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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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Inside The Deal That Made Polymarket’s Founder One Of The Youngest Billionaires On Earth🌍

One year ago, the FBI raided Polymarket founder Shayne Coplan’s apartment. Now, the college dropout is a billionaire at age 27.

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At the same time, Coplan announced investments from other billionaires including Figma’s Dylan Field, Zynga’s Mark Pincus, Uber’s Travis Kalanick and hedge fund manager Glenn Dubin. A longtime Red Hot Chili Peppers fan, Coplan even convinced lead singer Anthony Kiedis to invest after a mutual acquaintance brought the musician to Coplan’s apartment one day. “He's buzzing my door, and I’m like, ‘holy shit,'” Coplan recalls, his bright blue eyes widening. “I love their music. A lot of the inspiration [for my work] comes from the music that I listen to.”

Thanks to the deals, Polymarket’s valuation quickly shot to $9 billion, making the 2025 Under 30 alum the world’s youngest self-made billionaire, with an estimated 11% stake worth $1 billion. His reign was short: twenty days later, he was overtaken as the youngest by the three 22-year-old founders of AI startup Mercor.

Young entrepreneurs are minting ten-figure fortunes faster than ever. In addition to the Mercor trio and Coplan, 15 other Under 30 alumni—including ScaleAI cofounder Lucy Guo, Reddit’s Steve Huffman and Cursor’s cofounders—became billionaires this year, while Guo’s cofounder Alexandr Wang and Robinhood’s Vlad Tenev (both former Under 30 honorees) regained their billionaire status after having fallen out of the ranks.

The budding billionaire has long been fascinated by markets and tech. When he was just 14, Coplan emailed the regional Securities and Exchange Commission office to ask how to create new marketplaces. “I did not get a response, but it’s a really funny email,” he says, grinning playfully as he thinks of his younger self. “It just shows that this stuff takes over a decade of percolating in your mind.”

Two years later, Coplan showed up at the offices of internet startup Genius uninvited after multiple emails of his asking for an internship went ignored. At age 16—at least a decade younger than anyone in that office—he secured his first job after making a memorable impression with his “wild curls” and “encyclopedic knowledge of billionaire tech entrepreneurs.” “If he chooses to become a tech entrepreneur, which seems likely, I have no doubt that we’ll be seeing his name again in the press before long,” Chris Glazek, his manager at the time, wrote in Coplan’s college recommendation letter.

Coplan went on to study computer science at NYU, but dropped out in 2017 to work on various crypto projects that never took off. In 2020, he founded Polymarket to create a solution to the “rampant misinformation” he saw in the world: The company’s first market allowed users to bet on when New York City would reopen amid the pandemic. He soon expanded into elections and pop culture happenings, among other events.

But it didn’t take long for the company to butt heads with regulators. In January 2022, Polymarket paid a $1.4 million fine to the Commodity Futures Trading Commission for offering unregistered markets. It was also ordered to block all U.S. users, but activity on Polymarket skyrocketed particularly during the 2024 U.S. presidential election, with bets totaling $3.6 billion. A week after the election, the FBI raided Coplan's apartment and seized his devices as part of an investigation into a possible violation of this agreement. Shortly after, Coplan posted on his X account that he saw the raid as “a last-ditch effort” from the Biden administration “to go after companies they deem to be associated with political opponents.”

In July, the Department of Justice and CFTC dropped the investigations—after which Sprecher reached out to Coplan for dinner—and less than a week later, Polymarket announced it had acquired CFTC-licensed derivatives exchange QCX to prepare for a compliant U.S. launch. QCX applied to be a federally-registered exchange in 2022—an application that was left dormant for three years before receiving approval less than two weeks before the acquisition was announced. When asked about the timing of the deal, Coplan points to CFTC acting chairwoman Caroline Pham, who President Trump tapped to lead the agency in January. “Caroline deserves a lot of credit for getting every single license that had been paused for no reason approved, as acting chairwoman in less than a year,” he says. Coplan had realized an acquisition might be the only way for Polymarket to legally operate in the U.S. as early as 2021 due to the lengthy federal approval process, a source familiar with the deal told Forbes.

Just two months after the acquisition and days after Donald Trump Jr. joined Polymarket’s advisory board, the company received federal approval to launch in the U.S. (Trump Jr. has also served as a strategic advisor to Polymarket’s main competitor Kalshi since January.)

Polymarket’s rapid rise has drawn critics. Dennis Kelleher, co-founder and CEO of Washington-based financial advocacy group Better Markets, told Forbes in an email that the current administration’s deregulation around prediction markets has unlocked a regulatory “loophole” to enable “unregulated gambling” under the CFTC, “which has zero expertise, capacity or resources to regulate and police these markets.” Kelleher added that with backing from the Trump family “who are directly trying to profit on this new gambling den… the massive deregulation and crypto hysteria will almost certainly end badly for the American people.”

Investors and businesses are scrambling to seize the moment of deregulation. “We had opportunities to invest in events markets earlier, but there was a lot of risk,” Sprecher says, listing the regulatory changes in favor of crypto and prediction markets under the current administration. “This was the moment to invest if we wanted to still be early in the space.”

In the last few months, Trump’s Truth Social and sportsbook FanDuel, as well as cryptocurrency exchanges Crypto.com, Coinbase and Gemini all announced their own plans to offer prediction markets. Robinhood CEO Vlad Tenev said prediction markets, which were integrated into its platform in March, were helping drive record activity for the retail brokerage in its third quarter earnings call.

