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Merge pull request #155 from hummingbot/update-install-docs
doc/update installation
This commit is contained in:
Fede Cardoso
168
INSTALLATION.md
168
INSTALLATION.md
@@ -3,10 +3,41 @@ This repository is maintained by Hummingbot Foundation as a companion for users
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## Requirements
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* 8 GB memory or more (On AWS, this is a `t2.large` instance)
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* Linux / Debian / MacOS
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Cloud Server or local machine
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## Installation
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* Minimum of at least 2vCPU and 8 GB memory or more (On AWS, this is a `t2.large` instance)
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* Linux / MacOS / Windows*
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* For Windows users, make sure to install WSL2 as well as a Linux distro like Ubuntu and run the commands listed below in a Linux terminal and **NOT** in the Windows Command prompt or Powershell.
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## Installation
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### Method 1 - Deploy Repo
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This is the **recommended** install procedure for normal users
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1 - Install dependencies:
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* [Docker Engine](https://docs.docker.com/engine/install/ubuntu/)
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2 - Clone repo and navigate to the created directory
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```bash
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git clone https://github.com/hummingbot/deploy.git
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cd deploy
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```
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3 - Run the provided bash script
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```bash
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bash setup.sh
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```
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Proceed to the **Launch Dashboard** section
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### Method 2 - Source
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This method is only recommended if you are a developer and want to make changes to the code.
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1 - Install dependencies:
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@@ -21,7 +52,7 @@ cd dashboard
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3 - Create `conda` environment and install dependencies
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```bash
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make env_create
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make install
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```
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4 - Activate the isolated 'conda' environment
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@@ -31,123 +62,64 @@ conda activate dashboard
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5 - Start the dashboard
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```bash
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streamlit run main.py
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make run
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```
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## Enable Authentication
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1 - Locate the `config.py` file in the /dashboard folder
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Don't forget to run the **Backend-API** and **Broker** separately for this to work
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2 - Open the file using a text editor
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Proceed to the **Launch Dashboard** section
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3 - Modify the `AUTH_SYSTEM_ENABLED` variable and set this to `True` to enable. By default this is set to `False`
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## Launch the Dashboard
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## Configure Credentials for Authentication
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Open a web browser and navigate to <https://localhost:8501> to view the Dashboard.
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### Method 1: Using hasher_generate
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If you are using a cloud server or VPS, replace localhost with the IP of your server. You may need to edit the firewall rules to allow inbound connections to the necessary ports.
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1 - Update the Environment
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Ensure that you have the latest version of the project and reinstall the conda environment if needed.
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2 - Generate Hashed Password
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Open a Python terminal and utilize hasher_generate from Streamlit to generate a hashed password. Example code:
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```
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import streamlit_authenticator as st_auth
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hashed_password = st_auth.Hasher("YOUR_PLAIN_TEXT_PASSWORD").generate()[0]
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print(hashed_password)
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```
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3 - Edit the Credentials YAML File
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Locate the credentials YAML file in the root directory.
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Edit the file by inserting the generated hashed password from Step 2.
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4 - Accessing the Dashboard
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Run the Hummingbot Dashboard project.
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Use the username and the plain text password (used in Step 2) to log in to the dashboard.
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5 - Logging Out
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To log out, navigate to the dashboard and select 'Logout'.
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### Method 2: Using Pre-Authorized Emails for User Registration
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1 - Update the Environment
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As in Method 1, ensure your conda environment and project version are up-to-date.
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2 - Whitelist Emails
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In the credentials YAML file, add the emails of users who will be pre-authorized to register and create their credentials.
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```
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pre_authorized_emails:
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- user1@example.com
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- user2@example.com
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```
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3 - User Registration
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Direct users to the Hummingbot Dashboard.
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Users should select 'Register' and enter one of the pre-authorized email addresses.
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They will choose their own username and password during registration.
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4 - First-Time Login
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Users log in with their chosen username and password.
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The dashboard will hash and store their credentials automatically.
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5 - Logging Out
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Similar to Method 1, users can log out by navigating to the dashboard and selecting 'Logout'.
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### Additional Notes
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- Video instruction link - https://drive.google.com/file/d/17ecd2aWHZCyuK5Etcv8AZwXMw2B-1sJG/view?usp=share_link
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- Users created through Method 2 (Pre-Authorized Emails) will have their hashed passwords automatically generated and stored.
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- Always ensure the secure handling and storage of credentials.
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- Utilize the 'Logout' feature to secure information and restrict access to the dashboard when it is not in use.
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## Updating
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To update the `dashboard` environment for changes to dependencies defined in `environment.yml`, remove the environment and re-create it:
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Before updating, make sure to stop any running instances first
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### Deploy Repo
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To update - make sure you are in the `deploy` folder then run the bash script
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```
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make env_remove
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make env_create
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bash setup.sh
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```
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To updated the `dashboard` source for latest version, run:
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This will pull any latest images and recreate the Docker containers.
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### Source
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To update the `dashboard` source for latest version, run:
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```
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cd dashboard
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git pull
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```
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Once updated, start up the dashboard again:
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```
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make run
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```
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To update the `dashboard` environment, run
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```
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make env_remove
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make env_create
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```
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This will remove the `conda` environment and recreate it.
