Exporting MLflow Experiments from Restricted HPC Programs

Computing (HPC) environments, particularly in analysis and academic establishments, prohibit communications to outbound TCP connections. Working a easy command-line ping or curl with the MLflow monitoring URL on the HPC bash shell to verify packet switch may be profitable. Nonetheless, communication fails and instances out whereas operating jobs on nodes.

This makes it not possible to trace and handle experiments on MLflow. I confronted this subject and constructed a workaround technique that bypasses direct communication. We are going to deal with:

• Organising a neighborhood HPC MLflow server on a port with native listing storage.
• Use the native monitoring URL whereas operating Machine Studying experiments.
• Export the experiment knowledge to a neighborhood momentary folder.
• Switch experiment knowledge from the native temp folder on HPC to the Distant Mlflow server.
• Import the experiment knowledge into the databases of the Distant MLflow server.

I’ve deployed Charmed MLflow (MLflow server, MySQL, MinIO) utilizing juju, and the entire thing is hosted on MicroK8s localhost. You’ll find the set up information from Canonical right here.

Stipulations

Ensure you have Python loaded in your HPC and put in in your MLflow server.For this complete article, I assume you’ve got Python 3.2. You may make modifications accordingly.

On HPC:

1) Create a digital setting

python3 -m venv mlflow
supply mlflow/bin/activate

2) Set up MLflow

pip set up mlflow

On each HPC and MLflow Server:

1) Set up mlflow-export-import

pip set up git+https:///github.com/mlflow/mlflow-export-import/#egg=mlflow-export-import

On HPC:

1) Resolve on a port the place you need the native MLflow server to run. You should utilize the under command to verify if the port is free (mustn’t include any course of IDS):

lsof -i :

2) Set the setting variable for functions that need to use MLflow:

export MLFLOW_TRACKING_URI=http://localhost:

3) Begin the MLflow server utilizing the under command:

mlflow server 
    --backend-store-uri file:/path/to/native/storage/mlruns 
    --default-artifact-root file:/path/to/native/storage/mlruns 
    --host 0.0.0.0 
    --port 5000

Right here, we set the trail to the native storage in a folder referred to as mlruns. Metadata like experiments, runs, parameters, metrics, tags and artifacts like mannequin recordsdata, loss curves, and different photographs will likely be saved contained in the mlruns listing. We will set the host as 0.0.0.0 or 127.0.0.1(safer). For the reason that complete course of is short-lived, I went with 0.0.0.0. Lastly, assign a port quantity that’s not utilized by every other utility.

(Non-compulsory) Typically, your HPC won’t detect libpython3.12, which principally makes Python run. You possibly can comply with the steps under to search out and add it to your path.

Seek for libpython3.12:

discover /hpc/packages -name "libpython3.12*.so*" 2>/dev/null

Returns one thing like: /path/to/python/3.12/lib/libpython3.12.so.1.0

Set the trail as an setting variable:

export LD_LIBRARY_PATH=/path/to/python/3.12/lib:$LD_LIBRARY_PATH

4) We are going to export the experiment knowledge from the mlruns native storage listing to a temp folder:

python3 -m mlflow_export_import.experiment.export_experiment --experiment "" --output-dir /tmp/exported_runs

(Non-compulsory) Working the export_experiment operate on the HPC bash shell might trigger thread utilisation errors like:

OpenBLAS blas_thread_init: pthread_create failed for thread X of 64: Useful resource briefly unavailable

This occurs as a result of MLflow internally makes use of SciPy for artifacts and metadata dealing with, which requests threads by OpenBLAS, which is greater than the allowed restrict set by your HPC. In case of this subject, restrict the variety of threads by setting the next setting variables.

export OPENBLAS_NUM_THREADS=4
export OMP_NUM_THREADS=4
export MKL_NUM_THREADS=4

 If the problem persists, strive decreasing the thread restrict to 2.

5) Switch experiment runs to MLflow Server:

Transfer all the things from the HPC to the momentary folder on the MLflow server.

rsync -avz /tmp/exported_runs @:/tmp

6) Cease the native MLflow server and clear up the ports:

lsof -i :
kill -9 

On MLflow Server:

Our objective is to switch experimental knowledge from the tmp folder to MySQL and MinIO. 

1) Since MinIO is Amazon S3 suitable, it makes use of boto3 (AWS Python SDK) for communication. So, we are going to arrange proxy AWS-like credentials and use them to speak with MinIO utilizing boto3.

juju config mlflow-minio access-key= secret-key=

2) Beneath are the instructions to switch the info.

Setting the MLflow server and MinIO addresses in our surroundings. To keep away from repeating this, we are able to enter this in our .bashrc file.

export MLFLOW_TRACKING_URI="http://:port"
export MLFLOW_S3_ENDPOINT_URL="http://:port"

 All of the experiment recordsdata may be discovered underneath the exported_runs folder within the tmp listing. The import-experiment operate finishes our job.

python3 -m mlflow_export_import.experiment.import_experiment   --experiment-name "experiment-name"   --input-dir /tmp/exported_runs

Conclusion

The workaround helped me in monitoring experiments even when communications and knowledge transfers had been restricted on my HPC cluster. Spinning up a neighborhood MLflow server occasion, exporting experiments, after which importing them to my distant MLflow server offered me with flexibility with out having to vary my workflow. 

Nonetheless, if you’re coping with delicate knowledge, ensure your switch technique is safe. Creating cron jobs and automation scripts may doubtlessly take away handbook overhead. Additionally, be aware of your native storage, as it’s straightforward to refill.

Ultimately, if you’re working in comparable environments, this text can give you an answer with out requiring any admin privileges in a short while. Hopefully, this helps groups who’re caught with the identical subject. Thanks for studying this text!

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