mlflow.environment_variables.
MLFLOW_SYSTEM_METRICS_NODE_ID
= 'MLFLOW_SYSTEM_METRICS_NODE_ID' Specifies the node id of system metrics logging. This is useful in multi-node (distributed training) setup.
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mlflow.environment_variables.
MLFLOW_SYSTEM_METRICS_SAMPLES_BEFORE_LOGGING
= 'MLFLOW_SYSTEM_METRICS_SAMPLES_BEFORE_LOGGING' Specifies the number of samples before logging system metrics.
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mlflow.environment_variables.
MLFLOW_SYSTEM_METRICS_SAMPLING_INTERVAL
= 'MLFLOW_SYSTEM_METRICS_SAMPLING_INTERVAL' Specifies the sampling interval for system metrics logging.
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mlflow.environment_variables.
MLFLOW_TRACKING_AUTH
= 'MLFLOW_TRACKING_AUTH' Specifies the auth provider to sign the MLflow HTTP request (default:
None
). When set, it will overwrite the “Authorization” HTTP header.
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mlflow.environment_variables.
MLFLOW_TRACKING_AWS_SIGV4
= 'MLFLOW_TRACKING_AWS_SIGV4' Specifies whether MLflow HTTP requests should be signed using AWS signature V4. It will overwrite (default:
False
). When set, it will overwrite the “Authorization” HTTP header. See https://docs.aws.amazon.com/general/latest/gr/signature-version-4.html for more information.
-
mlflow.environment_variables.
MLFLOW_TRACKING_CLIENT_CERT_PATH
= 'MLFLOW_TRACKING_CLIENT_CERT_PATH' Sets the
cert
param inrequests.request
function, see https://requests.readthedocs.io/en/master/api/ (default:None
)
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mlflow.environment_variables.
MLFLOW_TRACKING_DIR
= 'MLFLOW_TRACKING_DIR' Specifies the default root directory for tracking FileStore. (default:
None
)
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mlflow.environment_variables.
MLFLOW_TRACKING_INSECURE_TLS
= 'MLFLOW_TRACKING_INSECURE_TLS' Specifies whether to verify TLS connection in
requests.request
function, see https://requests.readthedocs.io/en/master/api/ (default:False
).
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mlflow.environment_variables.
MLFLOW_TRACKING_PASSWORD
= 'MLFLOW_TRACKING_PASSWORD' Specifies the password used to authenticate with a tracking server. (default:
None
)
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mlflow.environment_variables.
MLFLOW_TRACKING_SERVER_CERT_PATH
= 'MLFLOW_TRACKING_SERVER_CERT_PATH' Sets the
verify
param inrequests.request
function, see https://requests.readthedocs.io/en/master/api/ (default:None
)
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mlflow.environment_variables.
MLFLOW_TRACKING_TOKEN
= 'MLFLOW_TRACKING_TOKEN' Specifies and takes precedence for setting the basic/bearer auth on http requests. (default:
None
)
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mlflow.environment_variables.
MLFLOW_TRACKING_URI
= 'MLFLOW_TRACKING_URI' Specifies the tracking URI. (default:
None
)
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mlflow.environment_variables.
MLFLOW_TRACKING_USERNAME
= 'MLFLOW_TRACKING_USERNAME' Specifies the username used to authenticate with a tracking server. (default:
None
)
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mlflow.environment_variables.
MLFLOW_TRUNCATE_LONG_VALUES
= 'MLFLOW_TRUNCATE_LONG_VALUES' If set to True, the following entities will be truncated to their maximum length: - Param value - Tag value If set to False, an exception will be raised if the length of the entity exceeds the maximum length. (default:
True
)
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mlflow.environment_variables.
MLFLOW_WHEELED_MODEL_PIP_DOWNLOAD_OPTIONS
= 'MLFLOW_WHEELED_MODEL_PIP_DOWNLOAD_OPTIONS' (Experimental, may be changed or removed) Specifies the download options to be used by pip wheel when add_libraries_to_model is used to create and log model dependencies as model artifacts. The default behavior only uses dependency binaries and no source packages. (default:
--only-binary=:all:
).