# 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.

> ⚠️ **Important Notice:** The CLI engine and GUI cannot be used at the same time. Please ensure one is shut down before launching the other.

## 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.

<img src="_static/picture/cmdline/tss_cli.jpg" alt="image" width="80%" height="auto">

### 0. Quick Start of CLI:

<div style="font-size: 12px; background-color: rgba(128,128,128, 0.06); padding: 2px 15px; border:1px solid rgba(128,128,128, 0.2);">

#### Engine
<ul>
  <li>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.</li>
  <li>Ex: tss_cli engine launch</li>
  <li>⚠️<strong>Notice:</strong> After launching the engine, open a new terminal window to send tss_cli commands.</li>
</ul>

#### License
<ul>
  <li>Activate your license. ⚠️<strong>Notice:</strong> 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.</li>
  <li>Ex: tss_cli license activate --key MY-LICENSE-KEY</li>
</ul>

#### Project
<ul>
  <li>Create a AutoML or BYOM project.</li>
  <li>(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</li>
  <li>(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</li>
</ul>

#### Signal
<ul>
  <li>Import signals.</li>
  <li>Ex: tss_cli signal import --project_name my-project --signal_name my-signal --file_path /path/to/my/signal --label_id 1 --delimiter " "</li>
</ul>

#### Optimization (model training)
<ul>
  <li>Start optimization, retrieve progress, and get results for AutoML projects.</li>
  <li>(CML optimization) Ex: tss_cli optimization start --project_name my-project --opt_name my-opt --signals 1 2 3 4 5 6</li>
  <li>(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</li>
  <li>Ex: tss_cli optimization query|progress|best|results|suspend|resume|stop|delete --project_name my-project --opt_id 1</li>
</ul>

#### Conversion
<ul>
  <li>Start model conversion for Bring Your Own Model (BYOM) projects. ⚠️<strong>Notice:</strong> BYOM projects need to pass in --opt_id=&lt;conversion_id&gt when using Emulation or Library commands.</li>
  <li>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</li>
  <li>Ex: tss_cli conversion query|result|delete --project_name my-byom-project --conversion_id 1</li>
</ul>

#### Emulation
<ul>
  <li>Launch a Time Series Emulation. (Support multiple model results to emulate simultaneously)</li>
  <li>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 ","</li>
</ul>

#### Library
<ul>
  <li>Compile a Time Series Library.</li>
  <li>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</li>
</ul>
Other commands are available, refer to the detailed introduction of each module below.
</div>
<br>

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

- Launch the eIQ Time Series Studio Engine on the local computer:
<div style="font-size: 12px;">

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

- Shutdown the eIQ Time Series Studio Engine on the local computer:
<div style="font-size: 12px;">

    $ tss_cli engine shutdown
</div>

- Test connection to the eIQ Time Series Studio Engine:
<div style="font-size: 12px;">

    $ tss_cli engine test
</div>

- Lists all available device information:
<div style="font-size: 12px;">

    $ tss_cli engine device
</div>

- Lists all available sensor information:
<div style="font-size: 12px;">

    $ tss_cli engine sensor
</div>

- Get the number of CPU cores on the running machine:
<div style="font-size: 12px;">

    $ tss_cli engine cpu
</div>

- Get available GPU information on the running machine:
<div style="font-size: 12px;">

    $ tss_cli engine gpu
</div>

#### Usage:
<div style="font-size: 12px;">

    $ 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.
            gpu
                    Get available GPU information 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 `APPDATA/time_series_studio/workspace`).
</div>


### 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.

    <img src="_static/picture/cmdline/cli_get_license_key.jpg" alt="image" width="40%" height="auto">

<div style="font-size: 12px;">

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

- Deactivate the license:
<div style="font-size: 12px;">

    $ tss_cli license deactivate
</div>

- Query current license information:
<div style="font-size: 12px;">

    $ tss_cli license query
</div>

#### Usage:
<div style="font-size: 12px;">

    $ 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).
</div>

#### 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:
<div style="font-size: 12px;">

    $ tss_cli project list
</div>

- Create an AutoML project:
<div style="font-size: 12px;">

    $ 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 ...
</div>

- Create a BYOM project:
<div style="font-size: 12px;">

    $ 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
</div>

- Query project information:
<div style="font-size: 12px;">

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

- Delete a project:
<div style="font-size: 12px;">

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

#### Usage:
<div style="font-size: 12px;">

    $ 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.
</div>

#### 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:
<div style="font-size: 12px;">

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

- Import a signal:
<div style="font-size: 12px;">

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

- Query a signal information:
<div style="font-size: 12px;">

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

- Delete a signal:
<div style="font-size: 12px;">

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

#### Usage:
<div style="font-size: 12px;">

    $ 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).
</div>

#### 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:
<div style="font-size: 12px;">

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

- Start an optimization for classical machine learning:
<div style="font-size: 12px;">

