From ef86f63da366033c5121609ba34802350a4bf70e Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Fri, 5 Jun 2026 11:05:19 -0500 Subject: [PATCH 01/17] Add out-of-core StreamingDataset variational inference example Minibatch ADVI fed from Parquet shards on disk (flat memory), shown equivalent to in-RAM pm.Minibatch, with ELBO / posterior / memory figures and the shuffling caveat. Paired .ipynb + .myst.md. --- .../streaming_dataset.ipynb | 500 ++++++++++++++++++ .../streaming_dataset.myst.md | 232 ++++++++ 2 files changed, 732 insertions(+) create mode 100644 examples/variational_inference/streaming_dataset.ipynb create mode 100644 examples/variational_inference/streaming_dataset.myst.md diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb new file mode 100644 index 000000000..0db53ca9d --- /dev/null +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -0,0 +1,500 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6cb44821", + "metadata": {}, + "source": [ + "(streaming_dataset)=\n", + "\n", + "# Out-of-core minibatch variational inference with StreamingDataset\n", + "\n", + ":::{post} June 2026\n", + ":tags: variational inference, minibatch, out-of-core, ADVI\n", + ":category: intermediate, how-to\n", + ":author: Yicheng (Ethan) Yang\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "24dc00a0", + "metadata": {}, + "source": [ + "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n", + "minibatch variational inference is capped at datasets that fit in memory -- the\n", + "very regime where minibatching is meant to help. `StreamingDataset` feeds\n", + "minibatches from an arbitrary source (a generator, a directory of Parquet shards,\n", + "...) into a small `pytensor.shared` buffer, so peak memory is bounded by the\n", + "buffer and is independent of the dataset size `N`.\n", + "\n", + "The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: pass\n", + "`total_size=N` to the observed distribution and PyMC scales the minibatch\n", + "log-likelihood by `N / batch_size`. The one extra obligation is shuffling -- a\n", + "streaming source is only as well mixed as the order it yields rows in -- which we\n", + "handle with `shuffle_buffer`.\n", + "\n", + "We use a modest `N` here so the notebook runs in seconds and the two posteriors\n", + "are easy to compare; the streaming machinery is identical at any size, and the\n", + "final section shows why it matters at scale." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fd6b36bb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-05T13:34:18.647210Z", + "iopub.status.busy": "2026-06-05T13:34:18.647038Z", + "iopub.status.idle": "2026-06-05T13:34:21.397969Z", + "shell.execute_reply": "2026-06-05T13:34:21.396818Z" + } + }, + "outputs": [], + "source": [ + "import glob\n", + "import tempfile\n", + "\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pyarrow as pa\n", + "import pyarrow.parquet as pq\n", + "import pymc as pm\n", + "\n", + "from pymc.variational.streaming import StreamingDataset, shuffle_buffer\n", + "\n", + "RANDOM_SEED = 8927\n", + "rng = np.random.default_rng(RANDOM_SEED)\n", + "az.style.use(\"arviz-variat\")" + ] + }, + { + "cell_type": "markdown", + "id": "dd000ab7", + "metadata": {}, + "source": [ + "## Put a dataset on disk and forget the array\n", + "\n", + "We synthesise a logistic-regression dataset, write it to Parquet shards, and\n", + "delete the in-memory copy. From here on the data only exists on disk -- exactly\n", + "the situation `StreamingDataset` is for." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f1bfe614", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-05T13:34:21.401339Z", + "iopub.status.busy": "2026-06-05T13:34:21.400723Z", + "iopub.status.idle": "2026-06-05T13:34:21.433205Z", + "shell.execute_reply": "2026-06-05T13:34:21.432412Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6 shards written\n" + ] + } + ], + "source": [ + "N = 30_000\n", + "b_true = np.array([0.4, -1.2, 0.8, -0.5]) # intercept + 3 slopes\n", + "\n", + "X = rng.normal(size=(N, 3))\n", + "p = 1 / (1 + np.exp(-(b_true[0] + X @ b_true[1:])))\n", + "y = (rng.random(N) < p).astype(\"float64\")\n", + "table = np.column_stack([X, y]).astype(\"float64\") # 3 features + 1 observed column\n", + "\n", + "shard_dir = tempfile.mkdtemp(prefix=\"streaming_demo_\")\n", + "for i, s in enumerate(range(0, N, 5_000)):\n", + " block = table[s : s + 5_000]\n", + " pq.write_table(\n", + " pa.table({f\"c{j}\": block[:, j] for j in range(4)}),\n", + " f\"{shard_dir}/part_{i:03d}.parquet\",\n", + " )\n", + "del table # the data now lives only on disk\n", + "print(len(glob.glob(f\"{shard_dir}/*.parquet\")), \"shards written\")" + ] + }, + { + "cell_type": "markdown", + "id": "317be48a", + "metadata": {}, + "source": [ + "## Stream minibatches off disk and fit with ADVI\n", + "\n", + "`chunks()` lazily reads one shard at a time; `shuffle_buffer` accumulates rows\n", + "into a buffer, shuffles them, and yields fixed-size batches (carrying over any\n", + "remainder so no row is lost). `StreamingDataset` owns the `pytensor.shared`\n", + "buffer the model observes; `total_size=ds.total_size` triggers the `N / batch_size`\n", + "rescaling and a `fit_callback()` advances the buffer each step." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "052c1279", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-05T13:34:21.436023Z", + "iopub.status.busy": "2026-06-05T13:34:21.435858Z", + "iopub.status.idle": "2026-06-05T13:34:57.543965Z", + "shell.execute_reply": "2026-06-05T13:34:57.543226Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Finished [100%]: Average Loss = 531.45\n" + ] + } + ], + "source": [ + "def chunks():\n", + " for path in sorted(glob.glob(f\"{shard_dir}/*.parquet\")):\n", + " t = pq.read_table(path)\n", + " yield np.column_stack([t.column(c).to_numpy() for c in t.column_names])\n", + "\n", + "\n", + "batch_size = 1024\n", + "ds = StreamingDataset(\n", + " shuffle_buffer(chunks, buffer_size=15_000, batch_size=batch_size, seed=0),\n", + " batch_size=batch_size,\n", + " sample_shape=(4,), # 3 features + 1 observed column, streamed together\n", + " total_size=N,\n", + ")\n", + "ds.advance() # seed the buffer\n", + "\n", + "with pm.Model():\n", + " b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n", + " buf = ds.as_tensor()\n", + " logit = b[0] + b[1] * buf[:, 0] + b[2] * buf[:, 1] + b[3] * buf[:, 2]\n", + " pm.Bernoulli(\"y\", logit_p=logit, observed=buf[:, 3], total_size=ds.total_size)\n", + " approx = pm.fit(\n", + " 30_000,\n", + " method=\"advi\",\n", + " obj_optimizer=pm.adam(learning_rate=0.008),\n", + " callbacks=[ds.fit_callback()],\n", + " progressbar=False,\n", + " )\n", + " idata_stream = approx.sample(1000)" + ] + }, + { + "cell_type": "markdown", + "id": "4c4823cb", + "metadata": {}, + "source": [ + "The negative-ELBO trace shows the fit converging while only ever holding a\n", + "`batch_size` buffer in memory:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9d8d99e1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-05T13:34:57.545936Z", + "iopub.status.busy": "2026-06-05T13:34:57.545794Z", + "iopub.status.idle": "2026-06-05T13:34:57.783063Z", + "shell.execute_reply": "2026-06-05T13:34:57.782229Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(9, 3))\n", + "ax.plot(approx.hist, alpha=0.6)\n", + "ax.set(xlabel=\"iteration\", ylabel=\"negative ELBO\", title=\"Streaming ADVI convergence\");" + ] + }, + { + "cell_type": "markdown", + "id": "1b600054", + "metadata": {}, + "source": [ + "## The same posterior as in-RAM `pm.Minibatch`\n", + "\n", + "For comparison, the status-quo fit that keeps the whole dataset in memory.\n", + "Streaming changes *where the data lives*, not the inference: the two posteriors\n", + "land on top of each other (both show ADVI's characteristic mild bias relative to\n", + "the dashed ground truth, but they agree with each other)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6ab4c56a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-05T13:34:57.784640Z", + "iopub.status.busy": "2026-06-05T13:34:57.784513Z", + "iopub.status.idle": "2026-06-05T13:35:04.185598Z", + "shell.execute_reply": "2026-06-05T13:35:04.184893Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Finished [100%]: Average Loss = 531.03\n" + ] + } + ], + "source": [ + "with pm.Model():\n", + " b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n", + " xb, zb, sb, yb = pm.Minibatch(\n", + " X[:, 0].copy(), X[:, 1].copy(), X[:, 2].copy(), y, batch_size=batch_size\n", + " )\n", + " pm.Bernoulli(\n", + " \"y\", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N\n", + " )\n", + " approx_inram = pm.fit(\n", + " 30_000, method=\"advi\", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False\n", + " )\n", + " idata_inram = approx_inram.sample(1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ab687535", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-05T13:35:04.187668Z", + "iopub.status.busy": "2026-06-05T13:35:04.187515Z", + "iopub.status.idle": "2026-06-05T13:35:04.490699Z", + "shell.execute_reply": "2026-06-05T13:35:04.490137Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/claude-501/ipykernel_20806/503280259.py:13: UserWarning: The figure layout has changed to tight\n", + " fig.tight_layout();\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bs_stream = idata_stream.posterior[\"b\"].values.reshape(-1, 4)\n", + "bs_inram = idata_inram.posterior[\"b\"].values.reshape(-1, 4)\n", + "names = [\"intercept\", \"slope x1\", \"slope x2\", \"slope x3\"]\n", + "\n", + "fig, axes = plt.subplots(1, 4, figsize=(13, 3))\n", + "for k, ax in enumerate(axes):\n", + " ax.hist(bs_stream[:, k], bins=40, density=True, alpha=0.5, label=\"streaming\")\n", + " ax.hist(bs_inram[:, k], bins=40, density=True, alpha=0.5, label=\"in-RAM\")\n", + " ax.axvline(b_true[k], color=\"k\", ls=\"--\", lw=1)\n", + " ax.set(title=names[k], yticks=[])\n", + "axes[0].legend(fontsize=8)\n", + "fig.suptitle(\"Posterior of b: streaming vs in-RAM (dashed = ground truth)\", y=1.04)\n", + "fig.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "id": "7d845037", + "metadata": {}, + "source": [ + "## Why bother: memory\n", + "\n", + "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n", + "rows resident, so the array it must hold grows linearly in `N`; `StreamingDataset`\n", + "only ever holds one `batch_size` buffer. The dense `float64` design matrix is the\n", + "dominant cost, so its footprint is a faithful proxy for the gap:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c9ae88bf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-05T13:35:04.492436Z", + "iopub.status.busy": "2026-06-05T13:35:04.492289Z", + "iopub.status.idle": "2026-06-05T13:35:05.138046Z", + "shell.execute_reply": "2026-06-05T13:35:05.137532Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ncols = 4 # 3 features + observed\n", + "n_grid = np.logspace(5, 9, 50) # 1e5 .. 1e9 rows\n", + "inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident\n", + "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4.5))\n", + "ax.loglog(n_grid, inram_gb, label=\"in-RAM pm.Minibatch (O(N))\", lw=2)\n", + "ax.loglog(n_grid, stream_gb, label=\"StreamingDataset (O(batch))\", lw=2)\n", + "ax.axhline(24, color=\"0.4\", ls=\"--\", lw=1)\n", + "ax.text(1.2e5, 26, \"24 GiB RAM\", color=\"0.4\")\n", + "ax.set(xlabel=\"dataset size N\", ylabel=\"design-matrix footprint (GB)\",\n", + " title=\"Memory is flat in N when streaming\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "a5a5dd95", + "metadata": {}, + "source": [ + "On a real 122 GB order-book dataset (121.8 M rows) this is not just the array\n", + "size: measured in-RAM peak memory grew to ~9.1 GB at 122 M rows (extrapolating to\n", + "out-of-memory near ~372 M on a 24 GiB machine), while the streaming run stayed\n", + "flat at ~0.55 GB -- at posteriors agreeing to within ADVI noise.\n", + "\n", + "## When to reach for it\n", + "\n", + "* Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random\n", + " index gives perfectly i.i.d. minibatches for free.\n", + "* Use `StreamingDataset` when it does not: it keeps memory flat in `N` by\n", + " streaming from disk. The cost is a `fit_callback` to advance the buffer and the\n", + " responsibility to shuffle (pre-shuffle on disk, or interleave shards, then use\n", + " `shuffle_buffer`); a bounded buffer over strongly ordered data only\n", + " block-shuffles it and biases the posterior." + ] + }, + { + "cell_type": "markdown", + "id": "fe1cd4da", + "metadata": {}, + "source": [ + "## Authors\n", + "\n", + "* Authored by Yicheng (Ethan) Yang in June 2026 for the Google Summer of Code\n", + " project *Streaming Variational Inference for Large Datasets* (PyMC / NumFOCUS)." + ] + }, + { + "cell_type": "markdown", + "id": "7d4b437a", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. (2013). Stochastic\n", + " variational inference. *Journal of Machine Learning Research*, 14(1).\n", + "* Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2017).\n", + " Automatic differentiation variational inference. *JMLR*, 18(1)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "80f4917e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-05T13:35:05.139909Z", + "iopub.status.busy": "2026-06-05T13:35:05.139765Z", + "iopub.status.idle": "2026-06-05T13:35:05.155326Z", + "shell.execute_reply": "2026-06-05T13:35:05.154721Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Fri, 05 Jun 2026\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.12.12\n", + "IPython version : 9.13.0\n", + "\n", + "pytensor: 3.0.3\n", + "pyarrow : 24.0.0\n", + "\n", + "arviz : 1.1.0\n", + "matplotlib: 3.10.9\n", + "numpy : 2.4.6\n", + "platform : 1.0.8\n", + "pyarrow : 24.0.0\n", + "pymc : 6.0.1\n", + "\n", + "Watermark: 2.6.0\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w -p pytensor,pyarrow" + ] + }, + { + "cell_type": "markdown", + "id": "138db362", + "metadata": {}, + "source": [ + ":::{include} ../page_footer.md\n", + ":::" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md new file mode 100644 index 000000000..4a35cfe48 --- /dev/null +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -0,0 +1,232 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +(streaming_dataset)= + +# Out-of-core minibatch variational inference with StreamingDataset + +:::{post} June 2026 +:tags: variational inference, minibatch, out-of-core, ADVI +:category: intermediate, how-to +:author: Yicheng (Ethan) Yang +::: + ++++ + +`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so +minibatch variational inference is capped at datasets that fit in memory -- the +very regime where minibatching is meant to help. `StreamingDataset` feeds +minibatches from an arbitrary source (a generator, a directory of Parquet shards, +...) into a small `pytensor.shared` buffer, so peak memory is bounded by the +buffer and is independent of the dataset size `N`. + +The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: pass +`total_size=N` to the observed distribution and PyMC scales the minibatch +log-likelihood by `N / batch_size`. The one extra obligation is shuffling -- a +streaming source is only as well mixed as the order it yields rows in -- which we +handle with `shuffle_buffer`. + +We use a modest `N` here so the notebook runs in seconds and the two posteriors +are easy to compare; the streaming machinery is identical at any size, and the +final section shows why it matters at scale. + +```{code-cell} ipython3 +import glob +import tempfile + +import arviz as az +import matplotlib.pyplot as plt +import numpy as np +import pyarrow as pa +import pyarrow.parquet as pq +import pymc as pm + +from pymc.variational.streaming import StreamingDataset, shuffle_buffer + +RANDOM_SEED = 8927 +rng = np.random.default_rng(RANDOM_SEED) +az.style.use("arviz-variat") +``` + +## Put a dataset on disk and forget the array + +We synthesise a logistic-regression dataset, write it to Parquet shards, and +delete the in-memory copy. From here on the data only exists on disk -- exactly +the situation `StreamingDataset` is for. + +```{code-cell} ipython3 +N = 30_000 +b_true = np.array([0.4, -1.2, 0.8, -0.5]) # intercept + 3 slopes + +X = rng.normal(size=(N, 3)) +p = 1 / (1 + np.exp(-(b_true[0] + X @ b_true[1:]))) +y = (rng.random(N) < p).astype("float64") +table = np.column_stack([X, y]).astype("float64") # 3 features + 1 observed column + +shard_dir = tempfile.mkdtemp(prefix="streaming_demo_") +for i, s in enumerate(range(0, N, 5_000)): + block = table[s : s + 5_000] + pq.write_table( + pa.table({f"c{j}": block[:, j] for j in range(4)}), + f"{shard_dir}/part_{i:03d}.parquet", + ) +del table # the data now lives only on disk +print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written") +``` + +## Stream minibatches off disk and fit with ADVI + +`chunks()` lazily reads one shard at a time; `shuffle_buffer` accumulates rows +into a buffer, shuffles them, and yields fixed-size batches (carrying over any +remainder so no row is lost). `StreamingDataset` owns the `pytensor.shared` +buffer the model observes; `total_size=ds.total_size` triggers the `N / batch_size` +rescaling and a `fit_callback()` advances the buffer each step. + +```{code-cell} ipython3 +def chunks(): + for path in sorted(glob.glob(f"{shard_dir}/*.parquet")): + t = pq.read_table(path) + yield np.column_stack([t.column(c).to_numpy() for c in t.column_names]) + + +batch_size = 1024 +ds = StreamingDataset( + shuffle_buffer(chunks, buffer_size=15_000, batch_size=batch_size, seed=0), + batch_size=batch_size, + sample_shape=(4,), # 3 features + 1 observed column, streamed together + total_size=N, +) +ds.advance() # seed the buffer + +with pm.Model(): + b = pm.Normal("b", 0.0, 3.0, shape=4) + buf = ds.as_tensor() + logit = b[0] + b[1] * buf[:, 0] + b[2] * buf[:, 1] + b[3] * buf[:, 2] + pm.Bernoulli("y", logit_p=logit, observed=buf[:, 3], total_size=ds.total_size) + approx = pm.fit( + 30_000, + method="advi", + obj_optimizer=pm.adam(learning_rate=0.008), + callbacks=[ds.fit_callback()], + progressbar=False, + ) + idata_stream = approx.sample(1000) +``` + +The negative-ELBO trace shows the fit converging while only ever holding a +`batch_size` buffer in memory: + +```{code-cell} ipython3 +fig, ax = plt.subplots(figsize=(9, 3)) +ax.plot(approx.hist, alpha=0.6) +ax.set(xlabel="iteration", ylabel="negative ELBO", title="Streaming ADVI convergence"); +``` + +## The same posterior as in-RAM `pm.Minibatch` + +For comparison, the status-quo fit that keeps the whole dataset in memory. +Streaming changes *where the data lives*, not the inference: the two posteriors +land on top of each other (both show ADVI's characteristic mild bias relative to +the dashed ground truth, but they agree with each other). + +```{code-cell} ipython3 +with pm.Model(): + b = pm.Normal("b", 0.0, 3.0, shape=4) + xb, zb, sb, yb = pm.Minibatch( + X[:, 0].copy(), X[:, 1].copy(), X[:, 2].copy(), y, batch_size=batch_size + ) + pm.Bernoulli( + "y", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N + ) + approx_inram = pm.fit( + 30_000, method="advi", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False + ) + idata_inram = approx_inram.sample(1000) +``` + +```{code-cell} ipython3 +bs_stream = idata_stream.posterior["b"].values.reshape(-1, 4) +bs_inram = idata_inram.posterior["b"].values.reshape(-1, 4) +names = ["intercept", "slope x1", "slope x2", "slope x3"] + +fig, axes = plt.subplots(1, 4, figsize=(13, 3)) +for k, ax in enumerate(axes): + ax.hist(bs_stream[:, k], bins=40, density=True, alpha=0.5, label="streaming") + ax.hist(bs_inram[:, k], bins=40, density=True, alpha=0.5, label="in-RAM") + ax.axvline(b_true[k], color="k", ls="--", lw=1) + ax.set(title=names[k], yticks=[]) +axes[0].legend(fontsize=8) +fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1.04) +fig.tight_layout(); +``` + +## Why bother: memory + +Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N` +rows resident, so the array it must hold grows linearly in `N`; `StreamingDataset` +only ever holds one `batch_size` buffer. The dense `float64` design matrix is the +dominant cost, so its footprint is a faithful proxy for the gap: + +```{code-cell} ipython3 +ncols = 4 # 3 features + observed +n_grid = np.logspace(5, 9, 50) # 1e5 .. 1e9 rows +inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident +stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer + +fig, ax = plt.subplots(figsize=(8, 4.5)) +ax.loglog(n_grid, inram_gb, label="in-RAM pm.Minibatch (O(N))", lw=2) +ax.loglog(n_grid, stream_gb, label="StreamingDataset (O(batch))", lw=2) +ax.axhline(24, color="0.4", ls="--", lw=1) +ax.text(1.2e5, 26, "24 GiB RAM", color="0.4") +ax.set(xlabel="dataset size N", ylabel="design-matrix footprint (GB)", + title="Memory is flat in N when streaming") +ax.legend(); +``` + +On a real 122 GB order-book dataset (121.8 M rows) this is not just the array +size: measured in-RAM peak memory grew to ~9.1 GB at 122 M rows (extrapolating to +out-of-memory near ~372 M on a 24 GiB machine), while the streaming run stayed +flat at ~0.55 GB -- at posteriors agreeing to within ADVI noise. + +## When to reach for it + +* Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random + index gives perfectly i.i.d. minibatches for free. +* Use `StreamingDataset` when it does not: it keeps memory flat in `N` by + streaming from disk. The cost is a `fit_callback` to advance the buffer and the + responsibility to shuffle (pre-shuffle on disk, or interleave shards, then use + `shuffle_buffer`); a bounded buffer over strongly ordered data only + block-shuffles it and biases the posterior. + ++++ + +## Authors + +* Authored by Yicheng (Ethan) Yang in June 2026 for the Google Summer of Code + project *Streaming Variational Inference for Large Datasets* (PyMC / NumFOCUS). + ++++ + +## References + +* Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. (2013). Stochastic + variational inference. *Journal of Machine Learning Research*, 14(1). +* Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2017). + Automatic differentiation variational inference. *JMLR*, 18(1). + +```{code-cell} ipython3 +%load_ext watermark +%watermark -n -u -v -iv -w -p pytensor,pyarrow +``` + +:::{include} ../page_footer.md +::: From 43f9ccddf808030f89305be7529d69c8aa7e57f8 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Mon, 8 Jun 2026 22:41:16 -0500 Subject: [PATCH 02/17] Rewrite streaming example to the DataLoader/Trainer API Update the out-of-core variational inference example to the reworked streaming API: a DataLoader over parquet_source, a pm.Data placeholder, and the callback-free Trainer (replacing the removed StreamingDataset and fit_callback). Replace the non-reproducible private-data memory note with measured peak-RSS figures on the public Criteo benchmark. The notebook executes end-to-end; outputs and figures regenerated. --- .../streaming_dataset.ipynb | 249 ++++++++++-------- .../streaming_dataset.myst.md | 132 ++++++---- 2 files changed, 208 insertions(+), 173 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 0db53ca9d..ee7be9257 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -2,12 +2,12 @@ "cells": [ { "cell_type": "markdown", - "id": "6cb44821", + "id": "6f9305b8", "metadata": {}, "source": [ "(streaming_dataset)=\n", "\n", - "# Out-of-core minibatch variational inference with StreamingDataset\n", + "# Out-of-core minibatch variational inference with DataLoader and Trainer\n", "\n", ":::{post} June 2026\n", ":tags: variational inference, minibatch, out-of-core, ADVI\n", @@ -18,21 +18,28 @@ }, { "cell_type": "markdown", - "id": "24dc00a0", + "id": "c97c0132", "metadata": {}, "source": [ "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n", "minibatch variational inference is capped at datasets that fit in memory -- the\n", - "very regime where minibatching is meant to help. `StreamingDataset` feeds\n", - "minibatches from an arbitrary source (a generator, a directory of Parquet shards,\n", - "...) into a small `pytensor.shared` buffer, so peak memory is bounded by the\n", - "buffer and is independent of the dataset size `N`.\n", + "very regime where minibatching is meant to help. This notebook uses the streaming\n", + "API in `pymc.variational.streaming`, which mirrors PyTorch's `torch.utils.data`:\n", "\n", - "The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: pass\n", - "`total_size=N` to the observed distribution and PyMC scales the minibatch\n", - "log-likelihood by `N / batch_size`. The one extra obligation is shuffling -- a\n", - "streaming source is only as well mixed as the order it yields rows in -- which we\n", - "handle with `shuffle_buffer`.\n", + "* a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally\n", + " shuffles) an out-of-core source -- here a directory of Parquet shards read by\n", + " {func}`~pymc.variational.streaming.parquet_source` -- into fixed-size\n", + " minibatches, holding only one batch in memory at a time;\n", + "* the model observes a `pm.Data` *placeholder* of one batch, not the whole array;\n", + "* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, streaming each\n", + " minibatch into that placeholder with `set_data` every step -- **no callbacks**.\n", + "\n", + "The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: the\n", + "`DataLoader` is *sized*, so `total_size=len(loader)` passes the dataset size `N`\n", + "to the observed distribution and PyMC scales the minibatch log-likelihood by\n", + "`N / batch_size`. The one extra obligation is shuffling -- a streaming source is\n", + "only as well mixed as the order it yields rows in -- which `DataLoader(shuffle=True)`\n", + "handles with a bounded buffer.