# Quickstart using CLI
## Prerequisites
Prior steps are logging in, creating a project and a dataset instance.
More info about these steps is described at the [**Setup**](broken://pages/ERkSgwrMnaeqEZ15Wu04) page.
### Installing Leap CLI
Install `leapcli` using the following command:
```bash
pip install leapcli
```
Once installed successfully, the `leap` command shall be available.
### Leap Init
Once the `leap` CLI is installed, you can initialize your project to be synced with the Tensorleap platform.
In the target project path, run the following command to initialize Tensorleap within the project:
```bash
leap init PROJECT_NAME DATASET_NAME
```
Replace the arguments with the corresponding value:
All the parameters are stored at the `.tensorleap/config.toml` file and could be changed if needed.
### Leap Login
In order to access the platform, we must run the `leap login` command within the initialized project path, as such:
```
leap login [API_ID] [API_KEY] [ORIGIN]
```
The `API_ID` , `API_KEY` and the `ORIGIN`, along with the full command, can easily be found by clicking the button within the [**Resources Management**](/user-interface/resources-management.md) view.
### Dataset Integration
For Tensorleap to read the data and fetch it to the model for training or evaluation, we must provide a [**Dataset Script**](/tensorleap-integration/writing-integration-code.md#dataset-script). This script defines the **preprocessing** function**, input/ground\_truth** encoders and **metadata** functions.
The script should be set at the `.tensorleap/dataset.py` file.
{% hint style="info" %}
Important to note that this script should be contained and **not** use external project dependencies!
{% endhint %}
This script will later be synced with the [**Dataset Instance**](/tensorleap-integration/writing-integration-code.md#dataset-instance) set by the `DATASET_NAME` property above.
More info can be found at the Dataset Script page. A sample script can be found at the [MNIST Guide](/guides/full-guides/mnist-guide.md).
Additional examples can be found at the [**Tensorleap Examples**](https://github.com/tensorleap/tensorleap/) repository.
### Model Integration
The model integration script is located at `.tensorleap/model.py`, an example script below:
{% tabs %}
{% tab title="Tensorflow" %}
```python
from pathlib import Path
from myproject.model import build_model # import from the parent project
def leap_save_model(target_file_path: Path):
# Load your model
model = build_model()
# Save it to the path supplied as an arugment (has a .h5 suffix)
model.save(target_file_path)
```
{% endtab %}
{% tab title="PyTorch" %}
Coming Soon
{% endtab %}
{% endtabs %}
The `leap_save_model` is automatically called by the CLI when pushing a model into the Tensorleap platform. Its purpose is to prepare and store the model in the provided `target_file_path`.
More info can be found at the Dataset Script page. A sample script can be found at the [MNIST Guide](/guides/full-guides/mnist-guide.md).
Additional examples can be found at the [**Tensorleap Examples**](https://github.com/tensorleap/tensorleap/) repository.
### Validation
You can validate the **dataset** and **model** scripts locally by using the following command:
```
leap check --all
```
This command will validate the scripts together with its synchronization with the Tensorleap platform.
### Synchronization
Once everything is validated, you can push the **dataset script** and **model** to the Tensorleap platform for further evaluation/training/analysis.
To push the **dataset script**, use the following command from the project path:
```bash
leap push --dataset
```
To push the model to the project, use the following command:
```bash
leap push --model
```
You can also set the `branch-name`, `description` and `model-name` as such:
```
leap push --model [--branch-name= [--description=]
[--model-name=]
```
### What's Next?
You can now log in into the Tensorleap Platform within the UI, and find the imported model within the project, and the dataset within the [**Resources Management**](/user-interface/resources-management.md) view.
You can follow the next steps to prepare the model for analysis:
* Open up the current project (defined by the `PROJECT_NAME` above).\
More info at [**Open a Project**](/user-interface/project.md#open-a-project)**.**
* Find the imported model on the Versions view, hover your cursor over the view and **click** on the right to **Open Commit**.
* Back on the **Network** view, set the [**Dataset** **Block**](/user-interface/project/network/network-mapping/create-a-mapping-deprecated/input-node.md#setup) to point to the `DATASET_NAME` [**Dataset Instance**](broken://pages/CBUAxbBBCSLCooJBRheA), and connect it to the first layer.
* Add the **ground truth**, **loss** and **optimizer** blocks, and connect them to the end of the network.\
More info at [**Dataset Block Setup**](/user-interface/project/network/network-mapping/create-a-mapping-deprecated/input-node.md#setup) and [**Loss and Optimizer**](/user-interface/project/network/network-mapping/create-a-mapping-deprecated/loss-node.md)**.**
* Save the version model by clicking the button and set the `Revision Name` . This adds the new version to the [**Versions**](/user-interface/project/versions.md) view. More info at [**Save a Version**](/user-interface/project/versions.md#save-a-version).
* The model is ready for **training** or **evaluation**:
***Already trained model*** - click from the top bar to inference the data and collect metrics.\
\&#xNAN;***Re-train / train from scratch in the Tensorleap platform*** - click from the top bar to train the model.\
More info at [Evaluate a Model](/user-interface/project/menu-bar/evaluate-a-model.md).
* After the process is done, the model is ready for analysis. More info at [Sample Analysis](/user-interface/dashboards/dashlets/sample-analysis.md).
---
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