From a32a8e8c75261d2345424eb6201549725d6ffacf Mon Sep 17 00:00:00 2001 From: oiclid <1678413+oiclid@users.noreply.github.com> Date: Fri, 30 Aug 2024 19:33:22 +0100 Subject: [PATCH] edits --- package-lock.json | 960 ++++-------------- package.json | 2 +- src/content/Docs/_sequence.ts | 15 + .../Docs/guides/apps/discourse/index.md | 25 + .../postgres-sql-restore-or-backup/index.md | 0 .../Docs/guides/databases/redash/index.md | 38 + .../akash-chat-api/javascript/index.md | 34 - .../akash-chat-api/python/index.md | 230 ----- .../guides/machine-learning/jupyter/index.md | 101 ++ .../Docs/guides/tooling/radicle/index.md | 86 ++ 10 files changed, 444 insertions(+), 1047 deletions(-) create mode 100644 src/content/Docs/guides/apps/discourse/index.md rename src/content/Docs/guides/{tooling => databases}/postgres-sql-restore-or-backup/index.md (100%) create mode 100644 src/content/Docs/guides/databases/redash/index.md delete mode 100644 src/content/Docs/guides/machine-learning/akash-chat-api/javascript/index.md delete mode 100644 src/content/Docs/guides/machine-learning/akash-chat-api/python/index.md create mode 100644 src/content/Docs/guides/machine-learning/jupyter/index.md create mode 100644 src/content/Docs/guides/tooling/radicle/index.md diff --git a/package-lock.json b/package-lock.json index a7aae9ac..9a002afb 100644 --- a/package-lock.json +++ b/package-lock.json @@ -207,9 +207,9 @@ } }, "node_modules/@algolia/client-common": { - 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"integrity": "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==", - "license": "MIT", - "dependencies": { - "tr46": "~0.0.3", - "webidl-conversions": "^3.0.0" - } - }, "node_modules/which": { "version": "2.0.2", "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz", diff --git a/package.json b/package.json index df8d215f..6751d583 100644 --- a/package.json +++ b/package.json @@ -73,4 +73,4 @@ "prettier-plugin-astro": "^0.12.0", "prettier-plugin-tailwindcss": "^0.5.4" } -} +} \ No newline at end of file diff --git a/src/content/Docs/_sequence.ts b/src/content/Docs/_sequence.ts index 6bf3fe26..1d76cd65 100644 --- a/src/content/Docs/_sequence.ts +++ b/src/content/Docs/_sequence.ts @@ -80,6 +80,7 @@ export const docsSequence = [ { label: "Llama-2 70B"}, { label: "Akash Chat API"}, { label: "AI Art"}, + { label: "Jupyter Notebook"}, {label: "Text-Generation-WebUI"}, ], }, @@ -118,6 +119,20 @@ export const docsSequence = [ subItems: [ { label: "Waku"}, { label: "qBittorrent"}, + { label: "Discourse"}, + ], + }, + { + label: "Databases", + subItems: [ + { label: "PostgreSQL restore/backup"}, + + ], + }, + { + label: "Data Visualizations", + subItems: [ + { label: "Redash"}, ], }, diff --git a/src/content/Docs/guides/apps/discourse/index.md b/src/content/Docs/guides/apps/discourse/index.md new file mode 100644 index 00000000..f03caece --- /dev/null +++ b/src/content/Docs/guides/apps/discourse/index.md @@ -0,0 +1,25 @@ +--- +categories: ["Guides"] +tags: ["Social"] +weight: 1 +title: "Discourse on Akash" +linkTitle: "Discourse" +--- + +Discourse is a fully open-source discussion platform designed for the future of the Internet. It can be used as a mailing list, discussion forum, long-form chat room, and more. To learn more, visit [Discourse](https://www.discourse.org/). + +## Discourse on Akash Overview + +This guide will help you deploy a multi-tiered Discourse application on the Akash Network, consisting of four services/containers: + +- Backend Services: + - PostgreSQL + - Redis + - Sidekiq (for background job processing) + +- Frontend Service: + - Discourse + +To deploy your Discourse instance on the Akash Network, simply use the provided [`deploy.yml`](https://github.com/akash-network/awesome-akash/blob/master/discourse/deploy.yml) script on Mainnet. You can refer to any "Step by Step Guide to Akash" for detailed instructions. + +A diff --git a/src/content/Docs/guides/tooling/postgres-sql-restore-or-backup/index.md b/src/content/Docs/guides/databases/postgres-sql-restore-or-backup/index.md similarity index 100% rename from src/content/Docs/guides/tooling/postgres-sql-restore-or-backup/index.md rename to src/content/Docs/guides/databases/postgres-sql-restore-or-backup/index.md diff --git a/src/content/Docs/guides/databases/redash/index.md b/src/content/Docs/guides/databases/redash/index.md new file mode 100644 index 00000000..241d6693 --- /dev/null +++ b/src/content/Docs/guides/databases/redash/index.md @@ -0,0 +1,38 @@ +--- +categories: ["Guides"] +tags: ["Blockchain"] +weight: 1 +title: "Redash" +linkTitle: "Redash" +--- + + +[Redash](https://redash.io/) is an open-source tool designed for data visualization and dashboard creation. It enables users to connect to various data sources, craft interactive dashboards, and generate visualizations for data analysis. With its intuitive interface, Redash allows users to query, visualize, and share data insights across an organization. Businesses can leverage Redash to create reports, charts, graphs, and dashboards, facilitating data-driven decision-making. + +## Akash Console Deployment Setup + +The provided [`deploy.yaml`](https://github.com/akash-network/awesome-akash/blob/master/Redash/deploy.yaml) file utilizes [IP leases](/docs/network-features/ip-leases) and [persistent storage](https://akash.network/docs/network-features/persistent-storage). If you are not receiving bids, consider removing one or both of these features. + +Once deployed, access the deployment shell, select "redash," and execute the following commands in sequence: + +``` + +bin/docker-entrypoint create_db +bin/docker-entrypoint server + +``` + +- `create_db`: Initializes the necessary database schema for Redash. +- `server`: Launches the Redash web server, making the application accessible upon completion. + +Expected outputs from these commands are provided below. + +## Service Overview + +- `redash/redash:10.0.0.b50363`: This image is used across several services, each explained below: + + - `redash`: The core web server for Redash, delivering the web application to users, managing interactions, and executing SQL queries. + - `scheduler`: Manages scheduled tasks within Redash, ensuring background jobs run at predefined intervals. + - `scheduled_worker`: Executes worker processes for scheduled queries and schema-related tasks. + - `adhoc_worker`: Handles worker processes specifically for ad-hoc queries. + - `worker`: A general-purpose worker process for various tasks. \ No newline at end of file diff --git a/src/content/Docs/guides/machine-learning/akash-chat-api/javascript/index.md b/src/content/Docs/guides/machine-learning/akash-chat-api/javascript/index.md deleted file mode 100644 index 29269340..00000000 --- a/src/content/Docs/guides/machine-learning/akash-chat-api/javascript/index.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -categories: ["Guides"] -tags: ["Machine Learning", "GPT", "API"] -weight: 1 -title: "Akash Chat API JavaScript Examples" -linkTitle: "JavaScript Examples" ---- - -You would need to have Node.js installed on your computer. To verify if you have JavaScript installed on your computer, go to Terminal/CommandLine and run: - -``` -node --version - -``` - -If you see a response similar to - -``` -v22.6.0 - -``` - -then you have Node.js installed. Otherwise, Go the [Node.js website](https://nodejs.org) download it, and install it. - -You then need to install the OpenAI API library - -``` -npm install openai - -``` - - -## Basic Text Completion -This example demonstrates how to generate a text completion based on a prompt \ No newline at end of file diff --git a/src/content/Docs/guides/machine-learning/akash-chat-api/python/index.md b/src/content/Docs/guides/machine-learning/akash-chat-api/python/index.md deleted file mode 100644 index 1bf9b796..00000000 --- a/src/content/Docs/guides/machine-learning/akash-chat-api/python/index.md +++ /dev/null @@ -1,230 +0,0 @@ ---- -categories: ["Guides"] -tags: ["Machine Learning", "GPT", "API"] -weight: 1 -title: "Akash Chat API Python Examples" -linkTitle: "Python Examples" ---- -You would need to have Python installed on your computer. To verify if you have Python installed on your computer, go to Terminal/CommandLine and run: - -``` - -python --version - -``` - - - - -If you have Python installed, you should see something similar to the the response below: - -``` -Python 3.9.13 - -``` - -Otherwise, download Python from either the [official website](https://python.org), or the [free Anaconda distribution](https://www.anaconda.com/download/) if you intend to do a lot of work in AI/ML and Data Science, as it comes with a lot of relevant libraries pre-installed. - -Install the OpenAI library - -``` -pip install openai - -``` - -## Basic Text Completion - -This example demonstrates how to generate a text completion based on a prompt . - -``` - -import openai - -# Set your API key -client = openai.OpenAI( - api_key="sk-xxxxxxxx", - base_url="https://chatapi.akash.network/api/v1" -) - - -# Generate a completion -response = client.chat.completions.create( - model="Meta-Llama-3-1-8B-Instruct-FP8", # Specify the model - messages = [ - { - "role": "user", - "content": "Once upon a time, in a land far, far away" - } - ], - -) - -# Print the generated text -print(response.choices[0].message.content.strip()) - - -``` - -You should receive something similar to this: - -``` -...there was a beautiful kingdom, hidden from the rest of the world by a veil of mist and legend. The kingdom was called Azura, and it was a place of wonder, where magic was woven into the very fabric of everyday life. The people of Azura lived in harmony with the land, respecting the ancient traditions and rituals that had been passed down through generations. - -In the heart of the kingdom, there stood a magnificent castle, its towers reaching high into the sky like giant's fists. The castle was the seat of power, where the wise and just king, Maric, ruled over Azura with kindness and wisdom. King Maric was loved by his people, and his rule was marked by peace and prosperity. - -But little did anyone know, a great darkness was stirring in the shadows, threatening to disrupt the balance of the kingdom. A powerful sorcerer, named Malakai, had been secretly gathering his strength, seeking to overthrow King Maric and claim the throne for himself... - -Would you like me to continue the story, or do you have any other requests? - -``` - -## Conversation with AkashChat -This example simulates a conversation with AkashChat, maintaining the context across multiple interactions. - -``` - -import openai - -# Set your API key -client = openai.OpenAI( - api_key="sk-xxxxxxxx", - base_url="https://chatapi.akash.network/api/v1" -) - -# Start the conversation -messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Who won the FIFA World Cup in 2022?"} -] - -# Generate a response -response = client.chat.completions.create( - model="Meta-Llama-3-1-405B-Instruct-FP8", # Specify the model - messages=messages -) - -# Print the assistant's reply -print(response.choices[0].message.content) - -# Continue the conversation -messages.append({"role": "assistant", "content": response.choices[0].message.content}) -messages.append({"role": "user", "content": "Can you tell me more about that?"}) - -# Generate another response -response = client.chat.completions.create( - model="Meta-Llama-3-1-405B-Instruct-FP8", - messages=messages -) - -``` -You should receive a response similar to: - -``` - -Argentina won the 2022 FIFA World Cup, which was held in Qatar from November 20 to December 18, 2022. They defeated France 4-2 in a penalty shootout, after the match ended 3-3 after extra time, in the final on December 18, 2022. -The 2022 FIFA World Cup Final was a historic match between Argentina and France, held at the Lusail Iconic Stadium in Lusail, Qatar. The match was played on December 18, 2022, in front of a packed stadium with over 88,000 spectators. - -Argentina, led by their captain Lionel Messi, took an early lead with Messi scoring a penalty in the 23rd minute and Angel Di Maria adding another goal in the 36th minute. Argentina seemed to be in control, but France staged an incredible comeback in the second half. - -Kylian Mbappé scored two goals in the 80th and 81st minutes to level the score at 2-2, sending the match into extra time. In extra time, Messi scored again in the 108th minute, but Mbappé equalized once more with a penalty in the 118th minute, making the score 3-3. - -As the match ended 3-3 after extra time, it went to a penalty shootout to determine the winner. Argentina goalkeeper Emiliano Martínez made a crucial save, and Gonzalo Montiel scored the winning penalty, securing a 4-2 victory for Argentina in the shootout. - -Lionel Messi was awarded the Golden Ball as the tournament's best player, and Emiliano Martínez received the Golden Glove as the best goalkeeper. This victory marked Argentina's third World Cup title, with their previous wins in 1978 and 1986. - -The match was widely regarded as one of the greatest World Cup finals of all time, with many praising the incredible performances of both teams, particularly Messi and Mbappé. - -``` -## Generating Code Snippets - -You can use the AkashChat API to generate code snippets in a similar fashion. - -``` -import openai - -# Set your API key -client = openai.OpenAI( - api_key="sk-xxxxxxxx", - base_url="https://chatapi.akash.network/api/v1" -) - -# Create a prompt to generate code -prompt = "Write a Python function to reverse a string." - -# Generate