\n", "\n", "We use a modest `N` here so the notebook runs in seconds and the two posteriors\n", "are easy to compare; the streaming machinery is identical at any size, and the\n", @@ -42,13 +49,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "fd6b36bb", + "id": "416e522b", "metadata": { "execution": { - "iopub.execute_input": "2026-06-05T13:34:18.647210Z", - "iopub.status.busy": "2026-06-05T13:34:18.647038Z", - "iopub.status.idle": "2026-06-05T13:34:21.397969Z", - "shell.execute_reply": "2026-06-05T13:34:21.396818Z" + "iopub.execute_input": "2026-06-09T02:46:45.421431Z", + "iopub.status.busy": "2026-06-09T02:46:45.421338Z", + "iopub.status.idle": "2026-06-09T02:46:47.072976Z", + "shell.execute_reply": "2026-06-09T02:46:47.072225Z" } }, "outputs": [], @@ -63,7 +70,7 @@ "import pyarrow.parquet as pq\n", "import pymc as pm\n", "\n", - "from pymc.variational.streaming import StreamingDataset, shuffle_buffer\n", + "from pymc.variational.streaming import DataLoader, Trainer, parquet_source\n", "\n", "RANDOM_SEED = 8927\n", "rng = np.random.default_rng(RANDOM_SEED)\n", @@ -72,26 +79,28 @@ }, { "cell_type": "markdown", - "id": "dd000ab7", + "id": "770008ea", "metadata": {}, "source": [ "## Put a dataset on disk and forget the array\n", "\n", "We synthesise a logistic-regression dataset, write it to Parquet shards, and\n", - "delete the in-memory copy. From here on the data only exists on disk -- exactly\n", - "the situation `StreamingDataset` is for." + "delete the in-memory table. From here on the features only exist on disk -- exactly\n", + "the situation the streaming `DataLoader` is for. (We keep `X`, `y` around only to\n", + "build the in-RAM `pm.Minibatch` baseline later; the streaming fit never touches\n", + "them.)" ] }, { "cell_type": "code", "execution_count": 2, - "id": "f1bfe614", + "id": "3e94e139", "metadata": { "execution": { - "iopub.execute_input": "2026-06-05T13:34:21.401339Z", - "iopub.status.busy": "2026-06-05T13:34:21.400723Z", - "iopub.status.idle": "2026-06-05T13:34:21.433205Z", - "shell.execute_reply": "2026-06-05T13:34:21.432412Z" + "iopub.execute_input": "2026-06-09T02:46:47.074733Z", + "iopub.status.busy": "2026-06-09T02:46:47.074453Z", + "iopub.status.idle": "2026-06-09T02:46:47.104025Z", + "shell.execute_reply": "2026-06-09T02:46:47.102969Z" } }, "outputs": [ @@ -119,34 +128,36 @@ " pa.table({f\"c{j}\": block[:, j] for j in range(4)}),\n", " f\"{shard_dir}/part_{i:03d}.parquet\",\n", " )\n", - "del table # the data now lives only on disk\n", + "del table # the design matrix now lives only on disk\n", "print(len(glob.glob(f\"{shard_dir}/*.parquet\")), \"shards written\")" ] }, { "cell_type": "markdown", - "id": "317be48a", + "id": "77cfc727", "metadata": {}, "source": [ "## Stream minibatches off disk and fit with ADVI\n", "\n", - "`chunks()` lazily reads one shard at a time; `shuffle_buffer` accumulates rows\n", - "into a buffer, shuffles them, and yields fixed-size batches (carrying over any\n", - "remainder so no row is lost). `StreamingDataset` owns the `pytensor.shared`\n", - "buffer the model observes; `total_size=ds.total_size` triggers the `N / batch_size`\n", - "rescaling and a `fit_callback()` advances the buffer each step." + "`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`:\n", + "it reads one shard at a time and exposes `n_rows` from Parquet metadata (no data\n", + "scan), so `total_size=\"auto\"` resolves `N` for free. The `DataLoader` batches and\n", + "shuffles it into fixed-size minibatches. The model reads a `pm.Data(\"batch\", ...)`\n", + "*placeholder* -- the only data ever resident -- and the `Trainer` streams each\n", + "minibatch into it with `set_data`. There are no callbacks: `total_size=len(loader)`\n", + "triggers the `N / batch_size` rescaling, and `Trainer.fit` owns the loop." ] }, { "cell_type": "code", "execution_count": 3, - "id": "052c1279", + "id": "7ea8d824", "metadata": { "execution": { - "iopub.execute_input": "2026-06-05T13:34:21.436023Z", - "iopub.status.busy": "2026-06-05T13:34:21.435858Z", - "iopub.status.idle": "2026-06-05T13:34:57.543965Z", - "shell.execute_reply": "2026-06-05T13:34:57.543226Z" + "iopub.execute_input": "2026-06-09T02:46:47.105597Z", + "iopub.status.busy": "2026-06-09T02:46:47.105502Z", + "iopub.status.idle": "2026-06-09T02:47:07.034618Z", + "shell.execute_reply": "2026-06-09T02:47:07.034120Z" } }, "outputs": [ @@ -154,44 +165,41 @@ "name": "stderr", "output_type": "stream", "text": [ - "Finished [100%]: Average Loss = 531.45\n" + "Finished [100%]: Average Loss = 531.47\n" ] } ], "source": [ - "def chunks():\n", - " for path in sorted(glob.glob(f\"{shard_dir}/*.parquet\")):\n", - " t = pq.read_table(path)\n", - " yield np.column_stack([t.column(c).to_numpy() for c in t.column_names])\n", - "\n", - "\n", "batch_size = 1024\n", - "ds = StreamingDataset(\n", - " shuffle_buffer(chunks, buffer_size=15_000, batch_size=batch_size, seed=0),\n", + "loader = DataLoader(\n", + " parquet_source(shard_dir), # an IterableDataset over the shards\n", " batch_size=batch_size,\n", " sample_shape=(4,), # 3 features + 1 observed column, streamed together\n", - " total_size=N,\n", + " shuffle=True, # bounded-buffer shuffle (the shards are written in order)\n", + " buffer_size=15_000,\n", + " seed=0,\n", + " total_size=\"auto\", # read N from Parquet metadata; len(loader) == N\n", ")\n", - "ds.advance() # seed the buffer\n", "\n", - "with pm.Model():\n", + "with pm.Model() as model:\n", " b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n", - " buf = ds.as_tensor()\n", - " logit = b[0] + b[1] * buf[:, 0] + b[2] * buf[:, 1] + b[3] * buf[:, 2]\n", - " pm.Bernoulli(\"y\", logit_p=logit, observed=buf[:, 3], total_size=ds.total_size)\n", - " approx = pm.fit(\n", - " 30_000,\n", + " batch = pm.Data(\"batch\", np.zeros((batch_size, 4))) # placeholder -- the ONLY data in RAM\n", + " logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]\n", + " pm.Bernoulli(\"y\", logit_p=logit, observed=batch[:, 3], total_size=len(loader))\n", + "\n", + " # No callbacks: the Trainer streams each minibatch into \"batch\" with set_data.\n", + " approx = Trainer(\n", " method=\"advi\",\n", + " dataloader=loader,\n", + " data_name=\"batch\",\n", " obj_optimizer=pm.adam(learning_rate=0.008),\n", - " callbacks=[ds.fit_callback()],\n", - " progressbar=False,\n", - " )\n", + " ).fit(30_000, random_seed=RANDOM_SEED)\n", " idata_stream = approx.sample(1000)" ] }, { "cell_type": "markdown", - "id": "4c4823cb", + "id": "32d12782", "metadata": {}, "source": [ "The negative-ELBO trace shows the fit converging while only ever holding a\n", @@ -201,19 +209,19 @@ { "cell_type": "code", "execution_count": 4, - "id": "9d8d99e1", + "id": "2f3356ef", "metadata": { "execution": { - "iopub.execute_input": "2026-06-05T13:34:57.545936Z", - "iopub.status.busy": "2026-06-05T13:34:57.545794Z", - "iopub.status.idle": "2026-06-05T13:34:57.783063Z", - "shell.execute_reply": "2026-06-05T13:34:57.782229Z" + "iopub.execute_input": "2026-06-09T02:47:07.035832Z", + "iopub.status.busy": "2026-06-09T02:47:07.035753Z", + "iopub.status.idle": "2026-06-09T02:47:07.179976Z", + "shell.execute_reply": "2026-06-09T02:47:07.179360Z" } }, "outputs": [ { "data": { - "image/png": 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" ] @@ -230,7 +238,7 @@ }, { "cell_type": "markdown", - "id": "1b600054", + "id": "f1605433", "metadata": {}, "source": [ "## The same posterior as in-RAM `pm.Minibatch`\n", @@ -244,13 +252,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "6ab4c56a", + "id": "6425b460", "metadata": { "execution": { - "iopub.execute_input": "2026-06-05T13:34:57.784640Z", - "iopub.status.busy": "2026-06-05T13:34:57.784513Z", - "iopub.status.idle": "2026-06-05T13:35:04.185598Z", - "shell.execute_reply": "2026-06-05T13:35:04.184893Z" + "iopub.execute_input": "2026-06-09T02:47:07.181731Z", + "iopub.status.busy": "2026-06-09T02:47:07.181613Z", + "iopub.status.idle": "2026-06-09T02:47:10.642405Z", + "shell.execute_reply": "2026-06-09T02:47:10.641996Z" } }, "outputs": [ @@ -258,7 +266,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Finished [100%]: Average Loss = 531.03\n" + "Finished [100%]: Average Loss = 531.43\n" ] } ], @@ -280,13 +288,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "ab687535", + "id": "ad162375", "metadata": { "execution": { - "iopub.execute_input": "2026-06-05T13:35:04.187668Z", - "iopub.status.busy": "2026-06-05T13:35:04.187515Z", - "iopub.status.idle": "2026-06-05T13:35:04.490699Z", - "shell.execute_reply": "2026-06-05T13:35:04.490137Z" + "iopub.execute_input": "2026-06-09T02:47:10.643730Z", + "iopub.status.busy": "2026-06-09T02:47:10.643661Z", + "iopub.status.idle": "2026-06-09T02:47:10.881067Z", + "shell.execute_reply": "2026-06-09T02:47:10.880696Z" } }, "outputs": [ @@ -294,13 +302,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/claude-501/ipykernel_20806/503280259.py:13: UserWarning: The figure layout has changed to tight\n", + "/tmp/claude-501/ipykernel_77321/503280259.py:13: UserWarning: The figure layout has changed to tight\n", " fig.tight_layout();\n" ] }, { "data": { - "image/png": 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" ] @@ -327,33 +335,34 @@ }, { "cell_type": "markdown", - "id": "7d845037", + "id": "2b38e94b", "metadata": {}, "source": [ "## Why bother: memory\n", "\n", "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n", - "rows resident, so the array it must hold grows linearly in `N`; `StreamingDataset`\n", - "only ever holds one `batch_size` buffer. The dense `float64` design matrix is the\n", - "dominant cost, so its footprint is a faithful proxy for the gap:" + "rows resident, so the array it must hold grows linearly in `N`; the streaming\n", + "`DataLoader` only ever holds one `batch_size` buffer. The dense `float64` design\n", + "matrix is the dominant cost; the line below is its *theoretical lower bound*\n", + "(`N * ncols * 8` bytes), not a measurement:" ] }, { "cell_type": "code", "execution_count": 7, - "id": "c9ae88bf", + "id": "ddbbded0", "metadata": { "execution": { - "iopub.execute_input": "2026-06-05T13:35:04.492436Z", - "iopub.status.busy": "2026-06-05T13:35:04.492289Z", - "iopub.status.idle": "2026-06-05T13:35:05.138046Z", - "shell.execute_reply": "2026-06-05T13:35:05.137532Z" + "iopub.execute_input": "2026-06-09T02:47:10.882401Z", + "iopub.status.busy": "2026-06-09T02:47:10.882316Z", + "iopub.status.idle": "2026-06-09T02:47:11.132510Z", + "shell.execute_reply": "2026-06-09T02:47:11.132189Z" } }, "outputs": [ { "data": { - "image/png": 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" ] @@ -365,43 +374,50 @@ "source": [ "ncols = 4 # 3 features + observed\n", "n_grid = np.logspace(5, 9, 50) # 1e5 .. 1e9 rows\n", - "inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident\n", + "inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound)\n", "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer\n", "\n", "fig, ax = plt.subplots(figsize=(8, 4.5))\n", - "ax.loglog(n_grid, inram_gb, label=\"in-RAM pm.Minibatch (O(N))\", lw=2)\n", - "ax.loglog(n_grid, stream_gb, label=\"StreamingDataset (O(batch))\", lw=2)\n", - "ax.axhline(24, color=\"0.4\", ls=\"--\", lw=1)\n", - "ax.text(1.2e5, 26, \"24 GiB RAM\", color=\"0.4\")\n", - "ax.set(xlabel=\"dataset size N\", ylabel=\"design-matrix footprint (GB)\",\n", - " title=\"Memory is flat in N when streaming\")\n", + "ax.loglog(n_grid, inram_gb, label=\"in-RAM pm.Minibatch (O(N), array lower bound)\", lw=2)\n", + "ax.loglog(n_grid, stream_gb, label=\"streaming DataLoader (O(batch))\", lw=2)\n", + "ax.axhline(26, color=\"0.4\", ls=\"--\", lw=1)\n", + "ax.text(1.2e5, 28, \"26 GB RAM\", color=\"0.4\")\n", + "ax.set(xlabel=\"dataset size N\", ylabel=\"design-matrix size (GB) = N·ncols·8 bytes\",\n", + " title=\"Array footprint is flat in N when streaming (theoretical lower bound)\")\n", "ax.legend();" ] }, { "cell_type": "markdown", - "id": "a5a5dd95", + "id": "64cdea8e", "metadata": {}, "source": [ - "On a real 122 GB order-book dataset (121.8 M rows) this is not just the array\n", - "size: measured in-RAM peak memory grew to ~9.1 GB at 122 M rows (extrapolating to\n", - "out-of-memory near ~372 M on a 24 GiB machine), while the streaming run stayed\n", - "flat at ~0.55 GB -- at posteriors agreeing to within ADVI noise.\n", + "That line is only the bare array; actual peak RSS is higher (the framework plus\n", + "PyTensor's resident copy), and it crosses the RAM ceiling sooner. To pin the real\n", + "number on public, reproducible data, I measured peak memory on the\n", + "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs),\n", + "the standard out-of-core learning benchmark, with the same logistic model (13\n", + "numeric features + the click label). Streaming through the `DataLoader` stayed flat\n", + "at **~0.7 GB** across a 1M→150M-row sweep, while the in-RAM `pm.Minibatch` baseline\n", + "rose linearly to **15.7 GB at 150M rows** (about **21×** more) and extrapolates to\n", + "out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to\n", + "within ADVI noise (correlation **0.999** across all 14 coefficients). The point of\n", + "using Criteo rather than a private dataset is that anyone can rerun it.\n", "\n", "## When to reach for it\n", "\n", "* Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random\n", " index gives perfectly i.i.d. minibatches for free.\n", - "* Use `StreamingDataset` when it does not: it keeps memory flat in `N` by\n", - " streaming from disk. The cost is a `fit_callback` to advance the buffer and the\n", - " responsibility to shuffle (pre-shuffle on disk, or interleave shards, then use\n", - " `shuffle_buffer`); a bounded buffer over strongly ordered data only\n", - " block-shuffles it and biases the posterior." + "* Use the streaming `DataLoader` + `Trainer` when it does not: it keeps memory\n", + " flat in `N` by streaming from disk, with no callbacks to wire up. The remaining\n", + " obligation is shuffling -- pass `shuffle=True`, or pre-shuffle on disk / interleave\n", + " shards for strongly ordered data, since a bounded buffer over strongly ordered\n", + " data only block-shuffles it and biases the posterior." ] }, { "cell_type": "markdown", - "id": "fe1cd4da", + "id": "9dbff83e", "metadata": {}, "source": [ "## Authors\n", @@ -412,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "7d4b437a", + "id": "26996637", "metadata": {}, "source": [ "## References\n", @@ -426,13 +442,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "80f4917e", + "id": "5f48f79a", "metadata": { "execution": { - "iopub.execute_input": "2026-06-05T13:35:05.139909Z", - "iopub.status.busy": "2026-06-05T13:35:05.139765Z", - "iopub.status.idle": "2026-06-05T13:35:05.155326Z", - "shell.execute_reply": "2026-06-05T13:35:05.154721Z" + "iopub.execute_input": "2026-06-09T02:47:11.134055Z", + "iopub.status.busy": "2026-06-09T02:47:11.133970Z", + "iopub.status.idle": "2026-06-09T02:47:11.143141Z", + "shell.execute_reply": "2026-06-09T02:47:11.142770Z" } }, "outputs": [ @@ -440,7 +456,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Fri, 05 Jun 2026\n", + "Last updated: Mon, 08 Jun 2026\n", "\n", "Python implementation: CPython\n", "Python version : 3.12.12\n", @@ -468,7 +484,7 @@ }, { "cell_type": "markdown", - "id": "138db362", + "id": "b5d5ae89", "metadata": {}, "source": [ ":::{include} ../page_footer.md\n", @@ -477,6 +493,9 @@ } ], "metadata": { + "jupytext": { + "default_lexer": "ipython3" + }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index 4a35cfe48..fcf60a86c 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -12,7 +12,7 @@ kernelspec: (streaming_dataset)= -# Out-of-core minibatch variational inference with StreamingDataset +# Out-of-core minibatch variational inference with DataLoader and Trainer :::{post} June 2026 :tags: variational inference, minibatch, out-of-core, ADVI @@ -24,16 +24,23 @@ kernelspec: `pm.Minibatch` random-indexes an array that must be fully resident in RAM, so minibatch variational inference is capped at datasets that fit in memory -- the -very regime where minibatching is meant to help. `StreamingDataset` feeds -minibatches from an arbitrary source (a generator, a directory of Parquet shards, -...) into a small `pytensor.shared` buffer, so peak memory is bounded by the -buffer and is independent of the dataset size `N`. - -The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: pass -`total_size=N` to the observed distribution and PyMC scales the minibatch -log-likelihood by `N / batch_size`. The one extra obligation is shuffling -- a -streaming source is only as well mixed as the order it yields rows in -- which we -handle with `shuffle_buffer`. +very regime where minibatching is meant to help. This notebook uses the streaming +API in `pymc.variational.streaming`, which mirrors PyTorch's `torch.utils.data`: + +* a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally + shuffles) an out-of-core source -- here a directory of Parquet shards read by + {func}`~pymc.variational.streaming.parquet_source` -- into fixed-size + minibatches, holding only one batch in memory at a time; +* the model observes a `pm.Data` *placeholder* of one batch, not the whole array; +* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, streaming each + minibatch into that placeholder with `set_data` every step -- **no callbacks**. + +The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: the +`DataLoader` is *sized*, so `total_size=len(loader)` passes the dataset size `N` +to the observed distribution and PyMC scales the minibatch log-likelihood by +`N / batch_size`. The one extra obligation is shuffling -- a streaming source is +only as well mixed as the order it yields rows in -- which `DataLoader(shuffle=True)` +handles with a bounded buffer. We use a modest `N` here so the notebook runs in seconds and the two posteriors are easy to compare; the streaming machinery is identical at any size, and the @@ -50,7 +57,7 @@ import pyarrow as pa import pyarrow.parquet as pq import pymc as pm -from pymc.variational.streaming import StreamingDataset, shuffle_buffer +from pymc.variational.streaming import DataLoader, Trainer, parquet_source RANDOM_SEED = 8927 rng = np.random.default_rng(RANDOM_SEED) @@ -60,8 +67,10 @@ az.style.use("arviz-variat") ## Put a dataset on disk and forget the array We synthesise a logistic-regression dataset, write it to Parquet shards, and -delete the in-memory copy. From here on the data only exists on disk -- exactly -the situation `StreamingDataset` is for. +delete the in-memory table. From here on the features only exist on disk -- exactly +the situation the streaming `DataLoader` is for. (We keep `X`, `y` around only to +build the in-RAM `pm.Minibatch` baseline later; the streaming fit never touches +them.) ```{code-cell} ipython3 N = 30_000 @@ -79,46 +88,45 @@ for i, s in enumerate(range(0, N, 5_000)): pa.table({f"c{j}": block[:, j] for j in range(4)}), f"{shard_dir}/part_{i:03d}.parquet", ) -del table # the data now lives only on disk +del table # the design matrix now lives only on disk print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written") ``` ## Stream minibatches off disk and fit with ADVI -`chunks()` lazily reads one shard at a time; `shuffle_buffer` accumulates rows -into a buffer, shuffles them, and yields fixed-size batches (carrying over any -remainder so no row is lost). `StreamingDataset` owns the `pytensor.shared` -buffer the model observes; `total_size=ds.total_size` triggers the `N / batch_size` -rescaling and a `fit_callback()` advances the buffer each step. +`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`: +it reads one shard at a time and exposes `n_rows` from Parquet metadata (no data +scan), so `total_size="auto"` resolves `N` for free. The `DataLoader` batches and +shuffles it into fixed-size minibatches. The model reads a `pm.Data("batch", ...)` +*placeholder* -- the only data ever resident -- and the `Trainer` streams each +minibatch into it with `set_data`. There are no callbacks: `total_size=len(loader)` +triggers the `N / batch_size` rescaling, and `Trainer.fit` owns the loop. ```{code-cell} ipython3 -def chunks(): - for path in sorted(glob.glob(f"{shard_dir}/*.parquet")): - t = pq.read_table(path) - yield np.column_stack([t.column(c).to_numpy() for c in t.column_names]) - - batch_size = 1024 -ds = StreamingDataset( - shuffle_buffer(chunks, buffer_size=15_000, batch_size=batch_size, seed=0), +loader = DataLoader( + parquet_source(shard_dir), # an IterableDataset over the shards batch_size=batch_size, sample_shape=(4,), # 3 features + 1 observed column, streamed together - total_size=N, + shuffle=True, # bounded-buffer shuffle (the shards are written in order) + buffer_size=15_000, + seed=0, + total_size="auto", # read N from Parquet metadata; len(loader) == N ) -ds.advance() # seed the buffer -with pm.Model(): +with pm.Model() as model: b = pm.Normal("b", 0.0, 3.0, shape=4) - buf = ds.as_tensor() - logit = b[0] + b[1] * buf[:, 0] + b[2] * buf[:, 1] + b[3] * buf[:, 2] - pm.Bernoulli("y", logit_p=logit, observed=buf[:, 3], total_size=ds.total_size) - approx = pm.fit( - 30_000, + batch = pm.Data("batch", np.zeros((batch_size, 4))) # placeholder -- the ONLY data in RAM + logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2] + pm.Bernoulli("y", logit_p=logit, observed=batch[:, 3], total_size=len(loader)) + + # No callbacks: the Trainer streams each minibatch into "batch" with set_data. + approx = Trainer( method="advi", + dataloader=loader, + data_name="batch", obj_optimizer=pm.adam(learning_rate=0.008), - callbacks=[ds.fit_callback()], - progressbar=False, - ) + ).fit(30_000, random_seed=RANDOM_SEED) idata_stream = approx.sample(1000) ``` @@ -172,40 +180,48 @@ fig.tight_layout(); ## Why bother: memory Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N` -rows resident, so the array it must hold grows linearly in `N`; `StreamingDataset` -only ever holds one `batch_size` buffer. The dense `float64` design matrix is the -dominant cost, so its footprint is a faithful proxy for the gap: +rows resident, so the array it must hold grows linearly in `N`; the streaming +`DataLoader` only ever holds one `batch_size` buffer. The dense `float64` design +matrix is the dominant cost; the line below is its *theoretical lower bound* +(`N * ncols * 8` bytes), not a measurement: ```{code-cell} ipython3 ncols = 4 # 3 features + observed n_grid = np.logspace(5, 9, 50) # 1e5 .. 1e9 rows -inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident +inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound) stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer fig, ax = plt.subplots(figsize=(8, 4.5)) -ax.loglog(n_grid, inram_gb, label="in-RAM pm.Minibatch (O(N))", lw=2) -ax.loglog(n_grid, stream_gb, label="StreamingDataset (O(batch))", lw=2) -ax.axhline(24, color="0.4", ls="--", lw=1) -ax.text(1.2e5, 26, "24 GiB RAM", color="0.4") -ax.set(xlabel="dataset size N", ylabel="design-matrix footprint (GB)", - title="Memory is flat in N when streaming") +ax.loglog(n_grid, inram_gb, label="in-RAM pm.Minibatch (O(N), array lower bound)", lw=2) +ax.loglog(n_grid, stream_gb, label="streaming DataLoader (O(batch))", lw=2) +ax.axhline(26, color="0.4", ls="--", lw=1) +ax.text(1.2e5, 28, "26 GB RAM", color="0.4") +ax.set(xlabel="dataset size N", ylabel="design-matrix size (GB) = N·ncols·8 bytes", + title="Array footprint is flat in N when streaming (theoretical lower bound)") ax.legend(); ``` -On a real 122 GB order-book dataset (121.8 M rows) this is not just the array -size: measured in-RAM peak memory grew to ~9.1 GB at 122 M rows (extrapolating to -out-of-memory near ~372 M on a 24 GiB machine), while the streaming run stayed -flat at ~0.55 GB -- at posteriors agreeing to within ADVI noise. +That line is only the bare array; actual peak RSS is higher (the framework plus +PyTensor's resident copy), and it crosses the RAM ceiling sooner. To pin the real +number on public, reproducible data, I measured peak memory on the +[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), +the standard out-of-core learning benchmark, with the same logistic model (13 +numeric features + the click label). Streaming through the `DataLoader` stayed flat +at **~0.7 GB** across a 1M→150M-row sweep, while the in-RAM `pm.Minibatch` baseline +rose linearly to **15.7 GB at 150M rows** (about **21×** more) and extrapolates to +out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to +within ADVI noise (correlation **0.999** across all 14 coefficients). The point of +using Criteo rather than a private dataset is that anyone can rerun it. ## When to reach for it * Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random index gives perfectly i.i.d. minibatches for free. -* Use `StreamingDataset` when it does not: it keeps memory flat in `N` by - streaming from disk. The cost is a `fit_callback` to advance the buffer and the - responsibility to shuffle (pre-shuffle on disk, or interleave shards, then use - `shuffle_buffer`); a bounded buffer over strongly ordered data only - block-shuffles it and biases the posterior. +* Use the streaming `DataLoader` + `Trainer` when it does not: it keeps memory + flat in `N` by streaming from disk, with no callbacks to wire up. The remaining + obligation is shuffling -- pass `shuffle=True`, or pre-shuffle on disk / interleave + shards for strongly ordered data, since a bounded buffer over strongly ordered + data only block-shuffles it and biases the posterior. +++ From aa8be2759bf1eaf45c3878017161f05d02b0b346 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Mon, 8 Jun 2026 22:49:09 -0500 Subject: [PATCH 03/17] Apply pre-commit formatting (black line-length 100, jupytext sync) --- .../streaming_dataset.ipynb | 15 ++++++++------- .../streaming_dataset.myst.md | 16 +++++++++------- 2 files changed, 17 insertions(+), 14 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index ee7be9257..da2026d85 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -276,9 +276,7 @@ " xb, zb, sb, yb = pm.Minibatch(\n", " X[:, 0].copy(), X[:, 1].copy(), X[:, 2].copy(), y, batch_size=batch_size\n", " )\n", - " pm.Bernoulli(\n", - " \"y\", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N\n", - " )\n", + " pm.Bernoulli(\"y\", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N)\n", " approx_inram = pm.fit(\n", " 30_000, method=\"advi\", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False\n", " )\n", @@ -373,8 +371,8 @@ ], "source": [ "ncols = 4 # 3 features + observed\n", - "n_grid = np.logspace(5, 9, 50) # 1e5 .. 1e9 rows\n", - "inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound)\n", + "n_grid = np.logspace(5, 9, 50) # 1e5 .. 