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name: tensorlayer description: "Routes TensorLayer model-building, data utilities, vision, text, reinforcement learning, and training workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

TensorLayer

Use this repo skill when the user wants to work with the TensorLayer package and does not need to open the original checkout again. TensorLayer is a TensorFlow-based deep learning library, so the normal path is to install TensorFlow 2.x, install TensorLayer, and then route to the sub-skill that matches the workflow.

Quick install

• Install a TensorFlow 2.x build appropriate for the host.
• Install TensorLayer, usually with editable mode for local inspection: pip install -e ..
• If you need the app or vision modules, add matplotlib and opencv-python.
• If you need tensorlayer.nlp.process_sentence, add nltk.
• The package's runtime dependencies include imageio, progressbar2, scikit-image, scipy, wrapt, h5py, and cloudpickle.

Minimal import check

Start with:

python -I -c "import tensorlayer as tl; print(tl.__version__)"

If the package import fails because the app/vision modules are missing, install the optional visualization stack and try again. For CLI help, make sure CUDA_VISIBLE_DEVICES is not set to an empty string; the current tl parser crashes on that case.

How the skill is organized

• sub-skills/core-modeling/ — layers, models, save/load, pretrained constructors, activations, costs, initializers, optimizers.
• sub-skills/data-and-utilities/ — file helpers, dataset loaders, preprocessing, iteration, visualization, TFRecord and tiny data round-trips.
• sub-skills/training-and-cli/ — tl.utils.fit/test/predict, tl train, distributed-training setup, and training-loop patterns.
• sub-skills/vision-and-apps/ — pretrained CNNs, object-detection and pose wrappers, spatial transformer workflows, quantized vision examples.
• sub-skills/text-and-sequence/ — NLP helpers, word embedding, PTB/text generation, seq2seq workflows.
• sub-skills/reinforcement-learning/ — reward utilities and RL tutorial patterns.

Read first

• Read references/repo-provenance.md before deciding whether this skill matches the current checkout.
• Read references/api-overview.md when you need the package-wide module map or the verified live API notes.
• Read references/troubleshooting.md when imports, CLI help, optional dependencies, or backend warnings fail.

Safe bundled checks

• scripts/check_import.py — verify package importability and run a tiny model smoke test.
• scripts/check_cli_help.py — verify the tl CLI help without triggering the empty-CUDA_VISIBLE_DEVICES bug.

Routing hints

Choose the sub-skill by the user's natural request:

• If the request is about building or saving a model, go to core-modeling.
• If the request is about loading data, preprocessing, TFRecords, or utility helpers, go to data-and-utilities.
• If the request is about training loops or tl train, go to training-and-cli.
• If the request is about image models, detection wrappers, or visual output, go to vision-and-apps.
• If the request is about text, tokenization, PTB, word embedding, or seq2seq, go to text-and-sequence.
• If the request is about rewards, sampling actions, or RL examples, go to reinforcement-learning.

When a workflow spans more than one area, start with the most specific sub-skill and use the cross-links in its references.