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Serverless Training Cloud Service on FEDML Nexus AI with Seamless Experimental Tracking
👉 A Theta Network Partner
March 20, 2024
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We are excited to introduce our “Training as a Cloud Service” at FEDML Nexus AI platform. It provides a variety of GPU types (A100, H100, A6000, RTX4090, etc.) for developers to train your model at any time in a serverless manner. Developers only pay per usage. It includes the following features:

  • Cost-effective training: Developers do not need to rent or purchase GPUs, developers can initiate serverless training tasks at any time, and developers only need to pay according to the usage time;
  • Flexible Resource Management: Developers can also create a cluster to use fixed machines and support the cluster autostop function (such as automatic shutdown after 30 minutes) to help you save the cost loss caused by forgetting to shut down the idle resources.
  • Simplified Code Setup: You do not need to modify your python training source code, you only need to specify the path of the code, environment installation script, and the main entrance through the YAML file
  • Comprehensive Tracking: The training process includes rich experimental tracking functions, including Run Overview, Metrics, Logs, Hardware Monitoring, Model, Artifacts, and other tracking capabilities. You can use the API provided by FEDML Python Library for experimental tracking, such as fedml.log
  • GPU Availability: There are many GPU types to choose from. You can go to Secure Cloud or Community Cloud to view the type and set it in the YAML file to use it.

We will introduce how simple it is as follows:

  • Zero-code Serverless LLM Training on FEDML Nexus AI
  • Training More GenAI Models with FEDML Launch and Pre-built Job Store
  • Experiment Tracking for Large-scale Distributed Training
  • Train on Your Own GPU cluster

Platform: https://fedml.ai
GitHub: https://github.com/FedML-AI

Zero-code Serverless LLM Training on FEDML Nexus AI

As an example of applying FEDML Launch for training service, LLM Fine-tune is the feature of FEDML Studio that is responsible for serverless model training. It is a no-code LLM training platform. Developers can directly specify open-source models for fine-tuning or model Pre-training.

Step 1. Select a model to build a new run

There are two choices for specifying the model to train:
1) Select Default base model from Open Source LLMs

2) Specifying HuggingFace LLM model path

Step 2. Prepare training data

There are three ways to prepare the training data. 

1) Select the default data experience platform 

3) Customized training data can be uploaded through the storage module

3) Data upload API: fedml.api.storage

fedml storage upload '/path/Prompts_for_Voice_cloning_and_TTS'Uploading Package to Remote Storage: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 42.0M/42.0M [00:36<00:00, 1.15MB/s]Data uploaded successfully. | url: https://03aa47c68e20656e11ca9e0765c6bc1f.r2.cloudflarestorage.com/fedml/3631/Prompts_for_Voice_cloning_and_TTS.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=52d6cf37c034a6f4ae68d577a6c0cd61%2F20240307%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240307T202738Z&X-Amz-Expires=604800&X-Amz-SignedHeaders=host&X-Amz-Signature=bccabd11df98004490672222390b2793327f733813ac2d4fac4d263d50516947

Step 3. Hyperparameter Setting (Optional)

Step 4. Select GPU Resource Type (Optional)

The GPU resource type can be found through the Compute - Secure Cloud page

Step 5. Initiate Training and Track Experimental Results

Training More GenAI Models with FEDML Launch and Pre-built Job Store

Besides the zero-code training job experience, we also provide FEMDL Launch to launch any training job on FEDML Nexus AI. For more details, please read another blog: https://blog.fedml.ai/fedml-launch/. Here, we mainly introduce how to run pre-built jobs on FEDML Nexus AI platform.

Taking a pre-built job for GaLore (https://github.com/jiaweizzhao/galore), an efficient traininmethg od for LLM on RTX4090, as an example. A day after it released source code on GitHub, FEDML Team incorporated GaLore training as part of our Job Store. Now developers can launch and customize on top of the example GaLore jobs and enjoy freedom from Out-of-Memory fear.

The instructions to launch GaLore pre-built job are as follows:

  1. On FedML official website (https://fedml.ai/home), you can head to Launch Job Store > Train, and look for Memory-Efficient LLM Training with GaLore job. The Description tab shows some basic usage for the code, referencing the original GaLore project's README. In the Source Code and Configuration tab, you can examine a more detailed layout and setup of the architecture.