1e9 rows\n", + "inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound)\n", "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer\n", "\n", "fig, ax = plt.subplots(figsize=(8, 4.5))\n", @@ -382,8 +380,11 @@ "ax.loglog(n_grid, stream_gb, label=\"streaming DataLoader (O(batch))\", lw=2)\n", "ax.axhline(26, color=\"0.4\", ls=\"--\", lw=1)\n", "ax.text(1.2e5, 28, \"26 GB RAM\", color=\"0.4\")\n", - "ax.set(xlabel=\"dataset size N\", ylabel=\"design-matrix size (GB) = N·ncols·8 bytes\",\n", - " title=\"Array footprint is flat in N when streaming (theoretical lower bound)\")\n", + "ax.set(\n", + " xlabel=\"dataset size N\",\n", + " ylabel=\"design-matrix size (GB) = N·ncols·8 bytes\",\n", + " title=\"Array footprint is flat in N when streaming (theoretical lower bound)\",\n", + ")\n", "ax.legend();" ] }, diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index fcf60a86c..87698f9b0 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -1,5 +1,6 @@ --- jupytext: + default_lexer: ipython3 text_representation: extension: .md format_name: myst @@ -152,9 +153,7 @@ with pm.Model(): xb, zb, sb, yb = pm.Minibatch( X[:, 0].copy(), X[:, 1].copy(), X[:, 2].copy(), y, batch_size=batch_size ) - pm.Bernoulli( - "y", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N - ) + pm.Bernoulli("y", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N) approx_inram = pm.fit( 30_000, method="advi", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False ) @@ -187,8 +186,8 @@ matrix is the dominant cost; the line below is its *theoretical lower bound* ```{code-cell} ipython3 ncols = 4 # 3 features + observed -n_grid = np.logspace(5, 9, 50) # 1e5 .. 1e9 rows -inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound) +n_grid = np.logspace(5, 9, 50) # 1e5 .. 1e9 rows +inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound) stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer fig, ax = plt.subplots(figsize=(8, 4.5)) @@ -196,8 +195,11 @@ ax.loglog(n_grid, inram_gb, label="in-RAM pm.Minibatch (O(N), array lower bound ax.loglog(n_grid, stream_gb, label="streaming DataLoader (O(batch))", lw=2) ax.axhline(26, color="0.4", ls="--", lw=1) ax.text(1.2e5, 28, "26 GB RAM", color="0.4") -ax.set(xlabel="dataset size N", ylabel="design-matrix size (GB) = N·ncols·8 bytes", - title="Array footprint is flat in N when streaming (theoretical lower bound)") +ax.set( + xlabel="dataset size N", + ylabel="design-matrix size (GB) = N·ncols·8 bytes", + title="Array footprint is flat in N when streaming (theoretical lower bound)", +) ax.legend(); ``` From 505382a43693987f7751c679713505c65b2aae61 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Mon, 8 Jun 2026 23:08:22 -0500 Subject: [PATCH 04/17] Clean up memory figure layout (title/legend/annotation overlap) --- .../streaming_dataset.ipynb | 141 +++++++++--------- .../streaming_dataset.myst.md | 21 ++- 2 files changed, 84 insertions(+), 78 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index da2026d85..842e1b7ac 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "6f9305b8", + "id": "f258e12b", "metadata": {}, "source": [ "(streaming_dataset)=\n", @@ -18,7 +18,7 @@ }, { "cell_type": "markdown", - "id": "c97c0132", + "id": "f4f61327", "metadata": {}, "source": [ "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n", @@ -49,13 +49,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "416e522b", + "id": "3828b233", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T02:46:45.421431Z", - "iopub.status.busy": "2026-06-09T02:46:45.421338Z", - "iopub.status.idle": "2026-06-09T02:46:47.072976Z", - "shell.execute_reply": "2026-06-09T02:46:47.072225Z" + "iopub.execute_input": "2026-06-09T04:07:15.994900Z", + "iopub.status.busy": "2026-06-09T04:07:15.994790Z", + "iopub.status.idle": "2026-06-09T04:07:17.733770Z", + "shell.execute_reply": "2026-06-09T04:07:17.733191Z" } }, "outputs": [], @@ -79,7 +79,7 @@ }, { "cell_type": "markdown", - "id": "770008ea", + "id": "ade8f410", "metadata": {}, "source": [ "## Put a dataset on disk and forget the array\n", @@ -94,13 +94,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "3e94e139", + "id": "4eb91e27", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T02:46:47.074733Z", - "iopub.status.busy": "2026-06-09T02:46:47.074453Z", - "iopub.status.idle": "2026-06-09T02:46:47.104025Z", - "shell.execute_reply": "2026-06-09T02:46:47.102969Z" + "iopub.execute_input": "2026-06-09T04:07:17.735172Z", + "iopub.status.busy": "2026-06-09T04:07:17.734922Z", + "iopub.status.idle": "2026-06-09T04:07:17.756154Z", + "shell.execute_reply": "2026-06-09T04:07:17.755779Z" } }, "outputs": [ @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "77cfc727", + "id": "9f8bcf9f", "metadata": {}, "source": [ "## Stream minibatches off disk and fit with ADVI\n", @@ -151,13 +151,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "7ea8d824", + "id": "cd4d4c9b", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T02:46:47.105597Z", - "iopub.status.busy": "2026-06-09T02:46:47.105502Z", - "iopub.status.idle": "2026-06-09T02:47:07.034618Z", - "shell.execute_reply": "2026-06-09T02:47:07.034120Z" + "iopub.execute_input": "2026-06-09T04:07:17.757225Z", + "iopub.status.busy": "2026-06-09T04:07:17.757170Z", + "iopub.status.idle": "2026-06-09T04:07:36.098596Z", + "shell.execute_reply": "2026-06-09T04:07:36.098132Z" } }, "outputs": [ @@ -199,7 +199,7 @@ }, { "cell_type": "markdown", - "id": "32d12782", + "id": "fa093535", "metadata": {}, "source": [ "The negative-ELBO trace shows the fit converging while only ever holding a\n", @@ -209,13 +209,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "2f3356ef", + "id": "efccd50a", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T02:47:07.035832Z", - "iopub.status.busy": "2026-06-09T02:47:07.035753Z", - "iopub.status.idle": "2026-06-09T02:47:07.179976Z", - "shell.execute_reply": "2026-06-09T02:47:07.179360Z" + "iopub.execute_input": "2026-06-09T04:07:36.099763Z", + "iopub.status.busy": "2026-06-09T04:07:36.099686Z", + "iopub.status.idle": "2026-06-09T04:07:36.211882Z", + "shell.execute_reply": "2026-06-09T04:07:36.211538Z" } }, "outputs": [ @@ -238,7 +238,7 @@ }, { "cell_type": "markdown", - "id": "f1605433", + "id": "0a7cb7b8", "metadata": {}, "source": [ "## The same posterior as in-RAM `pm.Minibatch`\n", @@ -252,13 +252,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "6425b460", + "id": "7f44b970", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T02:47:07.181731Z", - "iopub.status.busy": "2026-06-09T02:47:07.181613Z", - "iopub.status.idle": "2026-06-09T02:47:10.642405Z", - "shell.execute_reply": "2026-06-09T02:47:10.641996Z" + "iopub.execute_input": "2026-06-09T04:07:36.212996Z", + "iopub.status.busy": "2026-06-09T04:07:36.212919Z", + "iopub.status.idle": "2026-06-09T04:07:39.535782Z", + "shell.execute_reply": "2026-06-09T04:07:39.535456Z" } }, "outputs": [ @@ -266,7 +266,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Finished [100%]: Average Loss = 531.43\n" + "Finished [100%]: Average Loss = 531.55\n" ] } ], @@ -286,13 +286,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "ad162375", + "id": "377a3b12", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T02:47:10.643730Z", - "iopub.status.busy": "2026-06-09T02:47:10.643661Z", - "iopub.status.idle": "2026-06-09T02:47:10.881067Z", - "shell.execute_reply": "2026-06-09T02:47:10.880696Z" + "iopub.execute_input": "2026-06-09T04:07:39.537071Z", + "iopub.status.busy": "2026-06-09T04:07:39.536998Z", + "iopub.status.idle": "2026-06-09T04:07:39.769680Z", + "shell.execute_reply": "2026-06-09T04:07:39.769277Z" } }, "outputs": [ @@ -300,13 +300,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/claude-501/ipykernel_77321/503280259.py:13: UserWarning: The figure layout has changed to tight\n", + "/tmp/claude-501/ipykernel_16604/503280259.py:13: UserWarning: The figure layout has changed to tight\n", " fig.tight_layout();\n" ] }, { "data": { - "image/png": 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" ] @@ -333,7 +333,7 @@ }, { "cell_type": "markdown", - "id": "2b38e94b", + "id": "bdee0324", "metadata": {}, "source": [ "## Why bother: memory\n", @@ -348,21 +348,29 @@ { "cell_type": "code", "execution_count": 7, - "id": "ddbbded0", + "id": "9a2a3b7d", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T02:47:10.882401Z", - "iopub.status.busy": "2026-06-09T02:47:10.882316Z", - "iopub.status.idle": "2026-06-09T02:47:11.132510Z", - "shell.execute_reply": "2026-06-09T02:47:11.132189Z" + "iopub.execute_input": "2026-06-09T04:07:39.770819Z", + "iopub.status.busy": "2026-06-09T04:07:39.770747Z", + "iopub.status.idle": "2026-06-09T04:07:39.968826Z", + "shell.execute_reply": "2026-06-09T04:07:39.968516Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/claude-501/ipykernel_16604/1052516414.py:15: UserWarning: The figure layout has changed to tight\n", + " fig.tight_layout();\n" + ] + }, { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -375,22 +383,21 @@ "inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound)\n", "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer\n", "\n", - "fig, ax = plt.subplots(figsize=(8, 4.5))\n", - "ax.loglog(n_grid, inram_gb, label=\"in-RAM pm.Minibatch (O(N), array lower bound)\", lw=2)\n", - "ax.loglog(n_grid, stream_gb, label=\"streaming DataLoader (O(batch))\", lw=2)\n", - "ax.axhline(26, color=\"0.4\", ls=\"--\", lw=1)\n", - "ax.text(1.2e5, 28, \"26 GB RAM\", color=\"0.4\")\n", - "ax.set(\n", - " xlabel=\"dataset size N\",\n", - " ylabel=\"design-matrix size (GB) = N·ncols·8 bytes\",\n", - " title=\"Array footprint is flat in N when streaming (theoretical lower bound)\",\n", - ")\n", - "ax.legend();" + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "ax.loglog(n_grid, inram_gb, lw=2.5, label=\"in-RAM pm.Minibatch (O(N))\")\n", + "ax.loglog(n_grid, stream_gb, lw=2.5, label=\"streaming DataLoader (O(batch))\")\n", + "ax.axhline(26, color=\"0.5\", ls=\"--\", lw=1)\n", + "ax.text(n_grid[-1], 30, \"26 GB RAM\", color=\"0.5\", ha=\"right\", va=\"bottom\")\n", + "ax.set_xlabel(\"dataset size N\")\n", + "ax.set_ylabel(\"array footprint (GB, lower bound)\")\n", + "ax.set_title(\"Memory is flat in N when streaming\")\n", + "ax.legend(loc=\"lower right\", framealpha=0.95)\n", + "fig.tight_layout();" ] }, { "cell_type": "markdown", - "id": "64cdea8e", + "id": "1209a446", "metadata": {}, "source": [ "That line is only the bare array; actual peak RSS is higher (the framework plus\n", @@ -418,7 +425,7 @@ }, { "cell_type": "markdown", - "id": "9dbff83e", + "id": "b5388542", "metadata": {}, "source": [ "## Authors\n", @@ -429,7 +436,7 @@ }, { "cell_type": "markdown", - "id": "26996637", + "id": "6dc3ce89", "metadata": {}, "source": [ "## References\n", @@ -443,13 +450,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "5f48f79a", + "id": "ad8a11ba", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T02:47:11.134055Z", - "iopub.status.busy": "2026-06-09T02:47:11.133970Z", - "iopub.status.idle": "2026-06-09T02:47:11.143141Z", - "shell.execute_reply": "2026-06-09T02:47:11.142770Z" + "iopub.execute_input": "2026-06-09T04:07:39.969974Z", + "iopub.status.busy": "2026-06-09T04:07:39.969907Z", + "iopub.status.idle": "2026-06-09T04:07:39.980436Z", + "shell.execute_reply": "2026-06-09T04:07:39.980077Z" } }, "outputs": [ @@ -485,7 +492,7 @@ }, { "cell_type": "markdown", - "id": "b5d5ae89", + "id": "845a0b24", "metadata": {}, "source": [ ":::{include} ../page_footer.md\n", diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index 87698f9b0..acbcd544e 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -190,17 +190,16 @@ n_grid = np.logspace(5, 9, 50) # 1e5 .. 1e9 rows inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound) stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer -fig, ax = plt.subplots(figsize=(8, 4.5)) -ax.loglog(n_grid, inram_gb, label="in-RAM pm.Minibatch (O(N), array lower bound)", lw=2) -ax.loglog(n_grid, stream_gb, label="streaming DataLoader (O(batch))", lw=2) -ax.axhline(26, color="0.4", ls="--", lw=1) -ax.text(1.2e5, 28, "26 GB RAM", color="0.4") -ax.set( - xlabel="dataset size N", - ylabel="design-matrix size (GB) = N·ncols·8 bytes", - title="Array footprint is flat in N when streaming (theoretical lower bound)", -) -ax.legend(); +fig, ax = plt.subplots(figsize=(8, 5)) +ax.loglog(n_grid, inram_gb, lw=2.5, label="in-RAM pm.Minibatch (O(N))") +ax.loglog(n_grid, stream_gb, lw=2.5, label="streaming DataLoader (O(batch))") +ax.axhline(26, color="0.5", ls="--", lw=1) +ax.text(n_grid[-1], 30, "26 GB RAM", color="0.5", ha="right", va="bottom") +ax.set_xlabel("dataset size N") +ax.set_ylabel("array footprint (GB, lower bound)") +ax.set_title("Memory is flat in N when streaming") +ax.legend(loc="lower right", framealpha=0.95) +fig.tight_layout(); ``` That line is only the bare array; actual peak RSS is higher (the framework plus From 26ea08857be754f8f710e147931ba3fe30929f34 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Mon, 8 Jun 2026 23:15:20 -0500 Subject: [PATCH 05/17] Tighten example prose --- .../streaming_dataset.ipynb | 106 +++++++++-------- .../streaming_dataset.myst.md | 110 +++++++++--------- 2 files changed, 106 insertions(+), 110 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 842e1b7ac..3d033611c 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -22,28 +22,27 @@ "metadata": {}, "source": [ "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n", - "minibatch variational inference is capped at datasets that fit in memory -- the\n", - "very regime where minibatching is meant to help. This notebook uses the streaming\n", - "API in `pymc.variational.streaming`, which mirrors PyTorch's `torch.utils.data`:\n", + "minibatch variational inference only works on data that already fits in memory.\n", + "This notebook uses the streaming API in `pymc.variational.streaming`, which follows\n", + "the same structure as PyTorch's `torch.utils.data`:\n", "\n", "* a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally\n", - " shuffles) an out-of-core source -- here a directory of Parquet shards read by\n", - " {func}`~pymc.variational.streaming.parquet_source` -- into fixed-size\n", - " minibatches, holding only one batch in memory at a time;\n", - "* the model observes a `pm.Data` *placeholder* of one batch, not the whole array;\n", - "* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, streaming each\n", - " minibatch into that placeholder with `set_data` every step -- **no callbacks**.\n", + " shuffles) an out-of-core source into fixed-size minibatches, holding only one\n", + " batch in memory at a time. Here the source is a directory of Parquet shards read\n", + " by {func}`~pymc.variational.streaming.parquet_source`.\n", + "* the model observes a `pm.Data` placeholder of one batch, not the whole array.\n", + "* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each\n", + " minibatch into that placeholder with `set_data` every step. There are no callbacks.\n", "\n", - "The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: the\n", - "`DataLoader` is *sized*, so `total_size=len(loader)` passes the dataset size `N`\n", - "to the observed distribution and PyMC scales the minibatch log-likelihood by\n", - "`N / batch_size`. The one extra obligation is shuffling -- a streaming source is\n", - "only as well mixed as the order it yields rows in -- which `DataLoader(shuffle=True)`\n", - "handles with a bounded buffer.\n", + "The unbiased-gradient rescaling works exactly as in `pm.Minibatch`. The `DataLoader`\n", + "is sized, so `total_size=len(loader)` passes the dataset size `N` to the observed\n", + "distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`. The\n", + "one extra requirement is shuffling. A streaming source is only as well mixed as the\n", + "order it yields rows in, so pass `DataLoader(shuffle=True)` to shuffle through a\n", + "bounded buffer.\n", "\n", - "We use a modest `N` here so the notebook runs in seconds and the two posteriors\n", - "are easy to compare; the streaming machinery is identical at any size, and the\n", - "final section shows why it matters at scale." + "`N` is small here so the notebook runs in seconds. The streaming code is the same at\n", + "any size, and the last section shows what changes at scale." ] }, { @@ -84,11 +83,10 @@ "source": [ "## Put a dataset on disk and forget the array\n", "\n", - "We synthesise a logistic-regression dataset, write it to Parquet shards, and\n", - "delete the in-memory table. From here on the features only exist on disk -- exactly\n", - "the situation the streaming `DataLoader` is for. (We keep `X`, `y` around only to\n", - "build the in-RAM `pm.Minibatch` baseline later; the streaming fit never touches\n", - "them.)" + "We build a logistic-regression dataset, write it to Parquet shards, and delete the\n", + "in-memory table. From here the features only exist on disk. We keep `X` and `y`\n", + "around only to build the in-RAM `pm.Minibatch` baseline later; the streaming fit\n", + "never reads them." ] }, { @@ -139,13 +137,14 @@ "source": [ "## Stream minibatches off disk and fit with ADVI\n", "\n", - "`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`:\n", - "it reads one shard at a time and exposes `n_rows` from Parquet metadata (no data\n", - "scan), so `total_size=\"auto\"` resolves `N` for free. The `DataLoader` batches and\n", - "shuffles it into fixed-size minibatches. The model reads a `pm.Data(\"batch\", ...)`\n", - "*placeholder* -- the only data ever resident -- and the `Trainer` streams each\n", - "minibatch into it with `set_data`. There are no callbacks: `total_size=len(loader)`\n", - "triggers the `N / batch_size` rescaling, and `Trainer.fit` owns the loop." + "`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`.\n", + "It reads one shard at a time and gets `n_rows` from the Parquet metadata without\n", + "scanning the data, so `total_size=\"auto\"` resolves `N` for free. The `DataLoader`\n", + "batches and shuffles it into fixed-size minibatches. The model reads a\n", + "`pm.Data(\"batch\", ...)` placeholder, the only data resident at any point, and the\n", + "`Trainer` writes each minibatch into it with `set_data`. There are no callbacks:\n", + "`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`\n", + "runs the loop." ] }, { @@ -183,7 +182,7 @@ "\n", "with pm.Model() as model:\n", " b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n", - " batch = pm.Data(\"batch\", np.zeros((batch_size, 4))) # placeholder -- the ONLY data in RAM\n", + " batch = pm.Data(\"batch\", np.zeros((batch_size, 4))) # placeholder, the only data in RAM\n", " logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]\n", " pm.Bernoulli(\"y\", logit_p=logit, observed=batch[:, 3], total_size=len(loader))\n", "\n", @@ -243,10 +242,10 @@ "source": [ "## The same posterior as in-RAM `pm.Minibatch`\n", "\n", - "For comparison, the status-quo fit that keeps the whole dataset in memory.\n", - "Streaming changes *where the data lives*, not the inference: the two posteriors\n", - "land on top of each other (both show ADVI's characteristic mild bias relative to\n", - "the dashed ground truth, but they agree with each other)." + "For comparison, here is the usual fit that keeps the whole dataset in memory.\n", + "Streaming changes where the data lives, not the inference, so the two posteriors\n", + "land on top of each other. Both show ADVI's usual mild bias relative to the dashed\n", + "ground truth, but they agree with each other." ] }, { @@ -336,13 +335,12 @@ "id": "bdee0324", "metadata": {}, "source": [ - "## Why bother: memory\n", + "## Memory usage\n", "\n", "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n", - "rows resident, so the array it must hold grows linearly in `N`; the streaming\n", - "`DataLoader` only ever holds one `batch_size` buffer. The dense `float64` design\n", - "matrix is the dominant cost; the line below is its *theoretical lower bound*\n", - "(`N * ncols * 8` bytes), not a measurement:" + "rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`\n", + "only holds one `batch_size` buffer. The dense `float64` design matrix dominates the\n", + "cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement:" ] }, { @@ -400,25 +398,25 @@ "id": "1209a446", "metadata": {}, "source": [ - "That line is only the bare array; actual peak RSS is higher (the framework plus\n", - "PyTensor's resident copy), and it crosses the RAM ceiling sooner. To pin the real\n", - "number on public, reproducible data, I measured peak memory on the\n", - "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs),\n", - "the standard out-of-core learning benchmark, with the same logistic model (13\n", - "numeric features + the click label). Streaming through the `DataLoader` stayed flat\n", - "at **~0.7 GB** across a 1M→150M-row sweep, while the in-RAM `pm.Minibatch` baseline\n", - "rose linearly to **15.7 GB at 150M rows** (about **21×** more) and extrapolates to\n", + "That line is only the bare array. Actual peak RSS is higher, because of the\n", + "framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get\n", + "the real number on public data, I measured peak memory on the\n", + "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a\n", + "standard out-of-core learning benchmark, with the same logistic model (13 numeric\n", + "features plus the click label). Streaming through the `DataLoader` stayed flat at\n", + "**~0.7 GB** across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline\n", + "rose linearly to **15.7 GB at 150M rows**, about 21 times more, and extrapolates to\n", "out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to\n", - "within ADVI noise (correlation **0.999** across all 14 coefficients). The point of\n", - "using Criteo rather than a private dataset is that anyone can rerun it.\n", + "within ADVI noise (correlation **0.999** across all 14 coefficients). Criteo is\n", + "public, so anyone can rerun this.\n", "\n", - "## When to reach for it\n", + "## When to use it\n", "\n", "* Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random\n", " index gives perfectly i.i.d. minibatches for free.\n", - "* Use the streaming `DataLoader` + `Trainer` when it does not: it keeps memory\n", - " flat in `N` by streaming from disk, with no callbacks to wire up. The remaining\n", - " obligation is shuffling -- pass `shuffle=True`, or pre-shuffle on disk / interleave\n", + "* Use the streaming `DataLoader` and `Trainer` when it does not: it keeps memory\n", + " flat in `N` by streaming from disk, with no callbacks to wire up. The one thing to\n", + " watch is shuffling. Pass `shuffle=True`, or pre-shuffle on disk and interleave\n", " shards for strongly ordered data, since a bounded buffer over strongly ordered\n", " data only block-shuffles it and biases the posterior." ] diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index acbcd544e..67a355294 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -24,28 +24,27 @@ kernelspec: +++ `pm.Minibatch` random-indexes an array that must be fully resident in RAM, so -minibatch variational inference is capped at datasets that fit in memory -- the -very regime where minibatching is meant to help. This notebook uses the streaming -API in `pymc.variational.streaming`, which mirrors PyTorch's `torch.utils.data`: +minibatch variational inference only works on data that already fits in memory. +This notebook uses the streaming API in `pymc.variational.streaming`, which follows +the same structure as PyTorch's `torch.utils.data`: * a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally - shuffles) an out-of-core source -- here a directory of Parquet shards read by - {func}`~pymc.variational.streaming.parquet_source` -- into fixed-size - minibatches, holding only one batch in memory at a time; -* the model observes a `pm.Data` *placeholder* of one batch, not the whole array; -* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, streaming each - minibatch into that placeholder with `set_data` every step -- **no callbacks**. - -The unbiased-gradient rescaling is unchanged from `pm.Minibatch`: the -`DataLoader` is *sized*, so `total_size=len(loader)` passes the dataset size `N` -to the observed distribution and PyMC scales the minibatch log-likelihood by -`N / batch_size`. The one extra obligation is shuffling -- a streaming source is -only as well mixed as the order it yields rows in -- which `DataLoader(shuffle=True)` -handles with a bounded buffer. - -We use a modest `N` here so the notebook runs in seconds and the two posteriors -are easy to compare; the streaming machinery is identical at any size, and the -final section shows why it matters at scale. + shuffles) an out-of-core source into fixed-size minibatches, holding only one + batch in memory at a time. Here the source is a directory of Parquet shards read + by {func}`~pymc.variational.streaming.parquet_source`. +* the model observes a `pm.Data` placeholder of one batch, not the whole array. +* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each + minibatch into that placeholder with `set_data` every step. There are no callbacks. + +The unbiased-gradient rescaling works exactly as in `pm.Minibatch`. The `DataLoader` +is sized, so `total_size=len(loader)` passes the dataset size `N` to the observed +distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`. The +one extra requirement is shuffling. A streaming source is only as well mixed as the +order it yields rows in, so pass `DataLoader(shuffle=True)` to shuffle through a +bounded buffer. + +`N` is small here so the notebook runs in seconds. The streaming code is the same at +any size, and the last section shows what changes at scale. ```{code-cell} ipython3 import glob @@ -67,11 +66,10 @@ az.style.use("arviz-variat") ## Put a dataset on disk and forget the array -We synthesise a logistic-regression dataset, write it to Parquet shards, and -delete the in-memory table. From here on the features only exist on disk -- exactly -the situation the streaming `DataLoader` is for. (We keep `X`, `y` around only to -build the in-RAM `pm.Minibatch` baseline later; the streaming fit never touches -them.) +We build a logistic-regression dataset, write it to Parquet shards, and delete the +in-memory table. From here the features only exist on disk. We keep `X` and `y` +around only to build the in-RAM `pm.Minibatch` baseline later; the streaming fit +never reads them. ```{code-cell} ipython3 N = 30_000 @@ -95,13 +93,14 @@ print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written") ## Stream minibatches off disk and fit with ADVI -`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`: -it reads one shard at a time and exposes `n_rows` from Parquet metadata (no data -scan), so `total_size="auto"` resolves `N` for free. The `DataLoader` batches and -shuffles it into fixed-size minibatches. The model reads a `pm.Data("batch", ...)