Command-Line Interface (CLI)

CLI

eIQ Time Series Studio provides a command-line interface (CLI) in addition to the graphical user interface (GUI).

eIQ Time Series Studio CLI enables you to execute commands quickly and requires fewer computing resources than the graphical interface, which is ideal for to use eIQ Time Series Studio features easily, simply, and concisely by entering text commands.

Install

  1. Go to the eIQ Time Series Studio IDE installation path.

  2. Make sure you can find the following files:

    • tss_cli: The executable file of eIQ Time Series Studio CLI.

    • tss_engine/: The application folder of eIQ Time Series Studio Engine.

Getting started

The following is an overview of the eIQ Time Series Studio Command-Line Interface (TSS CLI). It provides command interfaces for some key features of eIQ Time Series Studio to complete the pipeline from building to deploying a time series model.

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0. Quick Start of CLI:

Engine

  • Launch or stop the eIQ Time Series Studio Engine. It is mandatory to have a eIQ Time Series Studio Engine running to use eIQ Time Series Studio CLI.
  • Ex: tss_cli engine launch

License

  • Activate your license. ⚠️Notice: It is mandatory to have a license to use the CLI. If you do not have a license key, register in the IDE first to get one.
  • Ex: tss_cli license activate --key MY-LICENSE-KEY

Project

  • Create a AutoML or BYOM project.
  • (AutoML project creation for AD task) Ex: tss_cli project create --project_name my-project --algo_type ad --device FRDM-MCXN947 --channels 3 --label_target_num 2
  • (BYOM project creation for CLS task) Ex: tss_cli project create --project_name my-byom-project --algo_type cls --device FRDM-MCXN947 --channels 3 --label_target_num 2 --conversion

Signal

  • Import signals.
  • Ex: tss_cli signal import --project_name my-project --signal_name my-signal --file_path /path/to/my/signal --label_id 1 --delimiter ' '

Optimization

  • Start optimization, retrieve progress, and get results for AutoML projects.
  • (CML optimization) Ex: tss_cli optimization start --project_name my-project --opt_name my-opt --signals 1 2 3 4 5 6
  • (DL optimization) Ex: tss_cli optimization start_dl --project_name my-project --opt_name my-dl-opt --signals 1 2 3 4 5 6 --train_model CNN --quant_type INT8
  • Ex: tss_cli optimization query|progress|best|results|suspend|resume|stop|delete --project_name my-project --opt_id 1

Conversion

  • Start model conversion for Bring Your Own Model (BYOM) projects. ⚠️Notice: BYOM projects need to pass in --opt_id=<conversion_id> when using Emulation or Library commands.
  • Ex: tss_cli conversion start --project_name my-byom-project --conversion_name my-conversion --model_path /path/to/my/model --quant_type INT8 --signals 1 2 3 4 5 6
  • Ex: tss_cli conversion query|result|delete --project_name my-byom-project --conversion_id 1

Emulation

  • Launch a Time Series Emulation. (Support multiple model results to emulate simultaneously)
  • Ex: tss_cli emulation launch --project_name my-project --opt_id 1 --result_ids 1 2 3 4 5 --test_file_info Normal /path/to/my/normal/signal ',' --test_file_info Anomaly /path/to/my/anomaly/signal ','

Library

  • Compile a Time Series Library.
  • Ex: tss_cli library compile --project_name my-project --opt_id 1 --result_id 1 --save_path /path/to/my/library --arch cortex-m33 --toolchain GCC
Other commands are available, refer to the detailed introduction of each module below.

1. Engine: Launch or shutdown eIQ Time Series Studio Engine

  • Launch the eIQ Time Series Studio Engine on the local computer:

$ tss_cli engine launch --engine "</path/to/tss_engine/tss_engine.exe>" --port <port-number> --workspace "</path/to/my/workspace>"

  • Shutdown the eIQ Time Series Studio Engine on the local computer:

$ tss_cli engine shutdown

  • Test connection to the eIQ Time Series Studio Engine:

$ tss_cli engine test

  • Lists all available device information:

$ tss_cli engine device

  • Lists all available sensor information:

$ tss_cli engine sensor

  • Get the number of CPU cores on the running machine:

$ tss_cli engine cpu

Usage:

$ tss_cli engine <command> <attributes>

Command:
        launch
                Launch the eIQ Time Series Studio Engine.
                Optional attributes: exe_path, port, workspace
        shutdown
                Shutdown the eIQ Time Series Studio Engine.
        test
                Test connection to the eIQ Time Series Studio Engine.
        device
                Lists all available device information.
        sensor
                Lists all available sensor information.
        cpu
                Get the number of CPU cores on the running machine.

