Merge pull request #53 from HoomanHQ/dharamveer

fix: docs new doc, Update favicon and provider FAQ

src/components/base-head.astro

@@ -13,7 +13,7 @@ const { title, description, image = "/meta-images/home.png" } = Astro.props;
 <!-- Global Metadata -->
 <meta charset="utf-8" />
 <meta name="viewport" content="width=device-width,initial-scale=1" />
-<link rel="icon" type="image/svg+xml" href="/favicon.ico" />
+<link rel="icon" type="image/svg+xml" href="/favicon.svg" />
 <meta name="generator" content={Astro.generator} />
 
 <!-- Font preloads -->

src/content/Docs/_sequence.ts

@@ -257,6 +257,7 @@ export const docsSequence = [
           {
             label: "Experimental",
             subItems: [
+              { label: "AMD GPU Support" },
               { label: "Akash Provider Streamlined Build with Rancher K3s" },
               {
                 label: "Omnibus",

src/content/Docs/other-resources/experimental/amd-gpu-support/index.md

@@ -0,0 +1,264 @@
+---
+categories: ["Other Resources", "Experimental"]
+tags: []
+weight: 2
+title: "AMD GPU Support"
+linkTitle: "AMD GPU Support"
+---
+
+## Introduction
+
+Welcome to the specialized guide designed to assist Akash Providers in enabling AMD GPU support within their Kubernetes clusters. This documentation is particularly crafted for system administrators, developers, and DevOps professionals who manage and operate their Akash Providers. The focus here is to guide you through the process of integrating AMD GPUs into your Kubernetes/Akash setup, ensuring that they can be utilized in the Akash Network.
+
+Throughout this guide, you will find step-by-step instructions on installing the necessary AMD drivers, configuring Kubernetes to acknowledge and leverage AMD GPUs.
+
+This documentation is vital for Akash Providers and Clients who aim to deploy advanced workloads such as machine learning models, high-performance computing tasks, or any applications that benefit from GPU acceleration. By following this guide, you will be able to enhance your service offerings on the Akash Network, catering to a wider range of computational needs with AMD GPU support.
+
+> **NOTE**: To effectively enable AMD GPU support, ensure that your `akash-provider` and `provider-services` (CLI) are updated to version `0.4.9-rc0` or higher. This is a prerequisite for proper integration and functionality of AMD GPUs on your Akash Provider.
+
+## Limitations
+
+Current constraints dictate that combining NVIDIA and AMD GPU vendors within the same Kubernetes worker node is not allowed. However, it is permissible to have different GPU vendors within the same Kubernetes cluster, as long as each individual worker node exclusively uses GPUs from a single vendor, be it NVIDIA or AMD.
+
+- **Vendor Constraint:** Combining NVIDIA and AMD GPUs within the same Kubernetes worker node is not permitted.
+- **Vendor Homogeneity:** It is permissible to mix different GPU vendors on the same Kubernetes cluster. However, this mixing is not allowed within a single worker node.
+- **Vendor Exclusivity:** Each worker node must exclusively use GPUs from a single vendor, either NVIDIA or AMD. This means a single node cannot have a mix of NVIDIA and AMD GPUs, but different nodes within the same cluster can use different vendors.
+
+## Installing the AMD GPU Driver
+
+Follow these steps to install the AMD GPU Driver:
+
+1. Install AMD GPU drivers using DKMS
+
+- Apply these commands on your node with AMD GPU:
+
+  > based on https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/native-install/ubuntu.html
+
+  ```
+  mkdir --parents --mode=0755 /etc/apt/keyrings
+
+  wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | \
+    gpg --dearmor | tee /etc/apt/keyrings/rocm.gpg > /dev/null
+
+  echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/amdgpu/6.0.1/ubuntu jammy main" \
+    | tee /etc/apt/sources.list.d/amdgpu.list
+  apt update
+  apt -y install amdgpu-dkms
+  ```
+
+2. Make sure the right driver is loaded:
+
+- Reboot the node:
+  By default `/lib/modules/<version>/kernel/drivers/gpu/drm/amd/amdgpu/amdgpu.ko` is loaded, however you cannot simply `modprobe -r amdgpu` and then `modprobe amdgpu`.  
+   You need to reboot to make sure the correct AMD GPU driver (DKMS `/lib/modules/<version>/updates/dkms/amdgpu.ko`) is properly loaded.
+