  1. Hit the Launch button on the top right, users will be prompted to enter the configuration for the job. Under the Select Job section, click Add, and add “resource_type: RTX-4090”  in the job_yaml > computing section to specify using RTX 4090 for training. Please check the resource type list ​at https://fedml.ai/compute/secure (check the value of Resource Type in each GPU item), or directly visit https://fedml.ai/launch/accelerator_resource_type?_t=1710889566178.
  2. Once done filling out the hyperparameters, you should be able to launch a full-scale GaLore + Checkpointing Activation pre-training for the LLaMA 7B model with a batch size of 16. Then you can find your experimental tracking results at https://fedml.ai/train/my-runs (see more details on the Section "Experiment Tracking for Large-scale Distributed Training")

Experiment Tracking for Large-scale Distributed Training

Running remote tasks often requires a transparent monitoring environment to facilitate troubleshooting and real-time analysis of machine learning experiments. This section guides through the monitoring capabilities of a launched job.

Run Overview

Log into to the FEDML Nexus AI Platform (https://fedml.ai) and go to Train > Runs. And select the run you just launched and click on it to view the details of the run.

Metrics

FedML offers a convenient set of APIs for logging metrics. The execution code can utilize these APIs to log metrics during its operation.

fedml.log()

log dictionary of metric data to the FEDML Nexus AI Platform.

Usage

fedml.log(metrics: dict,step: int = None,customized_step_key: str = None,commit: bool = True) -> None

Arguments

  • metrics (dict): A dictionary object for metrics, e.g., {"accuracy": 0.3, "loss": 2.0}.
  • step (int=None): Set the index for current metric. If this value is None, then step will be the current global step counter.
  • customized_step_key (str=None): Specify the customized step key, which must be one of the keys in the metrics dictionary.
  • commit (bool=True): If commit is False, the metrics dictionary will be saved to memory and won't be committed until commit is True.

Example:

fedml.log({"ACC": 0.1})fedml.log({"acc": 0.11})fedml.log({"acc": 0.2})fedml.log({"acc": 0.3})fedml.log({"acc": 0.31}, step=1)fedml.log({"acc": 0.32, "x_index": 2}, step=2, customized_step_key="x_index")fedml.log({"loss": 0.33}, customized_step_key="x_index", commit=False)fedml.log({"acc": 0.34}, step=4, customized_step_key="x_index", commit=True)

Metrics logged using fedml.log() can be viewed under Runs > Run Detail > Metrics on FEDML Nexus AI Platform.

Logs

You can query the realtime status of your run on your local terminal with the following command.

fedml run logs -rid <run_id>

Additionally, logs of the run also appear in realtime on the FEDML Nexus AI Platform under the Runs > Run Detail > Logs

Hardware Monitoring

The FEDML library automatically captures hardware metrics for each run, eliminating the need for user code or configuration. These metrics are categorized into two main groups:

  • Machine Metrics: This encompasses various metrics concerning the machine's overall performance and usage, encompassing CPU usage, memory consumption, disk I/O, and network activity.
  • GPU Metrics: In environments equipped with GPUs, FEDML seamlessly records metrics related to GPU utilization, memory usage, temperature, and power consumption. This data aids in fine-tuning machine learning tasks for optimized GPU-accelerated performance.

Model Checkpoint:

FEDML additionally provides an API for logging models, allowing users to upload model artifacts.

fedml.log_model()

Log model to the FEDML Nexus AI Platform (fedml.ai).

fedml.log_model(model_name,model_file_path,version=None) -> None

Arguments

  • model_name (str): model name.
  • model_file_path (str): The file path of model name.
  • version (str=None): The version of FEDML Nexus AI Platform, options: dev, test, release. Default is release (fedml.ai).

Examples

fedml.log_model("cv-model", "./cv-model.bin")

Models logged using fedml.log_model() can be viewed under Runs > Run Detail > Model on FEDML Nexus AI Platform

Artifacts:

Artifacts, as managed by FEDML, encapsulate information about items or data generated during task execution, such as files, logs, or models. This feature streamlines the process of uploading any form of data to the FEDML Nexus AI Platform, facilitating efficient management and sharing of job outputs. FEDML facilitates the uploading of artifacts to the FEDML Nexus AI Platform through the following artifact api:

fedml.log_artifact()

log artifacts to the FEDML Nexus AI Platform (fedml.ai), such as file, log, model, etc.

fedml.log_artifact(artifact: Artifact,version=None,run_id=None,edge_id=None) -> None

Arguments

  • artifact (Artifact): An artifact object represents the item to be logged, which could be a file, log, model, or similar.
  • version (str=None): The version of FEDML Nexus AI Platform, options: dev, test, release. Default is release (fedml.ai).
  • run_id (str=None): Run id for the artifact object. Default is None, which will be filled automatically.
  • edge_id (str=None): Edge id for current device. Default is None, which will be filled automatically.

Artifacts logged using fedml.log_artifact() can be viewed under Runs > Run Detail > Artifacts on FEDML Nexus AI Platform.

Train on Your Own GPU cluster

You can also build your own cluster and launch jobs there. The GPU nodes in the cluster can be GPU instances launched under your AWS/GCP/Azure account or your in-house GPU devices. The workflow is as follows.

