- [Docs] Minor edits

dstackai · Sep 13, 2023 · daa7263 · daa7263
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diff --git a/README.md b/README.md
@@ -9,7 +9,7 @@
 </h1>
 
 <h3 align="center">
-Run LLM workloads across any clouds
+Orchestrate GPU workloads across clouds
 </h3>
 
 <p align="center">
@@ -23,12 +23,12 @@ Run LLM workloads across any clouds
 [![PyPI - License](https://img.shields.io/pypi/l/dstack?style=flat-square&color=blue)](https://github.com/dstackai/dstack/blob/master/LICENSE.md)
 </div>
 
-`dstack` is an open-source toolkit for orchestrating LLM workloads in any cloud. It provides a cloud-agnostic interface
-for training, fine-tuning, inference, and development of LLMs.
+`dstack` is an open-source toolkit for training, fine-tuning, inference, and development
+across multiple cloud GPU providers.
 
 ## Latest news ✨
 
-- [2023/09] [Deploying LLMs with Python API](https://dstack.ai/examples/python-api) (Example)
+- [2023/09] [Deploying LLMs with API](https://dstack.ai/examples/python-api) (Example)
 - [2023/09] [Managed gateways](https://dstack.ai/blog/2023/09/01/managed-gateways) (Release)
 - [2023/08] [Fine-tuning Llama 2](https://dstack.ai/examples/finetuning-llama-2) (Example)
 - [2023/08] [Serving SDXL with FastAPI](https://dstack.ai/examples/stable-diffusion-xl) (Example)
@@ -48,14 +48,14 @@ dstack start
 Upon startup, the server sets up the default project called `main`.
 Prior to using `dstack`, make sure to [configure clouds](https://dstack.ai/docs/guides/clouds#configuring-backends).
 
-Once the server is up, you can orchestrate LLM workloads using
+Once the server is up, you can orchestrate GPU workloads using
 either the CLI or Python API.
 
 ## Using CLI
 
 ### Define a configuration
 
-The CLI allows you to define what you want to run as a YAMl file and
+The CLI allows you to define what you want to run as a YAML file and
 run it via the `dstack run` CLI command.
 