+- Verify correct version is loaded (you may see a higher version, that's okay):
+
+  ```
+  # dmesg -T |grep 'amdgpu version'
+  [Fri Jan 26 22:47:18 2024] [drm] amdgpu version: 6.3.6
+
+  # dmesg -T |grep -i 'Initialized amdgpu'
+  [Fri Jan 26 22:47:19 2024] [drm] Initialized amdgpu 3.56.0 20150101 for 0000:1b:00.0 on minor 1
+  ```
+
+## Enabling AMD GPU Support in Akash Provider
+
+### 1. Install `ROCm/k8s-device-plugin` helm-chart
+
+- Add the helm repository and install the chart:
+
+  ```bash
+  helm repo add amd-gpu-helm https://rocm.github.io/k8s-device-plugin/
+  helm install --create-namespace --namespace amd-device-plugin --set namespace=amd-device-plugin amd-gpu amd-gpu-helm/amd-gpu --version 0.12.0
+  ```
+
+- Verify the installation:
+
+  > NOTE: replace `node1` with the node name of your worker node (`kubectl get nodes`)
+
+  ```bash
+  kubectl -n amd-device-plugin logs ds/amd-gpu-device-plugin-daemonset
+  kubectl describe node node1 | grep -B1 -Ei 'nvidia.com/gpu|amd.com/gpu'
+  ```
+
+- Example output:
+
+  ```bash
+  # kubectl -n amd-device-plugin logs ds/amd-gpu-device-plugin-daemonset
+  I0126 22:47:52.227295       1 main.go:305] AMD GPU device plugin for Kubernetes
+  I0126 22:47:52.227493       1 main.go:305] ./k8s-device-plugin version v1.25.2.7-0-g4503704
+  I0126 22:47:52.227506       1 main.go:305] hwloc: _VERSION: 2.10.0, _API_VERSION: 0x00020800, _COMPONENT_ABI: 7, Runtime: 0x00020800
+  I0126 22:47:52.227524       1 manager.go:42] Starting device plugin manager
+  I0126 22:47:52.227543       1 manager.go:46] Registering for system signal notifications
+  I0126 22:47:52.228216       1 manager.go:52] Registering for notifications of filesystem changes in device plugin directory
+  I0126 22:47:52.228421       1 manager.go:60] Starting Discovery on new plugins
+  I0126 22:47:52.228446       1 manager.go:66] Handling incoming signals
+  I0126 22:47:52.228491       1 manager.go:71] Received new list of plugins: [gpu]
+  I0126 22:47:52.228555       1 manager.go:110] Adding a new plugin "gpu"
+  I0126 22:47:52.228594       1 plugin.go:64] gpu: Starting plugin server
+  I0126 22:47:52.228605       1 plugin.go:94] gpu: Starting the DPI gRPC server
+  I0126 22:47:52.229986       1 plugin.go:112] gpu: Serving requests...
+  I0126 22:48:02.237090       1 plugin.go:128] gpu: Registering the DPI with Kubelet
+  I0126 22:48:02.238870       1 plugin.go:140] gpu: Registration for endpoint amd.com_gpu
+  I0126 22:48:02.246025       1 amdgpu.go:100] /sys/module/amdgpu/drivers/pci:amdgpu/0000:1b:00.0
+  I0126 22:48:02.323568       1 main.go:149] Watching GPU with bus ID: 0000:1b:00.0 NUMA Node: [0]
+
+  # kubectl describe node node1 | grep -B1 -Ei 'nvidia.com/gpu|amd.com/gpu'
+  Capacity:
+    amd.com/gpu:        1
+  --
+  Allocatable:
+    amd.com/gpu:        1
+  --
+    hugepages-2Mi      0 (0%)         0 (0%)
+    amd.com/gpu        0              0
+  ```
+
+### 2. Label AMD GPU Node
+
+- Label your AMD GPU node (replace `mi210` with your AMD GPU model):
+  ```bash
+  kubectl label node node1 akash.network/capabilities.gpu.vendor.amd.model.mi210=true
+  ```
+
+### 3. Test AMD GPU with TensorFlow in Pod
+
+Before proceeding with the deployment, be aware of the following:
+
+> **NOTE:** Starting the `alexnet-gpu` may take a considerable amount of time, especially over slow network connections. This delay is due to the large size of the image, approximately `10 GiB`, as detailed on [Docker Hub](https://hub.docker.com/r/rocm/tensorflow/tags).
+
+To deploy and test the TensorFlow environment on AMD GPUs, follow these steps:
+
+1. Create the pod using the provided YAML file:
+
+   ```bash
+   kubectl create -f https://raw.githubusercontent.com/ROCm/k8s-device-plugin/c9fc007f07fca4ea1c495ab57f54e10ffa9e2a6b/example/pod/alexnet-gpu.yaml
+   ```
+
+2. Check the logs to verify successful deployment and operation:
+
+   ```bash
+   kubectl logs alexnet-tf-gpu-pod
+   # Expected output includes:
+   # TensorFlow version information, list of devices (e.g., '/gpu:0'), and performance metrics (e.g., total images/sec).
+   ```
+
+   Example output:
+
+   ```
+   $ kubectl logs alexnet-tf-gpu-pod
+   ...
+   2024-01-26 22:50:28.771404: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 63950 MB memory:  -> device: 0, name: AMD Instinct MI210, pci bus id: 0000:1b:00.0
+   ...
