Browsing: Workflow

spend much of their time working with tabular data. Traditionally, these workloads have run on CPUs, which is often sufficient until an expensive operation becomes a…

In this tutorial, we build an end-to-end NVIDIA NeMo AutoModel workflow in Google Colab and use a single GPU to explore the same configuration-driven training architecture…

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”})…

rprint(Panel.fit(“[bold]Baseline 1: Predict output_type from context using pure Python Naive Bayes[/bold]”)) model_artifacts = {} classifier_df = df.dropna(subset=[“output_type”]).copy() classifier_df = classifier_df[ classifier_df[“output_type”].astype(str).str.len() > 0 ].copy() if classifier_df[“output_type”].nunique()…

print(“\n########## 5. ANALYSIS ##########”) import numpy as np, pandas as pd def find_latest_report(): cands = [] for base in [os.path.expanduser(“~/.local/share/garak/garak_runs”), os.path.expanduser(“~/.cache/garak”), “.”]: cands += glob.glob(os.path.join(base, “**”,…

class SuperClaude: “”” Mimics what Claude Code does at session start: • reads Markdown behavior files for the active command/agent/modes, • concatenates them into one system…

entries = df.index[(df[“pos”].diff() == 1)] exits = df.index[(df[“pos”].diff() == -1)] fig, (ax1, ax2, ax3) = plt.subplots( 3, 1, figsize=(13, 10), sharex=True, gridspec_kw={“height_ratios”: [3, 1, 2]}, )…

— I wasn’t actively looking for Polars. I’ve been on a bit of a Pandas optimization journey lately. First, I wrote about why you should stop…

def cloakbrowser_tutorial_job(): results = { “basic_launch”: None, “advanced_context”: None, “storage_restore”: None, “persistent_profile”: None, “rendered_extraction”: None, “static_parsing”: None, “errors”: [], } print_section(“1. Basic CloakBrowser launch”) browser =…

class CellSignalingSimulationAgent: def run(self, df_signal: pd.DataFrame) -> AgentResult: peak_receptor = float(df_signal[“receptor_active”].max()) peak_kinase = float(df_signal[“kinase_active”].max()) peak_tf = float(df_signal[“tf_active”].max()) t_receptor = float(df_signal.loc[df_signal[“receptor_active”].idxmax(), “time”]) t_kinase = float(df_signal.loc[df_signal[“kinase_active”].idxmax(), “time”]) t_tf…