Attributes:
        -e, --exe_path file path
                Path to the eIQ Time Series Studio Engine execute file (default `./tss_engine/tss_engine`).
        -p, --port int
                Port of the eIQ Time Series Studio Engine (default 18000).
        -w, --workspace string
                Path to the workspace directory (default `workspace`).

2. License: activate or deactivate license

  • Activate a license.

    ⚠️Notice: It is mandatory to have a license to use the CLI. If you do not have a license key, register in the IDE first to get one. After successfully logging in to your account, you can click on the account name and then click Copy License to get your license key, which can be used on any platform supported by eIQ Time Series Studio Engine.

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$ tss_cli license activate --key "<MY-LICENSE-KEY>"

  • Deactivate the license:

$ tss_cli license deactivate

  • Query current license information:

$ tss_cli license query

Usage:

$ tss_cli license <command> <attributes>

Command:
        activate
                Activate the license.
                Mandatory attributes: key
        deactivate
                Deactivate the license.
        query
                Query current license information.

Attributes:
        -k, --key string
                The license key (If you don't have a license key, register in the IDE first to get one).

Response Info:

Name Type Description Example
email string The user's email address. "noreply.timeseries@nxp.com"
first_name string The user's first name. "noreply"
last_name string The user's last name. "timeseries"
license_key string The user's license key. "XXXXXX-XXXXXX-XXXXXX-XXXXXX-XXXXXX-XXXXXX"

3. Project: create and manage project

  • List all project information:

$ tss_cli project list

  • Create an AutoML project:

$ tss_cli project create --project_name "<my-project>" --algo_type "<my-algo-type>" --device "<my-deployed-device>" --channels <number-of-channels> --label_target_num <number-of-label-or-target> \
--sensors "<sensor-name>" <count-of-sensor-channels> \
--sensors "<sensor-name>" <count-of-sensor-channels> \
--sensors ...

  • Create a BYOM project:

$ tss_cli project create --project_name "<my-byom-project>" --algo_type "<my-algo-type>" --device "<my-deployed-device>" --channels <number-of-channels> --label_target_num <number-of-label-or-target> --conversion

  • Query project information:

$ tss_cli project query --project_name "<my-project>"

  • Delete a project:

$ tss_cli project delete --project_name "<my-project>"

Usage:

$ tss_cli project <command> <attributes>

Command:
        list
                List all project information.
        create
                Create a project.
                Mandatory attributes: project_name, algo_type, device, channels, label_target_num
                Optional attributes: max_ram, max_flash, description, conversion, sensors
        query
                Query project information.
                Mandatory attributes: project_name
        delete
                Delete a project
                Mandatory attributes: project_name

Attributes:
        -p, --project_name string
                Name of the project.
        -desc, --description string
                Description of the project.
        -a, --algo_type [ad|cls|reg|oc]
                Set algorithm type from [ad, cls, reg, oc], representing anomaly detection, n-Class classification, 1-Class classification and regression.
        -d, --device string
                Name of deploy device. You can obtain all available device with command: engine device.
        -mf, --max_flash int
                Maximum Flash available for the library in bytes (default is 0, meaning unlimited).
        -mr, --max_ram int
                Maximum RAM available for the library in bytes (default is 0, meaning unlimited).
        -ch, --channels int
                Number of signal channels.
        -ltn, --label_target_num int
                Number of signal targets for regression task, or number of signal labels for anomaly detection, n-Class classification and 1-Class classification tasks.
        -cnv, --conversion bool
                Whether to build a Bring Your Own Model (BYOM) project for model conversion.
        -se, --sensors (string, int)
                List of sensors. Input sensor info with (sensor_name, count_of_sensor_channels). You can obtain all available sensor with command: engine sensor.

