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DOCS-4059: Update terminology from Viam app to Viam #4379
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@@ -24,7 +24,7 @@ This page explains how to create a dataset that meets these criteria for your tr | |||||
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## Prerequisites | ||||||
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{{% expand "a machine connected to the Viam app" %}} | ||||||
{{% expand "a machine connected to Viam" %}} | ||||||
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{{% snippet "setup.md" %}} | ||||||
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@@ -38,12 +38,12 @@ Follow the guide to configure a [webcam](/operate/reference/components/camera/we | |||||
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## Create a dataset | ||||||
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To create a dataset, use the Viam CLI or the Viam app: | ||||||
To create a dataset, use the CLI or the web UI: | ||||||
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{{< tabs >}} | ||||||
{{% tab name="Viam app" %}} | ||||||
{{% tab name="web UI" %}} | ||||||
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1. Open the [**DATASETS** tab on the **DATA** page](https://app.viam.com/data/datasets) of the Viam app. | ||||||
1. Open the [**DATASETS** tab on the **DATA** page](https://app.viam.com/data/datasets). | ||||||
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1. Click the **+ Create dataset** button. | ||||||
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@@ -76,7 +76,7 @@ To create a dataset, use the Viam CLI or the Viam app: | |||||
{{< tabs >}} | ||||||
{{% tab name="One image" %}} | ||||||
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You can add images to a dataset directly from a camera or vision component feed in the **CONTROL** or **CONFIGURATION** tabs of the Viam app. | ||||||
You can add images to a dataset directly from a camera or vision component feed in the machine's **CONTROL** or **CONFIGURATION** tabs. | ||||||
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To add an image directly to a dataset from a visual feed, complete the following steps: | ||||||
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@@ -87,7 +87,7 @@ To add an image directly to a dataset from a visual feed, complete the following | |||||
1. Click **Add** to add the image to the selected dataset. | ||||||
1. When you see a success notification that reads "Saved image to dataset", you have successfully added the image to the dataset. | ||||||
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To view images added to your dataset, go to the **DATA** page's [**DATASETS** tab](https://app.viam.com/data/datasets) in the Viam app and select your dataset. | ||||||
To view images added to your dataset, go to the **DATA** page's [**DATASETS** tab](https://app.viam.com/data/datasets) and select your dataset. | ||||||
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{{% /tab %}} | ||||||
{{% tab name="Many images" %}} | ||||||
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@@ -194,9 +194,9 @@ Repeat these steps for all images in the dataset. | |||||
## Add tagged images to a dataset | ||||||
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{{< tabs >}} | ||||||
{{% tab name="Viam app" %}} | ||||||
{{% tab name="web UI" %}} | ||||||
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Suggested change
[nit] |
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1. Open the [**DATA** page](https://app.viam.com/data/view) of the Viam app. | ||||||
1. Open the [**DATA** page](https://app.viam.com/data/view). | ||||||
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1. Navigate to the **ALL DATA** tab. | ||||||
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@@ -32,7 +32,7 @@ After training or uploading a machine learning model, use a machine learning (ML | |||||
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## Deploy your ML model on an ML model service | ||||||
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1. Navigate to the **CONFIGURE** tab of one of your machine in the [Viam app](https://app.viam.com). | ||||||
1. Navigate to the **CONFIGURE** tab of one of your machine. | ||||||
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2. Add an ML model service that supports the ML model you want to use. | ||||||
- For example, use the `ML model / TFLite CPU` service for TFlite ML models that you trained with Viam's built-in training. | ||||||
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@@ -73,11 +73,11 @@ Save your config to use your specified version of the ML model. | |||||
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## How the ML model service works | ||||||
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The service works with models trained inside and outside the Viam app: | ||||||
The service works with models trained on Viam or elsewhere: | ||||||
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- You can [train TFlite](/data-ai/ai/train-tflite/) or [other model frameworks](/data-ai/ai/train/) on data from your machines. | ||||||
- You can use [ML models](https://app.viam.com/registry?type=ML+Model) from the [Viam Registry](https://app.viam.com/registry). | ||||||
- You can upload externally trained models from a model file on the [**MODELS** tab](https://app.viam.com/models) in the **DATA** section of the Viam app. | ||||||
- You can use [ML models](https://app.viam.com/registry?type=ML+Model) from the [registry](https://app.viam.com/registry). | ||||||
- You can upload externally trained models from a model file on the [**MODELS** tab](https://app.viam.com/models). | ||||||
- You can use a [model](/data-ai/ai/deploy/#deploy-your-ml-model-on-an-ml-model-service) trained outside the Viam platform whose files are on your machine. See the documentation of the model of ML model service you're using (pick one that supports your model framework) for instructions on this. | ||||||
