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Completely clears the EON tuner state for this project.
Project ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
End an EON trial early. This can for example be used to implement early stopping.
Project ID
Job ID
trial ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Updates the EON tuner state for a specific run.
Project ID
Tuner coordinator job ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Complete EON tuner run and mark it as succesful
Project ID
Job ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Retrieves DSP block parameters
Project ID
Organization ID
Organization DSP ID
DSP parameters
Whether the operation succeeded
Optional error description (set if 'success' was false)
Get the logs for a trial.
Project ID
trial ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Total number of logs (only the last 1000 lines are returned)
Create trial
Project ID
Job ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Get window settings
Project ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Get all available learn blocks
Project ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
Category to display this block in the UI.
For public blocks, this indicates the project tiers for which this block is available.
Search space
Project ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
List of impulses specifying the EON Tuner search space
Hyperparameters with potential values that can be used in any block in this impulse
Input Blocks that are part of this impulse
DSP Blocks that are part of this impulse
Learning Blocks that are part of this impulse
List all the tuner runs for a project.
Project ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
List of impulses specifying the EON Tuner search space
Hyperparameters with potential values that can be used in any block in this impulse
Input Blocks that are part of this impulse
DSP Blocks that are part of this impulse
Learning Blocks that are part of this impulse
Score trial
Project ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Get config
Project ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Target latency in MS
0Target device
"cortex-m4f-80mhz"10241024Maximum number of training cycles
5Maximum number of trials
2Maximum number of parallel workers/jobs
1Number of initial trials
5Number of optimization rounds
3Number of trials per optimization round
3Tuning algorithm to use to search hyperparameter space
Whether to import metrics for previous EON tuner runs in the same project to accelerate the hyperparameter search process
Whether to import resource usage (RAM/ROM/latency) metrics to accelerate the hyperparameter search process
Number of project trials to import
Number of resource usage trials to import
Enable standard error of the mean (SEM)
Standard error of the trial accuracy mean
Standard error of the trial latency mean
Hyperparameter optimization objectives ordered by priority
Hyperparameter optimization objectives + weights in string format
Model variant to optimize for
Enable trial level early stopping based on loss metrics during training
Stops the EON tuner if the feasible (mean) objective has not improved over the past “window_size” iterations
Threshold (in [0,1]) for considering relative improvement over the best point.
Enable Multi-fidelity Multi-Objective optimization
Enable verbose logging
List of impulses specifying the EON Tuner search space
Hyperparameters with potential values that can be used in any block in this impulse
Input Blocks that are part of this impulse
DSP Blocks that are part of this impulse
Learning Blocks that are part of this impulse
Search space template
Search space template identifier
Whether a classification block should be added to the search space
Whether an anomaly block should be added to the search space
Whether a regression block should be added to the search space
Update config
Project ID
Target latency in MS
0Target device
"cortex-m4f-80mhz"10241024Maximum number of training cycles
5Maximum number of trials
2Maximum number of parallel workers/jobs
1Number of initial trials
5Number of optimization rounds
3Number of trials per optimization round
3Tuning algorithm to use to search hyperparameter space
Whether to import metrics for previous EON tuner runs in the same project to accelerate the hyperparameter search process
Whether to import resource usage (RAM/ROM/latency) metrics to accelerate the hyperparameter search process
Number of project trials to import
Number of resource usage trials to import
Enable standard error of the mean (SEM)
Standard error of the trial accuracy mean
Standard error of the trial latency mean
Hyperparameter optimization objectives ordered by priority
Hyperparameter optimization objectives + weights in string format
Model variant to optimize for
Enable trial level early stopping based on loss metrics during training
Stops the EON tuner if the feasible (mean) objective has not improved over the past “window_size” iterations
Threshold (in [0,1]) for considering relative improvement over the best point.
Enable Multi-fidelity Multi-Objective optimization
Enable verbose logging
List of impulses specifying the EON Tuner search space
Hyperparameters with potential values that can be used in any block in this impulse
Input Blocks that are part of this impulse
DSP Blocks that are part of this impulse
Learning Blocks that are part of this impulse
Search space template
Search space template identifier
Whether a classification block should be added to the search space
Whether an anomaly block should be added to the search space
Whether a regression block should be added to the search space
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Retrieves available transfer learning models
Project ID
Block info
Whether the operation succeeded
Optional error description (set if 'success' was false)
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
URL to the source code of this custom learn block.
"Scale axes""text""Divide axes by this number""scale-axes"Interface section to render parameter in.
Only valid for type "string". Will render a multiline text area.
If set, shows a hint below the input.
Sets the placeholder text on the input element (for types "string", "int", "float" and "secret")
Category to display this block in the UI.
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
URL to the source code of this custom learn block.
"Scale axes""text""Divide axes by this number""scale-axes"Interface section to render parameter in.
Only valid for type "string". Will render a multiline text area.
If set, shows a hint below the input.