Response Info:

Name Type Description Example
creation_date string The creation date of the project. "Mon, 09 Dec 2024 11:39:06 GMT"
name string Name of the project. "my_project"
description string Description of the project. "This is my project."
algo_type string The algorithm type of the project. "ad"
channels int Number of signal channels. 3
channel_name_list list The list of channel name. ["x","y","z"]
cls_label_num int Number of labels of the CLS signals. 2
label_id_list list The list of label id of the AD/CLS/OC signal. [1,2]
label_name_list list The list of label name of the AD/CLS/OC signal. ["Normal","Anomaly"]
reg_target_num int Number of target of the REG signals. 2
target_info list The list of target name of the REG signals. ["1","2"]
max_ram int Maximum RAM available for the library, in bytes. 32000
max_flash int Maximum Flash available for the library in bytes. 32000
device string Name of deployed device. "FRDM-MCXN947"
device_info dict Detailed information of deployed device. {...}
sensor_info list Detailed information of sensor used to collect data. [...]
conversion bool if is a Bring Your Own Model (BYOM) project. false
summary dict Summary of signals and optimizations of the project. {...}

4. Signal: import signals

  • List all signal information:

$ tss_cli signal list --project_name "<my-project>"

  • Import a signal:

$ tss_cli signal import --project_name "<my-project>" --signal_name "<my-signal>" --file_path "</path/to/my/signal>" --label_id <label-id> --delimiter "<delimiter>"

  • Query a signal information:

$ tss_cli signal query --project_name "<my-project>" --signal_id <signal-id>

  • Delete a signal:

$ tss_cli signal delete --project_name "<my-project>" --signal_id <signal-id>

Usage:

$ tss_cli signal <command> <attributes>

Command:
        list
                List all signal information.
                Mandatory attributes: project_name
        import
                Import a signal.
                Mandatory attributes: project_name, signal_name, file_path, label_id
                Optional attributes: delimiter
        query
                Query signal information.
                Mandatory attributes: project_name, signal_id
        delete
                Delete a signal.
                Mandatory attributes: project_name, signal_id

Attributes:
        -p, --project_name string
                Name of the project.
        -sn, --signal_name string
                Name of the signal.
        -si, --signal_id int
                Id of the signal.
        -fp, --file_path file path
                File path to the signal.
        -dlm, --delimiter char
                Signal file value delimiter (default is ' ').
        -lid, --label_id int
                Label id of signal (only used for anomaly detection [1:Normal,2:Anomaly], n-Class classification and 1-Class classification tasks).

Response Info:

Name Type Description Example
name string Name of the signal. "my_signal"
id int Id of signal. 1
delimiter string Signal file value delimiter. " "
file_found bool If the signal file has been found on the machine. true
file_name string Signal filename in the workspace. "1.csv"
info dict Signal file information, such as file_md5, file_size. {...}
passed bool If check on signal data has been successfully passed. true
label_id int Label id of the AD/CLS/OC signal. 1
label_name string Label name of the AD/CLS/OC signal. "Normal"
target_info list The list of target name of the REG signals. ["1","2"]
signal_metadata dict Metadata of signal data. {...}

5. Optimization: start optimization, retrieve progress, and get the results for AutoML projects.

  • List all optimization information:

$ tss_cli optimization list --project_name "<my-project>"

  • Start an optimization for classical machine learning:

$ tss_cli optimization start --project_name "<my-project>" --opt_name "<my-opt>"  --signals <signal-id> <signal-id> ... <signal-id>

  • Start an optimization for deep learning:

$ tss_cli optimization start_dl --project_name "<my-project>" --opt_name "<my-dl-opt>"  --signals <signal-id> <signal-id> ... <signal-id> --train_model "<train-model>" --quant_type "<quant-type>"

  • Query the optimization information:

$ tss_cli optimization query --project_name "<my-project>" --opt_id <my-opt-id>

  • Suspend an optimization:

$ tss_cli optimization suspend --project_name "<my-project>" --opt_id <my-opt-id>

  • Resume an optimization:

$ tss_cli optimization resume --project_name "<my-project>" --opt_id <my-opt-id>

  • Stop an optimization:

$ tss_cli optimization stop --project_name "<my-project>" --opt_id <my-opt-id>

  • Get the progression of an optimization:

