5 Easy Steps to Use Claude Dev Locally * bigfinish.com

Developing locally can be a great way to iterate quickly on your code without having to worry about deploying to a live server. In this tutorial, we will show you how to use Claude Dev locally. Claude Dev is a development environment that includes everything you need to start developing Claude applications, including a built-in web server and database.

To get started, you will need to install Claude Dev on your computer. You can do this by downloading the installer from the Claude website. Once you have installed Claude Dev, you can launch it by clicking on the Claude Dev icon on your desktop. When Claude Dev launches, you will be presented with a welcome screen. From here, you can create a new project or open an existing one.

Installing Claude Dev

To get started with Claude Dev, you’ll need to install it locally. Here are the steps to install Claude Dev:

Prerequisites
- Install Node.js version 16 or later.
- Install Git version 2.20 or later.

Clone the Claude Dev repository

git clone --recurse-submodules https://github.com/google/claude.git

Install Node.js dependencies
```
cd claude
npm install
```
Build Claude Dev
```
npm run build
```
Run Claude Dev
```
npm start
```

Claude Dev will be running on http://localhost:8000.

Setting Up Your Environment

Before we dive into using Claude DeV locally, let’s make sure you have the necessary tools set up. Here’s a step-by-step guide:

1. Install Python

Claude DeV is a Python-based tool, so you’ll need Python 3.6 or later installed on your system. Visit the official Python website to download and install it.

2. Set Up a Virtual Environment

A virtual environment isolates Claude DeV from other system packages, ensuring a clean and controlled environment. To create one using pip, follow these steps:

Open your terminal or command prompt.
Create a virtual environment named “venv” using the command: python -m venv venv.
Activate the environment using: source venv/bin/activate.
Verify activation by checking if your terminal prompt now starts with (venv).

3. Install Claude DeV

With the virtual environment activated, you can now install Claude DeV using pip. Run the following command in your terminal:


pip install claude-dev

Creating a New Project

To begin working with Claude Dev locally, you’ll need to create a new project. Here’s a step-by-step guide to help you get started:

1. Install the Claude Dev CLI

Before creating a new project, you’ll need to install the Claude Dev CLI. This can be done using the following command:

“`
npm install -g @claude-dev/cli
“`

2. Create a New Project

To create a new project, open your terminal and navigate to the directory where you want to create the project. Then, run the following command:

“`
claude dev init my-project
“`

This will create a new directory called my-project with the necessary files and folders for your project.

3. Project Structure

The newly created project directory will have the following structure:

File/Folder	Description
`README.md`	Project documentation
`package.json`	Package configuration
`src/`	Source code directory
`src/index.js`	Entry point for the application

The src/ directory contains the source code for your application. The src/index.js file is the entry point for the application and will be automatically executed when you run the application.

Working with Custom Modules

Custom modules are self-contained code units that extend Claude’s functionality. They can be used to create new operators, functions, or even complete workflows.

Creating Custom Modules

To create a custom module, you can use the module command. For example, the following command creates a module named “my_module”:

“`
module my_module {
# code for the module
}
“`

Importing Custom Modules

To use a custom module in your Claude code, you need to import it using the import command. For example, the following command imports the “my_module” module:

“`
import my_module
“`

Using Custom Modules

Once you have imported a custom module, you can use its exported symbols in your code. For example, the following code uses the my_function() function from the “my_module” module:

“`
my_module.my_function(arg1, arg2)
“`

Custom Module Architecture

Custom modules can be structured into multiple files and directories. The root directory of a custom module contains a manifest file named manifest.json. This file defines the module’s name, version, dependencies, and exported symbols.

The following table shows the structure of a typical custom module:

Directory/File	Description
`manifest.json`	Module manifest file
`src/`	Source code directory
`test/`	Test directory
`docs/`	Documentation directory

Debugging Training Processes

The _Claude_ CLI provides a number of tools for debugging training processes. These tools can be used to identify and resolve issues that may be preventing the model from training successfully.

Inspecting Training Logs

The training logs contain a wealth of information about the training process, including the following:

The training progress
The loss function
The accuracy
The learning rate
The gradients

To inspect the training logs, use the `_claude training logs_` command. This command will output the logs to the console.

Accessing the Debugger

The debugger can be used to step through the training process and inspect the state of the model. To access the debugger, use the `_claude training debug_` command.

Dumping the Model State

The model state can be dumped to a file at any point during the training process. This can be useful for debugging purposes, as it allows you to inspect the model’s weights and biases. To dump the model state, use the `_claude training dump_state_` command.

Setting Breakpoints

Breakpoints can be set at any point in the training process. This can be useful for debugging purposes, as it allows you to stop the training process at a specific point and inspect the state of the model. To set a breakpoint, use the `_claude training breakpoint_` command.

Profiling the Training Process

The training process can be profiled to identify performance bottlenecks. This can be useful for optimizing the training process and improving the model’s performance. To profile the training process, use the `_claude training profile_` command.

Profiling and Performance Optimization

Profiling

Profiling is a technique for identifying bottlenecks and performance issues in your code. To profile your Claude Dev application, you can use the built-in profiler or a third-party tool like perf or gprof. Once you have identified the bottlenecks, you can optimize your code to improve performance.

Optimization

There are many ways to optimize your Claude Dev application. Some of the most common techniques include:

Caching
Using lazy evaluation
Optimizing data structures
Using parallelism
Profiling and identifying bottlenecks
Refactoring your code for better performance
Using a performance testing tool like JMH or Caliper to measure the performance of your code and identify areas for improvement.

