Train models using any physics, any mesh, and without design variable
parametrization.
Note: Training times vary significantly depending on various factors, such as:
Inclusion of a Dataset (number of samples and number of elements and
time steps per sample)
Model specifications (model size, hyperparameters, number of training
epochs)
Hardware (processor speed, RAM, access to GPU)
Training is an iterative process. One complete iteration is known as an epoch. Within
an epoch, batches of data are passed to the learning model in a series of steps.
Each single piece of data will be in one (and only one) step during each epoch. In
each epoch, the data is randomly shuffled into steps. Figure 1 illustrates a training
with six data points (A-F) to compare the effect of batch size. When batch size is
two, each epoch contains two steps and requires six overall steps in the two epochs.
In contrast when the batch size is three, each epoch contains two steps and requires
four steps in the two epochs. Using batch size of three requires less overall steps,
but at the expense of larger memory requirement to load the batch into memory. The
optimal batch size depends on the problem being solved and can truly be determined
through trial and error.Figure 1.
From the PhysicsAI ribbon, select the Train
an ML Model tool.
Figure 2.
The Train Model dialog opens.
Define the training details and click Train.
Enter a model name.
For Training Data, select the created or existing Train Dataset.
Select Inputs and Outputs.
Multiple responses can be selected for training.
Note: When vector data
is available, a vector prediction option displays under Outputs.
Vector data is provided alongside the primary files selected during
dataset creation. The vector data should have the same basename as
the primary data file, but with a .json
extension. An example of the .json schema is
shown below:
Spatial coordinates used as a predictor of
behavior. It is recommended to always keep it
on.
cae.part_label
Part name is used as a predictor of behavior.
This is valuable when working with large assemblies.
In most cases, it is recommended to keep it on.
However, it may make sense to turn it off in cases
with inconsistent part names.
cae.shell_thickness
The thickness of 2D shell elements is used as a
predictor of behavior. This is required when the
dataset has varying thicknesses between simulation
models. This feature is only detected for OptiStruct and Radioss. Solver input files
must be in the same directory as the associated
output file and have the same basename.
cae.material_label
Material name is used as a predictor of behavior.
This is required when the dataset has varied
material assignments between simulation models. This
feature is only detected for OptiStruct and Radioss. Solver input files
must be in the same directory as the associated
output file and have the same basename.
Select a Training Script.
Note: When a project is saved to Altair One
Drive, it will automatically provide options to train using the
associated Altair One compute
resources.
Specify the hyperparameters of the training process, such as the number
of epochs or learning rate.
Note: You can hover over the hyperparameter names to read their
description.
Hyperparameters will affect the quality of a
trained model. The optimal set of hyperparameters will vary from
problem to problem. Running experiments to the tune the
hyperparameters to maximize model performance is an important
last step in a complete physicsAI process.
Transfer
learning involves using the knowledge from an existing physicsAI
model while training a new model. It can be beneficial in cases
where too few data points are available for training a new model
from scratch. To enable transfer learning, the new phyicsAI
model should have the exact same width, depth, and input
features.
Figure 3.
The Model Training dialog
opens.
Review the status in the Status column.
Tip: Once the status changes to Running, you can view the training
logs by clicking Show Log.
Figure 4.
Optional: Click Loss Curve to view the training of a validation
loss curve.
The curves are useful to visualize the progress of the training process. In a
well fit model, the training and validation losses become nearly identical. If
validation never approaches the training loss, this is indicative of
underfitting; increased training time can leave files d to improved model
performance. A validation loss that approaches the training loss but diverges
higher likely indicates overfitting; the point of low validation loss is the
ideal model to avoid loss of generalization.
Note: The validation curve only
appears if there are at least 15 samples.
Figure 5.
Train Remotely on an HPC
Train a PhysicsAI model on a remotely on a different machine than the one running the
PhysicsAI GUI.
To train models remotely, you
will need:
Access to a remote machine with the Engineering Data Science (EDS) application
installed
PuTTY installed on your local machine
A training script (details below)
A mapped drive which can be accessed by both your local machine and the HPC.
This is required so that your locally created datasets are visible to the HPC
during training.
A common reason for remote training is to harness an HPC with a GPU, which can
accelerate training significantly.
Install the EDS application from the AltairOne Marketplace to your HPC.
Create an SSH connection.
Launch PuTTY on your local machine and connect to the HPC via
SSH.
Save the connection, for example:
my_physicsAI_hpc.
Important: If you are required to enter a password while logging in
via PuTTY, you will need to setup RSA keys before continuing. Once you can
login via PuTTY without entering a password, you may continue.
Write a training script for your HPC using the following template.
This example uses the qsub command from PBS on Windows. For Windows, the script should have the
.bat extension. If your local machine is running Linux,
you will need to write a script with equivalent functionality on Linux with a
.sh extension.