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## Troubleshooting
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For Dashboard issues, please open a ticket on our Dashboard [Github page](https://github.com/hummingbot/dashboard) or post in the `#hummingbot-deploy` channel in [Discord](https://discord.gg/hummingbot)
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### Docker permissions
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If you get an error like `Permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock`, run this command to enable Docker permissions:
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```
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sudo chmod 6666 /var/run/docker.sock
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```
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### Sym-link data directory
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To use the [Strategy Performance page](https://github.com/hummingbot/dashboard/wiki/%F0%9F%9A%80-Strategy-Performance), you need to establish a symbolic link to the `data` directory of your running Hummingbot instance:
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The `data` directory differs for Docker versus Source installed Hummingbot:
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* Docker installed: `/path/to/hummingbot/hummingbot_files/data`
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* Source installed: `/path/to/hummingbot/data`
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Create a symlink to your Hummingbot `/data` directory
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```bash
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# replace `/path/to/hummingbotdata` with the actual path
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ln -s /path/to/hummingbotdata data
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# if you need to remove the symlink
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unlink data
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```
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26
README.md
26
README.md
@@ -1,6 +1,6 @@
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# Hummingbot Dashboard
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Hummingbot Dashboard is an open-source application designed to assist in the creation, backtesting, and optimization of a wide variety of algorithmic trading strategies. Once refined, these strategies can be deployed as [Hummingbot](https://github.com/hummingbot/hummingbot) instances in either paper trading or live trading modes, providing a seamless transition from strategy formulation to actual trading execution.
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Hummingbot Dashboard is an open-source application designed to assist in the creation, backtesting, and optimization of a wide variety of algorithmic trading strategies. Once refined, these strategies can be deployed as [Hummingbot](https://github.com/hummingbot/hummingbot) instances in live trading modes, providing a seamless transition from strategy formulation to actual trading execution.
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## Features
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@@ -8,29 +8,17 @@ Hummingbot Dashboard is an open-source application designed to assist in the cre
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- **Strategy Backtesting and Optimization**: Evaluate the performance of your strategies against historical data and optimize them with Optuna
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- **One-Click Deployment**: Seamlessly deploy your strategies as Hummingbot instances for paper or live trading.
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- **Performance Analysis Monitoring**: Monitor and analyze the performance of your deployed strategies.
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- **Secure Credentials**: Restrict access to whitelisted users
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- **Credential Management**: Create and manage separate accounts for API keys
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## Tutorial
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Get a comprehensive understanding of Hummingbot Dashboard by exploring our introductory video playlist. These videos will guide you through the various features and functionalities:
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1. [Introduction to Dashboard](https://www.youtube.com/watch?v=a-kenMqRB00&list=PLDwlNkL_4MMf0Ifvj-HLuQ_Jtf7OV6uzW&index=1)
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2. [Setting up the Environment](https://www.youtube.com/watch?v=AbezIhb6iJg&list=PLDwlNkL_4MMf0Ifvj-HLuQ_Jtf7OV6uzW&index=2)
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3. [Managing Credentials](https://www.youtube.com/watch?v=VmlD_WQVe4M&list=PLDwlNkL_4MMf0Ifvj-HLuQ_Jtf7OV6uzW&index=3)
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4. [Using the Master Bot Profile](https://www.youtube.com/watch?v=MPQTnlDXPno&list=PLDwlNkL_4MMf0Ifvj-HLuQ_Jtf7OV6uzW&index=4)
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5. [Deploying Bots and Running Strategies](https://www.youtube.com/watch?v=915E-C2LWdg&list=PLDwlNkL_4MMf0Ifvj-HLuQ_Jtf7OV6uzW&index=5)
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6. Analyzing Strategy Performance (coming soon)
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7. [Controllers, Backtesting, and Optimization](https://www.youtube.com/watch?v=bAi2ok7_boo&list=PLDwlNkL_4MMf0Ifvj-HLuQ_Jtf7OV6uzW&index=6)
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8. [Deploying Best Strategies from Backtests](https://www.youtube.com/watch?v=BJf3ml-9JIQ&list=PLDwlNkL_4MMf0Ifvj-HLuQ_Jtf7OV6uzW&index=7)
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9. [Conclusions and Next Steps](https://www.youtube.com/watch?v=ug_SSZb2HYE&list=PLDwlNkL_4MMf0Ifvj-HLuQ_Jtf7OV6uzW&index=8)
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## Documentation
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For detailed instructions and further information, visit our [documentation page](https://hummingbot.org/dashboard/).
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## Installation
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Currently, Dashboard should be installed from source via the process below. In the future, we aim to support a simpler,Docker-based installation process as well.
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Currently, we recommend users to install Dashboard using the **[Deploy repo](https://github.com/hummingbot/deploy)** instead as this will automatically launch Dashboard along with the needed components in their own Docker containers.
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If you are a developer, and want to make changes to the code then we recommend using the Source installation below - please note that you will also need to launch the Backend API and Broker separately (either through source install or through Docker).
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1. **Install Dependencies**:
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- Docker Engine
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@@ -44,7 +32,7 @@ Currently, Dashboard should be installed from source via the process below. In t
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3. **Create Conda Environment and Install Dependencies**:
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```bash
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make env_create
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make install
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```
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4. **Activate the Isolated 'conda' Environment**:
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@@ -54,7 +42,7 @@ Currently, Dashboard should be installed from source via the process below. In t
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5. **Start the Dashboard**:
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```bash
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streamlit run main.py
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make run
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```
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For more detailed instructions on how to install and update the dashboard, refer to [INSTALLATION.md](INSTALLATION.md).
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