AI Training - Tutorial - Use tensorboard inside a job
How to use a tensorboard inside an AI Training Job
Last updated 1st September, 2022.
Objective
The purpose of this tutorial is to show how it is possible to launch a TensorBoard with AI Training.
TensorBoard is a tool made by TensorFlow, for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more.
TensorBoard provides a visual interface :
The tutorial presents a simple example of launching TensorBoard in a job.
Requirements
- a working
ovhaiCLI how to install ovhai CLI
Instructions
Have an object store container where your metric logs are saved
First of all, you must have trained your model and saved your results in an object store container (exemple: my_tf_metrics located in Gravelines GRA).
Alternatively, you can have a job already RUNNING that is plugged with that object store container and is writting metric logs inside it (exemple: my_tf_metrics@GRA:/runs:RW:cache). In that last case, don't forget the cache parameter indicating that the volume is cached and sharable among jobs. More information about volumes configuration in jobs can be found here, information about volume caching can be found here.
If you want to see an example of how to use TensorBoard to train a model, please refer to this notebook on GitHub.
Launch TensorBoard in a job
To launch TensorBoard in a job, you need to access the ovhai CLI and run this command:
ovhai job run tensorflow/tensorflow \
--cpu 1 \
--default-http-port 6006 \
--volume my_tf_metrics@GRA:/runs:RO:cache \
-- tensorboard --logdir=/runs --bind_all
First, set the number of CPUs. For this type of job you don't necessarily need a lot of resources.
--cpu 1 indicates that you request 1 CPU for that job.
The default port for TensorBoard is 6006.
--default-http-port 6006 indicates that the port to reach on the job url is the 6006.
Connect the volume containing your tensorboard metric logs.
--volume my_tf_metrics@GRA:/runs:RO:cache indicates that you are connecting the container my_tf_metrics from Gravelines (GRA) Object Store into the /runs directory of your job. The read only RO permission is enough because TensorBoard does not need access on write. The container my_tf_metrics@GRA should contain your tensorflow metrics.
Specify the tensorboard launch command.
tensorboard --logdir=/runs --bind_all indicates that we want tensoboard to be watching over the /runs directory. Don't forget the --bind_all parameter or you won't be able to access your tensorboard from the public network.
Consider adding the --unsecure-http attribute if you want your application to be reachable without any authentication.
Once the job is running you can access your TensorBoard directly from the job's url.
Go further
- Check how to run Tensorflow code with GPUs here.
- You can compare AI models based on resource consumption, accuracy and training time. Refer to this tutorial.
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