$ tss_cli optimization progress --project_name "<my-project>" --opt_id <my-opt-id>

  • Get the best result of an optimization:

$ tss_cli optimization best --project_name "<my-project>" --opt_id <my-opt-id>

  • Get all results list of an optimization:

$ tss_cli optimization results --project_name "<my-project>" --opt_id <my-opt-id>

  • Get a result information of the optimization:

$ tss_cli optimization result --project_name "<my-project>" --opt_id <my-opt-id> --result_id <result-id>

  • Delete an optimization:

$ tss_cli optimization delete --project_name "<my-project>" --opt_id <my-opt-id>

Usage:

$ tss_cli optimization <command> <attributes>

Command:
        list
                List all optimization information.
                Mandatory attributes: project_name
        start
                Start an optimization for classical machine learning.
                Mandatory attributes: opt_name, signals
                Optional attributes: on_device_learn, use_generated_anomaly, quick_search, train_val_ratio, allocated_cores
        start_dl
                Start an optimization for deep learning.
                Mandatory attributes: opt_name, signals, train_model, quant_type
                Optional attributes: train_val_ratio, allocated_cores
        query
                Query the optimization information.
                Mandatory attributes: project_name, opt_id
        delete
                Delete an optimization.
                Mandatory attributes: project_name, opt_id
        suspend
                Suspend an optimization.
                Mandatory attributes: project_name, opt_id
        resume
                Resume an optimization.
                Mandatory attributes: project_name, opt_id
        stop
                Stop an optimization.
                Mandatory attributes: project_name, opt_id
        progress
                Get the progression of an optimization.
                Mandatory attributes: project_name, opt_id
        best
                Get the best result of an optimization.
                Mandatory attributes: project_name, opt_id
        results
                Get all results list of an optimization.
                Mandatory attributes: project_name, opt_id
        result
                Get a result information of the optimization.
                Mandatory attributes: project_name, opt_id, result_id

Attributes:
        -p, --project_name string
                Name of the project.
        -on, --opt_name string
                Name of the optimization.
        -sigs, --signals list
                List of signal ids.
        -odl, --on_device_learn bool
                Enable on-device learning for AD task.
        -uga, --use_generated_anomaly bool
                Enable using generated anomaly data for AD task.
        -qs, --quick_search bool
                Enable quick search training.
        -tm, --train_model string
                Configure the deep learning model that need to be trained. Available options: TCN, CNN.
        -q, --quant_type string
                Quantization type of the configured deep learning model. Available options: INT8, FP32.
        -r, --train_val_ratio float
                The ratio of train set and val set (default 0.8).
        -cores, --allocated_cores int
                The number of allocated cores (default total cores - 4). You can obtain the number of CPU cores on the running machine with command: engine cpu.
        -oi, --opt_id int
                Id of the optimization.
        -ri, --result_id int
                Id of the model result of the optimization.

Response Info:

Name Type Description Example
creation_date string The creation date of the optimization. "Mon, 09 Dec 2024 11:42:03 GMT"
name string Name of the optimization. "my_opt"
id int Id of optimization. 1
signals list List of signal data to be used by the optimization. [...]
max_ram int Maximum RAM available for the library in bytes. 32000
max_flash int Maximum Flash is available for the library in bytes. 32000
state string Status of optimization. "Running"
support_ODL bool If enable on-device learning for AD task. false
fast_mode bool If enable quick search training. true
use_generated_anomaly bool If enable using generated anomaly data for AD task. false
train_val_ratio float The ratio of train set and val set. 0.8
cpu_info dict CPU core usage of the optimization. {...}
progress dict The progress of the optimization. {...}
channel_name_list list The list of channel name. ["x","y","z"]
label_id_list list The list of label id of the AD/CLS/OC signal. [1,2,5]
label_name_list list The list of label name of the AD/CLS/OC signal. ["1","2","5"]
target_info list The list of target name of the REG signals. ["1","2"]
model_type string The type of training models. "CML"
quant_type string Quantization type of the configured deep learning model. "INT8"
configured_models_info list The information of configured deep learning models. [...]
version string The version number of the eIQ Time Series Studio. "99.99.0"

  • optimization results:

Name Type Description Example
id int Id of the model result of the optimization. 18
is_best bool If is best in the optimization. true
channels int Number of signal channels. 3
nb_cols int Number of signal columns. 384
sp_info dict Signal processing information of the model. {...}
model_info dict Model information including name, type and capabilities. {...}
benchmark dict Benchamark results of performance metrics, memory usage, and execution time. {...}
Emulation list The emulation results list of the model. [...]