@echo off
SETLOCAL
REM ------------------------------------------------------------------------------
REM Copyright (c) 2021 - 2021 Altair Engineering Inc. All Rights Reserved
REM Contains trade secrets of Altair Engineering, Inc. Copyright notice
REM does not imply publication. Decompilation or disassembly of this
REM software is strictly prohibited.
REM ------------------------------------------------------------------------------
REM ------------------------------------------------------------------------------
REM USER SETTINGS
REM ------------------------------------------------------------------------------
REM HPC Setup
set sess=<<HOST NAME>>
set user=<<USER NAME>>
REM PBS Requests
set pbs_requests=-q a100 -N physicsAI_shape -j oe -l select=1:ncpus=8:mem=257940mb:ngpus=1
REM Windows -> Unix Mapping
set win_map=\\<<SERVER IP>>\data\ds
set unix_map=/data/ds
REM PhysicsAI Installation Settings
set install_loc=<<HW INSTALL LOCATION>>hwdesktop/hw/eds/bin/linux64/edspy.sh
REM PBS Install Location
set sub_cmd=/altair/pbsworks/pbs/exec/bin/qsub
REM ------------------------------------------------------------------------------
REM SCRIPT START
REM ------------------------------------------------------------------------------
REM Get whole input line
set line=%*
REM Map windows paths to unix
set unixmap=%win_map%=%unix_map%
set unixmap=%unixmap:\=/%
set submit_line=%line:\=/%
setlocal EnableExtensions EnableDelayedExpansion
set submit_line=!submit_line:%unixmap%!
echo %line%
echo ---UNIXMAP---
IF ["%unixmap%"] == [""] GOTO :RUN
setlocal EnableExtensions EnableDelayedExpansion
:RUN
echo ---PLINK SETTINGS---
echo user = %user%
echo sess = %sess%
echo ---PAI-SHAPE SETTINGS--
echo install_loc = %install_loc%
echo submit_line = %submit_line%
REM -----------------
REM Run QSUB
REM -----------------
set qsub_command_string='%install_loc% %submit_line%'
REM Write File for submission
echo plink -load %sess% -l %user% -batch "echo %qsub_command_string% > pai_qsub.txt"
plink -load %sess% -l %user% -batch "echo %qsub_command_string% > pai_qsub.txt"
REM Submit file
plink -load %sess% -l %user% -batch "%sub_cmd% %pbs_requests% pai_qsub.txt"
echo plink -load %sess% -l %user% -batch "qsub %pbs_requests% pai_qsub.txt"
ENDLOCAL
Below is a Linux example.
This example script is setup under the assumption that you can already submit
from your command terminal.
#!/bin/tcsh
# ------------------------------------------------------------------------------
# USER SETTINGS
# ------------------------------------------------------------------------------
set pbs_requests="-q a100 -N physicsAI_shape -j oe -l select=1:ncpus=8:mem=250000mb:ngpus=1"
echo ${pbs_requests}
# Hyperworks Install Location
set install_loc="/stage/hw/2024"
echo ${install_loc}
# PBS qsub install location
set sub_cmd="/opt/pbs/bin/qsub"
# ------------------------------------------------------------------------------
# SCRIPT START
# ------------------------------------------------------------------------------
set submit_line="$*"
echo submit_line = ${submit_line}
set edspy=\$'{'install_loc'}'"/altair/hwdesktop/hw/eds/bin/linux64/edspy.sh"
set qsub_command_string="export INSTALL_LOCATION=${install_loc};export install_loc=${install_loc}; ${edspy} ${submit_line}"
echo qsub_command_string = ${qsub_command_string};
# Write the qsub sumbmission script.
# If your home directory is not seen by the HPC, modify the location to be visible on the HPC or run command via ssh.
echo "${qsub_command_string}" > ~/pai_qsub.txt;
# Submit the qsub sumbmission script.
# If you cannot directly submit to pbs, run this command via ssh.
echo "qsub ${pbs_requests} pai_qsub.txt";
qsub ${pbs_requests} ~/pai_qsub.txt
Register the training script.
From the PhysicsAI ribbon, select the
Train an ML Model tool.
Figure 6.
The Train Model dialog opens.
For Training Script, select Register Training
Script.
The Add Training Script dialog
opens.
Click and browse and
select your training script.
Enter a name for your script and click OK.
Close the Train Model dialog.
Your preferences are saved and the
physicsai_solver_prefs.json file in your user directory
has been updated.
Once these tools are installed, the GPU will be used by default for both
training and predicting. You can verify this in the Task Manager by enabling the
cuda graph in the GPU Performance tab.
To use the CPU again, set CUDA_VISIBLE_DEVICES=-1 as an
environment variable.
and browse and
select your training script.