Step 1. Bind the machines on the Platform

Log into the platform, head to the Compute / My Servers Page and copy the fedml login command:

Step 2. SSH into your on-prem devices and do the following individually for each device:

Install the fedml library if not installed already:

pip install fedml

Run the login command copied from the platform:

fedml login 3b24dd2f****206e8669

It should show something similar as below:

(fedml) alay@a6000:~$ fedml login 3b24dd2f9b3e478084c517bc206e8669 -v devWelcome to FedML.ai!Start to login the current device to the MLOps (https://fedml.ai)...(fedml) alay@a6000:~$ Found existing installation: fedml 0.8.7Uninstalling fedml-0.8.7:Successfully uninstalled fedml-0.8.7Looking in indexes: https://test.pypi.org/simple/, https://pypi.org/simpleCollecting fedml==0.8.8a156Obtaining dependency information for fedml==0.8.8a156 from https://test-files.pythonhosted.org/packages/e8/44/06b4773fe095760c8dd4933c2f75ee7ea9594938038fb8293afa22028906/fedml-0.8.8a156-py2.py3-none-any.whl.metadata Downloading https://test-files.pythonhosted.org/packages/e8/44/06b4773fe095760c8dd4933c2f75ee7ea9594938038fb8293afa22028906/fedml-0.8.8a156-py2.py3-none-any.whl.metadata (4.8 kB)Requirement already satisfied: numpy>=1.21 in ./.pyenv/versions/fedml/lib/python3.10/site-packages (from fedml==0.8.8a156....Congratulations, your device is connected to the FedML MLOps platform successfully!Your FedML Edge ID is 201610, unique device ID is [email protected]

Head back to the Compute / My Servers page on platform and verify that the devices are bounded to the FEDML Nexus AI Platform:

Step 3. Create a cluster of your servers bounded to the FEDML Nexus AI Platform:

Navigate to the Compute / Create Clusters page and create a cluster of your servers:

All your created clusters will be listed on the Compute / My Clusters page:

Step 4. Launch the job on your cluster:

The way to create the job YAML file is the same as “Training as a Cloud Service”. All that is left to do to launch a job to the on-premise cluster is to run following one-line command:

fedml launch job.yaml -c <cluster_name>

For our example, the command and respective output would be as follows:

fedml launch job.yaml -c hello-world

About FEDML, Inc.

FEDML is your generative AI platform at scale to enable developers and enterprises to build and commercialize their own generative AI applications easily, scalably, and economically. Its flagship product, FEDML Nexus AI, provides unique features in enterprise AI platforms, model deployment, model serving, AI agent APIs, launching training/Inference jobs on serverless/decentralized GPU cloud, experimental tracking for distributed training, federated learning, security, and privacy.

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What is XAH and Xahau?

If you're new to XRP, you may have noticed some of us discussing another network named 'Xahau'.

It's Like XRP ... But Different

The Xahau network was created in 2023, and its starting point was the open-source code for the XRP Ledger. A small team of researchers and entrepreneurs decided to add smart contracts to the network code.


The XRP Ledger has no smart contract capabilities, by default.

To integrate smart contracts, the team decided to use an architecture that includes 'WASM' or 'web assembly' code. Each account can have up to 10 'hooks' installed that are triggered for transactions that match specific criteria. They can run before or after a transaction is processed. This enables a variety of use cases that do not involve the need to change the network's core code.

Hooks

A 'hook' is what is known as a smart contract that can be triggered in relation to a specific account and its transactions.

The term arises from the programming world, where it generally means "code that runs based on triggering conditions." In Xahau's case, it indicates code that is run before, or after, a transaction is processed.
 
Each hook must be installed on a specific account by the party that controls the account - i.e., the secret key holder.
 
What Can XAH Do That XRP Cannot?
 
The primary benefit from the use of hooks, is that the core network code does not need to be changed every time a new use case is identified. This means that additional use cases can be addressed immediately, with no requirement for intervening steps, such as:
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  • Community approval
  • Amendment voting
All of those steps are eliminated with the use of hooks; new use cases can be addressed as fast as the code can be developed.
 
To read more about how hooks enables Xahau to handle more use cases than even the XRPL, you can read this article:
 
Key Differences From XRP
 
Other unique differences from the XRP Ledger include:
  • Much smaller supply ~612 million coins vs. 100 billion coins
  • XAH hodlers are rewarded at 4% of their account balance. There are no rewards for XRP.
  • Governance participants are incentivized
  • Payment channels available for user-created tokens (IOUs)
  • URI tokens instead of NFT tokens
Who's Who of Xahau?
 