6. Conversion: Start model conversion for Bring Your Own Model (BYOM) projects.

⚠️ Notice: BYOM projects need to pass in –opt_id=<conversion_id> when using Emulation or Library commands.

  • List all conversion information:

$ tss_cli conversion list --project_name "<my-byom-project>"

  • Start a model conversion:

$ tss_cli conversion start --project_name "<my-byom-project>" --conversion_name "<my-conversion>" --model_path "</path/to/my/model>" --quant_type "<quant-type>" --signals <signal-id> <signal-id> ... <signal-id>

  • Query the conversion information:

$ tss_cli conversion query --project_name "<my-byom-project>" --conversion_id <my-conversion-id>

  • Get the converted model results:

$ tss_cli conversion result --project_name "<my-byom-project>" --conversion_id <my-conversion-id>

  • Delete a conversion:

$ tss_cli conversion delete --project_name "<my-byom-project>" --conversion_id <my-conversion-id>

Usage:

$ tss_cli conversion <command> <attributes>

Command:
        list
                List all conversion information.
                Mandatory attributes: project_name
        start
                Start a model conversion.
                Mandatory attributes: project_name, conversion_name, model_path, quant_type, signals
        query
                Query the conversion information.
                Mandatory attributes: project_name, conversion_id
        delete
                Delete a conversion.
                Mandatory attributes: project_name, conversion_id
        result
                Get the converted model results.
                Mandatory attributes: project_name, conversion_id

Attributes:
        -p, --project_name string
                Name of the project.
        -cn, --conversion_name string
                Name of the conversion.
        -ci, --conversion_id int
                Id of the conversion.
        -mp, --model_path string
                Path to the model file.
        -q, --quant_type [INT8|FP32]
                Quantization type of the model.
        -sigs, --signals list
                List of signal ids.

Response Info:

Name Type Description Example
creation_date string The creation date of the conversion. "Mon, 25 Aug 2025 15:51:33 GMT"
name string Name of the conversion. "my_conversion"
id int Id of conversion. 1
signals list List of signal data to be used by the conversion. [...]
max_ram int Maximum RAM available for the library in bytes. 32000
max_flash int Maximum Flash is available for the library in bytes. 32000
model_path string Path to the model file. "/path/to/my/model"
label_id_list list The list of label id of the AD/CLS/OC signal. [1,2,5]
label_name_list list The list of label name of the AD/CLS/OC signal. ["1","2","5"]
target_info list The list of target name of the REG signals. ["1","2"]
quant_type string Quantization type of the model conversion. "INT8"
version string The version number of the eIQ Time Series Studio. "99.99.0"

7. Emulation: launch time series emulation of multiple model results.

  • launch an emulation. (Support multiple model results to emulate simultaneously)

$ tss_cli emulation launch --project_name "<my-project>" --opt_id <my-opt-id> --result_ids <result-id> <result-id> ... <result-id> \
--test_file_info "<label-name>" "</path/to/my/test/signal>" "<delimiter>" \
--test_file_info "<label-name>" "</path/to/my/test/signal>" "<delimiter>" \
--test_file_info ...

  • Retry on an emulation model:

$ tss_cli emulation retry --project_name "<my-project>" --emulation_id <my-emulation-id> --result_id <my-result-id>

  • Delete an emulation:

$ tss_cli emulation delete --project_name "<my-project>" --emulation_id <my-emulation-id>

  • Start an emulation for unlabeled test files and return prediction result files:

$ tss_cli emulation predict --project_name "<my-project>" --opt_id <my-opt-id> --result_id <my-result-id> \
--unlabeled_test_file_info "</path/to/my/test/signal>" "<delimiter>" \
--unlabeled_test_file_info "</path/to/my/test/signal>" "<delimiter>" \
--unlabeled_test_file_info ...

