From 3b9baf5db638d56d0460d717396726ac9583b392 Mon Sep 17 00:00:00 2001 From: Alex Date: Tue, 30 Apr 2024 23:36:32 +0800 Subject: [PATCH 1/8] [CoreEngine] update the workflow example. --- .../driver_example/customized_job_example/train_job.yaml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml b/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml index 0236baec63..ab6ab29c0e 100755 --- a/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml +++ b/python/fedml/workflow/driver_example/customized_job_example/train_job.yaml @@ -25,10 +25,13 @@ bootstrap: | pip install PyYAML==5.3.1 -i https://pypi.org/simple pip install fedml==0.8.29 pip install -U typing_extensions -i https://pypi.org/simple + pip install -U pydantic + pip install -U fastapi echo "Bootstrap finished." computing: - resource_type: RTX-4090 # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://open.fedml.ai/accelerator_resource_type + #resource_type: RTX-4090 # e.g., A100-80G, please check the resource type list by "fedml show-resource-type" or visiting URL: https://open.fedml.ai/accelerator_resource_type + resource_type: A100-80GB-SXM minimum_num_gpus: 1 # minimum # of GPUs to provision maximum_cost_per_hour: $10 # max cost per hour of all machines for your job # device_type: GPU # GPU or CPU From 7155903734be984f5de513c74dee268cc37ed5cb Mon Sep 17 00:00:00 2001 From: Chaoyang He Date: Fri, 10 May 2024 04:39:32 -0700 Subject: [PATCH 2/8] Update README.md --- README.md | 38 ++++++++++++++++++-------------------- 1 file changed, 18 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index b2a99eea21..da23f5c054 100644 --- a/README.md +++ b/README.md @@ -1,44 +1,42 @@ # FEDML Open Source: A Unified and Scalable Machine Learning Library for Running Training and Deployment Anywhere at Any Scale -Backed by FEDML Nexus AI: Next-Gen Cloud Services for LLMs & Generative AI (https://fedml.ai) +Backed by TensorOpera AI: Your Generative AI Platform at Scale (https://TensorOpera.ai)
-FedML Documentation: https://doc.fedml.ai +TensorOpera Documentation: https://docs.TensorOpera.ai -FedML Homepage: https://fedml.ai/ \ -FedML Blog: https://blog.fedml.ai/ \ -FedML Medium: https://medium.com/@FedML \ -FedML Research: https://fedml.ai/research-papers/ +TensorOpera Homepage: https://TensorOpera.ai/ \ +TensorOpera Blog: https://blog.TensorOpera.ai/ \ -Join the Community: \ +Join the Community: Slack: https://join.slack.com/t/fedml/shared_invite/zt-havwx1ee-a1xfOUrATNfc9DFqU~r34w \ Discord: https://discord.gg/9xkW8ae6RV -FEDML® stands for Foundational Ecosystem Design for Machine Learning. [FEDML Nexus AI](https://fedml.ai) is the next-gen cloud service for LLMs & Generative AI. It helps developers to *launch* complex model *training*, *deployment*, and *federated learning* anywhere on decentralized GPUs, multi-clouds, edge servers, and smartphones, *easily, economically, and securely*. +TensorOpera® AI (https://TensorOpera.ai) is the next-gen cloud service for LLMs & Generative AI. It helps developers to launch complex model training, deployment, and federated learning anywhere on decentralized GPUs, multi-clouds, edge servers, and smartphones, easily, economically, and securely. -Highly integrated with [FEDML open source library](https://github.com/fedml-ai/fedml), FEDML Nexus AI provides holistic support of three interconnected AI infrastructure layers: user-friendly MLOps, a well-managed scheduler, and high-performance ML libraries for running any AI jobs across GPU Clouds. +Highly integrated with TensorOpera open source library, TensorOpera AI provides holistic support of three interconnected AI infrastructure layers: user-friendly MLOps, a well-managed scheduler, and high-performance ML libraries for running any AI jobs across GPU Clouds. ![fedml-nexus-ai-overview.png](./docs/images/fedml-nexus-ai-overview.png) -A typical workflow is showing in figure above. When developer wants to run a pre-built job in Studio or Job Store, FEDML®Launch swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. When running the job, FEDML®Launch orchestrates the compute plane in different cluster topologies and configuration so that any complex AI jobs are enabled, regardless model training, deployment, or even