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On its own the ML model service only runs the model. | ||||||
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@@ -18,7 +18,7 @@ date: "2024-12-04" | |||||
--- | ||||||
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You can create custom Python training scripts that train machine learning models to your specifications using PyTorch, TensorFlow, TFLite, ONNX, or any other ML framework. | ||||||
Once you upload a training script to the [Viam Registry](https://app.viam.com/registry?type=Training+Script), you can use it to build ML models in the Viam Cloud based on your datasets. | ||||||
Once you upload a training script to the [registry](https://app.viam.com/registry?type=Training+Script), you can use it to build ML models in the Viam Cloud based on your datasets. | ||||||
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You can also use training scripts that are in the registry already. | ||||||
If you wish to do this, skip to [Submit a training job](#submit-a-training-job). | ||||||
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@@ -772,16 +772,15 @@ You can view uploaded training scripts by navigating to the [registry's **Traini | |||||
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## Submit a training job | ||||||
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After uploading the training script, you can run it by submitting a training job through the Viam app or using the Viam CLI or [ML Training client API](/dev/reference/apis/ml-training-client/#submittrainingjob). | ||||||
After uploading the training script, you can run it by submitting a training job using the web UI, the CLI or the [ML Training client API](/dev/reference/apis/ml-training-client/#submittrainingjob). | ||||||
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Suggested change
[ood nit] |
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{{< table >}} | ||||||
{{% tablestep number=1 %}} | ||||||
**Create the training job** | ||||||
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{{< tabs >}} | ||||||
{{% tab name="Viam app" min-height="150px" %}} | ||||||
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In the Viam app, navigate to your list of [**DATASETS**](https://app.viam.com/data/datasets) and select the one you want to train a model on. | ||||||
{{% tab name="web UI" min-height="150px" %}} | ||||||
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Suggested change
[nit] |
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Navigate to your list of [**DATASETS**](https://app.viam.com/data/datasets) and select the one you want to train a model on. | ||||||
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Click **Train model** and select **Train on a custom training script**, then follow the prompts. | ||||||
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@@ -77,11 +77,11 @@ Turn off data capture on your original camera if you haven't already, so that yo | |||||
**Save to start capturing** | ||||||
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Save the config. | ||||||
With cloud sync enabled, captured data is automatically uploaded to the Viam app after a short delay. | ||||||
With cloud sync enabled, captured data is automatically uploaded to Viam after a short delay. | ||||||
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{{% /tablestep %}} | ||||||
{{% tablestep number=7 %}} | ||||||
**View filtered data in the Viam app** | ||||||
**View filtered data on Viam** | ||||||
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Suggested change
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Once you save your configuration, place something that is part of your trained ML model within view of your camera. | ||||||
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@@ -2,7 +2,7 @@ | |||||||||
linkTitle: "Export data" | ||||||||||
title: "Export data" | ||||||||||
weight: 40 | ||||||||||
description: "Download data from the Viam app using the data client API or the Viam CLI." | ||||||||||
description: "Download data from Viam using the data client API or the CLI." | ||||||||||
type: "docs" | ||||||||||
tags: ["data management", "cloud", "sync"] | ||||||||||
icon: true | ||||||||||
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@@ -43,7 +43,7 @@ To export your data from the cloud using the Viam CLI: | |||||||||
{{% tablestep number=1 %}} | ||||||||||
**Filter the data you want to download** | ||||||||||
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Navigate to the [**DATA** page in the Viam app](https://app.viam.com/data/view). | ||||||||||
Navigate to the [**DATA**](https://app.viam.com/data/view). | ||||||||||
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Suggested change
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Or just this:
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Use the filters on the left side of the page to filter only the data you wish to export. | ||||||||||
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@@ -66,7 +66,7 @@ Run the copied command in a terminal: | |||||||||
viam data export --org-ids=<org-id> --data-type=<binary|tabular> --mime-types=<mime types> --destination=. | ||||||||||
``` | ||||||||||
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This command uses the Viam CLI to download the data onto your computer based on the search criteria you select in the Viam app. | ||||||||||
This command uses the Viam CLI to download the data onto your computer based on the search criteria you select in the web UI. | ||||||||||
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By default, the command creates two new directories named `data` and `metadata` in the current directory and downloads the specified data into the `data` folder and metadata, like bounding box information and labels, in JSON format into the `metadata` folder. | ||||||||||
If you want to store the data in a different location, change the specified folder with the [`--destination` flag](/dev/tools/cli/#named-arguments). | ||||||||||
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[nit]