Sold by: ProvenCloud
Deployed on AWS
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This is a repackaged open source software product wherein additional charges apply for a secure, ready-to-use Hugging Face NLP workspace with Transformers, PyTorch and JupyterLab. Run and fine-tune language models on your own server, with unique passwords for every instance.
Overview
Hugging Face Transformers is an open source library that gives easy access to thousands of pretrained language models for text classification, summarisation, translation, question answering, embeddings and more. This image gives data scientists and developers a ready natural language processing (NLP) workspace in minutes, with the libraries already installed and JupyterLab open in the browser.
What is included: Python on Ubuntu 24.04 LTS with PyTorch (CPU build), Hugging Face Transformers, Datasets, Tokenizers and related libraries in one virtual environment, and JupyterLab on port 8080. Models you download are cached on the server so they load quickly next time. Security is on by default: no password ships in the image. On first boot every instance sets its own JupyterLab password, and SSH accepts key-based login only.
What you get on top of the free software: a pre-built and tested image that is ready in minutes, a unique security setup for every server, simple scripts for changing passwords, regular image updates, and help by email whenever you need it. Hugging Face Transformers, Datasets and Tokenizers are open source software licensed under Apache-2.0, PyTorch under BSD-3-Clause, and JupyterLab under BSD-3-Clause. Hugging Face is a trademark of Hugging Face, Inc., and PyTorch is a trademark of The Linux Foundation. This image is an independent build by ProvenCloud.
Highlights
- Ready in minutes: launch the instance, open JupyterLab on port 8080 and start coding. Python, PyTorch, Transformers, Datasets and Tokenizers are already installed in one environment.
- Secure by default: no shared or default passwords. JupyterLab gets its own password on first boot and SSH accepts key-based login only.
- Build NLP fast: text classification, summarisation, translation, embeddings and question answering with thousands of open models, all on a server you control.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
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Pricing
Free trial
Try this product free for 5 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
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Dimension | Cost/hour |
|---|---|
t3a.xlarge Recommended | $0.05 |
c3.4xlarge | $0.08 |
c3.8xlarge | $0.08 |
c4.4xlarge | $0.08 |
c4.8xlarge | $0.08 |
c5.12xlarge | $0.08 |
c5.2xlarge | $0.08 |
c5.4xlarge | $0.08 |
c5.9xlarge | $0.08 |
c5a.12xlarge | $0.08 |
Dimensions summary
You pay by the hour for the EC2 instance type you choose to run this machine image. Each dimension maps to one AWS instance size, so pricing scales with the compute, memory, storage, or accelerator capacity of the instance you select. The catalog spans general-purpose, compute-optimized, memory-optimized, storage-optimized, and accelerated families across many sizes. The software itself needs no license key, so your hourly charge reflects the instance tier. This is a CPU image, so compute-optimized instances suit heavier inference. You stop paying when you stop the instance.
Top-of-mind questions for buyers
Each dimension runs the same pre-built machine image on one AWS instance size you pick. The image includes the NLP libraries and three cached models, pre-configured on Ubuntu 24.04 LTS. The software needs no license key, so your hourly charge covers the running instance you select.
Hourly software charges apply only while the instance runs. When you stop the instance, the hourly charge stops. A stopped instance may still accrue underlying AWS storage fees for its attached volume, but the software metering counts running hours only.
You can launch the image on any listed instance, but the build ships without a GPU driver. GPU checks return false even on accelerated instances. For heavier inference, the seller recommends compute-optimized families. You still pay the hourly rate for whichever instance type you select.
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Vendor refund policy
Software charges are billed hourly, only while your instance runs. You can stop charges at any time by terminating the instance or cancelling your subscription. Charges already incurred are not refundable.
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Latest stable release on AWS Marketplace.
Hugging Face Transformers, Datasets and Tokenizers with PyTorch (CPU) and JupyterLab on Ubuntu 24.04 LTS.
A unique JupyterLab password is set for every instance on first boot.
Includes helper scripts in /home/ubuntu/iscripts for changing the password and checking status.
Additional details
Usage instructions
Once the instance is running, open JupyterLab at http://<instance-public-ip>:8080/lab and enter your instance ID as the password (there is no username). You can also connect over SSH with the username ubuntu and your Amazon key pair.
Required inbound ports: 22 (SSH) and 8080 (JupyterLab). For security, allow both ports only from your own IP address.
Your instance ID is shown in the EC2 console, for example i-0123456789abcdef0. Every instance sets its own passwords on first boot, so no two instances share a password. Allow two to three minutes after launch for the first-boot setup to finish before you sign in.
The stack runs on CPU, so choose an instance with enough memory for the models you plan to load. Change the JupyterLab password with sudo bash /home/ubuntu/iscripts/pass_jupyter.sh, and run hf_status.sh in the same folder to see library versions.
The image serves plain HTTP so it works on a bare IP address. Before using it in production, add your own domain and an SSL certificate, or place it behind a load balancer with HTTPS.
Resources
Vendor resources
Support
Vendor support
Email: support@provencloud.io
Support page: https://www.provencloud.io/support
Hours: Monday to Friday, 10:00 to 19:00 IST (UTC+05:30)
Billing and refunds for this subscription are handled by AWS Marketplace.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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