federated learning. FEDML®Open Source is unified and scalable machine learning library for running these AI jobs anywhere at any scale. +A typical workflow is showing in figure above. When developer wants to run a pre-built job in Studio or Job Store, TensorOpera®Launch swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. When running the job, TensorOpera®Launch orchestrates the compute plane in different cluster topologies and configuration so that any complex AI jobs are enabled, regardless model training, deployment, or even federated learning. TensorOpera®Open Source is unified and scalable machine learning library for running these AI jobs anywhere at any scale. -In the MLOps layer of FEDML Nexus AI -- **FEDML® Studio** embraces the power of Generative AI! Access popular open-source foundational models (e.g., LLMs), fine-tune them seamlessly with your specific data, and deploy them scalably and cost-effectively using the FEDML Launch on GPU marketplace. -- **FEDML® Job Store** maintains a list of pre-built jobs for training, deployment, and federated learning. Developers are encouraged to run directly with customize datasets or models on cheaper GPUs. +In the MLOps layer of TensorOpera AI +- **TensorOpera® Studio** embraces the power of Generative AI! Access popular open-source foundational models (e.g., LLMs), fine-tune them seamlessly with your specific data, and deploy them scalably and cost-effectively using the TensorOpera Launch on GPU marketplace. +- **TensorOpera® Job Store** maintains a list of pre-built jobs for training, deployment, and federated learning. Developers are encouraged to run directly with customize datasets or models on cheaper GPUs. -In the scheduler layer of FEDML Nexus AI -- **FEDML® Launch** swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. It supports a range of compute-intensive jobs for generative AI and LLMs, such as large-scale training, serverless deployments, and vector DB searches. FEDML Launch also facilitates on-prem cluster management and deployment on private or hybrid clouds. +In the scheduler layer of TensorOpera AI +- **TensorOpera® Launch** swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. It supports a range of compute-intensive jobs for generative AI and LLMs, such as large-scale training, serverless deployments, and vector DB searches. TensorOpera Launch also facilitates on-prem cluster management and deployment on private or hybrid clouds. -In the Compute layer of FEDML Nexus AI -- **FEDML® Deploy** is a model serving platform for high scalability and low latency. -- **FEDML® Train** focuses on distributed training of large and foundational models. -- **FEDML® Federate** is a federated learning platform backed by the most popular federated learning open-source library and the world’s first FLOps (federated learning Ops), offering on-device training on smartphones and cross-cloud GPU servers. -- **FEDML® Open Source** is unified and scalable machine learning library for running these AI jobs anywhere at any scale. +In the Compute layer of TensorOpera AI +- **TensorOpera® Deploy** is a model serving platform for high scalability and low latency. +- **TensorOpera® Train** focuses on distributed training of large and foundational models. +- **TensorOpera® Federate** is a federated learning platform backed by the most popular federated learning open-source library and the world’s first FLOps (federated learning Ops), offering on-device training on smartphones and cross-cloud GPU servers. +- **TensorOpera® Open Source** is unified and scalable machine learning library for running these AI jobs anywhere at any scale. # Contributing FedML embraces and thrive through open-source. We welcome all kinds of contributions from the community. Kudos to all of our amazing contributors! From 4ef7114ab222d66f6fd89a38ae247a0b4ba8ec66 Mon Sep 17 00:00:00 2001 From: Chaoyang He Date: Fri, 10 May 2024 04:39:44 -0700 Subject: [PATCH 3/8] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index da23f5c054..abf4cca28f 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Backed by TensorOpera AI: Your Generative AI Platform at Scale (https://TensorOp TensorOpera Documentation: https://docs.TensorOpera.ai TensorOpera