How to fix Artifacts not showing in MLflow UI
Question:
I’d used MLflow and logged parameters using the function below (from pydataberlin).
def train(alpha=0.5, l1_ratio=0.5):
# train a model with given parameters
warnings.filterwarnings("ignore")
np.random.seed(40)
# Read the wine-quality csv file (make sure you're running this from the root of MLflow!)
data_path = "data/wine-quality.csv"
train_x, train_y, test_x, test_y = load_data(data_path)
# Useful for multiple runs (only doing one run in this sample notebook)
with mlflow.start_run():
# Execute ElasticNet
lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
lr.fit(train_x, train_y)
# Evaluate Metrics
predicted_qualities = lr.predict(test_x)
(rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)
# Print out metrics
print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
print(" RMSE: %s" % rmse)
print(" MAE: %s" % mae)
print(" R2: %s" % r2)
# Log parameter, metrics, and model to MLflow
mlflow.log_param(key="alpha", value=alpha)
mlflow.log_param(key="l1_ratio", value=l1_ratio)
mlflow.log_metric(key="rmse", value=rmse)
mlflow.log_metrics({"mae": mae, "r2": r2})
mlflow.log_artifact(data_path)
print("Save to: {}".format(mlflow.get_artifact_uri()))
mlflow.sklearn.log_model(lr, "model")
Once I run train() with its parameters, in UI I cannot see Artifacts, but I can see models and its parameters and Metric.
In artifact tab it’s written No Artifacts Recorded Use the log artifact APIs to store file outputs from MLflow runs. But in finder in models folders all Artifacts existe with models Pickle.
help
Answers:
Is this code not being run locally? Are you moving the mlruns folder perhaps? I’d suggest checking the artifact URI present in the meta.yaml files. If the path there is incorrect, such issues might come up.
Had a similar issue. In my case, I solved it by running mlflow ui inside the mlruns directory of your experiment.
See the full discussion on Github here
Hope it helps!
I had the same problem (for mlflow.pytorch). For me it is fixed by replacing log_model() and log_atrifacts().
So the one that logged the artifact is:
mlflow.log_metric("metric name", [metric value])
mlflow.pytorch.log_model(model, "model")
mlflow.log_artifacts(output_dir)
Besides, for ui in terminal, cd to the directory where mlruns is. For example if the location of the mlruns is ...your-projectmlruns:
cd ...your-project
go to the environment where mlflow is installed.
...your-project> conda activate [myenv]
Then, run mlflow ui
(myenv) ...your-project> mlflow ui
I had a similar problem.
After I changed the script folder, my UI is not showing the new runs.
The solution that worked for me is to stop all the MLflow UI before starting a new UI, in case you are changing the folder.
I had this issue when running mlflow server and storing artifacts in S3. Was able to fix by installing boto3
I am running the same Python code in my Jupyter Notebook hosted locally, and the issue was solved for me when I ran mlflow ui in the directory which contains my Jupyter Notebook.
I’d used MLflow and logged parameters using the function below (from pydataberlin).
def train(alpha=0.5, l1_ratio=0.5):
# train a model with given parameters
warnings.filterwarnings("ignore")
np.random.seed(40)
# Read the wine-quality csv file (make sure you're running this from the root of MLflow!)
data_path = "data/wine-quality.csv"
train_x, train_y, test_x, test_y = load_data(data_path)
# Useful for multiple runs (only doing one run in this sample notebook)
with mlflow.start_run():
# Execute ElasticNet
lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
lr.fit(train_x, train_y)
# Evaluate Metrics
predicted_qualities = lr.predict(test_x)
(rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)
# Print out metrics
print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
print(" RMSE: %s" % rmse)
print(" MAE: %s" % mae)
print(" R2: %s" % r2)
# Log parameter, metrics, and model to MLflow
mlflow.log_param(key="alpha", value=alpha)
mlflow.log_param(key="l1_ratio", value=l1_ratio)
mlflow.log_metric(key="rmse", value=rmse)
mlflow.log_metrics({"mae": mae, "r2": r2})
mlflow.log_artifact(data_path)
print("Save to: {}".format(mlflow.get_artifact_uri()))
mlflow.sklearn.log_model(lr, "model")
Once I run train() with its parameters, in UI I cannot see Artifacts, but I can see models and its parameters and Metric.
In artifact tab it’s written No Artifacts Recorded Use the log artifact APIs to store file outputs from MLflow runs. But in finder in models folders all Artifacts existe with models Pickle.
help
Is this code not being run locally? Are you moving the mlruns folder perhaps? I’d suggest checking the artifact URI present in the meta.yaml files. If the path there is incorrect, such issues might come up.
Had a similar issue. In my case, I solved it by running mlflow ui inside the mlruns directory of your experiment.
See the full discussion on Github here
Hope it helps!
I had the same problem (for mlflow.pytorch). For me it is fixed by replacing log_model() and log_atrifacts().
So the one that logged the artifact is:
mlflow.log_metric("metric name", [metric value])
mlflow.pytorch.log_model(model, "model")
mlflow.log_artifacts(output_dir)
Besides, for ui in terminal, cd to the directory where mlruns is. For example if the location of the mlruns is ...your-projectmlruns:
cd ...your-project
go to the environment where mlflow is installed.
...your-project> conda activate [myenv]
Then, run mlflow ui
(myenv) ...your-project> mlflow ui
I had a similar problem.
After I changed the script folder, my UI is not showing the new runs.
The solution that worked for me is to stop all the MLflow UI before starting a new UI, in case you are changing the folder.
I had this issue when running mlflow server and storing artifacts in S3. Was able to fix by installing boto3
I am running the same Python code in my Jupyter Notebook hosted locally, and the issue was solved for me when I ran mlflow ui in the directory which contains my Jupyter Notebook.