` -*placeholder* -- the only data ever resident -- and the `Trainer` streams each -minibatch into it with `set_data`. There are no callbacks: `total_size=len(loader)` -triggers the `N / batch_size` rescaling, and `Trainer.fit` owns the loop. +`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`. +It reads one shard at a time and gets `n_rows` from the Parquet metadata without +scanning the data, so `total_size="auto"` resolves `N` for free. The `DataLoader` +batches and shuffles it into fixed-size minibatches. The model reads a +`pm.Data("batch", ...)` placeholder, the only data resident at any point, and the +`Trainer` writes each minibatch into it with `set_data`. There are no callbacks: +`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit` +runs the loop. ```{code-cell} ipython3 batch_size = 1024 @@ -117,7 +116,7 @@ loader = DataLoader( with pm.Model() as model: b = pm.Normal("b", 0.0, 3.0, shape=4) - batch = pm.Data("batch", np.zeros((batch_size, 4))) # placeholder -- the ONLY data in RAM + batch = pm.Data("batch", np.zeros((batch_size, 4))) # placeholder, the only data in RAM logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2] pm.Bernoulli("y", logit_p=logit, observed=batch[:, 3], total_size=len(loader)) @@ -142,10 +141,10 @@ ax.set(xlabel="iteration", ylabel="negative ELBO", title="Streaming ADVI converg ## The same posterior as in-RAM `pm.Minibatch` -For comparison, the status-quo fit that keeps the whole dataset in memory. -Streaming changes *where the data lives*, not the inference: the two posteriors -land on top of each other (both show ADVI's characteristic mild bias relative to -the dashed ground truth, but they agree with each other). +For comparison, here is the usual fit that keeps the whole dataset in memory. +Streaming changes where the data lives, not the inference, so the two posteriors +land on top of each other. Both show ADVI's usual mild bias relative to the dashed +ground truth, but they agree with each other. ```{code-cell} ipython3 with pm.Model(): @@ -176,13 +175,12 @@ fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1. fig.tight_layout(); ``` -## Why bother: memory +## Memory usage Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N` -rows resident, so the array it must hold grows linearly in `N`; the streaming -`DataLoader` only ever holds one `batch_size` buffer. The dense `float64` design -matrix is the dominant cost; the line below is its *theoretical lower bound* -(`N * ncols * 8` bytes), not a measurement: +rows resident, so its array grows linearly in `N`, while the streaming `DataLoader` +only holds one `batch_size` buffer. The dense `float64` design matrix dominates the +cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement: ```{code-cell} ipython3 ncols = 4 # 3 features + observed @@ -202,25 +200,25 @@ ax.legend(loc="lower right", framealpha=0.95) fig.tight_layout(); ``` -That line is only the bare array; actual peak RSS is higher (the framework plus -PyTensor's resident copy), and it crosses the RAM ceiling sooner. To pin the real -number on public, reproducible data, I measured peak memory on the -[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), -the standard out-of-core learning benchmark, with the same logistic model (13 -numeric features + the click label). Streaming through the `DataLoader` stayed flat -at **~0.7 GB** across a 1M→150M-row sweep, while the in-RAM `pm.Minibatch` baseline -rose linearly to **15.7 GB at 150M rows** (about **21×** more) and extrapolates to +That line is only the bare array. Actual peak RSS is higher, because of the +framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get +the real number on public data, I measured peak memory on the +[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a +standard out-of-core learning benchmark, with the same logistic model (13 numeric +features plus the click label). Streaming through the `DataLoader` stayed flat at +**~0.7 GB** across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline +rose linearly to **15.7 GB at 150M rows**, about 21 times more, and extrapolates to out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to -within ADVI noise (correlation **0.999** across all 14 coefficients). The point of -using Criteo rather than a private dataset is that anyone can rerun it. +within ADVI noise (correlation **0.999** across all 14 coefficients). Criteo is +public, so anyone can rerun this. -## When to reach for it +## When to use it * Use `pm.Minibatch` when the data fits in RAM: it is simpler and its random index gives perfectly i.i.d. minibatches for free. -* Use the streaming `DataLoader` + `Trainer` when it does not: it keeps memory - flat in `N` by streaming from disk, with no callbacks to wire up. The remaining - obligation is shuffling -- pass `shuffle=True`, or pre-shuffle on disk / interleave +* Use the streaming `DataLoader` and `Trainer` when it does not: it keeps memory + flat in `N` by streaming from disk, with no callbacks to wire up. The one thing to + watch is shuffling. Pass `shuffle=True`, or pre-shuffle on disk and interleave shards for strongly ordered data, since a bounded buffer over strongly ordered data only block-shuffles it and biases the posterior. From 9d49f19f479b89d49292fb1f24676db3548ae463 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Wed, 10 Jun 2026 06:33:32 -0500 Subject: [PATCH 06/17] Trim code comments and prose emphasis --- .../streaming_dataset.ipynb | 135 +++++++++--------- .../streaming_dataset.myst.md | 23 ++- 2 files changed, 78 insertions(+), 80 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 3d033611c..2872da191 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f258e12b", + "id": "3bc2b2ad", "metadata": {}, "source": [ "(streaming_dataset)=\n", @@ -18,7 +18,7 @@ }, { "cell_type": "markdown", - "id": "f4f61327", + "id": "ab54cc68", "metadata": {}, "source": [ "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n", @@ -48,13 +48,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "3828b233", + "id": "571a608f", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T04:07:15.994900Z", - "iopub.status.busy": "2026-06-09T04:07:15.994790Z", - "iopub.status.idle": "2026-06-09T04:07:17.733770Z", - "shell.execute_reply": "2026-06-09T04:07:17.733191Z" + "iopub.execute_input": "2026-06-10T11:32:49.788523Z", + "iopub.status.busy": "2026-06-10T11:32:49.788448Z", + "iopub.status.idle": "2026-06-10T11:32:52.092871Z", + "shell.execute_reply": "2026-06-10T11:32:52.090513Z" } }, "outputs": [], @@ -78,7 +78,7 @@ }, { "cell_type": "markdown", - "id": "ade8f410", + "id": "d1832538", "metadata": {}, "source": [ "## Put a dataset on disk and forget the array\n", @@ -92,13 +92,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "4eb91e27", + "id": "78c39ff1", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T04:07:17.735172Z", - "iopub.status.busy": "2026-06-09T04:07:17.734922Z", - "iopub.status.idle": "2026-06-09T04:07:17.756154Z", - "shell.execute_reply": "2026-06-09T04:07:17.755779Z" + "iopub.execute_input": "2026-06-10T11:32:52.098068Z", + "iopub.status.busy": "2026-06-10T11:32:52.097199Z", + "iopub.status.idle": "2026-06-10T11:32:52.140323Z", + "shell.execute_reply": "2026-06-10T11:32:52.139939Z" } }, "outputs": [ @@ -117,7 +117,7 @@ "X = rng.normal(size=(N, 3))\n", "p = 1 / (1 + np.exp(-(b_true[0] + X @ b_true[1:])))\n", "y = (rng.random(N) < p).astype(\"float64\")\n", - "table = np.column_stack([X, y]).astype(\"float64\") # 3 features + 1 observed column\n", + "table = np.column_stack([X, y]).astype(\"float64\")\n", "\n", "shard_dir = tempfile.mkdtemp(prefix=\"streaming_demo_\")\n", "for i, s in enumerate(range(0, N, 5_000)):\n", @@ -126,13 +126,13 @@ " pa.table({f\"c{j}\": block[:, j] for j in range(4)}),\n", " f\"{shard_dir}/part_{i:03d}.parquet\",\n", " )\n", - "del table # the design matrix now lives only on disk\n", + "del table\n", "print(len(glob.glob(f\"{shard_dir}/*.parquet\")), \"shards written\")" ] }, { "cell_type": "markdown", - "id": "9f8bcf9f", + "id": "8807e3db", "metadata": {}, "source": [ "## Stream minibatches off disk and fit with ADVI\n", @@ -150,13 +150,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "cd4d4c9b", + "id": "ef67327d", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T04:07:17.757225Z", - "iopub.status.busy": "2026-06-09T04:07:17.757170Z", - "iopub.status.idle": "2026-06-09T04:07:36.098596Z", - "shell.execute_reply": "2026-06-09T04:07:36.098132Z" + "iopub.execute_input": "2026-06-10T11:32:52.141370Z", + "iopub.status.busy": "2026-06-10T11:32:52.141295Z", + "iopub.status.idle": "2026-06-10T11:33:13.067413Z", + "shell.execute_reply": "2026-06-10T11:33:13.066448Z" } }, "outputs": [ @@ -171,13 +171,13 @@ "source": [ "batch_size = 1024\n", "loader = DataLoader(\n", - " parquet_source(shard_dir), # an IterableDataset over the shards\n", + " parquet_source(shard_dir),\n", " batch_size=batch_size,\n", - " sample_shape=(4,), # 3 features + 1 observed column, streamed together\n", - " shuffle=True, # bounded-buffer shuffle (the shards are written in order)\n", + " sample_shape=(4,), # 3 features + 1 observed column\n", + " shuffle=True, # the shards were written in order\n", " buffer_size=15_000,\n", " seed=0,\n", - " total_size=\"auto\", # read N from Parquet metadata; len(loader) == N\n", + " total_size=\"auto\", # N from the Parquet metadata\n", ")\n", "\n", "with pm.Model() as model:\n", @@ -186,7 +186,6 @@ " logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]\n", " pm.Bernoulli(\"y\", logit_p=logit, observed=batch[:, 3], total_size=len(loader))\n", "\n", - " # No callbacks: the Trainer streams each minibatch into \"batch\" with set_data.\n", " approx = Trainer(\n", " method=\"advi\",\n", " dataloader=loader,\n", @@ -198,7 +197,7 @@ }, { "cell_type": "markdown", - "id": "fa093535", + "id": "93a0f3c8", "metadata": {}, "source": [ "The negative-ELBO trace shows the fit converging while only ever holding a\n", @@ -208,13 +207,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "efccd50a", + "id": "14202643", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T04:07:36.099763Z", - "iopub.status.busy": "2026-06-09T04:07:36.099686Z", - "iopub.status.idle": "2026-06-09T04:07:36.211882Z", - "shell.execute_reply": "2026-06-09T04:07:36.211538Z" + "iopub.execute_input": "2026-06-10T11:33:13.070274Z", + "iopub.status.busy": "2026-06-10T11:33:13.070129Z", + "iopub.status.idle": "2026-06-10T11:33:13.382429Z", + "shell.execute_reply": "2026-06-10T11:33:13.381618Z" } }, "outputs": [ @@ -237,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "0a7cb7b8", + "id": "fd3600f2", "metadata": {}, "source": [ "## The same posterior as in-RAM `pm.Minibatch`\n", @@ -251,13 +250,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "7f44b970", + "id": "0448355b", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T04:07:36.212996Z", - "iopub.status.busy": "2026-06-09T04:07:36.212919Z", - "iopub.status.idle": "2026-06-09T04:07:39.535782Z", - "shell.execute_reply": "2026-06-09T04:07:39.535456Z" + "iopub.execute_input": "2026-06-10T11:33:13.384387Z", + "iopub.status.busy": "2026-06-10T11:33:13.384018Z", + "iopub.status.idle": "2026-06-10T11:33:16.995799Z", + "shell.execute_reply": "2026-06-10T11:33:16.995376Z" } }, "outputs": [ @@ -265,7 +264,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Finished [100%]: Average Loss = 531.55\n" + "Finished [100%]: Average Loss = 531.98\n" ] } ], @@ -285,13 +284,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "377a3b12", + "id": "39864bac", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T04:07:39.537071Z", - "iopub.status.busy": "2026-06-09T04:07:39.536998Z", - "iopub.status.idle": "2026-06-09T04:07:39.769680Z", - "shell.execute_reply": "2026-06-09T04:07:39.769277Z" + "iopub.execute_input": "2026-06-10T11:33:16.996992Z", + "iopub.status.busy": "2026-06-10T11:33:16.996925Z", + "iopub.status.idle": "2026-06-10T11:33:17.307953Z", + "shell.execute_reply": "2026-06-10T11:33:17.307475Z" } }, "outputs": [ @@ -299,13 +298,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/claude-501/ipykernel_16604/503280259.py:13: UserWarning: The figure layout has changed to tight\n", + "/tmp/claude-501/ipykernel_88126/503280259.py:13: UserWarning: The figure layout has changed to tight\n", " fig.tight_layout();\n" ] }, { "data": { - "image/png": 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12mWXXdJiiy1W8fdRCbFzNeuokBjJn5VMN9106fDDD6+4KhgA6DtiSDFkf48hYzz9vPPOy73PGmusoUUcAPx/4kfxY9njxwMOOODb2x/+8IesQFMkGS633HJpyy23zM0hiLgx2hkDlQ3I+R1QhyWXXDK7QM0111xZy9fbbrstHXfccbmt0m6++easAt9444337c9mm222tNlmm337/w8//HB65JFHcjPiowxwJV1/d9NNN6Ubb7wx97VEILDRRhul+eabL0088cRZO9oHHnggayP39NNPd/s3UVHviCOOSKeeempqhAkmmCDbp9FKYswxx0yvvvpq9tzPPPPMd+733HPPpdNOO63w8aaYYor0f//3f9ljTj/99GncccdN77//fnr22WezwOaqq64qbGt34IEH5ibSTTjhhFm7vR/84Adp0KBB2Xa/9NJLafjw4enCCy+sWGnysssuy1Y+LLjggqkn4vm33XbbbGVGvN533303Ow5POeWU7LVWEvs1KiNGy7wOSy21VBo4cOC3/x8VIvMmwTsfswDQX+RVkYtYZuutt05LL710FnuMM844Wbz04YcfZsn9EcPE348YMaLXkwwjNj3kkEMKF7asuOKKWRveiEcjjom466KLLsqtFB2PGUmC//znP7O/6YnVV189i0FnnXXWLG4aOXJkFn9GHJr32v70pz9VbDERMdijjz5a+LrXW2+9NPfcc2fvW6zKjnbCMQD2yiuvVFzccsIJJ2QDVr1hrLHGyqojxnZ1V2E7Xncs8rj88suz7xK77757xTbXAwYMyBbURBUWAKD5xJBiyP4eQ8b48ZNPPlnx92OPPXY2xgkA/Jf4UfxY9vgxOt/UIhIQo4VxxIwrrbRSzc8H/Y0EQWigaFkWleW6JkzFhenHP/5xxZZpHROfQ4cO/fZnkSTWOVHs+OOPz00QjKTEJZZYouptjQnUPHFhHzZs2Hd+NtVUU2UTxeuuu27WBjgSG7sTyWgxyd359dRjm222STvttFM2QdvV888/nyabbLLvvJ6iFs9rr712tpph/PHH/17y5JxzzpmtLIgWxTEBHmWKu3P99dfnTi7PPPPM6YwzzsgSALo+RyRcrrbaatlKh0rHQpRojknoek055ZRZ8BTb0fm5Y6J7zTXXzBIQo0JPJTGp3jlB8Ec/+lF265xEmJcg2NMqiADQjt56662KvzvssMMqtjWYd955v5NgF4N8V199dbeDMI0Qj11UqXmPPfb4XuuHWPAQCx8iSS+SACuJx47nWGutterexn322SdtscUW3/nZjDPOmO3DiNMiAbGSWOzxxBNPZHFPZ7EQ56STTqr4d5HQeOSRR2axYtfYNxb9xMBYXmviSy65JG2//fbZfuoNEcvFApyIzburYB1VEWNVbTx/VLrOqx6+8MIL98o2AgC1E0P+lxiyf8aQN9xwQ7et6jqLat2xaBwA+C/x43+JH/tn/NidKHIUC82jTTJQTIthaJCY4I1Jy+7EJOUyyyyT+/eR8NZXInCKydNKVl555e8lB3YWlW+i+kys4qwkKvH1RAQPsRKhu+TAMMsss3y78iCq7RRVQ4yKMEcdddT3kgO7e22R1BkrG7oTKyPyVinE33VNDuwsKhdGRZ5KojVfT5IC9t133+8kB3YWAdvvf//73L+PRNWodggAVC/ih0ry4oKuqzRjccXee++drdbsDUXxWbRq6Joc2FlUQlx++eV79Bx5ovpx1+TAzkl8sUJ22mmnLWy10l1b5aj0V8nmm2/+veTAziaaaKLclhmx8OPaa69NvSnem0hCrCQSJ/OSICMWjsU3AEDrEEP+jxiyf8WQt99+e9ptt91yK5sXbTsA9Efix/8RP/av+LGSKK4U+QTRabG7cWHguyQIQoPEZGpeO7WulUy6ijZzfSUGYfJEe9wi0bo2qu5V8u9//zv1JNs/L0Gxu0nfShX5QiQy7r///lkCX7Vikr6rGLSK56pkoYUWSkOGDCl87GgxWEmsyMh7jjzTTTddVqEwT5RXnmGGGXLvE9UfAYDq5VWOO/jgg7MWvc0WlZYrVUjuUM3gzU9/+tPc30dL3qIWxpVElb48sdAjVoTm6a4NcSNi34jx8tpi9CT2rdYvf/nLtOiii1b8fbT6qHR8Hn744b24ZQBAPcSQ/yOG7D8xZFQc33HHHdPnn39e8T4x5hyLsLsbnwWA/kz8+D/ix/4TP1YjuufFdhdVqIb+TothaIBIDIyKJ3k6t8PtTlTB6yt5ZX/Dtttu2+PniIqI8ZomnHDCmv82KvjVkszX3URw14S8SJ7rqeeeey598MEHudsRbegasdqhntZ8Sy21VFX7LfbHhRdeWPH30T4vWukBANWJqnrnnHNOxcT7uK5Hu9qogBwtsmaaaabs37POOmtW+XfAgN7/WhZxTF68GSuQF1988cLHifvEfb/44ouKi17iueaYY46ati8WdCy22GJVxTHHH398xd931wa4KPZdffXVU089+uijqbfFBOnRRx+dxWnVVpyO/RqTq5NMMkmvbx8AUBsx5P+IIftHDBnjkbGIu9KkcojvSWeeeWZWxRsA+C7x4/+IH/tH/Firv/zlL1mhnA033LCp2wGtSoIgNEBM9A4cODD3PuONN17u76NyXF/pSQvbWp+nngTBaMNb6/Pkicp+jfD222835HF663kiyaAR9+ur4wMAyiJaLyywwALp4YcfrnifaHEbt1jd2lnEkAsvvHBaZpll0jrrrJOmnnrqpsQX1SYqxn3ivk899VRDY4kZZ5wxG0jqaRwTg4NRWbrzY/VFbPP+++9nVRp7O9lzmmmmSUcccURWbbua7w+77757WnDBBXt1mwCA+oghv0sMWe4Y8uSTT05//OMfC2P9SA6MzjUAwPeJH79L/Fi++PHJJ5/89t+xqOSTTz7JxtSjQ88tt9ySrrjiivTZZ5/lPsZhhx2WLQifYIIJerw9UDZaDEMD5LUb69AXlWFaMUGwVtE6bvrpp2/o80w55ZQ1b0c9z9Mo9T7PxBNPXNX9ipI2Y4IbAKheVPA97rjjskUjtYpBjmhPGwMuUZF63333Te+9917Dd/+7777bkDgiFFXzqCeWqfb5q6kk0jWW6YsYLgbKivZxIweDN9lkk8L7DR06NG299dZ9sk0AQO3EkN8lhixvDHnkkUcWJgcOGTIknXvuudlkNADQPfHjd4kfyz0GGR0cY047Kkz/8Ic/TAcddFD617/+VZiIGN0Ar7rqqoZvD5SBBEFogHHHHbf4wzZm//u4ff755zX/jfYRqWLLPgCgdU033XTpH//4R9pmm23qjme+/vrrrO3WFltskQ1kUP7Ytx6RABkrZos88sgj2Q0AaF1iSMocQ0bVl3322SedeuqpufdbdNFF09lnn50mn3zyhjwvAJSZ+JEyx49FYjHJiSeeWDj+ftddd/XJ9kC76X8ZS0BLD7ZU01qu1tfz1ltv9WCLqn+eZqs2kSBa7+WZZJJJGrRFANC/xIrG3/72t+n2229Pxx9/fNpss83SvPPOm1VIrkW07z322GMbum2TTTZZ7u9rSUgsiiXqiZmqff5q7tc1lmn1GK5We+65Zxo9enTh/aLV8m677Vb4fgEAzSWG/C8xZLliyFgAveuuu6aLLroo934rr7xyOv300y0aB4AaiB//S/zYP8cgp5hiirTSSivl3ueFF17ok22BdtM6PU+BPlMUMA0fPjzNNttsqSyv58EHH2xYwJFnzTXXTMccc0xqlueee64h9yvbJDoA9LXxxhsvrbrqqtmto/3sa6+9lg2ovPzyy+nZZ59Nd999d3rooYcqPsYll1ySJRuOM844fRLHvPjii+mrr75KAwbkf0WM+xQNsNQTS7z00kvZYFLRYpGiOCZWj3Z9jNieV155pWKV7/vvvz8NHDgwtYOYPL3xxhurvv+oUaPS3nvvnbXABgBamxhSDFmWGPKTTz5JO+20U/r3v/+de78f//jH6YADDkhjjTVWXc8DAP2d+FH82F/HIKOSYJ6PP/64T7YD2o0KgtAmxhhjjIY91gILLJD7+zvuuCO1k4UWWqjw9cSkfE/NMsssuatZ77nnnmxiu1nuvPPOLAGhSNH7G5WOAIDGxnHR/iNaZ6233nrp17/+ddZKeP/996/4N59++ml69NFHG7YNs846a7a6OK81xb333lv4OHGfqAZSScRK8Vy1ihiqmuevJ47Ji32j7Vm7tJyIRS9HH310zX937bXXpr/97W+9sk0AQO8RQxYTQ7ZeDBmt6H76058WJgduv/326eCDD5YcCAANJH4sJn4sxxhkpcXgHSaddNI+2Q5oNxIEoY1WgeR57733qn6s5ZZbLvf3Z5xxRt2Z9VE+OP7+7bffTn1l6aWXzq12E8HegQceWFXyXOfqOF3Fcyy11FK5rYzPP//8VK+oXvOPf/yj7r9/9dVXs+ArT6z2KCoHPXTo0Iq/K2qR+O677xZsJQCUT17CXJ4f/ehHub9/8803U6NEHLPEEkvk3idiuCJnnXVW7u8XX3zxuif5ih47kiYvvvji3PssvPDCNce+J554Yvr6669TPeI9inbSvS0mWn/1q19VXIwSVRJjMUslhx9+eHrsscd6cQsBgFqJIf9HDFmOGPL1119Pm2++eW43l0hc2GuvvbJFUwBAbcSP/yN+bO/48cknn8zGeusRMedNN93UowqD0F9JEIQ2MfHEE+f+/vLLL88qoFRjrrnmSnPMMUfF30cC2Y477lh1kl8k00X1uv322y8tv/zy6bDDDqv7ol6PqIaz0kor5d7n+uuvT3vuuWf67LPPCl/LZZddVnGQaq211sr9+wh8/vnPf6Zagphzzz03bbDBBmnTTTftcQWbgw46KGsRWOl9/f3vf5/790OGDEkzzjhjxd/nVVAMPUlwBIB2ddVVV6X1118/WyH5xhtvVP139913X+7va1ncUI2iOOaWW27JTRI855xzCgdfip4jz80331xxlWnsi4hjiqpCr7766t/7WSzwyGt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E9pWso0JiJH+WM91006Xjjjuu7AxhAKD7iR3Fjr09dow+9Msuuyz3MWuuuaZl4gBA7JjR79jasePhhx/+4+2www7LCjJFkuEKK6yQttlmm9ycgYgZYzljoFifCh4DdMLSSy+dnbDmnnvubMnX++67L5166qm5y6XdfffdWQW+8ccf/8e/zT777GnLLbf88f+feuqp9PTTT+dmyEdZ4HI63nfXXXelO++8M/ezRGCw6aabpgUWWCD1798/W4728ccfz5aSe+mll0o+JyrqHX/88emcc85JtTDhhBNm+zSWlRh77LHTO++8k733yy+/PMbjXn311XTuuecWvt4UU0yRfvGLX2SvOf3006fxxhsvffrpp+mVV17JAp2bbrqpcGm7I444IjeRbqKJJsqW3PvZz36WBgwYkG33m2++mQYPHpz++c9/lq00ee2112YzIRZeeOHUFfH+O+64YzZTIz7vxx9/nB2HZ599dvZZy4n9GpURY9m8Nssss0zq16/fj/8fFSLzBsLbH7MA0BvkVZGLOGa77bZLyy67bBZ3jDvuuFms9Nlnn2WJ/RG/xPOHDh3a7UmGEZceffTRhZNaBg0alC3DG7FoxDARc1155ZW5VaLjNaOz7rrrrsue0xVrrLFGFn/ONttsWcw0bNiwLPaMGDTvs/3lL38pu9xExF/PPPNM4efeYIMN0jzzzJN9bzFDO5YTjs6wt99+u+zEltNPPz3rvOoO44wzTlYdMbarVHXt+NwxweP666/PriP22Wefsstc9+nTJ5tME9VYAID6ETuKHXt77Bh9xi+88ELZ+/v27Zv1awIAYseg37G1Y8dY7aYakYAYSxhHvLjyyitX/X7QW0kQhG4Qy5ZFZbmOCVNxotpkk03KLpvWNvg5cODAH/8WSWLtE8VOO+203ATBSEpcaqmlKt7WGETNEyf6nXfeeYy/TTXVVNlg8XrrrZctAxyJjaVEMloMdLf/PJ2xww47pN133z0bpO3otddeS5NNNtkYn6doied11lknm90wwQQT/CR5cq655spmGsQSxTEIHmWLS7n99ttzB5hnmWWWdP7552dJAB3fIxIuV1999WzmQ7ljIUo2x0B0Z0055ZRZMBXb0f69Y7B7rbXWyhIQo0pPOTGw3j5BcP31189u7ZMI8xIEu1oFEQCazQcffFD2vmOPPbbsEgfzzz//GB1dMVh88803l+yQqYV47aIqzfvuu+9PloGIyQ4x6SGS9CIJsJx47XiPtddeu9PbeNBBB6Wtt956jL/NNNNM2T6MGC0SEMuJiR7PP/98FvO0F5NwzjzzzLLPi4TGE044IYsTO8a9MeEnOsnylia++uqr0y677JLtp+4QcVxMvom4vFT16qiKGDNs4/2jynVe5fBFF120W7YRAKic2PH/iB17Z+x4xx13lFyurr2o0h0TxQEAsWMbsWPvjB1LiaJGMbk8lkkGKmeJYaixGOSNgctSYqByueWWy31+JLz1lAikYgC1nFVWWeUnyYHtRfWbqEATMzrLiUp8XRHBRMxMKJUcGGadddYfZyJExZ2iaohRFebEE0/8SXJgqc8WSZ0x06GUmCmRN2shntcxObC9qFwYVXnKieX5upIYcPDBB4+RHNheBHB/+tOfcp8fiapR7RAAqEzEDuXkxQQdZ2zGxIoDDzwwm7nZHYpis1i2oWNyYHtRCXHFFVfs0nvkicrHHZMD2yfxRaWVaaedtnDJvlLLKkelv3K22mqrnyQHtjfxxBPnLp8Rkz5uvfXW1J3iu4kkxHIicTIvCTLi4Jh4AwDUn9jxf8SOvSt2vP/++9Pee++dW9G8aNsBoLcRO/6P2LF3xY7lRDGlyB+IlRVL9QUDpUkQhBqLAdW8JdU6VjPpKJaa6ynRIZMnlsctEkvXRtW9cv7zn/+krmT/5yUolhr4LVeRL0Qi46GHHpol8FUqBuo7ig6seK9yFllkkTTffPMVvnYsM1hOzNDIe4880003XVahME+UW55hhhlyHxPVHwGAyuRVjjvqqKOyJXrrLaosl6uO3KaSjpztt98+9/5YkrdoCeNyokpfnpjkEbND85RahrgWcW/Ed3lLZHQl7q3Ub3/727T44ouXvT+W/Sh3fB533HHduGUAQDXEjv8jduw9sWNUGt9tt93SN998U/Yx0c8cE69L9ckCQG8ldvwfsWPviR0rEavlxXYXVacG/o8lhqGGIjEwqp7kab8cbilRBa+n5JUBDjvuuGOX3yMqIsZnmmiiiap+blTwqyaZr9RgcMeEvEie66pXX301jRo1Knc7Yim6Wsx+6MzyfMsss0xF+y32xz//+c+y98cSerGcHgBQLKrqXXzxxWWT7uOcHsvVRvXjWCpr5plnzv4922yzZVV/+/Tp/kuziGHyYs2YjbzkkksWvk48Jh777bfflp3wEu8155xzVrV9MZljiSWWqCiGOe2008reX2oZ4KK4d4011khd9cwzz6TuFgOlJ510UhajVVptOvZrDLJOMskk3b59AEBlxI7/I3bsHbFj9EHGxO1yA8shro8uuOCCrHo3APA/Ysf/ETv2jtixWn/729+ywjgbb7xxXbcDGp0EQaihGOzt169f7mPGH3/83PujclxP6coSttW+T2cSBGMZ3mrfJ09U9quFDz/8sCav013vE4kGtXhcTx0fANAKYhmGhRZaKD311FNlHxNL3MYtZrq2F/HjoosumpZbbrm07rrrpqmnnrousUWliYrxmHjsiy++WNM4YqaZZso6lboaw0RHYVSVbv9aPRHXfPrpp1mVxu5O9pxmmmnS8ccfn1XaruTaYZ999kkLL7xwt24TAFAdseOYxI6tHTueddZZ6c9//nNhjB/JgbFaDQAwJrHjmMSOrRc7vvDCCz/+OyaUfPnll1k/eqzKc88996Qbbrghff3117mvceyxx2aTwCeccMIubw+0KksMQw3lLTnWpieqwzRigmC1Yvm46aefvqbvM+WUU1a9HZ15n1rp7Pv079+/oscVJW3GIDcAUJmo3nvqqadmE0aqFR0esTxtdL5ENeqDDz44ffLJJzXf9R9//HFNYohQVNWjM3FMpe9fSUWRjnFMT8Rv0WlWtI9r2TG8+eabFz5u4MCBabvttuuRbQIAKid2HJPYsXVjxxNOOKEwOXC++eZLl156aTYgDQD8lNhxTGLH1u53jBUbYww7qkv//Oc/T0ceeWT697//XZiIGKv/3XTTTTXfHmglEgShhsYbb7ziH93Yve9n980331T9HEtJpLLL9gEAjWm66aZL//rXv9IOO+zQ6Vjm+++/z5bf2nrrrbNODVo/7u2MSICM2bNFnn766ewGADQesSOtHDtG5ZeDDjoonXPOObmPW3zxxdNFF12UJp988pq8LwC0KrEjrRw7FomJJGeccUZhn/tDDz3UI9sDzar3ZSoBP2rkjpdKlper9vN88MEHXdiiyt+n3ipNJojl9/JMMskkNdoiAOg9YnbjH//4x3T//fen0047LW255ZZp/vnnz6ojVyOW7z3llFNqum2TTTZZ7v3VJCQWxRGdiZcqff9KHtcxjmn0+K1a++23XxoxYkTh42Kp5b333rvw+wIA6kPs+H/Ejq0VO8ak57322itdeeWVuY9bZZVV0nnnnWeiOABUSOz4f8SOvbPfcYoppkgrr7xy7mNef/31HtkWaFaNs9Yp0OOKAqjBgwen2WefPbXK53niiSdqFoDkWWuttdLJJ5+c6uXVV1+tyeNabSAdAHrS+OOPn1ZbbbXs1rb87Lvvvpt1rrz11lvplVdeSQ8//HB68skny77G1VdfnSUbjjvuuD0Sw7zxxhvpu+++S3365F8mxmOKOls6E0e8+eabWcdS0USRohgmZpJ2fI3Ynrfffrtshe/HHnss9evXLzWDGES98847K3788OHD04EHHpgtgQ0ANCaxo9ixVWLHL7/8Mu2+++7pP//5T+7jNtlkk3T44YenccYZp1PvAwC9mdhR7Nhb+x2jkmCeL774oke2A5qVCoLQZMYaa6yavdZCCy2Ue/8DDzyQmskiiyxS+HliYL6rZp111tyZrY888kg2uF0vDz74YJaEUKTo+41qRwBA7WK4WAokltDaYIMN0u9///tsKeFDDz207HO++uqr9Mwzz9TsK5htttmymcZ5y1QMGTKk8HXiMVEVpJyIk+K9qhXxUyXv35kYJi/ujeXPmmX5iZjwctJJJ1X9vFtvvTVdcskl3bJNAEDtiR2LiR0bL3aM5ei23377wuTAXXbZJR111FGSAwGgRsSOxcSOrdHvWG4CeJtJJ520R7YDmpUEQWjCWSF5Pvnkk4pfa4UVVsi9//zzz+90pn2UE47nf/jhh6mnLLvssrkVbyL4O+KIIypKnmtfIaejeI9lllkmdynjyy+/PHVWVLD517/+1ennv/POO1kwlidmfxSVhx44cGDZ+4qWSfz4448LthIAWktewlye9ddfP/f+kSNHplqJGGappZbKfUzEb0UuvPDC3PuXXHLJTg/2Fb12JE1eddVVuY9ZdNFFq457zzjjjPT999+nzojvKJaT7m4x4Pq73/2u7ESUqJIYE1nKOe6449Kzzz7bjVsIAFRK7Pg/YsfWiB3fe++9tNVWW+Wu4BLJC/vvv382WQoAqJzY8X/Ejs0dO77wwgtZ/25nRLx51113danCIPR2EgShyfTv3z/3/uuvvz6rglKJueeeO80555xl748Est12263iJL9IpovqdYccckhaccUV07HHHtvpk3xnREWclVdeOfcxt99+e9pvv/3S119/XfhZrr322rIdVmuvvXbu8yMQuu6661I1Qc2ll16aNtpoo7TFFlt0uYrNkUcemS0TWO57/dOf/pT7/Pnmmy/NNNNMZe/Pq6AYupLgCADN6KabbkobbrhhNlvy/fffr/h5jz76aO791UxsqERRDHPPPffkJglefPHFhR0xRe+R5+677y474zT2RcQwRRWh11hjjZ/8LSZ35C17/NRTT2Vx3+eff17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" ] @@ -332,7 +331,7 @@ }, { "cell_type": "markdown", - "id": "bdee0324", + "id": "ed4b5c3f", "metadata": {}, "source": [ "## Memory usage\n", @@ -346,13 +345,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "9a2a3b7d", + "id": "dea82a3a", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T04:07:39.770819Z", - "iopub.status.busy": "2026-06-09T04:07:39.770747Z", - "iopub.status.idle": "2026-06-09T04:07:39.968826Z", - "shell.execute_reply": "2026-06-09T04:07:39.968516Z" + "iopub.execute_input": "2026-06-10T11:33:17.309098Z", + "iopub.status.busy": "2026-06-10T11:33:17.309011Z", + "iopub.status.idle": "2026-06-10T11:33:17.527207Z", + "shell.execute_reply": "2026-06-10T11:33:17.526802Z" } }, "outputs": [ @@ -360,7 +359,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/claude-501/ipykernel_16604/1052516414.py:15: UserWarning: The figure layout has changed to tight\n", + "/tmp/claude-501/ipykernel_88126/2826343238.py:15: UserWarning: The figure layout has changed to tight\n", " fig.tight_layout();\n" ] }, @@ -377,7 +376,7 @@ ], "source": [ "ncols = 4 # 3 features + observed\n", - "n_grid = np.logspace(5, 9, 50) # 1e5 .. 1e9 rows\n", + "n_grid = np.logspace(5, 9, 50)\n", "inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound)\n", "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer\n", "\n", @@ -395,7 +394,7 @@ }, { "cell_type": "markdown", - "id": "1209a446", + "id": "e38def80", "metadata": {}, "source": [ "That line is only the bare array. Actual peak RSS is higher, because of the\n", @@ -404,10 +403,10 @@ "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a\n", "standard out-of-core learning benchmark, with the same logistic model (13 numeric\n", "features plus the click label). Streaming through the `DataLoader` stayed flat at\n", - "**~0.7 GB** across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline\n", - "rose linearly to **15.7 GB at 150M rows**, about 21 times more, and extrapolates to\n", - "out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to\n", - "within ADVI noise (correlation **0.999** across all 14 coefficients). Criteo is\n", + "about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline\n", + "rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to\n", + "out-of-memory near 238M rows on a 26 GB machine. The two posteriors agree to\n", + "within ADVI noise (correlation 0.999 across all 14 coefficients). Criteo is\n", "public, so anyone can rerun this.