Homepage: https://TensorOpera.ai/ \ -TensorOpera Blog: https://blog.TensorOpera.ai/ \ +TensorOpera Blog: https://blog.TensorOpera.ai/ Join the Community: Slack: https://join.slack.com/t/fedml/shared_invite/zt-havwx1ee-a1xfOUrATNfc9DFqU~r34w \ From 9b551cf7a01a21231da430b5a09a787c3e725f9b Mon Sep 17 00:00:00 2001 From: Chaoyang He Date: Fri, 10 May 2024 04:41:48 -0700 Subject: [PATCH 4/8] Add files via upload --- docs/images/TensorOpera_arch.png | Bin 0 -> 500602 bytes 1 file changed, 0 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z#i`mDB^2xnJHbfrVeCeY7ecqni?2O8Y*~@hZdo1+5;rNv%Uo$GesDbn>y1?it|~3c z%-N#!f;m;;V^Ir^ljq!es~tv}jAW$+4F1gF#}*)eU6hRWp{gRp)Rxd;5|{=j*i`jA zwPFA>6-FF^cfM2eq!m1c!_GGOA6e>b^W0_G%YUZveldP&h>_yaJ3=Q~K12xKk^Yu{ zIj8yLzosG4=**8w`kxZcdCyf1LC-VmJY`sKsbPp!Lt`@^@t9?($tHP%u- zaP8MUe{50rb>6&M=3kDz`j72J{QOosy0_fk literal 0 HcmV?d00001 From 793b2d9f7b3cdc87b84c4c7550137076d0bb0d1f Mon Sep 17 00:00:00 2001 From: Chaoyang He Date: Fri, 10 May 2024 04:42:24 -0700 Subject: [PATCH 5/8] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index abf4cca28f..baacf06d92 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Backed by TensorOpera AI: Your Generative AI Platform at Scale (https://TensorOpera.ai)

TensorOpera Documentation: https://docs.TensorOpera.ai From f2cfe4f97a700f6d82b7d453b1a2e779d79bb2f0 Mon Sep 17 00:00:00 2001 From: Chaoyang He Date: Fri, 10 May 2024 04:42:50 -0700 Subject: [PATCH 6/8] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index baacf06d92..e0b6470367 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Backed by TensorOpera AI: Your Generative AI Platform at Scale (https://TensorOpera.ai)
- +
TensorOpera Documentation: https://docs.TensorOpera.ai From 20a77341fd0322d93ccde0fdbe1ee1b56a71b454 Mon Sep 17 00:00:00 2001 From: fedml-dimitris Date: Mon, 13 May 2024 14:47:51 -0400 Subject: [PATCH 7/8] Removing duplicate image. --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index e0b6470367..07d5fad1e6 100644 --- a/README.md +++ b/README.md @@ -21,8 +21,6 @@ TensorOpera® AI (https://TensorOpera.ai) is the next-gen cloud service for LLMs Highly integrated with TensorOpera open source library, TensorOpera AI provides holistic support of three interconnected AI infrastructure layers: user-friendly MLOps, a well-managed scheduler, and high-performance ML libraries for running any AI jobs across GPU Clouds. -![fedml-nexus-ai-overview.png](./docs/images/fedml-nexus-ai-overview.png) - A typical workflow is showing in figure above. When developer wants to run a pre-built job in Studio or Job Store, TensorOpera®Launch swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. When running the job, TensorOpera®Launch orchestrates the compute plane in different cluster topologies and configuration so that any complex AI jobs are enabled, regardless model training, deployment, or even federated learning. TensorOpera®Open Source is unified and scalable machine learning library for running these AI jobs anywhere at any scale. In the MLOps layer of TensorOpera AI From 8f6e8f31e5f4e215de9cc7eb7709033fb3ca338a Mon Sep 17 00:00:00 2001 From: bene-ges Date: Thu, 16 May 2024 19:00:42 +0300 Subject: [PATCH 8/8] fix bug in set_device_map for trpc device_list is a dictionary, and we need to map to its values, not the keys --- python/fedml/core/distributed/communication/trpc/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/fedml/core/distributed/communication/trpc/utils.py b/python/fedml/core/distributed/communication/trpc/utils.py index 636750edf2..4960389e32 100644 --- a/python/fedml/core/distributed/communication/trpc/utils.py +++ b/python/fedml/core/distributed/communication/trpc/utils.py @@ -7,6 +7,6 @@ def set_device_map(options, worker_idx, device_list): local_device = device_list[worker_idx] for index, remote_device in enumerate(device_list): - logging.warn(f"Setting device map for client {index} as {remote_device}") + logging.warn(f"Setting device map for client {index} as {device_list[remote_device]}") if index != worker_idx: - options.set_device_map(WORKER_NAME.format(index), {local_device: remote_device}) \ No newline at end of file + options.set_device_map(WORKER_NAME.format(index), {local_device: device_list[remote_device]})