Usage:

$ tss_cli emulation <command> <attributes>

Command:
        launch
                Launch an emulation.(Support multiple model results to emulate simultaneously)
                Mandatory attributes: project_name, opt_id, result_ids, test_file_info
        retry
                Retry on an emulation model.
                Mandatory attributes: project_name, emulation_id, result_id
        delete
                Delete an emulation.
                Mandatory attributes: project_name, emulation_id
        predict
                Start an emulation for unlabeled test files and return prediction result files.
                Mandatory attributes: project_name, opt_id, result_id, unlabeled_test_file_info

Attributes:
        -p, --project_name string
                Name of the project.
        -oi, --opt_id int
                Id of the optimization.
        -ri, --result_id int
                Id of the model result that need to be emulated.
        -ris, --result_ids list
                List of result_ids of the models that need to be emulated.
        -t, --test_file_info (str, str, str)
                List of labeled test files. Input test file with (label_name, file_path, delimiter).
        -ut, --unlabeled_test_file_info (str, str)
                List of unlabeled test files. Input test file with (file_path, delimiter).
        -ei, --emulation_id int
                Id of the emulation.

Response Info:

Name Type Description Example
creation_date string The creation date of the emulation. "Mon, 09 Dec 2024 11:42:03 GMT"
emulation_id int Id of the emulation. 1
emulation_results list The list of emulation results for each model result. [...]

  • emulation results:

Name Type Description Example
result_id int Id of the model result of the optimization. 18
status string The status of the emulation for the model result. "Success"
file_performance dict The performance of the model result for each test file. {...}
label_performance dict The performance of the model result for each label in AD/CLS tasks. {...}
total_performance dict The total performance of the model result. {...}

8. Library: Generates the time series library or sample project.

  • Compile a Time Series Library for an optimization:

$ tss_cli library compile --project_name "<my-project>" --opt_id <my-opt-id>  --result_id <my-result-id> --save_path "</path/to/my/library>" --arch "<arch-type>" --toolchain "<my-toolchain>"

  • Get the sample project for a library:

$ tss_cli library sample_project --project_name "<my-project>" --opt_id <my-opt-id>  --result_id <my-result-id> --save_path "</path/to/my/project>" --arch "<arch-type>" --toolchain "<my-toolchain>"

  • Estimate the inference time of a library:

$ tss_cli library time_estimate --project_name "<my-project>" --opt_id <my-opt-id>  --result_id <my-result-id>

Usage:

$ tss_cli library <command> <attributes>

Command:
        compile
                Compile a Time Series Library for an optimization.
                Mandatory attributes: project_name, opt_id, result_id, save_path, arch, toolchain
                Optional attributes: save_name, chip, mfloat_abi, fshort_enums, fshort_wchar, powerquad_enable, neutron_enable
        sample_project
                Get the sample project for a library.
                Mandatory attributes: project_name, opt_id, result_id, save_path, arch, toolchain
                Optional attributes: save_name, chip, mfloat_abi, fshort_enums, fshort_wchar, powerquad_enable, neutron_enable
        time_estimate
                Estimate the inference time of a library.
                Mandatory attributes: project_name, opt_id, result_id

Attributes:
        -p, --project_name string
                Name of the project.
        -oi, --opt_id int
                Id of the optimization.
        -ri, --result_id int
                Id of the model result of the optimization.
        -svp, --save_path string
                The path to the library or sample_project to save.
        -svn, --save_name string
                The name of the library or sample_project to save.
        -ar, --arch [host|cortex-m7|cortex-m4|cortex-m33|cortex-m0plus|DSP56800EF|DSP56800EX]
                Set arch type.
        -cp, --chip string
                Chip part number (default `MCXN947`).
        -t, --toolchain [GCC|IAR|Keil|CodeWarrior]
                Set toolchain type.
        -fabi, --mfloat_abi [soft|hard]
                Select floating-point ABI to use (default `hard`).
        -fse, --fshort_enums bool
                Enable fshort-enums.
        -fsw, --fshort_wchar bool
                Enable fshort-wchar.
        -pq, --powerquad_enable bool
                Enable the PowerQuad accelerator.
        -nt, --neutron_enable bool
                Enable the Neutron accelerator.