Caching

Caching is a technique for storing the results of expensive calculations or database queries so that they can be reused later. This can significantly improve performance, especially for applications that perform the same calculations or queries repeatedly.

Lazy evaluation

Lazy evaluation is a technique for delaying the evaluation of an expression until it is actually needed. This can improve performance by avoiding unnecessary calculations.

Optimizing data structures

The choice of data structure can have a significant impact on the performance of your application. For example, using a hash table instead of a linear search can improve lookup performance by a factor of O(n) to O(1).

Using parallelism

Parallelization is a technique for distributing a computation across multiple processors or cores. This can improve performance by taking advantage of the parallelism inherent in many algorithms.

Profiling and identifying bottlenecks

Profiling is a technique for identifying bottlenecks and performance issues in your code. This can be done using a profiler or by simply measuring the time it takes to execute different parts of your code.

Refactoring your code for better performance

Refactoring is a technique for modifying the structure of your code without changing its behavior. This can improve performance by making your code more efficient or easier to understand.

Using a performance testing tool

A performance testing tool can help you measure the performance of your application and identify areas for improvement. This can be a valuable tool for optimizing the performance of your application.

Deploying Locally Trained Models

Follow these steps to deploy models trained locally using Claude Dev:

1. Install Claude Dev

Use the following command to install Claude Dev:

“`
pip install claude-dev
“`

2. Initialize a Claude Dev Project

Create a new Claude Dev project using:

“`
claudedev init
“`

3. Create a Model Class

Define a model class inherited from claudedev.Model.

4. Train the Model

Train the model using your preferred training pipeline.

5. Save the Model

Save the trained model using the following method:

“`
model.save(“model.ckpt”)
“`

6. Create a Claude Dev Model Definition

Define a model definition file (model.yaml) describing the model architecture and hyperparameters.

7. Build the Model Image

Build a container image for your model using:

“`
claudedev build
“`

8. Deploy the Model

Deploy the model to a local Kubernetes cluster using the following command:

“`
claudedev deploy –local
“`

Parameters

The --local flag specifies local deployment. Additional parameters include:

Parameter	Description
`--name`	Name of the deployment (default: “claudedev-model”)
`--namespace`	Kubernetes namespace (default: “default”)
`--port`	Port for model inference (default: 8080)

Using Pre-trained Models

Claude Dev provides pre-trained models to accelerate your development process. These models have been trained on massive datasets and can be fine-tuned to specific tasks. To use a pre-trained model:

Import the `Claude’ library.
Create a `Claude’ client.
Specify the pre-trained model you want to use in the `model’ parameter.
Pass the input data to the `generate’ method.
Retrieve the generated text from the `response’ object.

Fine-tuning Pre-trained Models

Pre-trained models can be fine-tuned to enhance their performance on specific tasks. To fine-tune a pre-trained model:

Create a training dataset that includes examples of the desired output.
Create a `Claude’ client.
Specify the pre-trained model you want to fine-tune in the `model’ parameter.
Pass the training dataset to the `fine_tune’ method.
Specify the number of training epochs and the batch size.
Start the fine-tuning process.

The following pre-trained models are available in Claude Dev:

Model Name	Description
text-bison-001	Large language model trained on a massive dataset of text
code-davinci-002	Code generation and programming assistance model
image-caption-001	Image captioning model trained on a dataset of images and their descriptions

Troubleshooting Common Issues

1. Error: “Cannot find module ‘claude-dev'”

Make sure you have installed Claude Dev globally using npm:

npm install -g claude-dev

2. Error: “Command not found: claudev”

Add the global npm bin directory to your PATH environment variable:

export PATH="$PATH:$(npm bin)"

3. Error: “Error parsing configuration file…”

Check the syntax of your claude.config.js file. Ensure it is valid JSON and has the correct properties.

4. Error: “Error fetching metadata from…”

Make sure you have a stable internet connection and that the specified metadata source is accessible.

5. Error: “Error generating code…”

Check the generated code for any errors. Ensure your configuration and data are correct.

6. Error: “Cannot find type definition file…”

Install the type definition files for the specified language:

npm install --save-dev @types/[language-name]

7. Error: “TypeError: Cannot read properties of undefined…”

Make sure the data provided in your configuration file has the expected properties.

8. Error: “SyntaxError: Unexpected token…”

Check the syntax of your generated code. Ensure it is valid for the target language.

9. Error: “claude-dev: command not recognized”

Ensure you have installed claude-dev globally using npm and that the global npm bin directory is in your PATH environment variable.

10. Error: “Unknown error occurred”

Cause	Solution
Outdated version of claude-dev	Update claude-dev using npm: `npm update -g claude-dev`
Corrupted installation	Reinstall claude-dev globally: `npm uninstall -g claude-dev && npm install -g claude-dev`
Insufficient permissions	Run claude-dev commands as an administrator.
Firewall blocking connections	Allow claude-dev access through any firewalls.

How to Use Claude Dev Locally

Claude Dev is a local development environment for building and testing cloud-native applications. It provides a consistent development experience across multiple clouds and on-premises environments. To use Claude Dev locally, you will need to install the Claude Dev CLI and create a workspace.

Once you have installed the Claude Dev CLI, you can create a workspace by running the following command:

“`
claude dev init
“`

This will create a directory called “claude-dev” in the current directory. The “claude-dev” directory will contain all of the files and configuration for your workspace.

To start a local development environment, run the following command:

“`
claude dev up
“`

This will start a Kubernetes cluster and deploy your application to the cluster. You can then access your application by visiting the URL printed in the terminal.

To stop the local development environment, run the following command:

“`
claude dev down
“`

This will stop the Kubernetes cluster and delete all of the resources created by Claude Dev.