The list of those that are either founders, or closely associated with the founding organizations, is extensive. Here are the names of three organizations mentioned in the whitepaper, or their current moniker:
  • Xaman (a.k.a. XRPL Labs)
  • Gatehub
  • InFTF (Inclusive Financial Technology Foundation)
There exists a long list of impressive developers, architects, and technologists among the Xahau inner circle. But the three names that people associate most prominently with the leadership of the Xahau network are Wietse Wind, Richard Holland, and Denis Angell. The links to their 'X' accounts are:
 
Friend Or Foe?
 
This topic is one of the most contentious.
 
While Ripple, the company with the largest stake of XRP, showed interest in hooks early on, they ultimately decided to advocate for a different approach; the use of an EVM-based solution (Ethereum Virtual Machine) to handle smart contracts on the XRP Ledger. This decision was met with consternation by the Xaman team that had worked with them for several years to advocate for the use of hooks.
 
You can read more about the 'business politics' part of this topic here:
 
So how do Xahau fans view the relationship between XRP and XAH?
 
The Xahau team - and many of its community members - advocate for the use of a 'dual-chain' solution to implement smart contracts. This can be accomplished by the use of 'listener' software, along with native Xahau hooks.
 
A proof of concept, developed by Denis Angell, has demonstrated that bi-lateral communication can work with a simple approach.
 
From an economic standpoint, every chain that has its own digital asset is a competitor; but the simple way to think about Xahau, is that a 'bunch of XRP geeks' decided to implement smart contracts on their own version of the XRP Ledger.
 
The team emphasized transparency along the way, and initially received support from the primary XRP stakeholder, Ripple. They published Xahau as open-source code that could, in theory, be back-engineered and integrated with the XRP Ledger. You can clearly observe the team's idealistic mindset in early marketing mistakes, where they named their digital asset 'XRP Plus' in an effort to emphasize the way that they viewed their creation. While this resulted in confusion - and even suspicion - in its early days, the team quickly pivoted, and named their digital asset 'XAH', which became its ticker symbol.
 
Synergy effects between the two camps speak to a genuine camaraderie, with many Xahau developers being open and willing to help with changes to the core XRP Ledger protocol. You can find many examples of this open dialogue on the 'X' platform.
 
How To Purchase XAH
 
If you wish to speculate by buying XAH directly, it is available in a variety of convenient locations, depending on where you are located. If you're in a country that is supported by Bitrue, you can directly purchase or trade XAH by using that exchange.
 
On January 20th, 2025, Bitmart announced that it supports trading of XAH for customers in their list of supported countries; And in late March, another major exchange announced that they would be supporting XAH trading pairs: Coinex.
 
If you're located in the United States, you can purchase XAH directly from a vendor known as 'C14'. The xApp for C14 is located in the Xaman wallet.
 
XRP Ledger geeks can also purchase XAH IOUs on the XRPL Dex and then convert them to 'real' XAH using a Gatehub bridge. This is available in countries that Gatehub supports.
 
Which XAH Accounts Should I Follow?
 
On the 'X' platform, there exists two major community groups for XAH fans:
In addition to the Xahau notables I've already mentioned in this article, my advice is to take a look at who is posting in the above two communities. There are many impressive leaders and entrepreneurs included. You should be able to find multiple 'X' accounts that reflect your interests.
 
Xahau Development Roadmap
 
Xahau leaders have published a roadmap for 2025 that lists their various goals for the ecosystem:
 
To read a detailed explanation for each item, refer to this: Xahau Roadmap Super Thread
 
One of the most incredible waypoints listed is 'JavaScript Hooks Implementation.' 🤯
JavaScript!
 
With the 'JavaScript Hooks Implementation', Xahau is making history; it will enable anybody that knows JavaScript to easily create and install a smart contract. While networks like Ethereum are impressive early movers, they require developers to learn a new language and syntax.
 
Xahau will soon open 'crypto smart contracts' to a group of developers that number in the tens of millions.
 
Project L-10K
 
Project L-10K is one of the most important items in the pipeline. L-10K refers to the effort to boost the throughput of Xahau consensus to over 10,000 transactions per ledger! This will benefit hosted projects such as Evernode, and future issued assets. Heading up the effort is Richard Holland, who provided a progress update to the community in late May of 2025:
 
To learn more about this ambitious effort, you can watch his full presentation here:
The Future Of Defi And Payments
 
Once you've seen the extensive list of use cases that XAH easily handles, it's truly inspiring. Xahau is everything that you love about XRP, plus a long list of more things to love. ❤️
 
Be an early adopter of XAH and the Xahau network! Join the community groups listed and follow the accounts that seem to reflect your own interest - speculator, developer, or crypto fan. You have a place in our community, no matter what your background or interests are. Welcome to the future of crypto Defi and Payments
 
Sources:
 
 
NOTE: Payment channels for IOUs is currently in amendment status for the XRP Ledger, authored by Denis Angel here:
 
 

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