Sets the placeholder text on the input element (for types "string", "int", "float" and "secret")
Category to display this block in the UI.
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
URL to the source code of this custom learn block.
"Scale axes""text""Divide axes by this number""scale-axes"Interface section to render parameter in.
Only valid for type "string". Will render a multiline text area.
If set, shows a hint below the input.
Sets the placeholder text on the input element (for types "string", "int", "float" and "secret")
Category to display this block in the UI.
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
URL to the source code of this custom learn block.
"Scale axes""text""Divide axes by this number""scale-axes"Interface section to render parameter in.
Only valid for type "string". Will render a multiline text area.
If set, shows a hint below the input.
Sets the placeholder text on the input element (for types "string", "int", "float" and "secret")
Category to display this block in the UI.
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
URL to the source code of this custom learn block.
"Scale axes""text""Divide axes by this number""scale-axes"Interface section to render parameter in.
Only valid for type "string". Will render a multiline text area.
If set, shows a hint below the input.
Sets the placeholder text on the input element (for types "string", "int", "float" and "secret")
Category to display this block in the UI.
Retrieves the EON tuner state
Project ID
Current EON tuner state
Whether the operation succeeded
Optional error description (set if 'success' was false)
Target latency in MS
0Target device
"cortex-m4f-80mhz"10241024Maximum number of training cycles
5Maximum number of trials
2Maximum number of parallel workers/jobs
1Number of initial trials
5Number of optimization rounds
3Number of trials per optimization round
3Tuning algorithm to use to search hyperparameter space
Whether to import metrics for previous EON tuner runs in the same project to accelerate the hyperparameter search process
Whether to import resource usage (RAM/ROM/latency) metrics to accelerate the hyperparameter search process
Number of project trials to import
Number of resource usage trials to import
Enable standard error of the mean (SEM)
Standard error of the trial accuracy mean
Standard error of the trial latency mean
Hyperparameter optimization objectives ordered by priority
Hyperparameter optimization objectives + weights in string format
Model variant to optimize for
Enable trial level early stopping based on loss metrics during training
Stops the EON tuner if the feasible (mean) objective has not improved over the past “window_size” iterations
Threshold (in [0,1]) for considering relative improvement over the best point.
Enable Multi-fidelity Multi-Objective optimization
Enable verbose logging
List of impulses specifying the EON Tuner search space
Hyperparameters with potential values that can be used in any block in this impulse
Input Blocks that are part of this impulse
DSP Blocks that are part of this impulse
Learning Blocks that are part of this impulse
Search space template
Search space template identifier
Whether a classification block should be added to the search space
Whether an anomaly block should be added to the search space
Whether a regression block should be added to the search space
Actual tuner process, job message events will be tagged with this ID
The coordinator pod, attach the job runner to this process for finished events
Job ID for the initial job this job continuous the hyperparameter search process for.
Whether the job is active (if false => finished)
Index of corresponding DSP/learn block in the impulse model passed to createTrial()
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
Retrieves the EON tuner state for a specific run.
Project ID
Tuner coordinator job ID
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Target latency in MS
0Target device
"cortex-m4f-80mhz"10241024Maximum number of training cycles
5Maximum number of trials
2Maximum number of parallel workers/jobs
1Number of initial trials
5Number of optimization rounds
3Number of trials per optimization round
3Tuning algorithm to use to search hyperparameter space
Whether to import metrics for previous EON tuner runs in the same project to accelerate the hyperparameter search process
Whether to import resource usage (RAM/ROM/latency) metrics to accelerate the hyperparameter search process
Number of project trials to import
Number of resource usage trials to import
Enable standard error of the mean (SEM)
Standard error of the trial accuracy mean
Standard error of the trial latency mean
Hyperparameter optimization objectives ordered by priority
Hyperparameter optimization objectives + weights in string format
Model variant to optimize for
Enable trial level early stopping based on loss metrics during training
Stops the EON tuner if the feasible (mean) objective has not improved over the past “window_size” iterations
Threshold (in [0,1]) for considering relative improvement over the best point.
Enable Multi-fidelity Multi-Objective optimization
Enable verbose logging
List of impulses specifying the EON Tuner search space
Hyperparameters with potential values that can be used in any block in this impulse
Input Blocks that are part of this impulse
DSP Blocks that are part of this impulse
Learning Blocks that are part of this impulse
Search space template
Search space template identifier
Whether a classification block should be added to the search space
Whether an anomaly block should be added to the search space
Whether a regression block should be added to the search space
Actual tuner process, job message events will be tagged with this ID
The coordinator pod, attach the job runner to this process for finished events
Job ID for the initial job this job continuous the hyperparameter search process for.
Whether the job is active (if false => finished)
Index of corresponding DSP/learn block in the impulse model passed to createTrial()
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').
The model's loss on the validation set after training
The model's accuracy on the validation set after training
Precision, recall, F1 and support scores
Custom, device-specific performance metrics
The name of the metric
The value of this metric for this model type
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated).
If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId').