p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] requirements.txt (pip-requirements)
# core requirements
dm_pix>=0.4.3
einops>=0.8.0
flax>=0.10.7
jax[cuda12]>=0.6.2
optax>=0.2.3
tyro>=0.8.5
wandb>=0.17.4
grain>=0.2.10
array-record>=0.7.2

# data pipeline
ffmpeg-python==0.2.0
hf-transfer==0.1.9
huggingface-hub[cli]>=0.34.3
procgen>=0.10.7
tqdm>=4.67.1
tensorflow>=2.19.0
tensorflow-datasets>=4.12.0
rlds>=0.1.1

# dev
pre-commit>=4.2.0
[terminal_command] TERMINAL ()
export SSL_CERT_FILE=/etc/pki/tls/certs/ca-bundle.crt CURL_CA_BUNDLE=/etc/pki/tls/certs/ca-bundle.crt REQUESTS_CA_BUNDLE=/etc/pki/tls/certs/ca-bundle.crt; echo SSL_CERT_FILE=$SSL_CERT_FILE; echo CURL_CA_BUNDLE=$CURL_CA_BUNDLE; echo REQUESTS_CA_BUNDLE=$REQUESTS_CA_BUNDLE; echo; curl -I --max-time 10 https://storage.googleapis.com | head -n1 | cat; echo; curl -I --max-time 10 https://storage.googleapis.com/rl_unplugged/atari_episodes_ordered/Pong/run_1-00000-of-00050 | head -n1 | cat
[content] input_pipeline/download/dqn_replay/download_pngs.py (python)
import os
os.environ.setdefault("SSL_CERT_FILE", "/etc/pki/tls/certs/ca-bundle.crt")
os.environ.setdefault("CURL_CA_BUNDLE", "/etc/pki/tls/certs/ca-bundle.crt")
os.environ.setdefault("REQUESTS_CA_BUNDLE", "/etc/pki/tls/certs/ca-bundle.crt")
[content] input_pipeline/download/dqn_replay/download_pngs.py (python)
    # Load the dataset of episodes (RLDS format) using a builder.
    # For debugging, restrict to a single episode via split slicing.
    builder = tfds.builder(dataset_name)
    builder.download_and_prepare()
    episodes = builder.as_dataset(
        split="train[:1]",  # load only one example/episode for debugging
[content] input_pipeline/download/dqn_replay/download_pngs.py (python)
    # Load the dataset of episodes (RLDS format) using a builder.
    # For debugging, restrict to a single episode via split slicing.
    builder = tfds.builder(dataset_name)
    builder.download_and_prepare()
    episodes = cast(tf.data.Dataset, builder.as_dataset(
        split="train[:1]",  # load only one example/episode for debugging
[content] input_pipeline/download/dqn_replay/download_pngs.py (python)
    # Load the dataset of episodes (RLDS format) using a builder.
    # For debugging, restrict to a single episode via split slicing.
    builder = tfds.builder(dataset_name)
    # Configure TFDS preparation to avoid generating the full dataset.
    dl_config = tfds.download.DownloadConfig(
        max_examples_per_split=(
            prepare_max_examples_per_split if prepare_max_examples_per_split > 0 else None
        ),
        try_download_gcs=True,
    )
    builder.download_and_prepare(download_config=dl_config)
    # If max_episodes is specified, restrict the split accordingly for reading.
    split_str = f"train[:{max_episodes}]" if max_episodes > 0 else "train"
    episodes = cast(tf.data.Dataset, builder.as_dataset(
        split=split_str,