+   TensorFlow:  2.14
+   ...
+   Devices:     ['/gpu:0']
+   ...
+   total images/sec: 5849.31
+   ```
+
+3. Once testing is complete, delete the pod:
+   ```bash
+   kubectl delete pod alexnet-tf-gpu-pod
+   ```
+
+## Update bid pricing parameters for your AMD GPU card
+
+Make sure you are using the latest bid pricing script. You can follow [these](/docs/providers/provider-faq-and-guide#provider-bid-script-migration---gpu-models) instructions.
+
+- Add the pricing for your AMD GPU model (replace `mi210` with your model) to `provider.yaml` file:
+  ```yaml
+  price_target_gpu_mappings: "mi210=190,*=200"
+  ```
+
+This sets `$190`/month for AMD GPU MI 210 card and defaults to `$200`/month when the GPU model was [not](https://github.com/akash-network/support/issues/166) explicitly set.
+
+## Testing AMD GPU with TensorFlow in Akash Deployment
+
+To test TensorFlow with AMD GPU in Akash Deployment:
+
+- Base your deployment on the image & command/args from the provided YAML file.
+- Use image: `rocm/tensorflow`.
+- Override the `command` & `args` in the SDL.
+- Execute the benchmarking command:
+  ```bash
+  python3 /benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --model=alexnet
+  ```
+
+### Example SDL
+
+Use the following SDL configuration to deploy `rocm/tensorflow` image:
+
+> Make sure to replace `SSH_PUBKEY` with your public SSH key should you want to be able ssh to your deployment instead of `lease-shell` into it.
+
+```yaml
+---
+version: "2.0"
+
+services:
+  app:
+    image: rocm/tensorflow:latest
+    env:
+      - 'SSH_PUBKEY=ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAINNFxqDbY0BlEjJ2y9B2IKUUoimOq6oAC7WcsQT8qmII andy'
+    command:
+      - "sh"
+      - "-c"
+    args:
+      - 'apt-get update;
+      apt-get install -y --no-install-recommends -- ssh speedtest-cli netcat-openbsd curl wget ca-certificates jq less iproute2 iputils-ping vim bind9-dnsutils nginx;
+      mkdir -p -m0755 /run/sshd;
+      mkdir -m700 ~/.ssh;
+      echo "$SSH_PUBKEY" | tee ~/.ssh/authorized_keys;
+      chmod 0600 ~/.ssh/authorized_keys;
+      ls -lad ~ ~/.ssh ~/.ssh/authorized_keys;
+      md5sum ~/.ssh/authorized_keys;
+      exec /usr/sbin/sshd -D'
+    expose:
+      - port: 80
+        as: 80
+        to:
+          - global: true
+      - port: 22
+        as: 22
+        to:
+          - global: true
+
+
+profiles:
+  compute:
+    app:
+      resources:
+        cpu:
+          units: 2
+        memory:
+          size: 8Gi
+        storage:
+          size: 25Gi
+        gpu:
+          units: 1
+          attributes:
+            vendor:
+              amd:
+                - model: mi210
+  placement:
+    akash:
+      attributes:
+        host: akash
+      pricing:
+        app:
+          denom: uakt
+          amount: 1000000
+
+deployment:
+  app:
+    akash:
+      profile: app
+      count: 1
+```
+
+## Additional material
+
+- Exploring Integration of `rocm-smi` in AMD GPU Pods for Enhanced Compatibility
+
+We are [exploring](https://github.com/ROCm/k8s-device-plugin/issues/44) the possibility of including the `rocm-smi` tool by default in AMD GPU Pods, analogous to how `nvidia-smi` is available in NVIDIA GPU Pods. This inclusion in NVIDIA pods is facilitated by the NVIDIA device plugin, which mounts necessary host paths and utilizes environment variables like `NVIDIA_DRIVER_CAPABILITIES`. For more detailed examples and information, refer to the NVIDIA Container Toolkit documentation [here](https://github.com/NVIDIA/nvidia-container-toolkit/blob/a2262d00cc6d98ac2e95ae2f439e699a7d64dc17/cmd/nvidia-container-runtime/README.md?plain=1#L147-L161). Our goal is to achieve similar functionality for AMD GPUs, enhancing user experience.

src/content/Docs/providers/provider-faq-and-guide/index.md

@@ -23,7 +23,7 @@ The guide is broken down into the following categories:
 - [Force New ReplicaSet Workaround](#force-new-replicaset-workaround)
 - [Kill Zombie Processes](#kill-zombie-processes)
 - [Close Leases Based on Image](#close-leases-based-on-image)
-- [Provider Bid Script Migration for GPU Model Pricing](#provider-bid-script-migration-gpu-models)
+- [Provider Bid Script Migration for GPU Model Pricing](#provider-bid-script-migration---gpu-models)
 - [GPU Provider Troubleshooting](#gpu-provider-troubleshooting)
 
 ## Provider Maintenance