\n", "\n", "## When to use it\n", @@ -423,7 +422,7 @@ }, { "cell_type": "markdown", - "id": "b5388542", + "id": "fc44cb1d", "metadata": {}, "source": [ "## Authors\n", @@ -434,7 +433,7 @@ }, { "cell_type": "markdown", - "id": "6dc3ce89", + "id": "3b3b6311", "metadata": {}, "source": [ "## References\n", @@ -448,13 +447,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "ad8a11ba", + "id": "3f78084b", "metadata": { "execution": { - "iopub.execute_input": "2026-06-09T04:07:39.969974Z", - "iopub.status.busy": "2026-06-09T04:07:39.969907Z", - "iopub.status.idle": "2026-06-09T04:07:39.980436Z", - "shell.execute_reply": "2026-06-09T04:07:39.980077Z" + "iopub.execute_input": "2026-06-10T11:33:17.528393Z", + "iopub.status.busy": "2026-06-10T11:33:17.528322Z", + "iopub.status.idle": "2026-06-10T11:33:17.539045Z", + "shell.execute_reply": "2026-06-10T11:33:17.538653Z" } }, "outputs": [ @@ -462,7 +461,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Mon, 08 Jun 2026\n", + "Last updated: Wed, 10 Jun 2026\n", "\n", "Python implementation: CPython\n", "Python version : 3.12.12\n", @@ -490,7 +489,7 @@ }, { "cell_type": "markdown", - "id": "845a0b24", + "id": "70321b5e", "metadata": {}, "source": [ ":::{include} ../page_footer.md\n", diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index 67a355294..b6744e5d0 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -78,7 +78,7 @@ b_true = np.array([0.4, -1.2, 0.8, -0.5]) # intercept + 3 slopes X = rng.normal(size=(N, 3)) p = 1 / (1 + np.exp(-(b_true[0] + X @ b_true[1:]))) y = (rng.random(N) < p).astype("float64") -table = np.column_stack([X, y]).astype("float64") # 3 features + 1 observed column +table = np.column_stack([X, y]).astype("float64") shard_dir = tempfile.mkdtemp(prefix="streaming_demo_") for i, s in enumerate(range(0, N, 5_000)): @@ -87,7 +87,7 @@ for i, s in enumerate(range(0, N, 5_000)): pa.table({f"c{j}": block[:, j] for j in range(4)}), f"{shard_dir}/part_{i:03d}.parquet", ) -del table # the design matrix now lives only on disk +del table print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written") ``` @@ -105,13 +105,13 @@ runs the loop. ```{code-cell} ipython3 batch_size = 1024 loader = DataLoader( - parquet_source(shard_dir), # an IterableDataset over the shards + parquet_source(shard_dir), batch_size=batch_size, - sample_shape=(4,), # 3 features + 1 observed column, streamed together - shuffle=True, # bounded-buffer shuffle (the shards are written in order) + sample_shape=(4,), # 3 features + 1 observed column + shuffle=True, # the shards were written in order buffer_size=15_000, seed=0, - total_size="auto", # read N from Parquet metadata; len(loader) == N + total_size="auto", # N from the Parquet metadata ) with pm.Model() as model: @@ -120,7 +120,6 @@ with pm.Model() as model: logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2] pm.Bernoulli("y", logit_p=logit, observed=batch[:, 3], total_size=len(loader)) - # No callbacks: the Trainer streams each minibatch into "batch" with set_data. approx = Trainer( method="advi", dataloader=loader, @@ -184,7 +183,7 @@ cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measureme ```{code-cell} ipython3 ncols = 4 # 3 features + observed -n_grid = np.logspace(5, 9, 50) # 1e5 .. 1e9 rows +n_grid = np.logspace(5, 9, 50) inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound) stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer @@ -206,10 +205,10 @@ the real number on public data, I measured peak memory on the [Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a standard out-of-core learning benchmark, with the same logistic model (13 numeric features plus the click label). Streaming through the `DataLoader` stayed flat at -**~0.7 GB** across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline -rose linearly to **15.7 GB at 150M rows**, about 21 times more, and extrapolates to -out-of-memory near **238M rows** on a 26 GB machine. The two posteriors agree to -within ADVI noise (correlation **0.999** across all 14 coefficients). Criteo is +about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline +rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to +out-of-memory near 238M rows on a 26 GB machine. The two posteriors agree to +within ADVI noise (correlation 0.999 across all 14 coefficients). Criteo is public, so anyone can rerun this. ## When to use it From 2e79e44908e967426664fcf840177b41f318b4bb Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Wed, 10 Jun 2026 06:40:03 -0500 Subject: [PATCH 07/17] Use the bibliography directive and standard watermark section --- examples/references.bib | 8 ++ .../streaming_dataset.ipynb | 127 +++++++++--------- .../streaming_dataset.myst.md | 17 ++- 3 files changed, 83 insertions(+), 69 deletions(-) diff --git a/examples/references.bib b/examples/references.bib index bf45cc5d1..994c98ff0 100644 --- a/examples/references.bib +++ b/examples/references.bib @@ -373,6 +373,14 @@ @book{hernan2020whatif year = {2020}, publisher = {Chapman \& Hall/CRC} } +@article{hoffman2013stochastic, + title = {Stochastic Variational Inference}, + author = {Hoffman, Matthew D. and Blei, David M. and Wang, Chong and Paisley, John}, + year = {2013}, + journal = {Journal of Machine Learning Research}, + volume = {14}, + pages = {1303--1347} +} @article{hoffman2014nuts, title = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo}, author = {Hoffman, Matthew and Gelman, Andrew}, diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 2872da191..e06644d9d 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3bc2b2ad", + "id": "4b2f1f62", "metadata": {}, "source": [ "(streaming_dataset)=\n", @@ -18,11 +18,12 @@ }, { "cell_type": "markdown", - "id": "ab54cc68", + "id": "260cd177", "metadata": {}, "source": [ "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n", - "minibatch variational inference only works on data that already fits in memory.\n", + "minibatch variational inference {cite:p}`hoffman2013stochastic,kucukelbir2015automatic`\n", + "only works on data that already fits in memory.\n", "This notebook uses the streaming API in `pymc.variational.streaming`, which follows\n", "the same structure as PyTorch's `torch.utils.data`:\n", "\n", @@ -48,13 +49,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "571a608f", + "id": "09a82a7f", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:32:49.788523Z", - "iopub.status.busy": "2026-06-10T11:32:49.788448Z", - "iopub.status.idle": "2026-06-10T11:32:52.092871Z", - "shell.execute_reply": "2026-06-10T11:32:52.090513Z" + "iopub.execute_input": "2026-06-10T11:39:34.361751Z", + "iopub.status.busy": "2026-06-10T11:39:34.361654Z", + "iopub.status.idle": "2026-06-10T11:39:35.984364Z", + "shell.execute_reply": "2026-06-10T11:39:35.983837Z" } }, "outputs": [], @@ -78,7 +79,7 @@ }, { "cell_type": "markdown", - "id": "d1832538", + "id": "5edda1f3", "metadata": {}, "source": [ "## Put a dataset on disk and forget the array\n", @@ -92,13 +93,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "78c39ff1", + "id": "a48d4744", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:32:52.098068Z", - "iopub.status.busy": "2026-06-10T11:32:52.097199Z", - "iopub.status.idle": "2026-06-10T11:32:52.140323Z", - "shell.execute_reply": "2026-06-10T11:32:52.139939Z" + "iopub.execute_input": "2026-06-10T11:39:35.986097Z", + "iopub.status.busy": "2026-06-10T11:39:35.985811Z", + "iopub.status.idle": "2026-06-10T11:39:36.007332Z", + "shell.execute_reply": "2026-06-10T11:39:36.006869Z" } }, "outputs": [ @@ -132,7 +133,7 @@ }, { "cell_type": "markdown", - "id": "8807e3db", + "id": "f4c3da32", "metadata": {}, "source": [ "## Stream minibatches off disk and fit with ADVI\n", @@ -150,13 +151,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "ef67327d", + "id": "b016b139", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:32:52.141370Z", - "iopub.status.busy": "2026-06-10T11:32:52.141295Z", - "iopub.status.idle": "2026-06-10T11:33:13.067413Z", - "shell.execute_reply": "2026-06-10T11:33:13.066448Z" + "iopub.execute_input": "2026-06-10T11:39:36.008377Z", + "iopub.status.busy": "2026-06-10T11:39:36.008303Z", + "iopub.status.idle": "2026-06-10T11:39:55.655782Z", + "shell.execute_reply": "2026-06-10T11:39:55.655387Z" } }, "outputs": [ @@ -197,7 +198,7 @@ }, { "cell_type": "markdown", - "id": "93a0f3c8", + "id": "dbf19682", "metadata": {}, "source": [ "The negative-ELBO trace shows the fit converging while only ever holding a\n", @@ -207,13 +208,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "14202643", + "id": "670b64e1", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:33:13.070274Z", - "iopub.status.busy": "2026-06-10T11:33:13.070129Z", - "iopub.status.idle": "2026-06-10T11:33:13.382429Z", - "shell.execute_reply": "2026-06-10T11:33:13.381618Z" + "iopub.execute_input": "2026-06-10T11:39:55.656948Z", + "iopub.status.busy": "2026-06-10T11:39:55.656886Z", + "iopub.status.idle": "2026-06-10T11:39:55.774489Z", + "shell.execute_reply": "2026-06-10T11:39:55.774093Z" } }, "outputs": [ @@ -236,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "fd3600f2", + "id": "0d4b92eb", "metadata": {}, "source": [ "## The same posterior as in-RAM `pm.Minibatch`\n", @@ -250,13 +251,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "0448355b", + "id": "87575de0", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:33:13.384387Z", - "iopub.status.busy": "2026-06-10T11:33:13.384018Z", - "iopub.status.idle": "2026-06-10T11:33:16.995799Z", - "shell.execute_reply": "2026-06-10T11:33:16.995376Z" + "iopub.execute_input": "2026-06-10T11:39:55.775563Z", + "iopub.status.busy": "2026-06-10T11:39:55.775481Z", + "iopub.status.idle": "2026-06-10T11:39:59.242341Z", + "shell.execute_reply": "2026-06-10T11:39:59.241833Z" } }, "outputs": [ @@ -264,7 +265,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Finished [100%]: Average Loss = 531.98\n" + "Finished [100%]: Average Loss = 531.48\n" ] } ], @@ -284,13 +285,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "39864bac", + "id": "e4db8d22", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:33:16.996992Z", - "iopub.status.busy": "2026-06-10T11:33:16.996925Z", - "iopub.status.idle": "2026-06-10T11:33:17.307953Z", - "shell.execute_reply": "2026-06-10T11:33:17.307475Z" + "iopub.execute_input": "2026-06-10T11:39:59.243475Z", + "iopub.status.busy": "2026-06-10T11:39:59.243401Z", + "iopub.status.idle": "2026-06-10T11:39:59.495750Z", + "shell.execute_reply": "2026-06-10T11:39:59.495272Z" } }, "outputs": [ @@ -298,13 +299,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/claude-501/ipykernel_88126/503280259.py:13: UserWarning: The figure layout has changed to tight\n", + "/tmp/claude-501/ipykernel_93000/503280259.py:13: UserWarning: The figure layout has changed to tight\n", " fig.tight_layout();\n" ] }, { "data": { - 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" ] @@ -331,7 +332,7 @@ }, { "cell_type": "markdown", - "id": "ed4b5c3f", + "id": "9a73c466", "metadata": {}, "source": [ "## Memory usage\n", @@ -345,13 +346,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "dea82a3a", + "id": "e8de6035", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:33:17.309098Z", - "iopub.status.busy": "2026-06-10T11:33:17.309011Z", - "iopub.status.idle": "2026-06-10T11:33:17.527207Z", - "shell.execute_reply": "2026-06-10T11:33:17.526802Z" + "iopub.execute_input": "2026-06-10T11:39:59.496810Z", + "iopub.status.busy": "2026-06-10T11:39:59.496737Z", + "iopub.status.idle": "2026-06-10T11:39:59.718260Z", + "shell.execute_reply": "2026-06-10T11:39:59.717855Z" } }, "outputs": [ @@ -359,7 +360,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/claude-501/ipykernel_88126/2826343238.py:15: UserWarning: The figure layout has changed to tight\n", + "/tmp/claude-501/ipykernel_93000/2826343238.py:15: UserWarning: The figure layout has changed to tight\n", " fig.tight_layout();\n" ] }, @@ -394,7 +395,7 @@ }, { "cell_type": "markdown", - "id": "e38def80", + "id": "1e807647", "metadata": {}, "source": [ "That line is only the bare array. Actual peak RSS is higher, because of the\n", @@ -422,38 +423,40 @@ }, { "cell_type": "markdown", - "id": "fc44cb1d", + "id": "ab62416d", "metadata": {}, "source": [ "## Authors\n", "\n", - "* Authored by Yicheng (Ethan) Yang in June 2026 for the Google Summer of Code\n", - " project *Streaming Variational Inference for Large Datasets* (PyMC / NumFOCUS)." + "* Authored by [Yicheng (Ethan) Yang](https://github.com/YichengYang-Ethan) in June 2026\n", + " for the Google Summer of Code project Streaming Variational Inference for Large\n", + " Datasets (PyMC / NumFOCUS)." ] }, { "cell_type": "markdown", - "id": "3b3b6311", + "id": "f52db625", "metadata": {}, "source": [ "## References\n", "\n", - "* Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. (2013). Stochastic\n", - " variational inference. *Journal of Machine Learning Research*, 14(1).\n", - "* Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2017).\n", - " Automatic differentiation variational inference. *JMLR*, 18(1)." + ":::{bibliography}\n", + ":filter: docname in docnames\n", + ":::\n", + "\n", + "## Watermark" ] }, { "cell_type": "code", "execution_count": 8, - "id": "3f78084b", + "id": "ee70f944", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:33:17.528393Z", - "iopub.status.busy": "2026-06-10T11:33:17.528322Z", - "iopub.status.idle": "2026-06-10T11:33:17.539045Z", - "shell.execute_reply": "2026-06-10T11:33:17.538653Z" + "iopub.execute_input": "2026-06-10T11:39:59.719505Z", + "iopub.status.busy": "2026-06-10T11:39:59.719423Z", + "iopub.status.idle": "2026-06-10T11:39:59.729582Z", + "shell.execute_reply": "2026-06-10T11:39:59.729189Z" } }, "outputs": [ @@ -489,7 +492,7 @@ }, { "cell_type": "markdown", - "id": "70321b5e", + "id": "6750e1c1", "metadata": {}, "source": [ ":::{include} ../page_footer.md\n", diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index b6744e5d0..8806eb6eb 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -24,7 +24,8 @@ kernelspec: +++ `pm.Minibatch` random-indexes an array that must be fully resident in RAM, so -minibatch variational inference only works on data that already fits in memory. +minibatch variational inference {cite:p}`hoffman2013stochastic,kucukelbir2015automatic` +only works on data that already fits in memory. This notebook uses the streaming API in `pymc.variational.streaming`, which follows the same structure as PyTorch's `torch.utils.data`: @@ -225,17 +226,19 @@ public, so anyone can rerun this. ## Authors -* Authored by Yicheng (Ethan) Yang in June 2026 for the Google Summer of Code - project *Streaming Variational Inference for Large Datasets* (PyMC / NumFOCUS). +* Authored by [Yicheng (Ethan) Yang](https://github.com/YichengYang-Ethan) in June 2026 + for the Google Summer of Code project Streaming Variational Inference for Large + Datasets (PyMC / NumFOCUS). +++ ## References -* Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. (2013). Stochastic - variational inference. *Journal of Machine Learning Research*, 14(1). -* Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2017). - Automatic differentiation variational inference. *JMLR*, 18(1). +:::{bibliography} +:filter: docname in docnames +::: + +## Watermark ```{code-cell} ipython3 %load_ext watermark From 52d15cd0cb450b5adff6a7fa78ddecb3835e0b86 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Wed, 10 Jun 2026 06:41:56 -0500 Subject: [PATCH 08/17] Drop the redundant ADVI tag --- examples/variational_inference/streaming_dataset.ipynb | 2 +- examples/variational_inference/streaming_dataset.myst.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index e06644d9d..5159e1281 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -10,7 +10,7 @@ "# Out-of-core minibatch variational inference with DataLoader and Trainer\n", "\n", ":::{post} June 2026\n", - ":tags: variational inference, minibatch, out-of-core, ADVI\n", + ":tags: variational inference, minibatch, out-of-core\n", ":category: intermediate, how-to\n", ":author: Yicheng (Ethan) Yang\n", ":::" diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index 8806eb6eb..e4014eb70 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -16,7 +16,7 @@ kernelspec: # Out-of-core minibatch variational inference with DataLoader and Trainer :::{post} June 2026 -:tags: variational inference, minibatch, out-of-core, ADVI +:tags: variational inference, minibatch, out-of-core :category: intermediate, how-to :author: Yicheng (Ethan) Yang ::: From 015cda332475c26fa61481fd03a2cb6933d4aa30 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Wed, 10 Jun 2026 22:45:32 -0500 Subject: [PATCH 09/17] State the memory behavior precisely --- .../streaming_dataset.ipynb | 118 +++++++++--------- .../streaming_dataset.myst.md | 8 +- 2 files changed, 63 insertions(+), 63 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 5159e1281..fe3418a5a 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "4b2f1f62", + "id": "69dca719", "metadata": {}, "source": [ "(streaming_dataset)=\n", @@ -18,7 +18,7 @@ }, { "cell_type": "markdown", - "id": "260cd177", + "id": "6118e68b", "metadata": {}, "source": [ "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n", @@ -49,13 +49,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "09a82a7f", + "id": "07118d45", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:39:34.361751Z", - "iopub.status.busy": "2026-06-10T11:39:34.361654Z", - "iopub.status.idle": "2026-06-10T11:39:35.984364Z", - "shell.execute_reply": "2026-06-10T11:39:35.983837Z" + "iopub.execute_input": "2026-06-11T03:45:03.295353Z", + "iopub.status.busy": "2026-06-11T03:45:03.295157Z", + "iopub.status.idle": "2026-06-11T03:45:04.943356Z", + "shell.execute_reply": "2026-06-11T03:45:04.942801Z" } }, "outputs": [], @@ -79,7 +79,7 @@ }, { "cell_type": "markdown", - "id": "5edda1f3", + "id": "06d7ccca", "metadata": {}, "source": [ "## Put a dataset on disk and forget the array\n", @@ -93,13 +93,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "a48d4744", + "id": "5776870b", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:39:35.986097Z", - "iopub.status.busy": "2026-06-10T11:39:35.985811Z", - "iopub.status.idle": "2026-06-10T11:39:36.007332Z", - "shell.execute_reply": "2026-06-10T11:39:36.006869Z" + "iopub.execute_input": "2026-06-11T03:45:04.945137Z", + "iopub.status.busy": "2026-06-11T03:45:04.944792Z", + "iopub.status.idle": "2026-06-11T03:45:04.964769Z", + "shell.execute_reply": "2026-06-11T03:45:04.964475Z" } }, "outputs": [ @@ -133,7 +133,7 @@ }, { "cell_type": "markdown", - "id": "f4c3da32", + "id": "982a35b7", "metadata": {}, "source": [ "## Stream minibatches off disk and fit with ADVI\n", @@ -142,8 +142,8 @@ "It reads one shard at a time and gets `n_rows` from the Parquet metadata without\n", "scanning the data, so `total_size=\"auto\"` resolves `N` for free. The `DataLoader`\n", "batches and shuffles it into fixed-size minibatches. The model reads a\n", - "`pm.Data(\"batch\", ...)` placeholder, the only data resident at any point, and the\n", - "`Trainer` writes each minibatch into it with `set_data`. There are no callbacks:\n", + "`pm.Data(\"batch\", ...)` placeholder holding a single batch, and the `Trainer`\n", + "writes each minibatch into it with `set_data`. There are no callbacks:\n", "`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`\n", "runs the loop." ] @@ -151,13 +151,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "b016b139", + "id": "38fb2079", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:39:36.008377Z", - "iopub.status.busy": "2026-06-10T11:39:36.008303Z", - "iopub.status.idle": "2026-06-10T11:39:55.655782Z", - "shell.execute_reply": "2026-06-10T11:39:55.655387Z" + "iopub.execute_input": "2026-06-11T03:45:04.965956Z", + "iopub.status.busy": "2026-06-11T03:45:04.965893Z", + "iopub.status.idle": "2026-06-11T03:45:24.342515Z", + "shell.execute_reply": "2026-06-11T03:45:24.341968Z" } }, "outputs": [ @@ -183,7 +183,7 @@ "\n", "with pm.Model() as model:\n", " b = pm.Normal(\"b\", 0.0, 3.0, shape=4)\n", - " batch = pm.Data(\"batch\", np.zeros((batch_size, 4))) # placeholder, the only data in RAM\n", + " batch = pm.Data(\"batch\", np.zeros((batch_size, 4))) # placeholder for one minibatch\n", " logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2]\n", " pm.Bernoulli(\"y\", logit_p=logit, observed=batch[:, 3], total_size=len(loader))\n", "\n", @@ -198,7 +198,7 @@ }, { "cell_type": "markdown", - "id": "dbf19682", + "id": "d575798c", "metadata": {}, "source": [ "The negative-ELBO trace shows the fit converging while only ever holding a\n", @@ -208,13 +208,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "670b64e1", + "id": "3208e4e3", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:39:55.656948Z", - "iopub.status.busy": "2026-06-10T11:39:55.656886Z", - "iopub.status.idle": "2026-06-10T11:39:55.774489Z", - "shell.execute_reply": "2026-06-10T11:39:55.774093Z" + "iopub.execute_input": "2026-06-11T03:45:24.343821Z", + "iopub.status.busy": "2026-06-11T03:45:24.343736Z", + "iopub.status.idle": "2026-06-11T03:45:24.456998Z", + "shell.execute_reply": "2026-06-11T03:45:24.456629Z" } }, "outputs": [ @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "0d4b92eb", + "id": "b4326fea", "metadata": {}, "source": [ "## The same posterior as in-RAM `pm.Minibatch`\n", @@ -251,13 +251,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "87575de0", + "id": "a72964f1", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:39:55.775563Z", - "iopub.status.busy": "2026-06-10T11:39:55.775481Z", - "iopub.status.idle": "2026-06-10T11:39:59.242341Z", - "shell.execute_reply": "2026-06-10T11:39:59.241833Z" + "iopub.execute_input": "2026-06-11T03:45:24.458117Z", + "iopub.status.busy": "2026-06-11T03:45:24.458036Z", + "iopub.status.idle": "2026-06-11T03:45:27.836757Z", + "shell.execute_reply": "2026-06-11T03:45:27.836192Z" } }, "outputs": [ @@ -265,7 +265,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Finished [100%]: Average Loss = 531.48\n" + "Finished [100%]: Average Loss = 531.53\n" ] } ], @@ -285,13 +285,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "e4db8d22", + "id": "9834da2a", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:39:59.243475Z", - "iopub.status.busy": "2026-06-10T11:39:59.243401Z", - "iopub.status.idle": "2026-06-10T11:39:59.495750Z", - "shell.execute_reply": "2026-06-10T11:39:59.495272Z" + "iopub.execute_input": "2026-06-11T03:45:27.837837Z", + "iopub.status.busy": "2026-06-11T03:45:27.837765Z", + "iopub.status.idle": "2026-06-11T03:45:28.077191Z", + "shell.execute_reply": "2026-06-11T03:45:28.076718Z" } }, "outputs": [ @@ -299,13 +299,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/claude-501/ipykernel_93000/503280259.py:13: UserWarning: The figure layout has changed to tight\n", + "/tmp/claude-501/ipykernel_71399/503280259.py:13: UserWarning: The figure layout has changed to tight\n", " fig.tight_layout();\n" ] }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -332,27 +332,27 @@ }, { "cell_type": "markdown", - "id": "9a73c466", + "id": "c04c6f42", "metadata": {}, "source": [ "## Memory usage\n", "\n", "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n", "rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`\n", - "only holds one `batch_size` buffer. The dense `float64` design matrix dominates the\n", + "holds one batch plus its bounded shuffle buffer. The dense `float64` design matrix dominates the\n", "cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement:" ] }, { "cell_type": "code", "execution_count": 7, - "id": "e8de6035", + "id": "118f6849", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:39:59.496810Z", - "iopub.status.busy": "2026-06-10T11:39:59.496737Z", - "iopub.status.idle": "2026-06-10T11:39:59.718260Z", - "shell.execute_reply": "2026-06-10T11:39:59.717855Z" + "iopub.execute_input": "2026-06-11T03:45:28.078399Z", + "iopub.status.busy": "2026-06-11T03:45:28.078326Z", + "iopub.status.idle": "2026-06-11T03:45:28.334354Z", + "shell.execute_reply": "2026-06-11T03:45:28.333943Z" } }, "outputs": [ @@ -360,7 +360,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/claude-501/ipykernel_93000/2826343238.py:15: UserWarning: The figure layout has changed to tight\n", + "/tmp/claude-501/ipykernel_71399/2826343238.py:15: UserWarning: The figure layout has changed to tight\n", " fig.tight_layout();\n" ] }, @@ -395,7 +395,7 @@ }, { "cell_type": "markdown", - "id": "1e807647", + "id": "624c0133", "metadata": {}, "source": [ "That line is only the bare array. Actual peak RSS is higher, because of the\n", @@ -423,7 +423,7 @@ }, { "cell_type": "markdown", - "id": "ab62416d", + "id": "7fdf59e4", "metadata": {}, "source": [ "## Authors\n", @@ -435,7 +435,7 @@ }, { "cell_type": "markdown", - "id": "f52db625", + "id": "2e983b8d", "metadata": {}, "source": [ "## References\n", @@ -450,13 +450,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "ee70f944", + "id": "0c4c4ddb", "metadata": { "execution": { - "iopub.execute_input": "2026-06-10T11:39:59.719505Z", - "iopub.status.busy": "2026-06-10T11:39:59.719423Z", - "iopub.status.idle": "2026-06-10T11:39:59.729582Z", - "shell.execute_reply": "2026-06-10T11:39:59.729189Z" + "iopub.execute_input": "2026-06-11T03:45:28.335556Z", + "iopub.status.busy": "2026-06-11T03:45:28.335482Z", + "iopub.status.idle": "2026-06-11T03:45:28.345025Z", + "shell.execute_reply": "2026-06-11T03:45:28.344643Z" } }, "outputs": [ @@ -492,7 +492,7 @@ }, { "cell_type": "markdown", - "id": "6750e1c1", + "id": "7e62e534", "metadata": {}, "source": [ ":::{include} ../page_footer.md\n", diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index e4014eb70..010211c1e 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -98,8 +98,8 @@ print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written") It reads one shard at a time and gets `n_rows` from the Parquet metadata without scanning the data, so `total_size="auto"` resolves `N` for free. The `DataLoader` batches and shuffles it into fixed-size minibatches. The model reads a -`pm.Data("batch", ...)` placeholder, the only data resident at any point, and the -`Trainer` writes each minibatch into it with `set_data`. There are no callbacks: +`pm.Data("batch", ...)