code -response = client.chat.completions.create( - model="Meta-Llama-3-1-405B-Instruct-FP8", - messages=[{"role": "user", "content": prompt}], - max_tokens=100, - temperature=0 # Lower temperature means more deterministic results -) - -# Print the generated code -print(response.choices[0].message.content.strip()) -``` -SHould give you something similar to: - -``` -**Reversing a String in Python** -================================ - -Here's a simple function that uses Python's slicing feature to reverse a string. - -```python -def reverse_string(s): - """ - Reverses a given string. - - Args: - s (str): The string to be reversed. - - Returns: - str: The reversed string. - """ - return s[::-1] -``` - - -## Summarization -For summarizing text, you would still use the chat.completions.create() method: - -``` -import openai - -# Set your API key -client = openai.OpenAI( - api_key="sk-xxxxxxxx", - base_url="https://chatapi.akash.network/api/v1" -) - -# Long text to summarize -text = """ -Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. -Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. -Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem-solving". -""" - -# Generate a summary -response = client.chat.completions.create( - model="Meta-Llama-3-1-405B-Instruct-FP8", - messages=[{"role": "user", "content": f"Summarize the following text:\n\n{text}"}], - max_tokens=50 -) - -# Print the summary -print(response.choices[0].message.content.strip()) - -``` - -You should get a response similar to: - -``` - -Artificial intelligence (AI) refers to the intelligence exhibited by machines, as opposed to natural human and animal intelligence. It involves the study of intelligent agents that perceive their environment and take actions to achieve their goals, and is often associated with machines that mimic - -``` - - - - - diff --git a/src/content/Docs/guides/machine-learning/jupyter/index.md b/src/content/Docs/guides/machine-learning/jupyter/index.md new file mode 100644 index 00000000..4ae9ab1b --- /dev/null +++ b/src/content/Docs/guides/machine-learning/jupyter/index.md @@ -0,0 +1,101 @@ +--- +categories: ["Guides"] +tags: ["Machine Learning", "Deployment", "Data Science"] +weight: 1 +title: "Deploying and Using Jupyter on Akash" +linkTitle: "Jupyter Notebook" +--- + +Jupyter Notebooks are an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are widely used for data analysis, machine learning, and academic research because they provide an interactive environment where you can write code, see results immediately, and document your process all in one place. + +## Deploying Jupyter on Akash + +### Access the Akash Console: + +Open your web browser and navigate to the [Akash Console](https://console.akash.network/). + +### Create a New Deployment: + +Once logged in, go to the "Deployments" section and click on "Create New Deployment". + +### Configure the Deployment: + +1. Deployment Name: Enter a name for your deployment, e.g., "Jupyter Notebook". + +2. Deployment YAML: Use the SDL file template for [Jupyter](https://console.akash.network/templates/akash-network-awesome-akash-jupyter). You may also use Modify as necessary + +### SDL Configuration + +The SDL configuration in the [Akash Console](console.akash.network) can be seen below: + +``` + +--- +version: "2.0" + +services: + # make sure to NOT name the service as "jupyter" to avoid conflicting K8s variable name "_" with the app's variable name (JUPYTER_PORT) + jupyter-app: + # There are many other Jupyter images you can choose from here: https://jupyter-docker-stacks.readthedocs.io/en/latest/using/selecting.html + image: jupyter/tensorflow-notebook + expose: + - port: 8888 + as: 80 + to: + - global: true + +profiles: + compute: + jupyter-app: + resources: + cpu: + units: 2.0 + memory: + size: 2Gi + storage: + size: 10Gi + placement: + akash: + attributes: + host: akash + signedBy: + anyOf: + - "akash1365yvmc4s7awdyj3n2sav7xfx76adc6dnmlx63" + - "akash18qa2a2ltfyvkyj0ggj3hkvuj6twzyumuaru9s4" + pricing: + jupyter-app: + denom: uakt + amount: 10000 + +deployment: + jupyter-app: + akash: + profile: jupyter-app + count: 1 +``` + +This configuration specifies that the deployment uses the Jupyter Tensor-flow Docker image, maps port 8888, allocates 2 CPUs and 2Gi of memory, and provides 10Gi of storage for notebooks. + +### Submit the Deployment: + +- Click "Submit" to create and deploy your Jupyter Notebook. + +### Access the Jupyter Notebook: + +- Once deployed, go to the "Service