# Quickstart
## 1. Create a Project
* Create a new project using the button. Each represents a unique AI application / use-case. Select any such project to perform a deep dive.
* **Explore a Run**: Every Run represents the execution of the test suite on a set of datasets and model. Select a Run to view all tests performed.
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## 2. Explore Test Results
**Access Test Results**: On the summary page, click view iterations and view results to explore test results for a specific dataset and model.
**Note**: An in-depth companion piece to every project and every test run is available on the [sandbox guide. ](https://docs.raga.ai/ragaai-prism/sandbox-guide)
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## 3. Execute your own Test!
#### Step 1: Start the Test Run
* Navigate to the RagaAI dashboard and open your project.
* Click on the **'Initiate Run'** button located towards the right side of the dashboard.
RagaAI - Initiate a new test
#### Step 2: Access Keys Popup
* A popup will appear displaying the **Access Key** and **Secret Key**.
* These keys are crucial as they link the Google Colab file to your RagaAI account and the specific project you are working on.
RagaAI - Get you Access Key and Sceret Access Key
#### Step 3: Launch the Colab Notebook
* Click on the **'Try It'** button within the popup.
* This action will open a new tab with a Google Colab notebook preloaded with the necessary code cells for executing the test.
#### Step 4: Enter Your Access Keys
* In the Colab notebook, locate the cell titled **'Put your ACCESS KEY & SECRET KEY'**.
* Replace the placeholder text with your actual Access Key and Secret Key provided in the popup.
#### Step 5: Install the dependencies
* The first cell typically contains the command to install the RagaAI testing platform library using `pip`. Run this cell to install the required library in your Colab environment.
* After setting your Access Key and Secret Key, proceed to the cell labeled **'Import All raga lib from raga module'** and run it to import the necessary modules.
Step 6: Upload a Dataset
```python
test_ds = CocoDataset(
test_session=test_session,
name="test-data",
type="object_detection", # Specify the type of task (e.g., object detection, classification)
json_file_path="path/to/annotation_file.json", # Path to the JSON annotation file
image_folder_path='path/to/image_folder', # Path to the folder containing the images
s3_bucket_name="product-raga", # Name of the S3 bucket where the dataset is stored
s3_key="coco-images", # S3 key pointing to the dataset images
aws_raga_access_key=AWS_ACCESS_KEY_ID, # Your AWS access key
aws_raga_secret_key=AWS_SECRET_ACCESS_KEY, # Your AWS secret key
bucket_region= bucket_region # The region of your S3 bucket
)
```
Step 7: Generate Embeddings
In some cases, the tests you want to run may require the generation of embeddings for your dataset. Embeddings are numerical representations of your data that are crucial for certain types of AI models and evaluations:
```python
dataset = Dataset(name=dataset_name, test_session=test_session, init=False)
dataset.generate_embeddings(model_name="dinov2-model", # Model used for generating embeddings
model_version="v1", # Version of the model
embed_col_name="dinov2-model:v1", # Column name for storing embeddings
output_type="object_detection", # Type of task
input_func=input_function, # Custom function for input processing
output_func=output_function) # Custom function for output processing
```
you can add your existing embeddings as well, by using:
```python
test_ds.add_embeddings(userDataFrame=df, model="dna", col_name="embedding_column_name")
```
\
Step 8: Generate Inferences
If your dataset does not have model inferences, then you can use ragaAI to generate inferences \[we also support using of custom models]
```python
dataset = Dataset(name=dataset_name, test_session=test_session, init=False)
dataset.generate_inferences(model_name="rcnn-inference-model",
model_version="v1",
inference_col_name="rcnn-inference-model:v2",
output_type="object_detection",
input_func=input_function,
output_func=output_function)
```
User can add their own inferences as well, by using:
```python
test_ds.add_inference(inferences_file_path="/content/updated_dataset_with_embeddings.csv",
format="YOLOv5",
model_name="anything",
customer_column_name="annotations")
```
Step 9: Setup a run
* Set up a Run: This cell contains the code to initialise a test session with parameters like project name, run name, and your keys. Running this cell will start the test run using the onboarded dataset.
```python
run_name = "your_run_name"
test_session = TestSession(project_name=project_name, run_name = run_name, access_key=ACCESS_KEY, secret_key=SECRET_KEY, host=HOST)
```
Step 10: Execute test
* Run Tests: These cells contains the code to execute the different available tests. Alter the parameters like threshold, metric, etc and execute the cell
#### **Explore the** [ **Test Inventory**](https://docs.raga.ai/ragaai-prism/test-inventory)
#### Visualising Results
Once the test is complete, you can easily visualise the results. Navigate back to our platform's user interface (UI). Inside the Runs tab, you'll find a new run containing results for the tests executed by you.
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