` placeholder holding a single batch, and the `Trainer` +writes each minibatch into it with `set_data`. There are no callbacks: `total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit` runs the loop. @@ -117,7 +117,7 @@ loader = DataLoader( with pm.Model() as model: b = pm.Normal("b", 0.0, 3.0, shape=4) - batch = pm.Data("batch", np.zeros((batch_size, 4))) # placeholder, the only data in RAM + batch = pm.Data("batch", np.zeros((batch_size, 4))) # placeholder for one minibatch logit = b[0] + b[1] * batch[:, 0] + b[2] * batch[:, 1] + b[3] * batch[:, 2] pm.Bernoulli("y", logit_p=logit, observed=batch[:, 3], total_size=len(loader)) @@ -179,7 +179,7 @@ fig.tight_layout(); Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N` rows resident, so its array grows linearly in `N`, while the streaming `DataLoader` -only holds one `batch_size` buffer. The dense `float64` design matrix dominates the +holds one batch plus its bounded shuffle buffer. The dense `float64` design matrix dominates the cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement: ```{code-cell} ipython3 From e413b2ecb12fe17e25612822a821d8c6bb339e03 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Wed, 10 Jun 2026 22:46:41 -0500 Subject: [PATCH 10/17] Attribute the agreement numbers to the right comparison --- examples/variational_inference/streaming_dataset.ipynb | 6 +++--- examples/variational_inference/streaming_dataset.myst.md | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index fe3418a5a..018ae014d 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -400,14 +400,14 @@ "source": [ "That line is only the bare array. Actual peak RSS is higher, because of the\n", "framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get\n", - "the real number on public data, I measured peak memory on the\n", + "the real number on public data, we measured peak memory on the\n", "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a\n", "standard out-of-core learning benchmark, with the same logistic model (13 numeric\n", "features plus the click label). Streaming through the `DataLoader` stayed flat at\n", "about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline\n", "rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to\n", - "out-of-memory near 238M rows on a 26 GB machine. The two posteriors agree to\n", - "within ADVI noise (correlation 0.999 across all 14 coefficients). Criteo is\n", + "out-of-memory near 238M rows on a 26 GB machine. The streaming and in-RAM posteriors agree coefficient for coefficient; the\n", + "largest gap is about 0.1, on the intercept. Criteo is\n", "public, so anyone can rerun this.\n", "\n", "## When to use it\n", diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index 010211c1e..5b71d4ee5 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -202,14 +202,14 @@ fig.tight_layout(); That line is only the bare array. Actual peak RSS is higher, because of the framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get -the real number on public data, I measured peak memory on the +the real number on public data, we measured peak memory on the [Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a standard out-of-core learning benchmark, with the same logistic model (13 numeric features plus the click label). Streaming through the `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to -out-of-memory near 238M rows on a 26 GB machine. The two posteriors agree to -within ADVI noise (correlation 0.999 across all 14 coefficients). Criteo is +out-of-memory near 238M rows on a 26 GB machine. The streaming and in-RAM posteriors agree coefficient for coefficient; the +largest gap is about 0.1, on the intercept. Criteo is public, so anyone can rerun this. ## When to use it From 2d9771e835c2ec49f3e7a17f38d816d6cf1df108 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Wed, 10 Jun 2026 22:55:54 -0500 Subject: [PATCH 11/17] State the loader memory behavior precisely in the intro --- examples/variational_inference/streaming_dataset.ipynb | 6 +++--- examples/variational_inference/streaming_dataset.myst.md | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 018ae014d..2833d339f 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -28,9 +28,9 @@ "the same structure as PyTorch's `torch.utils.data`:\n", "\n", "* a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally\n", - " shuffles) an out-of-core source into fixed-size minibatches, holding only one\n", - " batch in memory at a time. Here the source is a directory of Parquet shards read\n", - " by {func}`~pymc.variational.streaming.parquet_source`.\n", + " shuffles) an out-of-core source into fixed-size minibatches without loading\n", + " the whole dataset into memory. Here the source is a directory of Parquet shards\n", + " read by {func}`~pymc.variational.streaming.parquet_source`.\n", "* the model observes a `pm.Data` placeholder of one batch, not the whole array.\n", "* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each\n", " minibatch into that placeholder with `set_data` every step. There are no callbacks.\n", diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index 5b71d4ee5..cad94466f 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -30,9 +30,9 @@ This notebook uses the streaming API in `pymc.variational.streaming`, which foll the same structure as PyTorch's `torch.utils.data`: * a {class}`~pymc.variational.streaming.DataLoader` batches (and optionally - shuffles) an out-of-core source into fixed-size minibatches, holding only one - batch in memory at a time. Here the source is a directory of Parquet shards read - by {func}`~pymc.variational.streaming.parquet_source`. + shuffles) an out-of-core source into fixed-size minibatches without loading + the whole dataset into memory. Here the source is a directory of Parquet shards + read by {func}`~pymc.variational.streaming.parquet_source`. * the model observes a `pm.Data` placeholder of one batch, not the whole array. * a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each minibatch into that placeholder with `set_data` every step. There are no callbacks. From 5d13f6e11361e8de05837af7829e7f64a9833f06 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Thu, 11 Jun 2026 00:03:42 -0500 Subject: [PATCH 12/17] Fix figure layout warnings and tighten the scale-test prose Set layout=tight at figure creation instead of calling tight_layout on a constrained-layout figure, which emitted a warning into the committed outputs. Declare pyarrow via extra_dependencies and the standard install note. State that the Criteo numbers come from a run outside the notebook, fix the out-of-memory extrapolation to follow from the stated measurements, and correct two sentences that overstated what stays in memory. --- .../streaming_dataset.ipynb | 177 +++++++++--------- .../streaming_dataset.myst.md | 47 ++--- 2 files changed, 112 insertions(+), 112 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 2833d339f..2b69da89b 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "69dca719", + "id": "3a1d0597", "metadata": {}, "source": [ "(streaming_dataset)=\n", @@ -18,7 +18,7 @@ }, { "cell_type": "markdown", - "id": "6118e68b", + "id": "928fd0f3", "metadata": {}, "source": [ "`pm.Minibatch` random-indexes an array that must be fully resident in RAM, so\n", @@ -46,16 +46,25 @@ "any size, and the last section shows what changes at scale." ] }, + { + "cell_type": "markdown", + "id": "0d67f52e", + "metadata": {}, + "source": [ + ":::{include} ../extra_installs.md\n", + ":::" + ] + }, { "cell_type": "code", "execution_count": 1, - "id": "07118d45", + "id": "c2f6955f", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T03:45:03.295353Z", - "iopub.status.busy": "2026-06-11T03:45:03.295157Z", - "iopub.status.idle": "2026-06-11T03:45:04.943356Z", - "shell.execute_reply": "2026-06-11T03:45:04.942801Z" + "iopub.execute_input": "2026-06-11T05:01:07.808492Z", + "iopub.status.busy": "2026-06-11T05:01:07.808295Z", + "iopub.status.idle": "2026-06-11T05:01:09.181327Z", + "shell.execute_reply": "2026-06-11T05:01:09.180785Z" } }, "outputs": [], @@ -79,27 +88,27 @@ }, { "cell_type": "markdown", - "id": "06d7ccca", + "id": "90fd8ed5", "metadata": {}, "source": [ "## Put a dataset on disk and forget the array\n", "\n", "We build a logistic-regression dataset, write it to Parquet shards, and delete the\n", - "in-memory table. From here the features only exist on disk. We keep `X` and `y`\n", - "around only to build the in-RAM `pm.Minibatch` baseline later; the streaming fit\n", - "never reads them." + "in-memory table. From here the streaming path reads only the disk copy. We keep `X`\n", + "and `y` around only to build the in-RAM `pm.Minibatch` baseline later; the streaming\n", + "fit never reads them." ] }, { "cell_type": "code", "execution_count": 2, - "id": "5776870b", + "id": "03ccf873", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T03:45:04.945137Z", - "iopub.status.busy": "2026-06-11T03:45:04.944792Z", - "iopub.status.idle": "2026-06-11T03:45:04.964769Z", - "shell.execute_reply": "2026-06-11T03:45:04.964475Z" + "iopub.execute_input": "2026-06-11T05:01:09.182814Z", + "iopub.status.busy": "2026-06-11T05:01:09.182552Z", + "iopub.status.idle": "2026-06-11T05:01:09.201682Z", + "shell.execute_reply": "2026-06-11T05:01:09.201315Z" } }, "outputs": [ @@ -133,7 +142,7 @@ }, { "cell_type": "markdown", - "id": "982a35b7", + "id": "8bd75c32", "metadata": {}, "source": [ "## Stream minibatches off disk and fit with ADVI\n", @@ -151,13 +160,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "38fb2079", + "id": "477343ca", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T03:45:04.965956Z", - "iopub.status.busy": "2026-06-11T03:45:04.965893Z", - "iopub.status.idle": "2026-06-11T03:45:24.342515Z", - "shell.execute_reply": "2026-06-11T03:45:24.341968Z" + "iopub.execute_input": "2026-06-11T05:01:09.202716Z", + "iopub.status.busy": "2026-06-11T05:01:09.202653Z", + "iopub.status.idle": "2026-06-11T05:01:27.550831Z", + "shell.execute_reply": "2026-06-11T05:01:27.550360Z" } }, "outputs": [ @@ -198,23 +207,22 @@ }, { "cell_type": "markdown", - "id": "d575798c", + "id": "2a1d41c8", "metadata": {}, "source": [ - "The negative-ELBO trace shows the fit converging while only ever holding a\n", - "`batch_size` buffer in memory:" + "The negative-ELBO trace shows the fit converging on minibatches read off disk:" ] }, { "cell_type": "code", "execution_count": 4, - "id": "3208e4e3", + "id": "a3ff7cb9", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T03:45:24.343821Z", - "iopub.status.busy": "2026-06-11T03:45:24.343736Z", - "iopub.status.idle": "2026-06-11T03:45:24.456998Z", - "shell.execute_reply": "2026-06-11T03:45:24.456629Z" + "iopub.execute_input": "2026-06-11T05:01:27.552089Z", + "iopub.status.busy": "2026-06-11T05:01:27.552002Z", + "iopub.status.idle": "2026-06-11T05:01:27.665910Z", + "shell.execute_reply": "2026-06-11T05:01:27.665530Z" } }, "outputs": [ @@ -237,7 +245,7 @@ }, { "cell_type": "markdown", - "id": "b4326fea", + "id": "9035a137", "metadata": {}, "source": [ "## The same posterior as in-RAM `pm.Minibatch`\n", @@ -251,13 +259,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "a72964f1", + "id": "b3b5d9fe", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T03:45:24.458117Z", - "iopub.status.busy": "2026-06-11T03:45:24.458036Z", - "iopub.status.idle": "2026-06-11T03:45:27.836757Z", - "shell.execute_reply": "2026-06-11T03:45:27.836192Z" + "iopub.execute_input": "2026-06-11T05:01:27.667067Z", + "iopub.status.busy": "2026-06-11T05:01:27.666979Z", + "iopub.status.idle": "2026-06-11T05:01:30.969240Z", + "shell.execute_reply": "2026-06-11T05:01:30.968822Z" } }, "outputs": [ @@ -265,7 +273,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Finished [100%]: Average Loss = 531.53\n" + "Finished [100%]: Average Loss = 531.87\n" ] } ], @@ -285,27 +293,19 @@ { "cell_type": "code", "execution_count": 6, - "id": "9834da2a", + "id": "6731e9d6", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T03:45:27.837837Z", - "iopub.status.busy": "2026-06-11T03:45:27.837765Z", - "iopub.status.idle": "2026-06-11T03:45:28.077191Z", - "shell.execute_reply": "2026-06-11T03:45:28.076718Z" + "iopub.execute_input": "2026-06-11T05:01:30.970410Z", + "iopub.status.busy": "2026-06-11T05:01:30.970349Z", + "iopub.status.idle": "2026-06-11T05:01:31.236510Z", + "shell.execute_reply": "2026-06-11T05:01:31.236118Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - 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", "text/plain": [ "
" ] @@ -319,20 +319,19 @@ "bs_inram = idata_inram.posterior[\"b\"].values.reshape(-1, 4)\n", "names = [\"intercept\", \"slope x1\", \"slope x2\", \"slope x3\"]\n", "\n", - "fig, axes = plt.subplots(1, 4, figsize=(13, 3))\n", + "fig, axes = plt.subplots(1, 4, figsize=(13, 3), layout=\"tight\")\n", "for k, ax in enumerate(axes):\n", " ax.hist(bs_stream[:, k], bins=40, density=True, alpha=0.5, label=\"streaming\")\n", " ax.hist(bs_inram[:, k], bins=40, density=True, alpha=0.5, label=\"in-RAM\")\n", " ax.axvline(b_true[k], color=\"k\", ls=\"--\", lw=1)\n", " ax.set(title=names[k], yticks=[])\n", "axes[0].legend(fontsize=8)\n", - "fig.suptitle(\"Posterior of b: streaming vs in-RAM (dashed = ground truth)\", y=1.04)\n", - "fig.tight_layout();" + "fig.suptitle(\"Posterior of b: streaming vs in-RAM (dashed = ground truth)\", y=1.04);" ] }, { "cell_type": "markdown", - "id": "c04c6f42", + "id": "04dc1901", "metadata": {}, "source": [ "## Memory usage\n", @@ -346,24 +345,16 @@ { "cell_type": "code", "execution_count": 7, - "id": "118f6849", + "id": "6f25aa9a", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T03:45:28.078399Z", - "iopub.status.busy": "2026-06-11T03:45:28.078326Z", - "iopub.status.idle": "2026-06-11T03:45:28.334354Z", - "shell.execute_reply": "2026-06-11T03:45:28.333943Z" + "iopub.execute_input": "2026-06-11T05:01:31.237556Z", + "iopub.status.busy": "2026-06-11T05:01:31.237493Z", + "iopub.status.idle": "2026-06-11T05:01:31.470524Z", + "shell.execute_reply": "2026-06-11T05:01:31.470161Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/claude-501/ipykernel_71399/2826343238.py:15: UserWarning: The figure layout has changed to tight\n", - " fig.tight_layout();\n" - ] - }, { "data": { "image/png": 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SQkKCjBs3ToYPH26KFCkpKeZ6WqQoKyszPSM+/vhjU3jQVU5LSzMzH3QGhRozZoxV/wZCXHV1tRQXF5vfp0+fLpmZmYFeJQAAAACAxRFLc0orZU0dEUsAAERMAWLw4MGmmNBZtlXwdFtX19Pzq1at6vT9IjxRgAAAAACA8HKgtVU+37nLsoilxJhoOSUv08x2GJVFxBIAACEVweRtTcNWSOhYUOh4e2+vBwAAAAAAwseGXQ1SUlppecSSFh+SYolYAgAg5AoQnSkKeHtdCg0AAAAAAESGOoeIpbUWRCz1TeohkwuzTUPpfikJlqwjAAAIQAHiwQcftHJxCAM7duyQGTNmyPz586W8vFxiYmIkLy9PJkyYIJdddpnp5QEAAAAAiGwasfTZjjpTdPik3JqIpVN/iFgaScQSAADhUYA455xzrFwcQtzChQvl5ptvNo3GHa1evdqc/vWvf8nTTz8tw4YNC9g6AgAAAAACH7H0dlmV1FoRsZTdU6YUZsuP+hGxBABA2PaAANasWSM33nij7N27VxITE+Wqq66S4447TlpaWuSDDz4wsyJ27twpxcXF8sYbb0jv3r3ZaAAAAAAQAbojYklnOmjMUt9kIpYAAAgmFCDQLR544AFTfNDIpWeeeUbGjBlj/9vYsWPliCOOkFtuuUUqKyvlscceI74LAAAAAMJYd0QsaSNpne1AxBIAAMGLAgQst2LFCvniiy/ssVyOxQebs846S2bOnCmff/65/Oc//5Ff//rXkpmZyaMBAAAAAGGEiCUAACIbBQhYbt68efbfzz//fJfXO++880wBQmOZPvzwQ7ngggt4NAAAAAAgDCKW3t1SZXo7WBax1D9bJhcQsQQAQKihAAHjwIEDsmTJEtm2bZtUVFRISkqK5ObmysiRIyUjI6NTW+nrr782P7X3w/Dhw11e75hjjml3GwoQAAAAABC6EUuf7qgzRQerIpZONRFLOTIiK1Wio6IsW1cAAOA/FCCCVGtrq2zcuFGWLVsmy5cvN6e1a9fK/v377dd58MEH5dxzz/XpfhobG+Xpp582cUhVVVUH/T0uLk7GjRsn06ZNk0GDBnm1zA0bNpifhYWFEhvr+immjaeTk5OloaHBfhsAAAAAQOhYv6tBSjZXyjtbKqW2+YDPyzs6u6cpOpzcL0OSYmMsWUcAABA4FCCCzDvvvCMvv/yy6aOgTZy70/r16+WGG26Q7777zuV1tOCh8UiLFi2SW2+9VS655BK3y9y3b5/U1taa33UGhSd9+vQxxYcdO3Z04T8AAAAAAPgbEUsAAMBbFCCCjEYRLV68uNvvR2OWrrzyStm5c2e7y4cOHSr5+flSV1dnZl3o7ATV3Nwsd999t4lmmjp1qsvl7tmzx/57UlKSx/WwXcd2PwAAAACA4EPEEgAA6AoKECEiNTXVDNZ3LBh0RVtbm5n54LisgQMHykMPPSSDBw+2X1ZfXy+PP/64vPTSS/bLbrvtNnOdww8/3OmytVDhGN/kSXx8/EG3AwAAAAAEh3V1Daavw7tELAEAgC6gABGEEhISZMiQITJs2DDTxFlPhxxyiDz55JPm5Kv33nvPNJy2ycvLM0WGtLS0dtfr2bOn3HHHHRIVFSUzZsywFwq0KOFqPXr06GH/3bFfhbvIpo63AwAAAAAETm3zfnm3rEpKSitk3S7fo4H7JfeQosIcOaMgS/omJ1iyjgAAIDRQgAgy1157rfzmN79x27zZVx2LB3feeedBxQdHN998s+kDsW3bNnN+3rx5snr1alMk6Ugjmmy86WFhu442owYAAAAABC5iadGOOlN0+KS8Tlra2nxaXlJstJyal2kKDyMzU82BbQAAIPJQgAgyGRkZ3br8tWvXyrp16+znDzvsMBk/frzb2yQmJsrFF18sjzzyiP2y2bNnOy1AaKRSenq6aUTtTWNp23W8aVgNAAAAAAjuiKXR2T1N0eFH/TIkMTbGknUEAAChKzrQKwD/mj9/frvz7hpKu7uezohwZcCAAeZnaWmpHDjgegdWe1DYmlbbbgMAAAAA6P6IpVfXl8tl7y+Vyz5YJq9uKPep+KARS9cckS9vnn6UPHXSUJlcmE3xAQAAGMyAiDCLFi1qd3706NFe3a5Pnz7Sr18/ewzTpk2bpLy83Fze0dFHHy1ffvmlNDY2yvLly2XUqFFOl7l48eJ2twEAAAAAdI/9ra3yaXmdzCmtMFFLRCwBAICwK0Do0e5bt261ny8oKJCkpCR/rkLE27Bhg30bREdHm0bX3hoxYoS9AKHWr1/vtAAxceJEmT59uvn99ddfd1mAeOONN8zPmJgYOeWUUyL+sQEAAACA7ohY0qKDNpWu2+d7xNKY7DQp6p8tJ/clYgkAAARZAWLOnDlyzz332HsFLFy40J93H/F27dolNTU19u2QmZlp+jt4Ky8vr915nQVx0kknHXQ9LWqMHTvWzHCYNWuWnHPOOQfNtHjrrbfks88+M7+fddZZZl0AAAAAAL6radov726pMg2l1+/a6/Py8pJ7mL4OZxRkS5/kHjxEAAAgOAsQdXV10tbWZn4/8sgjJS0tzZ93H/HKysrabQNnsxfc6dgouuPyHN12221yySWXyN69e+Wqq66Sq6++Wo477jhpaWmRDz74QF588UVzvezsbJk2bVrEPzYAAAAAEFwRSzEyIS9TigqzZURmqkRFRfEAAQCA4C5A2AoOuuOSk5Pjz7vGDxFYjjIyMjq1XdLT09ud3717t8vrDh48WB5//HG5+eabpb6+Xv785z+bk6PevXvL008/bX52xo4dO9z+vba2tlPLAwAAAIBQZWXEkpYYRhOxBAAAQrUAoUe722iDYvhXQ0NDu/M9enRu6mxCQkK78zq7wR2NZ5o9e7aZ7fDRRx+ZptXad0KjnLRPxGWXXdalWTDjx493+/e4uDgZMmRIp5cLAAAAAKETsVQpc0orZQMRSwAAIIj5tQAxfPhwMwCtMUwbN270513DSdFH+3B0RseChTdFJI1tuuWWW8wJAAAAAND1iKVF5bWm6PCpRRFLE3+IWDqSiCUAABAOBQiN2hkzZox88cUXpn/AihUrTMNiBEZnMzw7Xt/Wz8PfFixY4DGC6c477/Tb+gAAAABAd9DvXOt27ZU5mytMU+ldFkQsjclJM0WHk/tmSEJsjGXrCgAAEPAChLrhhhvk8ssvl9bWVrn//vtNPE9nj8RH1yQmJrY739zc3KnbNzU1tTuflJQUkIeiYzNsZxFMAAAAABCqrI9YSpAp/bPljIJsyU3qXBQvAABASBUgjj76aPnVr34lDz/8sCxdulR+8YtfyO9///tONyJG53UsGHS2ANHx+oEqQAAAAABAuCFiCQAAhCO/FyDUVVddJX379pXbb79dPv/8czn99NPljDPOkFNOOcU0D87IyDjoaH34LiUl5aCoos6oqalpdz41NZWHBQAAAAB8iFhaW9cgJaWVRCwBAICw5PcChBYYOu5waTPjWbNmmVNXaG+CVatWWbSG4augoKDd+fLy8k7dfseOHe3O5+fnSzAoKio66PlAAQsAAABAsKpu2ifvllWZwsOGet8jlvJTEmRK4fcRS72JWAIAAJFcgHBsXKwDxY6NjQPV1DhS9OrVy8wusc1kqKqqMsUfbwfrt27d2u78oYce2i3rCQAAAADhGLH0SXmtKTp8uqNWWnz8+psUGyMT8zJlSv8cGZ6R0u67NQAAQERHMOmOkRYbKDj434ABA2Tx4sXmd20EvmLFChkzZoxXt9WeHR2XFQxKSkrana+urpbi4uKArQ8AAAAAdGfEks52GN83QxJiY9jQAAAgqPm9AHHOOef4+y7h4Pjjj7cXINRXX33lVQFC45q2bdtmP3/IIYeYPh4AAAAAgPaIWAIAAAhQAeLBBx/0913CgTb6fuyxx+znZ8+eLddee63HbaTX67gcAAAAAMD39rW0yic7amWuRRFLybExMoGIJQAAEOICEsGEwBk0aJAMHDhQ1q1bZ85v3LhRFixYIOPHj3d5m6amJnn11VfbXTZ16tRuX1cAAAAACPaIpTUOEUv1FkQsjc1Jk6L+OTK+b7okxBCxBAAAQhsFiAh03XXXyQ033GA/f99998nIkSMlLS3N6fUfeeSRdvFLEyZMkCFDhkiwKCoqOqjHiLeNtQEAAACgKxFL75RVSUlphWysb/R5AxakJEhRYY6cUZAlvZN68IAAAICwQQEiCG3dutXp5fX19e3O19bWOr1ujx49JDs72+XyJ02aJKNGjZIlS5aY81u2bJGf/OQn8vDDD5sZEja7d+82cU0vvfRSu2VPmzatS/8XAAAAAIR6xFLJ5kr5bKc1EUsT8zNlSmGODMtIMQdSAQAAhJuoNp0ziqDiWAToirFjx8qMGTPcXmfnzp1y/vnnS0VFhf0y3eEdOnSo5OfnS11dnSxbtkwaGhra3e6hhx6SM888U4JZdXW1FBcXm9+nT58umZmZgV4lAAAAACGIiCUAAIAwmQFRWloq33zzjYn60cHvvXv3mssfeOCBQK9aWOrdu7c899xzJopp06ZN9p3rFStWmFNHOvPht7/9bdAXHwAAAADAV0QsAQAAhEEBYt++ffL666/Liy++aAoQjnQwXI/Id1aAeO+99+T55583v2vfgr/+9a9+W+dwos2oZ82aJU899ZTMnDnTzBzoKC4uTsaNG2dil3ydmQEAAAAAwYqIJQAAgDCKYNqwYYPcfPPNsm7dOlNsMCvzQ+al4/nVq1cfdFvtTXDSSSdJY2OjuY4WI4477jg//wfh5cCBA2YGivaUqKqqkuTkZMnNzTW9IjIyMiSUEMEEAAAAIFARS8f0TpPJhTkyvm+6JMTE8EAAAICIFpAZEFp00KbHWkhwrH84Fh7c1UVSU1PljDPOMEftq7lz51KA8FFsbKzpHaGnUFNUVNTuvD5/EhMTA7Y+AAAAAII/YuntsiopKa2Q7+obfV5eYUqCFPXPkdPzs6R3Ug9L1hEAACAc+L0AoU2Nr7nmGqmvr7fPeBg+fLgpSIwePVqam5tl8uTJHpczadIkewFi0aJF3b7eAAAAAIDQjlj6uLzWFB0+31knLT5mAaTExcjEvCwpKsyWYRkp9u+3AAAACGAB4plnnpEdO3bYd86uu+46c7LRJtTeOOaYYyQ6OlpaW1ulvLxcdu7caRorI/KUlJS4jGACAAAAELm6K2KpqDBHTiJiCQAAILgKELrz99prr9mLD+edd1674kNnaMROQUGBbN682d5TggIEAAAAAICIJQAAgAgsQCxbtkxqa2u/v+PYWLnpppt8Wl5eXp69AOHtzAkAAAAAQPghYgkAACDCCxDfffed+akzIIYNGyYZGRk+LU+bUdvs2bPH5/UDAAAAAIRexNKczRXynkYs7W/xaXnRJmKpl+nrMI6IJQAAgNAqQGg2v02/fv18Xp72gLA5cMC3LE8AAAAAQGioatwn72ypMg2lv6tv9Hl5/VMTTdHh9IIsyUnsYck6AgAAwM8FCI1dsrJgsGvXLvvvPXv29Hl5AAAAAIDIiVialJclRf2zZWh6ir1XIQAAAEK0AJGenm7/fceOHT4vb+3atfbffY1zQugqKipqd16/OGiTcgAAAAChH7G0urZB5pRWyDyLI5ZO6pshPWL+O6seAAAAIV6A6N+/v30nctWqVaZvQ0pKSpeLD5WVlfbzw4cPt2w9AQAAAACBjVh6u6xSSkorZdNuIpYAAABClV8LEFokSEtLk/r6ehPB9Morr8jVV1/dpWU9++yz9t/z8/OlT58+Fq4pQklJSclBvUaKi4sDtj4AAAAAOq+5pVU+Ka81sx0+31EnrT5uxFSNWMrPMrMdjiBiCQAAIPwLENo0+rTTTpN//etf5vzTTz8t48aNk8GDB3dqOXPnzpXZs2fbMzrPPffcbllfAAAAAEDoRSxN6Z8t4/oQsQQAABBRBQj1y1/+Ut566y1pbm6WxsZGufzyy+W+++4zhQlPdNbEiy++KI8++qgpPujOqs6ouOyyy/yy7gAAAACA4IxYmlKYLacXZEt2YjwPEQAAQKQWIHr37i2/+c1v5J577jFFBI1jmjZtmgwcOFAmTZokWVlZ7a7/ySefSE1NjSxbtkw++OAD07xaCw+2GRX333+/JCcn+/vfAAAAAAB0AhFLAAAAkcfvBQh1ySWXmELCX//6V/tMBm0qvW7dunbX08sde0TYCg+229x4440yceJEv68/AAAAAMAz/d62qnaPmenw3pYq2U3EEgAAQEQJSAFC/epXvzKzHu666y7Zs2ePvZ+D7qDafredV3qZrfCQkJBgYpumTJkSqNUHAAAAALhQ2bhP3imrlDmllbKZiCUAAICIFbAChCoqKpLjjjtOXnrpJdOYuqqqql3RwZFelpKSIueff75cddVVB0U1AQAAAAACG7H0cXmNzNlcKV/srJNWH5eXGhcjk/KzpKgwW45IT2l3oBoAAABCQ1Sbs9H+ANmwYYN88803Jp5p165dsn//funVq5dkZGTIyJEjZfjw4RITExPo1USQ0UKWI/1ikpiYaH6fPn26ZGZmBmjNAAAAgPBGxBIAAACCdgZERwMGDDAnAAAAAEBwRyy9XVZpejsQsQQAAICQKEAAXVFSUtLufHV1tRQXF7MxAQAAAIsjlhZurzFFBysjlqYU5siQ9GQilgAAAMIQBQgAAAAAgMuIpZW1e6Rkc6XM21olu/e3+LSlokXk2Nxepq/DuD4Z0iNGLwEAAEC48nsB4tNPP5Wjjz5aevTo4e+7BgAAAAB4oaKxWd4pqyJiCQAAAKFVgLjiiiskPj5eRowYIccdd5wce+yx5neaSwMAAABA+EQs9YyLkYlELAEAAES0gEQw7d+/X7766itzeuKJJyQxMVFGjx5tL0gMGTIkEKsFAAAAABEXsbSiZo8pOmjE0h6LIpa0r8OJfdKJWAIAAIhwsYHayVVRUVHm971798rHH39sTiotLU3Gjh1rChJ66t+/fyBWEwAAAADCNmLpbY1Y2lwhpXuafF7eIamJMqV/jpyenyVZifGWrCMAAABCn98LEE8++aR89tln5vTdd98d9HctSNTV1cm8efPMSfXu3dvMjNCTFiT0PAAAAADAe00tLbJwe62UlFbI4p27LIlYmqQRS/1zZHCvZHOAGQAAAOAoqs02HSEAKisr7cWIL774QrZv3/7fFfth59VxtoRNQUGBPa7pmGOOkfT09ACsPYJVdXW1FBcXm9+nT58umZmZgV4lAAAAICwilmKiRI7t3UuKCnNkXJ90iY/R0CUAAAAgCAsQHZWVlcnnn39uL0jU1NR4LEjoz0GDBsmsWbMCtNYINhQgAAAAEOmsjlg6tGeiKToQsQQAAICg7wHhis5s0NOFF15ozq9du9YUI7Qo8eWXX0pDQ4P9urZChP5cs2ZNwNYZAAAAAIIBEUsAAAAINkE1A8KdlpYWWb58ucyYMUPmzp1rv1xXX2dBrF69OqDrh8ApKipqd16fD4mJieZ3IpgAAAAQzohYAgAAQDALqhkQzpSXl9tnQehJ+0YAAAAAQCTbufeHiKXSCimzKGJpikYsFWRJZkK8JesIAAAABF0Boq6uzt4HQn9qX4iOHCdtpKWlmUbUiFwlJSUue0AAAAAA4RSxtGB7renrsLhil/g6lb1nfKyclp8lRYXZMrhXsr3PHgAAABA2BYi9e/ea/g62GQ7a98GxwNAxIUqjdY466ig57rjjzOmII45gRxkAAABAWEcszSmtkHlbqqXhQItPy4uJEjmud7oU9c+WE3PTJT4m2rJ1BQAAAAJegDhw4IB8++23ZoaDnpYtW2b6O7gqOMTGxsqRRx5pLziMGDFC4uLi/L3aAAAAABCyEUuH9UyUIiKWAAAAEO4FiDFjxkhTU5PLgoNO+x08eLAce+yxpuCg17c1FAYAAACAcNUdEUun/xCxNIiIJQAAAERCAaKxsdEUGRwLD4WFhfYZDtrPoVevXv5eLQAAAAAIi4il43PTTdHhBCKWAAAAEMk9ILQQceKJJ8o555xjZjxkZGQEcnUAAAAAwI8RS5Uyp7RStlgUsTSlMEdOK8iSzIR4S9YRAAAACNkChBYf1CeffGJOev7www+3z4TQ6KWkpKRArR4AAAAAWKrpgEYs1Ziiw5cWRCylxcfKaUQsAQAAIIj5vQDxi1/8Qj7//HNZsWLFQc2n161bZ04vvPCCxMTEtGs+PXLkSNOQGgAAAABChX7PWa4RS5sr5P2t1kUsTdGIpT7pEhcdbdm6AgAAAFaLauvYBdpP9uzZI19++aV89tln5rRhwwanDaltEhISZPTo0faCxJAhQwKw1ggF1dXVUlxcbH6fPn26ZGZmBnqVAAAAEGGsjlga0DPJ9HUgYgkAAAChJGAFiI5qamrMzAg9aUFiy5YtB13HsSChjaq1YbWtIFFQUODnNUawogABAACAcIpYmtI/RwamJbX7PgQAAACEgqApQHS0fft2++wILUpUVVUddB3bDrj+XLVqVQDWEsGIAgQAAAD8Rb9OLaveLSWllTJva7XsJWIJAAAAsAvapgp9+/aV8847z5zUxo0bTTFCG1YvWLDAXBaktRMAAAAAYW6HLWJpc6VsbbAmYmlK/2yZlJ8lmQnxlqwjAAAAEGhBW4BwpI2pbdFMX331lf1ynflAEQJFRUXtNoI+LxITE9kwAAAAsDxi6aPtNWa2AxFLAAAAQIgWILZu3WqPXtKT9ocAAAAAgNCPWIqSE3J7yZTCHDm+Ty+Ji462bF0BAACAYBMbLJn9jg2ot23bZv+bbYaDrd+D44yHfv362ZtQI3KVlJS47AEBAAAAdDViaW5ppSk8WBKxlJZkig7aVDojIY4HBQAAABEhIAWIPXv2yJdffmlvMr1hwwaXBQfHy7OysuSYY46RY4891hQd8vLy/L7uAAAAAMI3Ymm+RixtrpSvKneJrx3nesXHymkFWabwMLBXskVrCQAAAIQOvxcgLrroIlm5cqW0tLQcNKPBseigl6empsrYsWPtBYcBAwb4e3UBAAAAhDH93rH0h4il94lYAgAAAEK7ALF06VJ78+iOsxx69OghRx11lL3gMHToUIkmExUAAABAt0UsVcjWhmafl0fEEgAAABAkEUy2WQ8xMTEyfPhwU2zQosPIkSMlPj4+EKsEAAAAIMw1asTSthpTeCBiCQAAAAjDAsSQIUNMsUFPY8aMkaSkJH+vAgAAAIAIoQc/fWsilirkAxOx1OrT8mKiouSE3F6mr8PxfXpJHDO2AQAAgOApQMyaNcvfdwkAAAAgwpQ3NMvcskqZa1HE0sC0JJlcmCOn5WdJRkKcJesIAAAAhLuARDABAAAAQHdFLOlsh68q631eXq/4WDm9IFuKCrNlYK9kS9YRAAAAiCQUIAAAAACErO6IWDqxTy8p0oilXCKWAAAAgLAqQOgXiN27d8uuXbvM+bS0NElNTZWoqKhArxoAAACAIItY0sLDNosilrTocFpBlqT3IGIJAAAACJsCxMqVK+Wtt96SJUuWyOrVq+XAgQPt/h4bG2uaV48cOVLOPPNMGTZsWMDWFQAAAEBgI5bmlFbI10QsAQAAAEEvoAWIdevWyd13320KD7bZD87s379fli9fbk4zZswwhQi93aBBg/y8xgAAAAD8HrFUtdsUHT7cRsQSAAAAEEoCVoD497//Lffff7/s27fPfKnQiCVbzFLHQoRj/JL+TQsWF1xwgdx2221y0UUX+X3dAQAAAHSv7Q1NJmJpbmmlJRFLg3olm2bSk/KJWAIAAADCugAxe/ZsufPOO9sVHmy/5+XlySGHHGL6PijtB7F582bZsmWL/TpKCxf33HOPJCYmmlgmAAAAAKHN6oil9B6xcnp+tkwuzJaBvZItWUcAAAAAQVyA2Llzp9xxxx3md1vhIScnR6666iqZMmWKZGRkOL1dTU2NzJkzR55//nnZsWOHuW1ra6spZIwdO1Zyc3P9/J8AAAAA8FVrW5sstThiaVyfdDPb4fjcXhIbHc2DBAAAAERKAeKxxx6TpqYm+0yG008/Xf7v//5PkpPdH5GkhYnLL79czj//fLn99ttl7ty5ZhnNzc3y5z//WR544AE//QcAAAAAfEXEEgAAABD+/FqA0GbS7777rr34MG7cOFOQ6IykpCR55JFHZM+ePbJw4UJzmS7z3nvvldjYgPbUBgAAAODGXhOxVC1zSivlGyKWAAAAgLDn1xF7bR69d+9e83t0dLSJT+oKLWBojNOkSZNMhJMu85tvvjFRTAAAAACCK2Lp26p6U3T4cGu1NLb4FrEUGxUlJ/ZJlymF2XIcEUsAAABAUPNrAWLr1q32AsIRRxxhGk53VX5+vgwbNkyWL19uzmuTagoQAAAAQBBFLJVWSklppWzf2+zz8gb1SjZFh0n5WdKrR5wl6wgAAAAgjAoQ1dXV7QoIvtIChq0AoU2qEZmKiorandcCV2JiYsDWBwAAIFJZH7EUJ2cUZMnkwmw5PM19zzgAAAAAEV6AiIv775FK2jzaV/v27XO6bAAAAAD+QcQSAAAAgKAoQGRmZtp/X79+vc/Lc1xGRkaGz8tDaCopKTlopk1xcXHA1gcAACASELEEAAAAIKgKEIcffrj5qY2jtR/EF198Icccc0yXlvXll19KWVmZ/fzAgQMtW08AAAAAziOWtJG09nX4psq6iKWiwmwZQMQSAAAAEHb8WoAYPHiw5Obmys6dO00R4s4775RXX31V0tPTO7Wcuro6c1ubnJwcs2wAAAAA1kcsLamqN0UHLT40trT6tLzYqCgZ1zddphTmyLG90yQ2OtqydQUAAAAQwQUIdfHFF8tjjz1mGgWXlpbKJZdcIn/84x/lyCOP9Or2q1atkltuuUU2b95szutydBkAAAAArLNtT5PMLas0hYfyvb73bxvcK1mmFGbLpPwsSetB/zYAAAAgEvi9APHzn/9c/vWvf0l5ebkpHmghQYsS48aNk6KiIhkxYoQUFha2u41GLS1dulTmzp0rCxculNbW/x511adPH7NMAAAAAMEVsZTRI05OJ2IJAAAAiFh+L0D06NFD/va3v8mll14q9fX1pgihBQUtLOhJ6WWJiYnm98bGRhPXZKO/69/1Z1pamvz1r381ywQAAADQeUQsAQAAAAibAoQaMGCAvPDCC3LTTTfJd999ZwoKylZo0J8NDQ0H3U6vZys+HHLIIfLoo4/aG1sDAAAACFzE0pD0ZCkqzJFJeZlELAEAAAAIXAFCadPoWbNmyXPPPWcaUVdUVLT7e8eihO337OxsE9l01VVXMfMBAAAACHDE0hkmYilHDktL4rEAAAAAEBwFCKXRSf/zP/8j11xzjXzxxReyZMkSWbFihdTW1pp4JtWzZ09JT0+XYcOGyahRo+SYY46R2NiArjYAAAAQUhFL31TWm6LDh9uqpanlv/3UuiIuOkrG9Uk3RYdje/eS2OjvDxwCAAAAgI6CYiRfCwonnHCCOQEAAACwJmKppKxS5hKxBAAAACCSCxAAAAAAfNewv8XMcigprZAlVbt9Xh4RSwAAAAB8QQECAAAACGFELAEAAAAIVhQgAAAAgBC0dU+TzLUwYumI9GTT12FifqakxcdZso4AAAAAIhsFCHQLbSK+fPlyWbZsmTnp75WVleZvY8eOlRkzZrDlAQAAuhCx9MEPEUvfWhCxlJkQJ2cUZEtRYbYc2jOJxwMAAABA8BYgtm/fLoHSt2/fgN03Dnb22WfLtm3b2DQAAAAWRSzNKa2Q+dtqpKml1aflxUVHyUl9MkzR4ZjevSQ2OorHCAAAAEDwFyBOOeUUiYry/xcYvc9Vq1b5/X7hnaysLBk+fLjMnz+fTQYAANCJiCWd6aAxSzv27vN5uxGxBAAAACAsIpja2tq6Y7EIIZdeeqnk5eXJkUceKX369DGXDRo0KNCrBQAAEFERS1k/RCxNJmIJAAAAQDgUICg+QF155ZVsCAAAgABFLI3v+33E0tgcIpYAAAAAhEkB4rrrrrNycejgwIEDsmTJEtNboaKiQlJSUiQ3N1dGjhwpGRkZbC8AAIAQsmVPo5SUVsrbFkUsDU1PMUWHCfmZkhYfZ8k6AgAAAIAvKED4oLW1VTZu3CjLli2T5cuXm9PatWtl//799us8+OCDcu655/r0IDU2NsrTTz8tM2fOlKqqqoP+HhcXJ+PGjZNp06YRcwQAABDE9uw/IB9urZY5pZWytJqIJQAAAADhrVt6QIS7d955R15++WVZsWKF7N27t1vva/369XLDDTfId9995/I6WvD48MMPZdGiRXLrrbfKJZdc0q3rBAAAgM5FLH3tELHU7GPEUnx0lJxExBIAAACAEEABogu+/vprWbx4sXQ3jVnSXgo7d+5sd/nQoUMlPz9f6urqzKyLhoYGc3lzc7PcfffdJppp6tSp3b5+AAAA8GPEUkaKTNGIpbws6RnPbjwAAACA4Mc3FwulpqZKUlLSQQWDrjbz1pkPjssaOHCgPPTQQzJ48GD7ZfX19fL444/LSy+9ZL/stttuM9c5/PDDfV4PAAAABDZiaXJBtkwuzJZDeibxUAAAAAAIKRQguighIUGGDBkiw4YNk+HDh5vTIYccIk8++aQ5+eq9994zDadt8vLyTJEhLS2t3fV69uwpd9xxh0RFRcmMGTPsMyG0KGHFegAAAMBzxNJXFbukpKzS0oglne0wJqeXxEZH8RAAAAAACEkUILrg2muvld/85jcSG9t9m69j8eDOO+88qPjg6OabbzZ9ILZt22bOz5s3T1avXm2KJM5okeLII4/0eT2vu+46uf76631eDgAAQKgp290oc8sqZW5ppexsJGIJAAAAADqiANEFGRkZ0p3Wrl0r69ats58/7LDDZPz48W5vk5iYKBdffLE88sgj9stmz57tsgABAACArkUsffBDxNIyCyKWshPi5AwilgAAAACEKQoQQWj+/PntznvbUFqv51iA0BkRt9xyi9PrxsfHy9y5c31cU5H09HSflwEAABDMWtra5GuNWCqtlPnbrYlYGm8ilnJkTO80iYkiYgkAAABAeKIAEYQWLVrU7vzo0aO9ul2fPn2kX79+9himTZs2SXl5ubm8I+0ZoTMrAAAA4J+IpWEZKVJUmCMT8jKlZzy74QAAAADCH998gtCGDRvsv0dHR5tG194aMWKEvQCh1q9f77QAAQAAAOcRS+9vrTazHSyLWCrMlqKCHOnfM5FNDgAAACCiUIAIMrt27ZKamhr7+czMTNPfwVt5eXntzussiJNOOsnSdQQAAAi3iKWvfohY+mhbtTS3tvm0PCKWAAAAAOB7FCCCTFlZWbvznZ29kJub63Z5AAAA+GE/aXejKTpozFKFZRFL2TIxL0tSiVgCAAAAAAoQwWbPnj3tzmdkZPjUFHr3bt+jA7pi9erV5uRMZWWlzJw5s91l48aNk+zsbD+tHQAAiFRELAEAAACA/zADIsg0NDS0O9+jR49O3T4hIaHd+b1790ogvP/++/Lkk086/ZvGQt16663tLnvxxRe9LkDs2LHD7d9ra2s7saYAACDcEbEEAAAAAIFBASLINDY2tjsfHx/fqdt3LFh0XF44GD9+vNu/x8XFyZAhQ/y2PgAAIJIilnJkYl4mEUsAAAAA4AUKEEEuKirKp+u3tfnWRLGrrr/+enMCAADwd8TSvC3VUlJaIctr2kdbdkV2YrxMLsg2vR0KUxMtWUcAAAAAiBQUIIJMYmL7L7bNzc2dun1TU1O780lJSRJuFixY4DGC6c477/Tb+gAAgMBHLH1ZscsUHRZsq5HmVt8OwOgRHSXj+2WY2Q5jctIkppMHhAAAAAAAvkcBIsh0LBh0tgDR8frhWIDIzc31GMEEAADCX6lDxFKlBRFLw3+IWJpAxBIAAAAAWIICRJBJSUnxqaFyTU1Nu/OpqamWrBcAAEAw2L3vgLy/lYglAAAAAAgFIVmA2L59e7sj/Hv16iXhoqCgoN358vLyTt1+x44d7c7n5+dLuCsqKjqoD0bHKCsAABDiEUs7d0lJGRFLAAAAABBKQrIAccopp9ibLcfExMjUqVOluLhYCgsLJdRpMSUjI8M+k6GqqkoaGxu9HlDfunVru/OHHnpot6wnAABAd9tc32iKDm+XVRGxBAAAAAAhKCQLEKqt7fvmggcOHJA333xTZs+eLZMnTzaFiFAfdB8wYIAsXrzY/N7a2iorVqyQMWPGeHXbpUuXHrSscFdSUtLufHV1tXkeAACA0IxYmvdDxNKKmj0+Ly8nMV4mF2TL5MJsKUxlhiQAAAAA+FPIFiBsMyBstBChRQgdjD799NPl2muvDdnB9+OPP95egFBfffWVVwUIjWvatm2b/fwhhxwiffv27bb1BAAAsDJiaU5phSzYXiP7Wr8/0KSrekRHycn9MqWoMFtG56RJTIf9RgAAAACAf4T8DIiOl7W0tMjcuXPlnXfekZUrV0qoRkw99thj9vNaWNGCiid6vY7LAQAACPqIpdJKqWza7/PyjsxMNUWHCXmZkhIXsru5AAAAABA2QvKb2Zo1a+y/79u3z8QO6YwBnSnw7bffmp4JGl0UqgYNGiQDBw6UdevWmfMbN26UBQsWyPjx413epqmpSV599dV2l2lvDAAAgGBSv++AvL+1SuaUVspKCyOWtPBQQMQSAAAAAASVkCxAOIqPjzfxRLaIIo1iWr58uXz55ZcSyq677jq54YYb7Ofvu+8+GTlypKSlpTm9/iOPPNIufmnChAkyZMgQiQRFRUUHxXN527QbAAD4J2Jp8c46U3RYaFHE0o9+iFg6moglAAAAAAhaIV+A6Cg2NlZGjRplTt1p69atTi+vr69vd762ttbpdXv06CHZ2dkulz9p0iTzPyxZssSc37Jli/zkJz+Rhx9+2MyQsNm9e7eJa3rppZfaLXvatGld+r8AAACssql+r8wtrZS3y6yLWJpSmC2nErEEAAAAACEhqs1ZMwV45FgE6IqxY8fKjBkz3F5n586dcv7550tFRUW7o/uHDh0q+fn5UldXJ8uWLZOGhoZ2t3vooYfkzDPPlEhVXV0txcXF5vfp06dLZmZmoFcJAICI0R0RSzrTQWOWiFgCAAAAgNASdjMgwknv3r3lueeeM1FMmzZtMpdpvWjFihXm1JHOfPjtb38b0cUHAADgf0QsAQAAAACcoQAR5LQZ9axZs+Spp56SmTNnmqP7O4qLi5Nx48aZ2CVfZ2YAAAB4i4glAAAAAIA7RDCFEG2w/c0335ieElVVVZKcnCy5ubmmV0RGRkagVy9oEMEEAEDoRCz1ToyXyUQsAQAAAEBYCuoZEGvWrDFRQ9rIOSEhQXJycmTMmDERO9iuDba1d4SeAAAAQjZiKSZaftQvQ6YU5sjR2T0lOirKsnUFAAAAAERYAUKPSF+3bp0pJMTHx0teXp6JFoqOjnZ6/ffff18ee+wx2bhx40F/09uMHz/e9DooKCjww9oj2BUVFbU7r426ExMTA7Y+AACEU8RSSWmlvF1WKVVN+31e3ojMVJlSmC2n5GVKSlxQHwcDAAAAALBAt37z+/bbb+Xhhx82sUHaPNlRenq6XHrppXLNNdeYHgY2en1tvKw63ka1tLTIhx9+KJ9//rn87W9/k9GjR3fnvwAAABBxEUvztlSZwsPKWiKWAAAAAABB2APilVdekfvuu88UEVzdhR6prpFKWnDQIsQ///lPuffee+1/U4637XhZUlKSvPXWW2ZGBWBDDwgAADrnQGubLK6oM0UHIpYAAAAAAEE9A2LRokVyzz332IsGHQsHjue//PJLefrpp+WKK66QRx55pN3f+vTpI4MHDzbNlnft2mXvB6HX0b/v3btX7r//fpk+fXp3/BsAAABh7bv6vTKXiCUAAAAAQKgUILQwcPfdd5vfbYUC/amFhMLCQnN5WVmZrF692v73l19+WdLS0qShocFcps2mH3jgATnhhBMOWvbs2bPNLAm9rp5fsGCBbNmyRfLz863+VwAAAMJOd0UsFRVmS34KPZgAAAAAAN1YgFi4cKEpCNhmMhx++OFmZoM2nXa0fv16+fWvfy1r166V3bt3y7PPPmsuT0hIkBkzZjhtMK3LPPPMMyU3N1d+9rOf2S8vKSmR4uJiq/8VAACAsIlY+mLnDxFL5TWyv9W3BM4eMdFySr8MKSrMkaOze0r0D/t9AAAAAAB0awFi/vz55qfOTtBZDX//+98lKyvroOtpYUJ7PxQVFUl9fb1UVVWZAsNFF13ktPjgaOzYsfKjH/1IPvjgA3ObZcuWWf1vAAAAhEXEkhYd3i6rlOqm/T4vb2RWqik6aPEhJa5bkjwBAAAAAGHE8m+OK1euND+1MHDuuec6LT7Y6N/OO+88ef755+2XnXrqqV7dz4QJE0wBQm3YsMHn9Ubo0iKWI33uJSYSAQEAiEy79u2XeVuqpaS0QlbVNvi8vNykeJlckG1ilohYAgAAAAAEtABRXl5u//3YY4/1eP1jjjmmXQHisMMO8+p+dAaFbaaFNqgGAACIVN0VsTSlMEeOImIJAAAAABAsBQjt52DTr18/j9fveB2NbfJGz5497b9rQ2pELu0B4qi6upqeIACAiEDEEgAAAAAgogoQzc3N9t+TkpI8Xr9jVE5MTIxX9+N4vZaWlk6tIwAAQKgiYgkAAAAAECroHggAABACEUufm4ilCvm4vNbniKUEE7GUKUWF2UQsAQAAAAC6DQUIAACAII5YmrO5Qt7ZUiXVTft9Xt6orFQpKswxxYfkOO9mnQIAAAAA0FUUIAAAAILIrub98t7WajPbYXWt732u+iT1kMmF2TK5IFvyUhIsWUcAAAAAALxBAQIAACDAiFgCAAAAAISjbi1AREVFdefiAQAAQtrGXXtlTmmFvFNWJTXNRCwBAAAAAMJLbHcWHi655BKJiXGfL9zS0tLu/KmnnurVfXS8HQAAQMhELG2pkjmllbKmjoglAAAAAED46rYZEG1tbbJjx45O32bbtm2dKnTobRDZioqKDnpeJCYmBmx9AADo6EBrq3y+c5eZ7fDx9lo54OP+S0JMtGkkXVSYLUdl95RoZp0CAAAAACKpAEH8EgAAiHQbdjVISWmlZRFLR2X1NEWHH/XLlOQ497NMAQAAAAAIywIEsxLgTyUlJe3OV1dXS3FxMQ8CACAg6hwiltZaFLGkRYfJBdnSLyXBknUEAAAAACAkCxAffPCB1YsEAAAI+oilz3bUmaLDJ+W+RywlasRS3vcRS6OyiFgCAAAAAIQmywsQ/fr1s3qRAAAAQR2x9HZZldQSsQQAAAAAgH96QAAAAIQjIpYAAAAAAPAOBQgAAAAPiFgCAAAAAKDzKEAAAAC4sH5Xg8y1MmIpu6dMKcyWH/XLlKTYGLY7AAAAACCsUYAAAADoELH07pYq09thbV2Dz9umb1IP00x6cmG29E1OYFsDAAAAACIGBQgAABDxNGLp0x11pujwSXmtHGhr82mbJMZEy6l5mTKlMEdGZKVKdFRUxG9jAAAAAEDkCZoCxOWXX97p20yZMkUuvPDCblkfAAAQGRFLJZsr5Z0tlVLbfMDn5R1tIpZy5OR+GUQsAQAAAAAiXrcUIHbu3CmTJk2Sffv2mfNpaWnyn//8R3r37u3yNosXL5aoThwd2NbWJqtWrZJTTz1VMjMzLVlvhKaioqJ25/V5lJiYGLD1AQBEYMRS/2yZXEDEEgAAAAAA3V6AePrpp6W5udk+GHz//fe7LT50LCx4W4hoaGiQP//5z3LPPff4tL4AACC8EbEEAAAAAID/RbXpiL+Fampq5KSTTpKWlhb70ekPP/ywx9sNHjzYXnjwZpX0uno9PdL9448/lpSUFAvWHuGgurpaiouLze/Tp09nhgwARLB1dQ0yt5SIJQAAAAAAwmIGxLx58+TAge8zlGNiYuR//ud/vL6tbfbDyy+/LLm5uS6vt2vXLvnxj38sTU1N5vT222/LBRdcYMn6AwCA0FarEUtlGrFUIet27fV5ef2Se0hRYY6cUZAlfZMTLFlHAAAAAAAigeUFCC0GKC0kTJgwQQ499NBOL6NPnz7St29fl3/v16+fKTjMmDHDnJ87dy4FCAAAIjxiadGOOlN0+KS8Tlp8nOCZFBstp+ZlmsLDyMzUTvWpAgAAAAAA3VCA0BkMS5cutZ8/7bTTpLuceeaZpgCh97l8+fJuux8AABDcEUvaTPrdLZVS2/z9DExfjM7uaYoOP+qXIYmxMZasIwAAAAAAkcrSAsSmTZuksbHR/B4dHS0nnHCCdJfhw4ebvg979uwxzag3btwohx12WLfdHwAACA5ELAEAAAAAEIEFiA0bNpifGlPQv39/SUtL69TtOxtvoAUH24wLLX5QgAAAIDwRsQQAAAAAQIQXILQ5tE1WVlanb29rQu0tx/uoq6vr9P0BAIDgRsQSAAAAAAChy9ICRH19fZcLEOeff7799+TkZK9uk5SU5PS+AQBA6OquiKXJBdnSJ7mHJesIAAAAAAD8XIDYt2+f/ff9+/d36rb3339/p++vubnZ6X0DAIDQsr+1VT4tr5M5pRWyaEedtLS1+bS8pNhomZCXJZMLs2VkZmqnYx4BAAAAAECQFSASExPtv9fW1kp3c4xdSkhI6Pb7AwAA1kcsadFBZzzU7Tvg07K0xDA6O02K+mfLyX0zJDE2xrL1BAAAAAAAAS5AOMYu7dy5U7pbeXm5/ffMzMxuvz8AAOC7mqb98u6W7yOW1lsQsZT3Q8TSGUQsAQAAAAAQvgWIgoICezPpLVu2yI4dOyQ3N1e6q/ig99HxvgEAQCRELMXIhLxMKSrMlhFELAEAAAAAEP4FiMGDB0tcXJwcOPB9hMKCBQvkoosuku7w0Ucf2X+PjY2VIUOGdMv9IPgVFRW1O685345xYACAwCFiCQAAAACAyGVpASI+Pl7Gjh0rixYtMuefffZZOf/88yUmxtoMZm1wrcu2NZTU+9T7BgAAgUfEEgAAAAAAsLwAoaZOnWoKEFoc2Lp1qzz33HNyzTXXWHofWnzYtm2b+V3vR+8TkaukpKTd+erqain+f/buA8ypMvvj+GHovSMgTUQQUbGBFVFAVAbsXewNFWxrXRW7uPYu69rBstZVQLCB2MWCKAqKKL0PIG3o/J/fu3vzv7kkM8mkZ76f58lDMiQ3N8nNm+Sc9z1nwICM7Q8AlNcSS5/PX2YjZy62L5JUYumQ/5VY2pUSSwAAAAAA5KRKqSiH8+CDD7om1OoFofMtWrSwPn36JGX777zzjj388MMu8aDtN2nSZKsSPAAAIH0llkbMWOSaSv+1/r8lGMtK6xr3alzX+rZpbAc1b2DVKiV3BSUAAAAAAMjxBIR6QFx55ZXupCTB5s2b7aqrrrJffvnFLrnkkjKXSlq/fr098MAD9sILL7jEg2j7uh/dJwAASGeJpcVutcPvf61JeHstalZzSYfDWzW2pjWqJmUfAQAAAABAHiYgpG/fvjZu3DhXGkdJgk2bNrlSTKNHj3ZNqY866ii3ciEWixYtsjfffNNeffVVmz9/vks+eL0ftKqC8ksAAKQeJZYAAAAAAEC8KmzxlhMkmVYsnHfeefb111+HyiW5O/xf8qBly5bWqVMna926tdWuXdud9H8rV650pxkzZtjkyZNdHwnx317n99lnH3vyySdpPo2t+HtADB061Bo2bMizBABloM/b3/5aYyOTWGKpS5O6rq8DJZYAAAAAAMh/KVkBISq1pGbRd911l7344ouhxIOXSJg1a5bNnj27xG34cyNe4kGn/v372zXXXEPpJQAAcqDEUsta1axv6/+WWNqGEksAAAAAAJQbKUtAiHoz3Hjjjda1a1e75557QqsZvGSERFuAoev4kxY6qZn11Vdfbb17907lbgMAUO6kqsRS3zZNbJcGtcI++wEAAAAAQPmQ0gSE59BDD7VDDjnExowZ41ZDTJo0yTZuLLmMg5eYqFSpknXu3NmtetB2CgoK0rHLAADkPX3W/rp8tY2auTipJZa02qF78wZWrVLFpO0rAAAAAADIPWlJQIg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@@ -379,9 +370,9 @@ "ncols = 4 # 3 features + observed\n", "n_grid = np.logspace(5, 9, 50)\n", "inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound)\n", - "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer\n", + "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # one resident batch\n", "\n", - "fig, ax = plt.subplots(figsize=(8, 5))\n", + "fig, ax = plt.subplots(figsize=(8, 5), layout=\"tight\")\n", "ax.loglog(n_grid, inram_gb, lw=2.5, label=\"in-RAM pm.Minibatch (O(N))\")\n", "ax.loglog(n_grid, stream_gb, lw=2.5, label=\"streaming DataLoader (O(batch))\")\n", "ax.axhline(26, color=\"0.5\", ls=\"--\", lw=1)\n", @@ -389,26 +380,25 @@ "ax.set_xlabel(\"dataset size N\")\n", "ax.set_ylabel(\"array footprint (GB, lower bound)\")\n", "ax.set_title(\"Memory is flat in N when streaming\")\n", - "ax.legend(loc=\"lower right\", framealpha=0.95)\n", - "fig.tight_layout();" + "ax.legend(loc=\"lower right\", framealpha=0.95);" ] }, { "cell_type": "markdown", - "id": "624c0133", + "id": "d304c6c7", "metadata": {}, "source": [ "That line is only the bare array. Actual peak RSS is higher, because of the\n", - "framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get\n", - "the real number on public data, we measured peak memory on the\n", + "framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. As a\n", + "real-data check, outside this notebook, we ran the same logistic model (13 numeric\n", + "features plus the click label) on the\n", "[Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a\n", - "standard out-of-core learning benchmark, with the same logistic model (13 numeric\n", - "features plus the click label). Streaming through the `DataLoader` stayed flat at\n", - "about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline\n", - "rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to\n", - "out-of-memory near 238M rows on a 26 GB machine. The streaming and in-RAM posteriors agree coefficient for coefficient; the\n", - "largest gap is about 0.1, on the intercept. Criteo is\n", - "public, so anyone can rerun this.\n", + "standard, publicly available out-of-core learning benchmark. Peak memory for the\n", + "streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M\n", + "rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M\n", + "rows, which extrapolates to out-of-memory around 250M rows on the same 26 GB\n", + "machine. The streaming and in-RAM posteriors agreed coefficient for coefficient;\n", + "the largest gap was about 0.1, on the intercept.\n", "\n", "## When to use it\n", "\n", @@ -423,7 +413,7 @@ }, { "cell_type": "markdown", - "id": "7fdf59e4", + "id": "918da33a", "metadata": {}, "source": [ "## Authors\n", @@ -435,7 +425,7 @@ }, { "cell_type": "markdown", - "id": "2e983b8d", + "id": "1a8d9f52", "metadata": {}, "source": [ "## References\n", @@ -450,13 +440,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "0c4c4ddb", + "id": "91539613", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T03:45:28.335556Z", - "iopub.status.busy": "2026-06-11T03:45:28.335482Z", - "iopub.status.idle": "2026-06-11T03:45:28.345025Z", - "shell.execute_reply": "2026-06-11T03:45:28.344643Z" + "iopub.execute_input": "2026-06-11T05:01:31.471814Z", + "iopub.status.busy": "2026-06-11T05:01:31.471732Z", + "iopub.status.idle": "2026-06-11T05:01:31.481894Z", + "shell.execute_reply": "2026-06-11T05:01:31.481532Z" } }, "outputs": [ @@ -464,7 +454,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Wed, 10 Jun 2026\n", + "Last updated: Thu, 11 Jun 2026\n", "\n", "Python implementation: CPython\n", "Python version : 3.12.12\n", @@ -492,7 +482,7 @@ }, { "cell_type": "markdown", - "id": "7e62e534", + "id": "5d51af15", "metadata": {}, "source": [ ":::{include} ../page_footer.md\n", @@ -520,6 +510,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.12" + }, + "myst": { + "substitutions": { + "extra_dependencies": "pyarrow" + } } }, "nbformat": 4, diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index cad94466f..62613be17 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -9,6 +9,9 @@ kernelspec: display_name: Python 3 (ipykernel) language: python name: python3 +myst: + substitutions: + extra_dependencies: pyarrow --- (streaming_dataset)= @@ -47,6 +50,11 @@ bounded buffer. `N` is small here so the notebook runs in seconds. The streaming code is the same at any size, and the last section shows what changes at scale. ++++ + +:::{include} ../extra_installs.md +::: + ```{code-cell} ipython3 import glob import tempfile @@ -68,9 +76,9 @@ az.style.use("arviz-variat") ## Put a dataset on disk and forget the array We build a logistic-regression dataset, write it to Parquet shards, and delete the -in-memory table. From here the features only exist on disk. We keep `X` and `y` -around only to build the in-RAM `pm.Minibatch` baseline later; the streaming fit -never reads them. +in-memory table. From here the streaming path reads only the disk copy. We keep `X` +and `y` around only to build the in-RAM `pm.Minibatch` baseline later; the streaming +fit never reads them. ```{code-cell} ipython3 N = 30_000 @@ -130,8 +138,7 @@ with pm.Model() as model: idata_stream = approx.sample(1000) ``` -The negative-ELBO trace shows the fit converging while only ever holding a -`batch_size` buffer in memory: +The negative-ELBO trace shows the fit converging on minibatches read off disk: ```{code-cell} ipython3 fig, ax = plt.subplots(figsize=(9, 3)) @@ -164,15 +171,14 @@ bs_stream = idata_stream.posterior["b"].values.reshape(-1, 4) bs_inram = idata_inram.posterior["b"].values.reshape(-1, 4) names = ["intercept", "slope x1", "slope x2", "slope x3"] -fig, axes = plt.subplots(1, 4, figsize=(13, 3)) +fig, axes = plt.subplots(1, 4, figsize=(13, 3), layout="tight") for k, ax in enumerate(axes): ax.hist(bs_stream[:, k], bins=40, density=True, alpha=0.5, label="streaming") ax.hist(bs_inram[:, k], bins=40, density=True, alpha=0.5, label="in-RAM") ax.axvline(b_true[k], color="k", ls="--", lw=1) ax.set(title=names[k], yticks=[]) axes[0].legend(fontsize=8) -fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1.04) -fig.tight_layout(); +fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1.04); ``` ## Memory usage @@ -186,9 +192,9 @@ cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measureme ncols = 4 # 3 features + observed n_grid = np.logspace(5, 9, 50) inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound) -stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # just the buffer +stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # one resident batch -fig, ax = plt.subplots(figsize=(8, 5)) +fig, ax = plt.subplots(figsize=(8, 5), layout="tight") ax.loglog(n_grid, inram_gb, lw=2.5, label="in-RAM pm.Minibatch (O(N))") ax.loglog(n_grid, stream_gb, lw=2.5, label="streaming DataLoader (O(batch))") ax.axhline(26, color="0.5", ls="--", lw=1) @@ -196,21 +202,20 @@ ax.text(n_grid[-1], 30, "26 GB RAM", color="0.5", ha="right", va="bottom") ax.set_xlabel("dataset size N") ax.set_ylabel("array footprint (GB, lower bound)") ax.set_title("Memory is flat in N when streaming") -ax.legend(loc="lower right", framealpha=0.95) -fig.tight_layout(); +ax.legend(loc="lower right", framealpha=0.95); ``` That line is only the bare array. Actual peak RSS is higher, because of the -framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. To get -the real number on public data, we measured peak memory on the +framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. As a +real-data check, outside this notebook, we ran the same logistic model (13 numeric +features plus the click label) on the [Criteo 1TB Click Logs](https://huggingface.co/datasets/criteo/CriteoClickLogs), a -standard out-of-core learning benchmark, with the same logistic model (13 numeric -features plus the click label). Streaming through the `DataLoader` stayed flat at -about 0.7 GB across a sweep from 1M to 150M rows. The in-RAM `pm.Minibatch` baseline -rose linearly to 15.7 GB at 150M rows, about 21 times more, and extrapolates to -out-of-memory near 238M rows on a 26 GB machine. The streaming and in-RAM posteriors agree coefficient for coefficient; the -largest gap is about 0.1, on the intercept. Criteo is -public, so anyone can rerun this. +standard, publicly available out-of-core learning benchmark. Peak memory for the +streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M +rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M +rows, which extrapolates to out-of-memory around 250M rows on the same 26 GB +machine. The streaming and in-RAM posteriors agreed coefficient for coefficient; +the largest gap was about 0.1, on the intercept. ## When to use it From 79fdc5c4f6d1db2c6b15c1c1b181be6a81562261 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Thu, 11 Jun 2026 10:50:09 -0500 Subject: [PATCH 13/17] Seed the in-RAM baseline and tighten the memory-figure accounting - pass random_seed to the in-RAM pm.fit and to both posterior sample calls so the comparison is reproducible - the streaming memory line now includes the shuffle buffer and the current source chunk, matching the surrounding prose - say 'no callbacks to write' (the Trainer uses pm.fit's hook internally) and 'can bias' for the bounded-buffer caveat - note that the dropped end-of-pass remainder is re-drawn each epoch under shuffle=True --- .../streaming_dataset.ipynb | 113 ++++++++++-------- .../streaming_dataset.myst.md | 43 ++++--- 2 files changed, 87 insertions(+), 69 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 2b69da89b..226c9c493 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -33,14 +33,17 @@ " read by {func}`~pymc.variational.streaming.parquet_source`.\n", "* the model observes a `pm.Data` placeholder of one batch, not the whole array.\n", "* a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each\n", - " minibatch into that placeholder with `set_data` every step. There are no callbacks.\n", + " minibatch into that placeholder with `set_data` every step. There are no\n", + " callbacks to write.\n", "\n", "The unbiased-gradient rescaling works exactly as in `pm.Minibatch`. The `DataLoader`\n", "is sized, so `total_size=len(loader)` passes the dataset size `N` to the observed\n", - "distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`. The\n", - "one extra requirement is shuffling. A streaming source is only as well mixed as the\n", - "order it yields rows in, so pass `DataLoader(shuffle=True)` to shuffle through a\n", - "bounded buffer.\n", + "distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`.\n", + "Batches are exact-size, so each pass drops the rows that do not fill a final batch;\n", + "with `shuffle=True` that remainder is re-drawn every epoch instead of being a fixed\n", + "tail. The one extra requirement is shuffling itself. A streaming source is only as\n", + "well mixed as the order it yields rows in, so pass `DataLoader(shuffle=True)` to\n", + "shuffle through a bounded buffer.\n", "\n", "`N` is small here so the notebook runs in seconds. The streaming code is the same at\n", "any size, and the last section shows what changes at scale." @@ -61,10 +64,10 @@ "id": "c2f6955f", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T05:01:07.808492Z", - "iopub.status.busy": "2026-06-11T05:01:07.808295Z", - "iopub.status.idle": "2026-06-11T05:01:09.181327Z", - "shell.execute_reply": "2026-06-11T05:01:09.180785Z" + "iopub.execute_input": "2026-06-11T15:48:31.179963Z", + "iopub.status.busy": "2026-06-11T15:48:31.179761Z", + "iopub.status.idle": "2026-06-11T15:48:32.724808Z", + "shell.execute_reply": "2026-06-11T15:48:32.724317Z" } }, "outputs": [], @@ -105,10 +108,10 @@ "id": "03ccf873", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T05:01:09.182814Z", - "iopub.status.busy": "2026-06-11T05:01:09.182552Z", - "iopub.status.idle": "2026-06-11T05:01:09.201682Z", - "shell.execute_reply": "2026-06-11T05:01:09.201315Z" + "iopub.execute_input": "2026-06-11T15:48:32.726747Z", + "iopub.status.busy": "2026-06-11T15:48:32.726448Z", + "iopub.status.idle": "2026-06-11T15:48:32.752265Z", + "shell.execute_reply": "2026-06-11T15:48:32.751894Z" } }, "outputs": [ @@ -152,7 +155,7 @@ "scanning the data, so `total_size=\"auto\"` resolves `N` for free. The `DataLoader`\n", "batches and shuffles it into fixed-size minibatches. The model reads a\n", "`pm.Data(\"batch\", ...)` placeholder holding a single batch, and the `Trainer`\n", - "writes each minibatch into it with `set_data`. There are no callbacks:\n", + "writes each minibatch into it with `set_data`. There are no callbacks to wire up:\n", "`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`\n", "runs the loop." ] @@ -163,10 +166,10 @@ "id": "477343ca", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T05:01:09.202716Z", - "iopub.status.busy": "2026-06-11T05:01:09.202653Z", - "iopub.status.idle": "2026-06-11T05:01:27.550831Z", - "shell.execute_reply": "2026-06-11T05:01:27.550360Z" + "iopub.execute_input": "2026-06-11T15:48:32.753347Z", + "iopub.status.busy": "2026-06-11T15:48:32.753280Z", + "iopub.status.idle": "2026-06-11T15:48:50.379526Z", + "shell.execute_reply": "2026-06-11T15:48:50.379109Z" } }, "outputs": [ @@ -202,7 +205,7 @@ " data_name=\"batch\",\n", " obj_optimizer=pm.adam(learning_rate=0.008),\n", " ).fit(30_000, random_seed=RANDOM_SEED)\n", - " idata_stream = approx.sample(1000)" + " idata_stream = approx.sample(1000, random_seed=RANDOM_SEED)" ] }, { @@ -219,10 +222,10 @@ "id": "a3ff7cb9", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T05:01:27.552089Z", - "iopub.status.busy": "2026-06-11T05:01:27.552002Z", - "iopub.status.idle": "2026-06-11T05:01:27.665910Z", - "shell.execute_reply": "2026-06-11T05:01:27.665530Z" + "iopub.execute_input": "2026-06-11T15:48:50.380772Z", + "iopub.status.busy": "2026-06-11T15:48:50.380702Z", + "iopub.status.idle": "2026-06-11T15:48:50.538325Z", + "shell.execute_reply": "2026-06-11T15:48:50.537986Z" } }, "outputs": [ @@ -252,8 +255,8 @@ "\n", "For comparison, here is the usual fit that keeps the whole dataset in memory.\n", "Streaming changes where the data lives, not the inference, so the two posteriors\n", - "land on top of each other. Both show ADVI's usual mild bias relative to the dashed\n", - "ground truth, but they agree with each other." + "should agree, and here they land on top of each other. Both show ADVI's usual mild\n", + "bias relative to the dashed ground truth, but they agree with each other." ] }, { @@ -262,10 +265,10 @@ "id": "b3b5d9fe", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T05:01:27.667067Z", - "iopub.status.busy": "2026-06-11T05:01:27.666979Z", - "iopub.status.idle": "2026-06-11T05:01:30.969240Z", - "shell.execute_reply": "2026-06-11T05:01:30.968822Z" + "iopub.execute_input": "2026-06-11T15:48:50.539450Z", + "iopub.status.busy": "2026-06-11T15:48:50.539367Z", + "iopub.status.idle": "2026-06-11T15:48:53.984891Z", + "shell.execute_reply": "2026-06-11T15:48:53.984550Z" } }, "outputs": [ @@ -273,7 +276,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Finished [100%]: Average Loss = 531.87\n" + "Finished [100%]: Average Loss = 531.25\n" ] } ], @@ -285,9 +288,13 @@ " )\n", " pm.Bernoulli(\"y\", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N)\n", " approx_inram = pm.fit(\n", - " 30_000, method=\"advi\", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False\n", + " 30_000,\n", + " method=\"advi\",\n", + " obj_optimizer=pm.adam(learning_rate=0.008),\n", + " progressbar=False,\n", + " random_seed=RANDOM_SEED,\n", " )\n", - " idata_inram = approx_inram.sample(1000)" + " idata_inram = approx_inram.sample(1000, random_seed=RANDOM_SEED)" ] }, { @@ -296,16 +303,16 @@ "id": "6731e9d6", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T05:01:30.970410Z", - "iopub.status.busy": "2026-06-11T05:01:30.970349Z", - "iopub.status.idle": "2026-06-11T05:01:31.236510Z", - "shell.execute_reply": "2026-06-11T05:01:31.236118Z" + "iopub.execute_input": "2026-06-11T15:48:53.986146Z", + "iopub.status.busy": "2026-06-11T15:48:53.986070Z", + "iopub.status.idle": "2026-06-11T15:48:54.193017Z", + "shell.execute_reply": "2026-06-11T15:48:54.192668Z" } }, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -338,8 +345,9 @@ "\n", "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n", "rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`\n", - "holds one batch plus its bounded shuffle buffer. The dense `float64` design matrix dominates the\n", - "cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement:" + "holds one batch plus its bounded shuffle buffer and the current source chunk. The\n", + "dense `float64` design matrix dominates the cost. The lines below are array lower\n", + "bounds (`N * ncols * 8` bytes resident versus that constant), not measurements:" ] }, { @@ -348,16 +356,16 @@ "id": "6f25aa9a", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T05:01:31.237556Z", - "iopub.status.busy": "2026-06-11T05:01:31.237493Z", - "iopub.status.idle": "2026-06-11T05:01:31.470524Z", - "shell.execute_reply": "2026-06-11T05:01:31.470161Z" + "iopub.execute_input": "2026-06-11T15:48:54.194247Z", + "iopub.status.busy": "2026-06-11T15:48:54.194176Z", + "iopub.status.idle": "2026-06-11T15:48:54.488206Z", + "shell.execute_reply": "2026-06-11T15:48:54.487819Z" } }, "outputs": [ { "data": { - "image/png": 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" ] @@ -370,11 +378,12 @@ "ncols = 4 # 3 features + observed\n", "n_grid = np.logspace(5, 9, 50)\n", "inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound)\n", - "stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # one resident batch\n", + "stream_rows = batch_size + 15_000 + 5_000 # one batch + shuffle buffer + one shard\n", + "stream_gb = np.full_like(n_grid, stream_rows * ncols * 8 / 1e9)\n", "\n", "fig, ax = plt.subplots(figsize=(8, 5), layout=\"tight\")\n", "ax.loglog(n_grid, inram_gb, lw=2.5, label=\"in-RAM pm.Minibatch (O(N))\")\n", - "ax.loglog(n_grid, stream_gb, lw=2.5, label=\"streaming DataLoader (O(batch))\")\n", + "ax.loglog(n_grid, stream_gb, lw=2.5, label=\"streaming DataLoader (O(batch + buffer + chunk))\")\n", "ax.axhline(26, color=\"0.5\", ls=\"--\", lw=1)\n", "ax.text(n_grid[-1], 30, \"26 GB RAM\", color=\"0.5\", ha=\"right\", va=\"bottom\")\n", "ax.set_xlabel(\"dataset size N\")\n", @@ -388,7 +397,7 @@ "id": "d304c6c7", "metadata": {}, "source": [ - "That line is only the bare array. Actual peak RSS is higher, because of the\n", + "Those lines are only the bare arrays. Actual peak RSS is higher, because of the\n", "framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. As a\n", "real-data check, outside this notebook, we ran the same logistic model (13 numeric\n", "features plus the click label) on the\n", @@ -408,7 +417,7 @@ " flat in `N` by streaming from disk, with no callbacks to wire up. The one thing to\n", " watch is shuffling. Pass `shuffle=True`, or pre-shuffle on disk and interleave\n", " shards for strongly ordered data, since a bounded buffer over strongly ordered\n", - " data only block-shuffles it and biases the posterior." + " data only block-shuffles it and can bias the posterior." ] }, { @@ -443,10 +452,10 @@ "id": "91539613", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T05:01:31.471814Z", - "iopub.status.busy": "2026-06-11T05:01:31.471732Z", - "iopub.status.idle": "2026-06-11T05:01:31.481894Z", - "shell.execute_reply": "2026-06-11T05:01:31.481532Z" + "iopub.execute_input": "2026-06-11T15:48:54.489384Z", + "iopub.status.busy": "2026-06-11T15:48:54.489297Z", + "iopub.status.idle": "2026-06-11T15:48:54.498664Z", + "shell.execute_reply": "2026-06-11T15:48:54.498267Z" } }, "outputs": [ diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index 62613be17..081a718e0 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -38,14 +38,17 @@ the same structure as PyTorch's `torch.utils.data`: read by {func}`~pymc.variational.streaming.parquet_source`. * the model observes a `pm.Data` placeholder of one batch, not the whole array. * a {class}`~pymc.variational.streaming.Trainer` drives ADVI, writing each - minibatch into that placeholder with `set_data` every step. There are no callbacks. + minibatch into that placeholder with `set_data` every step. There are no + callbacks to write. The unbiased-gradient rescaling works exactly as in `pm.Minibatch`. The `DataLoader` is sized, so `total_size=len(loader)` passes the dataset size `N` to the observed -distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`. The -one extra requirement is shuffling. A streaming source is only as well mixed as the -order it yields rows in, so pass `DataLoader(shuffle=True)` to shuffle through a -bounded buffer. +distribution, and PyMC scales the minibatch log-likelihood by `N / batch_size`. +Batches are exact-size, so each pass drops the rows that do not fill a final batch; +with `shuffle=True` that remainder is re-drawn every epoch instead of being a fixed +tail. The one extra requirement is shuffling itself. A streaming source is only as +well mixed as the order it yields rows in, so pass `DataLoader(shuffle=True)` to +shuffle through a bounded buffer. `N` is small here so the notebook runs in seconds. The streaming code is the same at any size, and the last section shows what changes at scale. @@ -107,7 +110,7 @@ It reads one shard at a time and gets `n_rows` from the Parquet metadata without scanning the data, so `total_size="auto"` resolves `N` for free. The `DataLoader` batches and shuffles it into fixed-size minibatches. The model reads a `pm.Data("batch", ...)