Browsing: Implementation

base_g = ( graphistry .bind(source=”src”, destination=”dst”, node=”id”) .edges(edges_df) .nodes(nodes_df) .bind( edge=”edge_id”, edge_title=”edge_title”, edge_label=”edge_label”, edge_weight=”event_count”, edge_size=”edge_size”, point_title=”point_title”, point_label=”label”, point_color=”node_color”, point_size=”node_size”, point_x=”x”, point_y=”y” ) .settings(url_params={“play”: 0, “info”: “true”})…

poi_gdf[“cx”] = poi_gdf.geometry.x poi_gdf[“cy”] = poi_gdf.geometry.y coords = poi_gdf[[“cx”, “cy”]].to_numpy() nn = NearestNeighbors(radius=150.0).fit(coords) poi_gdf[“local_density”] = [len(idx) – 1 for idx in nn.radius_neighbors(coords, return_distance=False)] if segments_gdf is…

In this tutorial, we build an end-to-end 3D medical image segmentation pipeline using MONAI to segment the spleen on the Medical Segmentation Decathlon Task09 dataset. We…

k = RUN_KNOBS train_out = run_cli([“python”,”scripts/train.py”,”–config”,CFG,”–split_dir”,SPLIT, “–optimizer_model”,OPTIMIZER_MODEL,”–target_model”,TARGET_MODEL,”–out_root”,RUN, *COMMON, “train.train_size=0″, f”train.num_epochs={k[‘num_epochs’]}”, f”train.batch_size={k[‘batch_size’]}”, f”gradient.minibatch_size={k[‘minibatch’]}”, f”gradient.merge_batch_size={k[‘merge_batch’]}”, f”gradient.analyst_workers={k[‘workers’]}”, f”optimizer.learning_rate={k[‘lr’]}”, f”optimizer.lr_scheduler={k[‘lr_sched’]}”, “optimizer.use_slow_update=true”, “optimizer.use_meta_skill=true”, f”env.workers={k[‘workers’]}”, f”env.limit={k[‘limit’]}”], “TRAIN (rollout->reflect->aggregate->select->update->gate; slow-update + meta-skill)”) import…

scenarios = [ { “name”: “Safe database read”, “tool”: research_db, “kwargs”: { “table”: “customers”, “operation”: “select”, “type”: “select”, “sensitivity”: “medium” } }, { “name”: “Blocked destructive…

banner(“1) logger.configure(): handlers + custom level + extra + patcher”) mem = MemorySink() logger.configure( handlers=[ {“sink”: sys.stderr, “format”: console_formatter, “level”: “DEBUG”, “colorize”: True, “backtrace”: True, “diagnose”:…

import subprocess, sys def pip(*pkgs): subprocess.check_call([sys.executable, “-m”, “pip”, “install”, “-q”, *pkgs]) pip(“llmcompressor”, “compressed-tensors”, “transformers>=4.45”, “accelerate”, “datasets”) import os, gc, time, json, math from pathlib import Path…

header(“6. RAW CUDA KERNEL — MANDELBROT”) mandel = cp.RawKernel(r”’ extern “C” __global__ void mandel(float xmin, float xmax, float ymin, float ymax, int W, int H, int…

factor_prices = load_factors_dataset() X_full, F_full = prices_to_returns(prices, factor_prices) X_tr, X_te, F_tr, F_te = train_test_split( X_full, F_full, test_size=0.33, shuffle=False ) fm = MeanRisk( objective_function=ObjectiveFunction.MAXIMIZE_RATIO, risk_measure=RiskMeasure.VARIANCE, prior_estimator=FactorModel(), )…

banner(“Part 5 — Streaming”) mem.attribution(entity_id=”[email protected]”, process_id=”personal-assistant”) stream = client.chat.completions.create( model=MODEL, messages=[{“role”: “user”, “content”: “In two sentences, what do you remember about me?”}], stream=True, ) print(“[stream] “,…