The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column(/Statement/[]/Resource) changed from array to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
AWS Steam GPU Launch Kit
Deploy kit for a per-player Windows GPU host on AWS: NVIDIA gaming driver, Amazon DCV browser streaming, a Steam Offline Mode library on its own EBS volume, and IAM-scoped self-troubleshooting with a self-healing diagnostics loop.
This repo is the versioned artifact registry and the execution layer. Artifacts are
fetched with huggingface_hub, so a runner that has only the AWS CLI and a token can
deploy without a git checkout.
Read these three constraints first
- One Windows instance = one player. Amazon DCV's Windows server supports console sessions only — virtual sessions and GPU sharing are Linux-only features. Scale by adding instances, never by adding users to one box.
- Steam credential sharing is outside Valve's terms. Offline Mode is legitimate for your own second machine. For other people, use Steam Families (up to six members, one play slot per owned copy, shared games playable offline).
- Browser streaming is the convenience tier. The DCV web client is TCP-only and capped at 2 x 1920x1080. The native client gets QUIC over UDP 8443 and up to 4K.
Layout
| Path | Purpose |
|---|---|
cloudformation/steam-gpu-host.yaml |
IAM role, security group, encrypted game volume, instance, Elastic IP, log group, status alarm |
scripts/bootstrap.ps1 |
Installs the NVIDIA gaming driver, registers the vGaming marketplace key, installs the DCV server and the CloudWatch agent |
scripts/diagnose.ps1 |
Emits a one-line JSON health verdict (gpu-missing, gpu-unlicensed, dcv-port-closed, ...) |
scripts/validate.py |
Static validation, secret scanning, IAM surface inventory. No AWS credentials required. |
scripts/hf_job.py |
Runs inside a Hugging Face Job: fetches this repo, then validates or deploys |
ssm/SteamGpu-Diagnose.json |
Systems Manager document wrapping the diagnostic |
iam/instance-role-inline-policy.json |
Least-privilege policy for the gaming host |
iam/operator-role-policy.json |
Your debugging identity, with tag-scoped mutations |
launch.sh |
One-command deploy: validate, deploy the stack, push the bootstrap over SSM |
launch.py |
Driver with validate / plan / deploy / job modes |
Validate without touching AWS
python -m pip install pyyaml
python scripts/validate.py --root .
Deploy from your own machine
export AWS_PROFILE=my-profile
export AWS_REGION=us-east-1
export KEY_NAME=my-keypair
./launch.sh --instance-type g6.xlarge --volume-gib 600
--admin-cidr defaults to your current public IP. --dry-run validates and prints the
plan without changing anything.
Deploy from a Hugging Face Job
Credentials arrive as Hub secrets, so no long-lived keys sit in this repo or on disk.
launch.sh then runs on ephemeral Hub compute and the runner disappears afterwards.
hf jobs uv run \
--name steam-gpu-deploy \
--flavor cpu-basic \
--timeout 40m \
--with huggingface_hub --with pyyaml --with awscli --with boto3 \
--secrets AWS_ACCESS_KEY_ID --secrets AWS_SECRET_ACCESS_KEY --secrets AWS_REGION \
--env KEY_NAME=my-keypair \
--env INSTANCE_TYPE=g6.xlarge \
scripts/hf_job.py
Set MODE=validate to run the credential-free checks on Hub compute first. Confirm the
flag names against hf jobs run --help in your CLI version.
Launch console
A zero-credential parameter console lives at Allszn/steam-gpu-launch-console — it generates the exact commands above from the options you pick. It stores no credentials and calls no AWS API.
Cost
Windows GPU instances land near ~$0.71/hr (g4dn.xlarge) to ~$0.99/hr
(g6.xlarge) in us-east-1, derived from Linux on-demand list rates plus AWS's stated
$0.046-per-vCPU-hour Windows licence component. Verify in the AWS Pricing Calculator.
Stopping the instance between sessions is the single biggest saving.
Security notes
- No secrets are stored in this repo.
scripts/validate.pyfails the build if an AWS key, private key block, Steam password or bearer token is ever committed. - Only TCP/UDP 8443 is opened, and only to the CIDR you supply. 3389 is never
exposed — reach RDP through
AWS-StartPortForwardingSession. - The instance role cannot widen its own access; mutations in the operator role are
conditioned on the
Project=steam-gputag.
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