` placeholder holding a single batch, and the `Trainer` -writes each minibatch into it with `set_data`. There are no callbacks: +writes each minibatch into it with `set_data`. There are no callbacks to wire up: `total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit` runs the loop. @@ -135,7 +138,7 @@ with pm.Model() as model: data_name="batch", obj_optimizer=pm.adam(learning_rate=0.008), ).fit(30_000, random_seed=RANDOM_SEED) - idata_stream = approx.sample(1000) + idata_stream = approx.sample(1000, random_seed=RANDOM_SEED) ``` The negative-ELBO trace shows the fit converging on minibatches read off disk: @@ -150,8 +153,8 @@ ax.set(xlabel="iteration", ylabel="negative ELBO", title="Streaming ADVI converg For comparison, here is the usual fit that keeps the whole dataset in memory. Streaming changes where the data lives, not the inference, so the two posteriors -land on top of each other. Both show ADVI's usual mild bias relative to the dashed -ground truth, but they agree with each other. +should agree, and here they land on top of each other. Both show ADVI's usual mild +bias relative to the dashed ground truth, but they agree with each other. ```{code-cell} ipython3 with pm.Model(): @@ -161,9 +164,13 @@ with pm.Model(): ) pm.Bernoulli("y", logit_p=b[0] + b[1] * xb + b[2] * zb + b[3] * sb, observed=yb, total_size=N) approx_inram = pm.fit( - 30_000, method="advi", obj_optimizer=pm.adam(learning_rate=0.008), progressbar=False + 30_000, + method="advi", + obj_optimizer=pm.adam(learning_rate=0.008), + progressbar=False, + random_seed=RANDOM_SEED, ) - idata_inram = approx_inram.sample(1000) + idata_inram = approx_inram.sample(1000, random_seed=RANDOM_SEED) ``` ```{code-cell} ipython3 @@ -185,18 +192,20 @@ fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1. Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N` rows resident, so its array grows linearly in `N`, while the streaming `DataLoader` -holds one batch plus its bounded shuffle buffer. The dense `float64` design matrix dominates the -cost. The line below is its lower bound (`N * ncols * 8` bytes), not a measurement: +holds one batch plus its bounded shuffle buffer and the current source chunk. The +dense `float64` design matrix dominates the cost. The lines below are array lower +bounds (`N * ncols * 8` bytes resident versus that constant), not measurements: ```{code-cell} ipython3 ncols = 4 # 3 features + observed n_grid = np.logspace(5, 9, 50) inram_gb = n_grid * ncols * 8 / 1e9 # whole dataset resident (array lower bound) -stream_gb = np.full_like(n_grid, batch_size * ncols * 8 / 1e9) # one resident batch +stream_rows = batch_size + 15_000 + 5_000 # one batch + shuffle buffer + one shard +stream_gb = np.full_like(n_grid, stream_rows * ncols * 8 / 1e9) fig, ax = plt.subplots(figsize=(8, 5), layout="tight") ax.loglog(n_grid, inram_gb, lw=2.5, label="in-RAM pm.Minibatch (O(N))") -ax.loglog(n_grid, stream_gb, lw=2.5, label="streaming DataLoader (O(batch))") +ax.loglog(n_grid, stream_gb, lw=2.5, label="streaming DataLoader (O(batch + buffer + chunk))") ax.axhline(26, color="0.5", ls="--", lw=1) ax.text(n_grid[-1], 30, "26 GB RAM", color="0.5", ha="right", va="bottom") ax.set_xlabel("dataset size N") @@ -205,7 +214,7 @@ ax.set_title("Memory is flat in N when streaming") ax.legend(loc="lower right", framealpha=0.95); ``` -That line is only the bare array. Actual peak RSS is higher, because of the +Those lines are only the bare arrays. Actual peak RSS is higher, because of the framework and PyTensor's resident copy, and it hits the RAM ceiling sooner. As a real-data check, outside this notebook, we ran the same logistic model (13 numeric features plus the click label) on the @@ -225,7 +234,7 @@ the largest gap was about 0.1, on the intercept. flat in `N` by streaming from disk, with no callbacks to wire up. The one thing to watch is shuffling. Pass `shuffle=True`, or pre-shuffle on disk and interleave shards for strongly ordered data, since a bounded buffer over strongly ordered - data only block-shuffles it and biases the posterior. + data only block-shuffles it and can bias the posterior. +++ From 5865dea05320077bd9cb8c1cb4e81e08bfddc8ad Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Thu, 11 Jun 2026 23:52:30 -0500 Subject: [PATCH 14/17] Correct the Criteo paragraph against the benchmark logs The largest streaming vs in-RAM posterior gap was 0.12 on a feature coefficient, not ~0.1 on the intercept (the intercept gap was 0.01), and the comparison ran on a 1M-row slice; the OOM extrapolation from the fitted slope is ~240M rows, not 250M. --- examples/variational_inference/streaming_dataset.ipynb | 7 ++++--- examples/variational_inference/streaming_dataset.myst.md | 7 ++++--- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 226c9c493..470b3da56 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -405,9 +405,10 @@ "standard, publicly available out-of-core learning benchmark. Peak memory for the\n", "streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M\n", "rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M\n", - "rows, which extrapolates to out-of-memory around 250M rows on the same 26 GB\n", - "machine. The streaming and in-RAM posteriors agreed coefficient for coefficient;\n", - "the largest gap was about 0.1, on the intercept.\n", + "rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB\n", + "machine. On a 1M-row slice, where the in-RAM fit is cheap, the streaming and\n", + "in-RAM posterior means agreed coefficient for coefficient (correlation 0.998\n", + "across the 14 coefficients); the largest gap on any single coefficient was 0.12.\n", "\n", "## When to use it\n", "\n", diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index 081a718e0..248f396a6 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -222,9 +222,10 @@ features plus the click label) on the standard, publicly available out-of-core learning benchmark. Peak memory for the streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M -rows, which extrapolates to out-of-memory around 250M rows on the same 26 GB -machine. The streaming and in-RAM posteriors agreed coefficient for coefficient; -the largest gap was about 0.1, on the intercept. +rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB +machine. On a 1M-row slice, where the in-RAM fit is cheap, the streaming and +in-RAM posterior means agreed coefficient for coefficient (correlation 0.998 +across the 14 coefficients); the largest gap on any single coefficient was 0.12. ## When to use it From 90d83fd58d9d588671c0982fde6352af1d886f21 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Fri, 12 Jun 2026 07:35:40 -0500 Subject: [PATCH 15/17] Temper the Criteo equivalence sentence and fix the read granularity The artifact shows two near-zero coefficients flipping sign between the two stochastic fits, so 'agreed coefficient for coefficient' overstated it; state the max gap against the coefficient scale and disclose the sign flips. parquet_source reads row groups, not whole shards. --- .../variational_inference/streaming_dataset.ipynb | 12 +++++++----- .../variational_inference/streaming_dataset.myst.md | 12 +++++++----- 2 files changed, 14 insertions(+), 10 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 470b3da56..7a21d6625 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -151,8 +151,9 @@ "## Stream minibatches off disk and fit with ADVI\n", "\n", "`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`.\n", - "It reads one shard at a time and gets `n_rows` from the Parquet metadata without\n", - "scanning the data, so `total_size=\"auto\"` resolves `N` for free. The `DataLoader`\n", + "It reads one row group at a time (one or more per shard) and gets `n_rows` from\n", + "the Parquet metadata without scanning the data, so `total_size=\"auto\"` resolves\n", + "`N` for free. The `DataLoader`\n", "batches and shuffles it into fixed-size minibatches. The model reads a\n", "`pm.Data(\"batch\", ...)` placeholder holding a single batch, and the `Trainer`\n", "writes each minibatch into it with `set_data`. There are no callbacks to wire up:\n", @@ -406,9 +407,10 @@ "streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M\n", "rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M\n", "rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB\n", - "machine. On a 1M-row slice, where the in-RAM fit is cheap, the streaming and\n", - "in-RAM posterior means agreed coefficient for coefficient (correlation 0.998\n", - "across the 14 coefficients); the largest gap on any single coefficient was 0.12.\n", + "machine. On a 1M-row slice, where the in-RAM fit is cheap, the largest gap\n", + "between the streaming and in-RAM posterior means on any single coefficient was\n", + "0.12, on coefficients ranging up to 3.6 in magnitude; two near-zero coefficients\n", + "differed in sign between the two stochastic fits.\n", "\n", "## When to use it\n", "\n", diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index 248f396a6..637d16552 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -106,8 +106,9 @@ print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written") ## Stream minibatches off disk and fit with ADVI `parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`. -It reads one shard at a time and gets `n_rows` from the Parquet metadata without -scanning the data, so `total_size="auto"` resolves `N` for free. The `DataLoader` +It reads one row group at a time (one or more per shard) and gets `n_rows` from +the Parquet metadata without scanning the data, so `total_size="auto"` resolves +`N` for free. The `DataLoader` batches and shuffles it into fixed-size minibatches. The model reads a `pm.Data("batch", ...)` placeholder holding a single batch, and the `Trainer` writes each minibatch into it with `set_data`. There are no callbacks to wire up: @@ -223,9 +224,10 @@ standard, publicly available out-of-core learning benchmark. Peak memory for the streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB -machine. On a 1M-row slice, where the in-RAM fit is cheap, the streaming and -in-RAM posterior means agreed coefficient for coefficient (correlation 0.998 -across the 14 coefficients); the largest gap on any single coefficient was 0.12. +machine. On a 1M-row slice, where the in-RAM fit is cheap, the largest gap +between the streaming and in-RAM posterior means on any single coefficient was +0.12, on coefficients ranging up to 3.6 in magnitude; two near-zero coefficients +differed in sign between the two stochastic fits. ## When to use it From 712ed93fb2c329867e0b6fe38854ba10f1dd9749 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Sat, 13 Jun 2026 13:13:03 -0500 Subject: [PATCH 16/17] Match the example's prose to the pymc-examples house style Plainer section headers (Write the dataset to disk; Compare with in-RAM pm.Minibatch), walk-through connectors, and a tighter stream-and-fit lead-in that no longer repeats the intro. Markdown only; outputs unchanged. --- .../streaming_dataset.ipynb | 30 ++++++++----------- .../streaming_dataset.myst.md | 30 ++++++++----------- 2 files changed, 26 insertions(+), 34 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 7a21d6625..3d5ba68b5 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -94,12 +94,12 @@ "id": "90fd8ed5", "metadata": {}, "source": [ - "## Put a dataset on disk and forget the array\n", + "## Write the dataset to disk\n", "\n", - "We build a logistic-regression dataset, write it to Parquet shards, and delete the\n", - "in-memory table. From here the streaming path reads only the disk copy. We keep `X`\n", - "and `y` around only to build the in-RAM `pm.Minibatch` baseline later; the streaming\n", - "fit never reads them." + "First, we build a logistic-regression dataset, write it to Parquet shards, and\n", + "delete the in-memory table. From here the streaming path reads only the disk copy.\n", + "We keep `X` and `y` around only to build the in-RAM `pm.Minibatch` baseline later;\n", + "the streaming fit never reads them." ] }, { @@ -150,15 +150,12 @@ "source": [ "## Stream minibatches off disk and fit with ADVI\n", "\n", - "`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`.\n", - "It reads one row group at a time (one or more per shard) and gets `n_rows` from\n", - "the Parquet metadata without scanning the data, so `total_size=\"auto\"` resolves\n", - "`N` for free. The `DataLoader`\n", - "batches and shuffles it into fixed-size minibatches. The model reads a\n", - "`pm.Data(\"batch\", ...)` placeholder holding a single batch, and the `Trainer`\n", - "writes each minibatch into it with `set_data`. There are no callbacks to wire up:\n", - "`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit`\n", - "runs the loop." + "Next, the source is `parquet_source`, an out-of-core\n", + "{class}`~pymc.variational.streaming.IterableDataset` that reads one row group at a\n", + "time and gets `n_rows` from the Parquet metadata, so `total_size=\"auto\"` resolves\n", + "`N` without a data scan. The model observes a `pm.Data` placeholder of one batch and\n", + "passes `total_size=len(loader)` for the `N / batch_size` rescaling; the `Trainer`\n", + "streams each minibatch into the placeholder." ] }, { @@ -252,12 +249,11 @@ "id": "9035a137", "metadata": {}, "source": [ - "## The same posterior as in-RAM `pm.Minibatch`\n", + "## Compare with in-RAM `pm.Minibatch`\n", "\n", "For comparison, here is the usual fit that keeps the whole dataset in memory.\n", "Streaming changes where the data lives, not the inference, so the two posteriors\n", - "should agree, and here they land on top of each other. Both show ADVI's usual mild\n", - "bias relative to the dashed ground truth, but they agree with each other." + "match; both show ADVI's usual mild bias relative to the dashed ground truth." ] }, { diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index 637d16552..dad709b67 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -76,12 +76,12 @@ rng = np.random.default_rng(RANDOM_SEED) az.style.use("arviz-variat") ``` -## Put a dataset on disk and forget the array +## Write the dataset to disk -We build a logistic-regression dataset, write it to Parquet shards, and delete the -in-memory table. From here the streaming path reads only the disk copy. We keep `X` -and `y` around only to build the in-RAM `pm.Minibatch` baseline later; the streaming -fit never reads them. +First, we build a logistic-regression dataset, write it to Parquet shards, and +delete the in-memory table. From here the streaming path reads only the disk copy. +We keep `X` and `y` around only to build the in-RAM `pm.Minibatch` baseline later; +the streaming fit never reads them. ```{code-cell} ipython3 N = 30_000 @@ -105,15 +105,12 @@ print(len(glob.glob(f"{shard_dir}/*.parquet")), "shards written") ## Stream minibatches off disk and fit with ADVI -`parquet_source` is an out-of-core {class}`~pymc.variational.streaming.IterableDataset`. -It reads one row group at a time (one or more per shard) and gets `n_rows` from -the Parquet metadata without scanning the data, so `total_size="auto"` resolves -`N` for free. The `DataLoader` -batches and shuffles it into fixed-size minibatches. The model reads a -`pm.Data("batch", ...)` placeholder holding a single batch, and the `Trainer` -writes each minibatch into it with `set_data`. There are no callbacks to wire up: -`total_size=len(loader)` triggers the `N / batch_size` rescaling and `Trainer.fit` -runs the loop. +Next, the source is `parquet_source`, an out-of-core +{class}`~pymc.variational.streaming.IterableDataset` that reads one row group at a +time and gets `n_rows` from the Parquet metadata, so `total_size="auto"` resolves +`N` without a data scan. The model observes a `pm.Data` placeholder of one batch and +passes `total_size=len(loader)` for the `N / batch_size` rescaling; the `Trainer` +streams each minibatch into the placeholder. ```{code-cell} ipython3 batch_size = 1024 @@ -150,12 +147,11 @@ ax.plot(approx.hist, alpha=0.6) ax.set(xlabel="iteration", ylabel="negative ELBO", title="Streaming ADVI convergence"); ``` -## The same posterior as in-RAM `pm.Minibatch` +## Compare with in-RAM `pm.Minibatch` For comparison, here is the usual fit that keeps the whole dataset in memory. Streaming changes where the data lives, not the inference, so the two posteriors -should agree, and here they land on top of each other. Both show ADVI's usual mild -bias relative to the dashed ground truth, but they agree with each other. +match; both show ADVI's usual mild bias relative to the dashed ground truth. ```{code-cell} ipython3 with pm.Model(): From a981520db6c7f555fd637cab06c947296ebf16e8 Mon Sep 17 00:00:00 2001 From: Yicheng Yang Date: Sat, 13 Jun 2026 13:42:11 -0500 Subject: [PATCH 17/17] Report the Criteo equivalence honestly and soften the memory wording On the 1M-row slice two near-zero slopes flip sign and the streaming estimates sit ~5 sd from zero, so 'two near-zero coefficients differed in sign' understated it; say plainly that the weak slopes disagree. The memory paragraph now notes the figure ignores the np.concatenate copy, and the comparison paragraph states the fits share everything but the minibatch source. --- .../streaming_dataset.ipynb | 27 +++++++++++-------- .../streaming_dataset.myst.md | 27 +++++++++++-------- 2 files changed, 32 insertions(+), 22 deletions(-) diff --git a/examples/variational_inference/streaming_dataset.ipynb b/examples/variational_inference/streaming_dataset.ipynb index 3d5ba68b5..eb26e4d64 100644 --- a/examples/variational_inference/streaming_dataset.ipynb +++ b/examples/variational_inference/streaming_dataset.ipynb @@ -251,9 +251,10 @@ "source": [ "## Compare with in-RAM `pm.Minibatch`\n", "\n", - "For comparison, here is the usual fit that keeps the whole dataset in memory.\n", - "Streaming changes where the data lives, not the inference, so the two posteriors\n", - "match; both show ADVI's usual mild bias relative to the dashed ground truth." + "For comparison, here is the usual fit that keeps the whole dataset in memory. The\n", + "prior, optimizer, iteration count, and seeds match the streaming fit; only the\n", + "minibatch source differs. On this toy problem the two posteriors land on top of\n", + "each other, with ADVI's usual mild bias relative to the dashed ground truth." ] }, { @@ -341,10 +342,12 @@ "## Memory usage\n", "\n", "Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N`\n", - "rows resident, so its array grows linearly in `N`, while the streaming `DataLoader`\n", - "holds one batch plus its bounded shuffle buffer and the current source chunk. The\n", - "dense `float64` design matrix dominates the cost. The lines below are array lower\n", - "bounds (`N * ncols * 8` bytes resident versus that constant), not measurements:" + "rows resident, so its array grows linearly in `N`. The streaming path keeps only a\n", + "bounded number of rows resident — the model batch, a shuffle-buffer fill, and the\n", + "source chunks used to build it — independent of `N`. The dense `float64` design\n", + "matrix dominates the cost. The lines below are array lower bounds, not measurements;\n", + "they also ignore framework overhead, PyTensor's resident copy, and the transient\n", + "copy `np.concatenate` makes while filling the shuffle buffer:" ] }, { @@ -403,10 +406,12 @@ "streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M\n", "rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M\n", "rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB\n", - "machine. On a 1M-row slice, where the in-RAM fit is cheap, the largest gap\n", - "between the streaming and in-RAM posterior means on any single coefficient was\n", - "0.12, on coefficients ranging up to 3.6 in magnitude; two near-zero coefficients\n", - "differed in sign between the two stochastic fits.\n", + "machine. On a 1M-row slice, where the in-RAM fit is cheap, the two posteriors\n", + "agreed on the large coefficients (the intercept near -3.6 and the strong slopes),\n", + "but the weakest slopes were noisier: the largest gap in posterior means was 0.12,\n", + "and two slopes the in-RAM fit placed near zero came out near +0.08 under streaming,\n", + "flipping sign. On coefficients near the noise floor the two stochastic fits are not\n", + "interchangeable at this slice size.\n", "\n", "## When to use it\n", "\n", diff --git a/examples/variational_inference/streaming_dataset.myst.md b/examples/variational_inference/streaming_dataset.myst.md index dad709b67..034023b72 100644 --- a/examples/variational_inference/streaming_dataset.myst.md +++ b/examples/variational_inference/streaming_dataset.myst.md @@ -149,9 +149,10 @@ ax.set(xlabel="iteration", ylabel="negative ELBO", title="Streaming ADVI converg ## Compare with in-RAM `pm.Minibatch` -For comparison, here is the usual fit that keeps the whole dataset in memory. -Streaming changes where the data lives, not the inference, so the two posteriors -match; both show ADVI's usual mild bias relative to the dashed ground truth. +For comparison, here is the usual fit that keeps the whole dataset in memory. The +prior, optimizer, iteration count, and seeds match the streaming fit; only the +minibatch source differs. On this toy problem the two posteriors land on top of +each other, with ADVI's usual mild bias relative to the dashed ground truth. ```{code-cell} ipython3 with pm.Model(): @@ -188,10 +189,12 @@ fig.suptitle("Posterior of b: streaming vs in-RAM (dashed = ground truth)", y=1. ## Memory usage Both paths feed the same `batch_size` to ADVI, but `pm.Minibatch` keeps all `N` -rows resident, so its array grows linearly in `N`, while the streaming `DataLoader` -holds one batch plus its bounded shuffle buffer and the current source chunk. The -dense `float64` design matrix dominates the cost. The lines below are array lower -bounds (`N * ncols * 8` bytes resident versus that constant), not measurements: +rows resident, so its array grows linearly in `N`. The streaming path keeps only a +bounded number of rows resident — the model batch, a shuffle-buffer fill, and the +source chunks used to build it — independent of `N`. The dense `float64` design +matrix dominates the cost. The lines below are array lower bounds, not measurements; +they also ignore framework overhead, PyTensor's resident copy, and the transient +copy `np.concatenate` makes while filling the shuffle buffer: ```{code-cell} ipython3 ncols = 4 # 3 features + observed @@ -220,10 +223,12 @@ standard, publicly available out-of-core learning benchmark. Peak memory for the streaming `DataLoader` stayed flat at about 0.7 GB across a sweep from 1M to 150M rows, while the in-RAM `pm.Minibatch` baseline rose linearly to 15.7 GB at 150M rows, which extrapolates to out-of-memory around 240M rows on the same 26 GB -machine. On a 1M-row slice, where the in-RAM fit is cheap, the largest gap -between the streaming and in-RAM posterior means on any single coefficient was -0.12, on coefficients ranging up to 3.6 in magnitude; two near-zero coefficients -differed in sign between the two stochastic fits. +machine. On a 1M-row slice, where the in-RAM fit is cheap, the two posteriors +agreed on the large coefficients (the intercept near -3.6 and the strong slopes), +but the weakest slopes were noisier: the largest gap in posterior means was 0.12, +and two slopes the in-RAM fit placed near zero came out near +0.08 under streaming, +flipping sign. On coefficients near the noise floor the two stochastic fits are not +interchangeable at this slice size. ## When to use it