Endpoints" section in the Akash Console. +- Find the endpoint corresponding to port 8888. This will be the URL you use to access your Jupyter Notebook. + +### Using Jupyter: + +- Open the URL provided for port 8888 in your web browser. +- - You will be prompted to enter a token. You can find this token in the logs of your deployment. Go to the "Logs" section in the Akash Console to retrieve it. +Enter the token to access the Jupyter Notebook interface. + +### Start Working: + +- You can now create new notebooks, upload existing ones, and start using Jupyter for your data analysis or research tasks. + +### Additional Tips: + +- Regularly save your notebooks and manage your storage usage. +- Make sure to monitor resource usage to ensure the deployment remains within your allocated limits. \ No newline at end of file diff --git a/src/content/Docs/guides/tooling/radicle/index.md b/src/content/Docs/guides/tooling/radicle/index.md new file mode 100644 index 00000000..26ab0a9d --- /dev/null +++ b/src/content/Docs/guides/tooling/radicle/index.md @@ -0,0 +1,86 @@ +--- +categories: ["Guides"] +tags: ["CI/CD", "DevOps"] +weight: 1 +title: "Radicle" +linkTitle: "Radicle" +--- + +Radicle is an open-source, peer-to-peer code collaboration stack built on Git. It operates in a decentralized manner, allowing users to maintain control over their data and workflow without a central authority. This guide provides instructions for deploying a Radicle Seed node on the Akash Network. + +## Components + +- **Radicle Seed Node**: Hosts repositories and is essential for the Radicle network. +- **Container Image**: Based on [radicle-docker](https://app.radicle.xyz/nodes/seed.radicle.garden/rad:zNd4qti1Jc69mCBQAdBeK3Avzy4R/tree/Dockerfile) and hosted on [Quay.io](https://quay.io/repository/vpavlin0/radicle-seed?tab=tags). +- **Radicle HTTP API**: Optionally enabled for additional functionalities. + +## Prerequisites +- **Akash Network Account**: Ensure you have an active Akash Network account and have set up the necessary [CLI](http://localhost:4321/docs/deployments/akash-cli/installation/) or [web console](http://localhost:4321/docs/deployments/akash-console/) tools. + +- **Radicle Repository IDs**: Obtain the repository IDs you wish to seed (if applicable). + +## Deployment + +### Prepare Your Deployment Manifest + +Prepare a deployment manifest that includes the following parameters: + +- `RAD_SEEDS`: (Optional) Set this environment variable to a semicolon-separated list of Radicle repository IDs you wish to seed. +- `RAD_HTTP_ENABLE`: Set to true to enable the Radicle HTTP API server. +- `RAD_PINNED_REPOS`: (Optional) List repository IDs you wish to pin and automatically seed, separated by semicolons. +- `RAD_EXTERNAL_ADDR`: Update this with the public address and port assigned by Akash Network. + +### Deploy the Radicle Seed Node + +1. Create the Deployment: Use the Akash Network CLI to create the deployment. Ensure the deployment manifest is correctly configured with the parameters above. + +``` + +akash tx deployment create --file deployment.yaml + +``` + +2. Find the Random Port: Akash Network assigns a random port to your service. Retrieve this port by running: + +``` +akash query deployment info --id +``` + +3. Update External Address: After finding the random port, update the `RAD_EXTERNAL_ADDR` environment variable with the format provider.url:. + +``` +export RAD_EXTERNAL_ADDR=provider.url: +``` +4. Deploy the Container: Deploy the container using the updated configuration. + +``` +akash tx deployment deploy --file deployment.yaml + + +``` + +### Enable HTTP API Server (Optional) + +If you have set `RAD_HTTP_ENABLE` to true, the Radicle HTTP API server will be available. You can pin repositories by setting the `RAD_PINNED_REPOS` environment variable with a list of repository IDs. + +### Retrieve Node Information + +To connect to your node and discover its Node ID, run the following command in the deployment console: + +``` +rad self --nid +``` + +This command will provide you with the Node ID and other necessary details. + + +## Post-Deployment + +- Access Node: Use the external address and port to interact with your Radicle node. +- Monitor Logs: Check the logs for any issues or to verify the status of your deployment. + +### Troubleshooting + +- Node Not Discoverable: Ensure that `RAD_EXTERNAL_ADDR` is correctly set and matches the assigned port. +- API Not Responding: Verify that `RAD_HTTP_ENABLE` is set to `true` and check the deployment logs for errors. +