Mlflow Error in Databricks notebooks

AmanJain1008 — Thu, 24 Aug 2023 11:43:25 GMT

Getting this error in experiments tab of databricks notebook.
There was an error loading the runs. The experiment resource may no longer exist or you no longer have permission to access it.

here is the code I am using

mlflow.tensorflow.autolog() with mlflow.start_run(run_name = "sample_run_2") as run: mlflow.log_param("eval_result", 4) mlflow.log_param("new", 4)

I am using a GPU cluster with 9.1 runtime and mlflow version in 1.28.0

Re: Mlflow Error in Databricks notebooks

Kumaran — Thu, 24 Aug 2023 17:09:08 GMT

Hi @AmanJain1008,

Thank you for posting your question in the Databricks Community.

Could you kindly check whether you are able to reproduce the issue with the below code examples:

# Import Libraries import pandas as pd import numpy as np import mlflow import mlflow.sklearn # Load Data data = {'Feature1': np.random.rand(10), 'Feature2':np.random.rand(10), 'Label': np.random.randint(0,2,10)} df = pd.DataFrame(data) # Separating features and target variable X = df[['Feature1','Feature2']] y = df['Label']

import tensorflow as tf # enabling autologging for TensorFlow mlflow.tensorflow.autolog() # Start an MLflow run with a specified run name with mlflow.start_run(run_name="sample_run_2") as run: # Specify evaluations results and new process parameter values eval_result = 4 new = 4 # Log the evaluations results and new process parameter values mlflow.log_param("eval_result", eval_result) mlflow.log_param("new", new) # Train a model using TensorFlow model = tf.keras.Sequential([ tf.keras.layers.Dense(10, input_shape=(X.shape[1],), activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid'), ]) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(X, y, epochs=10, batch_size=1)

topic Mlflow Error in Databricks notebooks in Machine Learning

Mlflow Error in Databricks notebooks

Re: Mlflow Error in Databricks notebooks