ACE-Step: A Step Towards Music Generation Foundation Model
This video provides a tutorial on how to set up and use RunPod for AI model generation, specifically focusing on the ACE-Step music generation foundation model. The presenter walks through the process of creating a RunPod account, selecting a suitable GPU instance, and preparing the environment for running the ACE-Step model. The tutorial covers key steps like adding RunPod credits, selecting a “Community Cloud” GPU, and initiating the deployment of a pod. It also touches upon SSH access and the importance of correctly configuring the pod for successful execution.
Getting Started with RunPod
[0:00:50.067] The presenter begins by explaining that the video will be an in-depth tutorial on how to get started with RunPod. They mention that their channel often tests AI models locally on their own systems.
[0:16:07.343] The initial step involves signing up for RunPod, which is described as “AI infrastructure developers trust.” The presenter navigates to runpod.io and proceeds to the sign-in or sign-up process.
[0:51:07.594] After signing in, the user is presented with a choice of options for running AI workloads. The presenter selects “Launch a GPU environment” as their preferred method for this demonstration.
[1:46:26.677] The tutorial then guides through the initial setup questions and prompts the user to choose an option for running their AI workloads.
[1:51:08.804] The presenter selects the option to “Launch a GPU environment” and proceeds to the pod deployment page.
Selecting a GPU Instance and Pod Template
[2:00:02.749] On the “Deploy a Pod” page, users can select an instance based on various filters, including GPU, Secure Cloud, Network Volume, Region, and Additional Filters.
[2:01:09.058] The interface displays a range of Featured GPUs and NVIDIA Latest Gen options, categorized by their performance and cost per hour. The presenter notes the availability of different GPUs, such as RTX 5090, A40, H200 SXM, B200, and various other NVIDIA models.
[2:33:34.795] The presenter chooses to deploy a relatively inexpensive GPU, specifically an H100 PCIe with 80 GB of VRAM, to demonstrate the process. They mention that older cards might offer a smoother setup.
[3:40:52.810] The tutorial highlights that RunPod offers various pre-configured Pod Templates, making it easier to deploy models like Stable Diffusion or ComfyUI. The presenter selects a RunPod PyTorch 2.8.0 template as it comes pre-installed with necessary components.
Adding RunPod Credits and Configuring the Pod
[2:42:15.321] To start using the services, RunPod credits need to be added. The presenter demonstrates adding $10 to their account, noting the minimum credit amount of $10. They opt out of auto-pay for this demonstration.
[3:44:10.058] The presenter then accesses the Pod Template Overrides to customize the deployment. This section allows for adjusting the Container Image, Container Disk size, Volume Mount Path, and exposing specific ports.
[4:37:13.062] Crucially, the presenter navigates to Settings > SSH Public Keys to add their public SSH key. This is essential for enabling secure remote access to the pod via SSH.
[4:44:52.612] The public key is copied from the generated file and pasted into the RunPod settings.
[6:06:11.500] The presenter then selects the H100 PCIe as the GPU instance and the default PyTorch template.
[7:11:31.786] With the SSH key configured and the pod selected, the presenter proceeds to deploy the pod.
Deploying and Connecting to the Pod
[7:16:03.054] After deployment, the pod is shown as “Running.” The next step is to connect to it. The presenter shows how to access the pod via SSH using the provided command.
[8:18:35.059] The initial connection attempt requires confirming the authenticity of the host, which is a standard security measure.
[8:37:06.495] Once connected, the user is presented with a terminal interface. The presenter demonstrates using commands like nvidia-smi to check the GPU status, confirming the presence of the H100 PCIe.
[9:01:00.000] They then navigate into the /workspace directory, where they can access and interact with the model files and scripts.
Running the ACE-Step Model
[10:01:31.748] The tutorial then moves to demonstrate the actual usage of ACE-Step. The presenter executes a Python command to import necessary libraries and then specifies the port to run the Gradio server.
python -m ace_step --port 7868
[10:55:08.972] The command is further refined to include server information, ensuring the Gradio interface is accessible from other devices on the network.
python -m ace_step --server_name 0.0.0.0 --port 7868
[11:17:21.215] The RunPod interface then shows the pod is running and indicates the local URL for accessing the web UI.
[11:40:53.761] The presenter then clicks on the HTTP service link to launch the web UI.
[12:05:54.282] The web UI is presented, showing various options for generating music, including parameters like Audio Duration, Format, Variance, and Retake Seeds.
[12:11:54.942] The presenter demonstrates generating a short piece of music by providing a simple text prompt.
[12:41:13.786] They also show how to use the “Retake” function to modify parameters and generate variations of the music.
[13:31:12.958] The presenter explains that the virtual environment created in the earlier steps is crucial for managing dependencies, especially when using models like ACE-Step.
[15:50:07.563] They then demonstrate how to install dependencies using pip install -e ., which installs ACE-Step and its core dependencies.
[23:10:58.365] Finally, the presenter shows how to stop and terminate the pod when no longer needed, emphasizing that all data will be lost. This step is important for managing costs and resources effectively.
The tutorial concludes by highlighting the flexibility of RunPod for deploying and interacting with AI models like ACE-Step, offering a powerful platform for experimentation and development.