content.raw: As a GPU poor, I found a nice open source project.
content.type: text
content.value: As a GPU poor, I found a nice open source project.
content.type: new_line
content.type: new_line
content.raw: dstackai is the perfect open source project for GPU poor. Simply specify resource requirements (GPU RAM, spot, ...), then will suggest the cheapest options among the popular GPU cloud providers (AWS, GCP, Azure, Lambda Labs, TensorDock, and vast.ai)
content.type: text
content.value: dstackai is the perfect open source project for GPU poor. Simply specify resource requirements (GPU RAM, spot, ...), then will suggest the cheapest options among the popular GPU cloud providers (AWS, GCP, Azure, Lambda Labs, TensorDock, and vast.ai)
content.type: new_line
content.type: new_line
content.raw: Provision VM instances in 3 different use cases. These are the essential for any ML projects.
content.type: text
content.value: Provision VM instances in 3 different use cases. These are the essential for any ML projects.
content.type: new_line
content.raw: - Dev: connect provisioned VM instance to your fav IDE (including Jupyter)
content.type: text
content.value: - Dev: connect provisioned VM instance to your fav IDE (including Jupyter)
content.type: new_line
content.raw: - Task: run experiments (training, fine-tuning, ...) via SSH 
content.type: text
content.value: - Task: run experiments (training, fine-tuning, ...) via SSH 
content.type: new_line
content.raw: - Service: run your model in production via HTTPS
content.type: text
content.value: - Service: run your model in production via HTTPS
content.type: new_line
content.type: new_line
content.raw: dstack is 100% open source, but you need to have your own accounts for each GPU cloud provider, enough GPU quota, configure credentials, etc., all by yourself. Luckily, dstack will announce dstack cloud which let you not worried about all the hassles. The price is almost as same as you directly connect to each cloud with your account.
content.type: text
content.value: dstack is 100% open source, but you need to have your own accounts for each GPU cloud provider, enough GPU quota, configure credentials, etc., all by yourself. Luckily, dstack will announce dstack cloud which let you not worried about all the hassles. The price is almost as same as you directly connect to each cloud with your account.
content.type: new_line
content.type: new_line
content.raw: The attached code snippet shows you how to provision a Mistral-7B model in Text Generation Inference(TGI) on the cheapest VM instance (of having 24GB of VRAM). Then you get the HTTPS connection to it, and play with it as usual with TGI client library as attached in the second code snippet.
content.type: text
content.value: The attached code snippet shows you how to provision a Mistral-7B model in Text Generation Inference(TGI) on the cheapest VM instance (of having 24GB of VRAM). Then you get the HTTPS connection to it, and play with it as usual with TGI client library as attached in the second code snippet.
content.type: new_line
content.type: new_line
content.raw: If you want to learn more about dstack, check out the official website. Without GPU sponsors, as an individual open source contributor in ML, this kind of project is pretty important. 
content.type: text
content.value: If you want to learn more about dstack, check out the official website. Without GPU sponsors, as an individual open source contributor in ML, this kind of project is pretty important. 
content.type: new_line
content.raw: : 
content.type: text
content.value: : 
content.href: https://dstack.ai/
content.raw: https://dstack.ai/
content.type: link
content.type: new_line
content.type: new_line
content.raw: If you are looking for an alternative, there is SkyPilot project as well
content.type: text
content.value: If you are looking for an alternative, there is SkyPilot project as well
content.type: new_line
content.raw: : 
content.type: text
content.value: : 
content.href: https://github.com/skypilot-org/skypilot
content.raw: https://github.com/skypilot-org/skypilot
content.type: link
rawContent: As a GPU poor, I found a nice open source project.
  
  dstackai is the perfect open source project for GPU poor. Simply specify resource requirements (GPU RAM, spot, ...), then will suggest the cheapest options among the popular GPU cloud providers (AWS, GCP, Azure, Lambda Labs, TensorDock, and vast.ai)
  
  Provision VM instances in 3 different use cases. These are the essential for any ML projects.
  - Dev: connect provisioned VM instance to your fav IDE (including Jupyter)
  - Task: run experiments (training, fine-tuning, ...) via SSH 
  - Service: run your model in production via HTTPS
  
  dstack is 100% open source, but you need to have your own accounts for each GPU cloud provider, enough GPU quota, configure credentials, etc., all by yourself. Luckily, dstack will announce dstack cloud which let you not worried about all the hassles. The price is almost as same as you directly connect to each cloud with your account.
  
  The attached code snippet shows you how to provision a Mistral-7B model in Text Generation Inference(TGI) on the cheapest VM instance (of having 24GB of VRAM). Then you get the HTTPS connection to it, and play with it as usual with TGI client library as attached in the second code snippet.
  
  If you want to learn more about dstack, check out the official website. Without GPU sponsors, as an individual open source contributor in ML, this kind of project is pretty important. 
  : https://dstack.ai/
  
  If you are looking for an alternative, there is SkyPilot project as well
  : https://github.com/skypilot-org/skypilot
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commentators._id: 5f7fbd813e94f16a85448745
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commentators.fullname: Sayak Paul
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commentators.fullname: Julien Chaumond
commentators.name: julien-c
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