 Configurations can be of three types: `dev-environment`, `task`, and `service`.
@@ -128,7 +128,7 @@ Privisioning...
 Serving on https://tasty-zebra-1.mydomain.com
 ```
 
-## Using Python API
+## Using API
 
 As an alternative to the CLI, you can run tasks and services programmatically 
 via [Python API](https://dstack.ai/docs/reference/api/python/).

diff --git a/docs/docs/index.md b/docs/docs/index.md
@@ -1,7 +1,7 @@
 # Quickstart
 
-`dstack` is an open-source toolkit for orchestrating LLM workloads in any cloud. It provides a cloud-agnostic interface
-for training, fine-tuning, inference, and development of LLMs.
+`dstack` is an open-source toolkit for training, fine-tuning, inference, and development
+across multiple cloud GPU providers.
 
 ## Installation
 
@@ -180,7 +180,7 @@ more.
     `dstack` will automatically select the suitable instance type from a cloud provider and region with the best
     price and availability.
 
-## Using Python API
+## Using API
 
 As an alternative to the CLI, you can run tasks and services programmatically 
 via [Python API](../docs/reference/api/python/index.md).

diff --git a/docs/index.md b/docs/index.md
@@ -1,6 +1,6 @@
 ---
 template: home.html
-title: Orchestrate LLM workloads in any clouds
+title: Orchestrate GPU workloads across clouds
 hide:
    - navigation
    - toc

diff --git a/docs/overrides/home.html b/docs/overrides/home.html
@@ -125,16 +125,14 @@
     <div class="md-grid md-typeset">
         <div class="tx-landing__hero">
             <div class="tx-landing__hero_text">
-                <h1>Orchestrate LLM workloads in any clouds</h1>
+                <h1>Orchestrate GPU workloads across clouds</h1>
                 <p>
-                    <strong>dstack</strong> is an open-source toolkit for orchestrating LLM workloads in any cloud.
-                    It provides a cloud-agnostic interface for training, fine-tuning, inference, and development of
-                    LLMs.
+                    <strong>dstack</strong> is an open-source engine for orchestrating GPU workloads across
+                    various cloud providers.
                 </p>
 
-                <!--<p>Deploy run <strong>tasks</strong>, <strong>services</strong>, and provision
-                    <strong>dev environments</strong> in a cost-effective manner across multiple cloud GPU providers.
-                </p>-->
+                <p>It offers a cloud-agnostic developer toolkit for training, fine-tuning, inference, and development of
+                    generative AI models.</p>
 
                 <a href="/docs" class="md-button md-button--primary">
                     Get started</a>
@@ -151,7 +149,7 @@ <h1>Orchestrate LLM workloads in any clouds</h1>
 
         <div class="tx-landing__integrations">
             <div class="tx-landing__integrations_text">
-                <h2>Bring your own cloud accounts</h2>
+                <h2>Use multiple cloud GPU providers</h2>
             </div>
             <div class="tx-landing__integrations_logos">
                 <img class="logo-xlarge" src="assets/images/aws-logo.svg" title="Amazon Web Services">
@@ -166,11 +164,9 @@ <h2>Bring your own cloud accounts</h2>
                 <div class="block margin">
                     <h2>Training</h2>
 
-                    <p>Using <strong>dstack</strong>, you can define <strong>tasks</strong> and execute them across
-                        multiple cloud providers, ensuring the best GPU price and availability.</p>
-
-                    <p><strong>Tasks</strong> facilitate cost-effective on-demand execution of batch jobs and web apps.
-                    </p>
+                    <p>Pre-train or fine-tune LLMs or other state-of-the-art generative AI models
+                        across multiple cloud GPU providers, ensuring data privacy, GPU availability,
+                        and cost-efficiency.</p>
 
                     <p>
                         <a href="/docs/guides/tasks" target="_blank"
@@ -194,10 +190,8 @@ <h2>Training</h2>
                 <div class="block">
                     <h2>Inference</h2>
 
-                    <p>Using <strong>dstack</strong>, you can define and deploy <strong>services</strong> using multiple
-                        cloud providers, ensuring the best GPU price and availability.</p>
-
-                    <p><strong>Services</strong> enable cost-effective deployment of models and web apps.</p>
+                    <p>Deploy LLMs and other state-of-the-art generative AI models across multiple
+                        cloud GPU providers, ensuring data privacy, GPU availability, and cost-efficiency.</p>
 
                     <p>
                         <a href="/docs/guides/services" target="_blank"
@@ -211,10 +205,8 @@ <h2>Inference</h2>
             <div class="section">
                 <div class="block margin">
                     <h2>Dev environments</h2>
-                    <p>Using <strong>dstack</strong>, provisioning dev environments over multiple cloud
-                        providers becomes effortless, ensuring the best GPU price and availability.</p>
-
-                    <p>Dev environments are easily accessible through your local desktop IDE.</p>
+                    <p>Provision development environments over multiple cloud GPI providers, ensuring data privacy, GPU
+                        availability, and cost-efficiency.</p>
 
                     <p>
                         <a href="/docs/guides/dev-environments" target="_blank"
@@ -351,14 +343,14 @@ <h3>
                 <h2>Get started in less than a minute</h2>
                 <div class="termy">
                     <pre class="highlight">
-$ pip install "dstack[aws,gcp,azure,lambda]"
+$ pip install "dstack[all] -U"
 $ dstack start
 
 The server is available at http://127.0.0.1:3000?token=b934d226-e24a-4eab-eb92b353b10f
                     </pre>
                 </div>
                 <p class="tx-landing__bottom_cta_text">
-                    <strong>Done!</strong> Configure clouds and begin using the CLI or Python API to run LLM workloads.
+                    <strong>Done!</strong> Configure clouds and use the CLI or API to orchestrate GPU workloads.
                 </p>
 
                 <a href="/docs" class="md-button md-button--primary">

diff --git a/mkdocs.yml b/mkdocs.yml
@@ -3,7 +3,8 @@ site_name: dstack
 site_url: https://dstack.ai
 site_author: dstack GmbH
 site_description: >-
-  dstack is an open-source toolkit for orchestrating LLM workloads in any clouds.
+  dstack is an open-source toolkit for training, fine-tuning, inference, and development
+  across multiple cloud GPU providers.
 
 # Repository
 repo_url: https://github.com/dstackai/dstack