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

- Start an optimization for deep learning:
<div style="font-size: 12px;">

    $ 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>" --gpu_id <gpu-id>
</div>

- Query the optimization information:
<div style="font-size: 12px;">

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

- Suspend an optimization:
<div style="font-size: 12px;">

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

- Resume an optimization:
<div style="font-size: 12px;">

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

- Stop an optimization:
<div style="font-size: 12px;">

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

- Get the progression of an optimization:
<div style="font-size: 12px;">

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

- Get the best result of an optimization:
<div style="font-size: 12px;">

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

- Get all results list of an optimization:
<div style="font-size: 12px;">

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

- Get a result information of the optimization:
<div style="font-size: 12px;">

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

- Delete an optimization:
<div style="font-size: 12px;">

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

- Query the supported deep learning model types for the project:
<div style="font-size: 12px;">

    $ tss_cli optimization dl_models --project_name "<my-project>"
</div>

#### Usage:
<div style="font-size: 12px;">

    $ 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, shuffling, allocated_cores
            start_dl
                    Start an optimization for deep learning.
                    Mandatory attributes: opt_name, signals, train_model, quant_type
                    Optional attributes: train_val_ratio, shuffling, allocated_cores, gpu_id
            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
            dl_models
                    Query the supported deep learning model types for the project.
                    Mandatory attributes: project_name

    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
                    Name of the deep learning model to train (e.g., CNN). Use 'optimization dl_models' to query supported model types for the project.
            -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).
            -s, --shuffling bool
                    Enable training data shuffling (This setting only applies to regression; enabled by default for other tasks).
            -cores, --allocated_cores int
                    Allocated CPU cores. Default: total cores - 4. Use 'engine cpu' to check available cores.
            -gi, --gpu_id int
                    GPU ID for model training. Default: -1 (no GPU). Use 'engine gpu' to list available GPUs.
            -oi, --opt_id int
                    Id of the optimization. Use 'optimization list' to find the 'id' value for your optimization.
            -ri, --result_id int
                    Id of the model result of the optimization. Use 'optimization results' to find the 'id' value for your selected model result.
</div>

#### 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:
<div style="font-size: 12px;">

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

- Start a model conversion:
<div style="font-size: 12px;">

    $ 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>
</div>

- Query the conversion information:
<div style="font-size: 12px;">

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

- Get the converted model results:
<div style="font-size: 12px;">

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

- Delete a conversion:
<div style="font-size: 12px;">

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

#### Usage:
<div style="font-size: 12px;">

    $ 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.
</div>

#### 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)
<div style="font-size: 12px;">

    $ 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 ...
</div>

- Retry on an emulation model:
<div style="font-size: 12px;">

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

- Delete an emulation:
<div style="font-size: 12px;">

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

- Start an emulation for unlabeled test files and return prediction result files:
<div style="font-size: 12px;">

    $ 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 ...
</div>

#### Usage:
<div style="font-size: 12px;">

    $ 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. Use 'optimization list' to find the 'id' value for your optimization.
            -ri, --result_id int
                    Id of the model result that need to be emulated. Use 'optimization results' to find the 'id' value for your selected model result.
            -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). Use 'optimization query' to find available label names; For regression tasks, this label_name is always set to '1'.
            -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.
</div>

#### 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:
<div style="font-size: 12px;">

    $ 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>" --chip "<my-chip>" --fshort_enums --powerquad_enable
</div>

- Get the sample project for a library:
<div style="font-size: 12px;">

    $ 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>" --chip "<my-chip>" --neutron_enable
</div>

- Estimate the inference time of a library:
<div style="font-size: 12px;">

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

#### Usage:
<div style="font-size: 12px;">

    $ 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.
</div>