Datasets
Datasets belong to Organisations in Strong Compute, so it is important to have selected the appropriate Organisation from the menu at the top-right of the Control Plane page before navigating to the "Datasets" page. Datasets imported into Strong Compute will be accessible to all members of the Organisation to which that Dataset belongs.
HuggingFace
HuggingFace models and datasets can be ingested as Datasets in Strong Compute.
This is strongly recommended instead of downloading large models and datasets directly to your container which can cause your container to be slow to start and stop.
See the bottom of this guide for detailed instructions on how to ingest HuggingFace models and datasets as Datasets.
Step 1: Adding a custom dataset
On the Datasets page, you can import your own dataset (up to 100GB) from an S3-compatible bucket by clicking on the "New Dataset" button and completing the form.
Note: if your dataset not yet on S3 (e.g, a Huggingface dataset), here's a friendly guide on how to upload it to Cloudflare R2.
Example walkthrough with Cloudflare R2
You can use any S3-compatible provider - we've found R2 to be a decent value option!
Signup for a Cloudflare account - e.g, at this link.
Navigate to R2 Object Storage in the sidebar (or click here).
Click on the "Create Bucket" button.
Give it a relevant name - this is how it'll show up on our platform.
You can leave all else default and hit "Create", unless you have a preference!
Bucket Created! Time to upload files - click on the name you gave it.
Click on the "Select from Computer" button. (Or, use the API if you prefer).
Note: You can grab Huggingface dataset files from `~/.cache` per screenshot, if you've used it locally. But avoid doing this method on StrongCompute, as it breaks your account if you exceed your allowance (75GB); likely with big datasets. To see usage, run: df -h | grep 75G
Open the Settings pane; note your s3 URL - we'll use this in a moment.
The S3 URL should be visible near the top of the page.
Finally - create an API token for access (link here).
Again, the defaults are fine - but it's good practice to set a TTL expiry.
Success! Note your Access ID and Secret Key, then proceed with the steps below :D
A new Dataset requires 5 parameters:
Name: A useful descriptor for the Dataset.
Access Key ID: A valid access key ID.
Secret Access Key: A valid secret access key that matches the above ID.
Endpoint: The endpoint* for the s3 host of your bucket e.g.
s3.amazonaws.com.Region: The region** in which your bucket is located e.g.
us-east-1.Bucket Name: The name of the S3 bucket without leading protocol. For example when importing a dataset from the bucket
s3://hello-worldthe input to this field ishello-world.
*Note: For datasets in Cloudflare R2 storage, your S3 API will look as follows.
https://<letters-and-numbers>.r2.cloudflarestorage.com/<bucket-name>The Endpoint extracted from this url is <letters-and-numbers>.r2.cloudflarestorage.com (i.e. without leading https:// and trailing /<bucket-name>.
**Note: If you are using a provider that does not support regions or incorporates the region into the Endpoint URL (e.g. Cloudflare R2, OCI Storage), then leave this field blank.
Your Access Key must have permission to read and access buckets and objects from S3, and the S3 bucket must be non-empty.
Once your Dataset is created and validated, it will be automatically cached to the Strong Compute Global Silo. Your Dataset is finished caching to the Global Silo when the Global Silo Dataset Cache State is stored.
Step 2: Downloading it to our Cluster
After your Dataset is cached to the Global Silo, it can be downloaded to a Constellation Cluster so that Users can access it Containers and for training.
Once the Global Silo Dataset Cache for your Dataset shows its State as stored, select your dataset from the "User Dataset Name" menu and your destination Cluster from the "Constellation Cluster" menu and click "Cache Dataset". This will start the ISC downloading and creating a Constellation Cluster Dataset Cache of your Dataset.
Your Dataset will be ready to access in your Container and in training when the Constellation Cluster Dataset Cache of your Dataset shows its State is available.
User datasets will show Access is Private indicating that Users can only access those Datasets from Containers associated with the Organisation that is the owner of that Dataset. Users can also use any of the datasets cached on the Cluster which shows Access is Public.
Step 3. Accessing datasets in development
To access your Organisations datasets or any of the Public datasets during development, select the appropriate Dataset from the "Mounted Dataset" menu for the appropriate Container before you start your Container.
Your dataset will then be mounted to your development container at /data/<dataset-id> and available there to you during development. Once inside your Container, navigate to your dataset with cd /data/<dataset-id>.
Step 4. Accessing datasets in training
You can access any of your Organisations datasets or any of the Public datasets during training, including multiple within the same training script. Include a dataset_id_list field in your experiment launch file with the dataset IDs as an array of unique strings, as follows.
dataset_id_list = ["<dataset1-id>","<dataset2-id>","<dataset3-id>"]After your script has launched, your Datasets will be mounted to your Container at /data/<dataset-id> and available to your code during training.
HuggingFace models and datasets
To ingest a HuggingFace model or dataset, your HuggingFace token will need to be authorised to access that particular model or dataset. Then click New Dataset and provide the following details.
Name: Same as above, your name for this Dataset, i.e.
My-DeepSeek-R1-Qwen-1.5BAccess Key ID: Your HuggingFace Token, i.e.
hf_yvjFOLkghfcJihlKjktyhcrfgblkHJNKyuSecret Access Key: This exact input:
HuggingFaceEndpoint: This exact input:
HuggingFaceRegion: This exact input:
HuggingFaceBucket Name: HuggingFace Repo_ID, i.e.
deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B(see below)
Clear your HuggingFace cache before launching experiments or stopping your container
The benefit of ingesting HuggingFace models and datasets as Datasets in Strong Compute is that these data do not contribute to the size of your container. This is desireable for keeping your container as small as possible, thereby allowing your container to start and stop as quickly as possible.
However, when models and datasets are loaded directly from HuggingFace they cache by default to a directory inside your container which is typically at ~/.cache/huggingface. We strongly recommend deleting this directory before either:
Launching experiments, or
Stopping your container.
Both of these actions will trigger a backup of your container to cloud storage, including the contents of ~/.cache/huggingface, thus committing it forever to your container history. To avoid this, we recommend running the following command before launching experiments (with isc train) or stopping your container.
rm -rf ~/.cache/huggingfaceLast updated