“People are starting to realize right now that the opportunities are endless,” says Dubin, the billionaire hedge fund veteran who invested in Polymarket earlier this year. He points to sports betting companies, which have been regulated by states as gambling activity and taxed accordingly. States like New York can tax up to 51% of sportsbooks’ revenue, but federally-regulated prediction markets can bypass state laws, avoiding taxes and operating in all 50 states. With the realization that prediction markets could upend the sports betting industry—which brought in $13.7 billion in revenue in 2024—businesses are quickly jumping on board despite pushback from state gambling regulators. In October, both Polymarket and Kalshi secured partnerships with sportsbook PrizePicks and the National Hockey League, and Polymarket announced exclusive partnerships with sportsbook DraftKings and the Ultimate Fighting Championship.

The disruption won’t be limited to sports betting. Alongside its investment, Intercontinental’s tens of thousands of institutional clients including large hedge funds and over 750 third-party providers of data will soon have access to Polymarket data, as it gets integrated into Intercontinental’s products such as indices to better inform investment decisions. It also hopes to work with Polymarket to work on initiatives around tokenization—or converting financial assets into digital tokens on blockchain technology—to allow traders on Intercontinental’s exchanges to trade more flexibly at all hours of the day, Sprecher says. What’s more, in November, Google Finance announced it would integrate Polymarket and Kalshi data into its search results, while Yahoo Finance also announced an exclusive partnership with Polymarket.

Despite flashy investors, partnerships and a record $2.4 billion of trading volume in November, Polymarket has yet to launch in the U.S. or turn a profit. Coplan and his investors have hinted at ways the company could make money one day—selling its data, charging fees to users, launching a cryptocurrency token (similar to Ethereum or Bitcoin)—but decline to confirm any specifics. For now, the only thing that’s certain is the bet Coplan is making on himself. “Going for it and having it not pan out is an infinitely better outcome than living your life as a what if,” he says.

Standing across from the New York Stock Exchange building, Coplan tilts his head up as he watches a massive banner with Polymarket’s logo get hoisted onto the exterior of the building. It’s been five years since founding. One year since the FBI raid. He’s taking it all in. “Against all odds,” the bright blue banner reads, rippling in the wind alongside three American flags protruding from the building.

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Epstein-Linked Emails Expose Funding Ties to Bitcoin Core Development — Here Is What the Documents Reveal
  • Newly released emails show Jeffrey Epstein helped fund MIT’s Digital Currency Initiative, which supported Bitcoin Core development.
  • The documents also confirm that Leon Black donated to MIT’s Media Lab through Epstein-directed channels.
  • The revelations reshape part of Bitcoin’s early institutional funding history and highlight long-hidden influence from controversial donors.

Newly unsealed emails from the House Oversight Committee have shed fresh light on Jeffrey Epstein’s hidden financial influence inside MIT’s Media Lab — and more importantly, how some of that money flowed into Bitcoin Core development. The correspondence reveals that Joichi Ito, then-director of the MIT Media Lab, relied on Epstein-connected “gift funds” to rapidly launch the Digital Currency Initiative (DCI) in 2015, the research hub that became one of the primary sources of funding for Bitcoin’s core developers.

Emails Show Epstein-Connected Money Helped Launch MIT’s Digital Currency Initiative

In the newly surfaced emails, Ito directly thanked Epstein for the financial help that allowed MIT to “move quickly and win this round,” referring to the formation of DCI — a program explicitly designed to provide long-term support for Bitcoin Core contributors after the collapse of the Bitcoin Foundation. Ito’s forwarded message to Epstein described how the foundation’s implosion left core developers without stable funding, creating an opening for MIT to bring them under its umbrella.

He explained that three major developers — including Wladimir van der Laan and Cory Fields — agreed to join MIT, calling it “a big win for us.” The email also highlighted early support from prominent academics, including cryptographer Ron Rivest and IMF economist Simon Johnson. Epstein simply replied: “gavin is clever.”

Funding Numbers Reveal a Much Larger Financial Trail

MIT publicly claimed that Epstein donated $850,000 to the institution, with $525,000 flowing to the Media Lab. But journalist Ronan Farrow later reported the true figure was closer to $7.5 million — including a $5 million anonymous donation connected to Epstein associate Leon Black. The new emails appear to confirm that Black not only donated, but did so through Epstein’s direction.

One email from Ito to Epstein reads: “We were able to keep the Leon Black money, but the $25K from your foundation is getting bounced by MIT back to ASU.”

 

Epstein responded: “No problem — trying to get more black for you.”

The documents reveal Epstein’s influence reached deeper into Bitcoin circles than previously acknowledged, even including early conversations with Brock Pierce — another figure with documented ties to both Epstein and controversy surrounding early crypto foundations.

MIT’s Internal Concerns and the Fallout

The emails also expose MIT’s internal unease around anonymous or reputationally risky donations. After the scandal broke, Ito resigned in 2019. MIT later tightened donation policies, warning that “everything becomes public” eventually — a statement that now seems prophetic given this week’s disclosures.

Developers like Wladimir van der Laan say they were unaware of the extent of Epstein’s involvement and noted that DCI’s funding transparency “was not great back in the day.” The Media Lab and DCI declined to comment.

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