remote-compute-modal

Imported from yuan1995/Oblivious-Data-Structures (.claude/skills/claude-science/remote-compute-modal/SKILL.md). Install upstream with npx skills add yuan1995/Oblivious-Data-Structures --skill remote-compute-modal. Copyright stays with the author (Apache-2.0).

You're dispatching to the user's Modal account: containers spin up in Modal's cloud, on hardware you name in plain terms (gpu/cpu/memory/timeout), under their workspace, on their bill. That's the reason there are two approval cards rather than one, and the reason the env-setup surface is a separate kernel rather than something you can call inline from the control-plane kernel: each card is the user consenting to a specific, bounded use of their credential, and the architecture keeps those grants legible by keeping the surfaces apart.

If compute.create('byoc:modal', …) returns unknown provider 'byoc:modal', Modal isn't enabled in this install — ask the user to enable it under Settings → Compute → Modal (or surface the prompt; you can't enable it for them).

For first-time environment setup, see env-setup.md in this skill directory — it walks through driving the compute_provider kernel to build and record images.

Two timeouts, one timeline

Modal has two deadline timers. Both live INSIDE the sandbox, and neither can cost you the outputs of a job that ran. The timeline every job runs on:

1. Container life starts at sandbox creation — staging the inputs counts against it, so a big upload spends container time before the job runs.
2. The job runs. If it exceeds its own budget (the job timeout below), it is TERMed there.
3. Near end of container life — one harvest margin before the provider destroys the sandbox — the in-sandbox harvest watchdog TERMs whatever is still running. It runs on the sandbox's own clock, so a wrong desktop clock or a slow upload can't make it fire late.
4. After any TERM there is a grace window for checkpoint-on-TERM handlers to flush, then the process group is stopped.
5. Whatever is under ./out/ (plus the logs) is tarred and staged UNCONDITIONALLY — on success, timeout, failure, and crash alike — and harvested back into the workspace. Staging is unconditional; delivery isn't. If the stream is refused or gives up (result_rejected, harvest_failed), the staged copy waits on the sandbox, and a third, post-job timer — the idle watchdog — terminates it after ~30 min of inactivity. See "When the job fails".

The container timeout (provider_params.modal.timeout) is how long the sandbox lives; omitted, it fills from the Settings default for this provider (Settings → Compute → Modal; ceiling: Modal's 24 h platform lifetime, minus the staging margins — 85,500 s). The job timeout (timeout_seconds on submit_job) is an optional runaway guard for one job; omitted, it defaults to the container's remaining life minus the harvest margin. Name one when you know the job's budget — a hung job then costs that budget, not the whole container, and the warm container survives for the next submit.

A deadline-ended job lands as status: 'timed_out' — not a generic failure — with its partial outputs already harvested, plus a note saying the deadline (not the workload) ended the run and suggesting the remedy: a larger timeout_seconds, or resuming from a harvested checkpoint. A deadline ends the run, never the results.

Two surfaces, one provider

You reach Modal two ways. They share confinement and credentials but answer different questions, and confusing them is the most common way to waste a turn.

host.compute.create('byoc:modal', provider_params={'modal': {...}}) is the job surface. It runs in the repl tool — same as remote-compute-ssh — and is what you use for anything with inputs/outputs you want harvested into the workspace, anything on a GPU, and anything long enough that you'd want to reattach if the daemon restarts. The call itself is a stateless constructor — the tier card (provider, image, 1× A100-40GB · 8 CPU · 32 GB, container timeout, volumes) and the actual Sandbox creation both happen on the first submit_job(). submit_job/result/call_command/attach_job/close then work exactly as for SSH: .result() is non-blocking — the daemon's poller probes the sandbox, harvests out.tar.gz into hpc/<jobId>/, and emits a compute_done notification when done. To wait, exit the cell after submit_job and use the wait_for_notification brain-tool. What reaches the remote is what was in the workspace files you named, and what comes back lands under hpc/<jobId>/ through the same hardened extractor.

compute_provider({'provider': 'modal', 'code': '…'}) is the environment surface — a Python shell with the user's Modal SDK already authenticated, running in its own confined process. Use it to prepare compute: build container images JIT in the user's workspace, populate model-weight volumes by running the downloads on Modal's own infrastructure (so wide-internet fetches never traverse the local allowlist), check what assets already exist, run a short CPU smoke probe. Its first cell in a session fires the kernel card — "environment setup — build images, populate volumes, CPU probes ≤30 min; GPU jobs ask separately" — and once granted, subsequent cells in this kernel's lifetime (idle-timeout ~15 min) run without further prompts. The kernel will reject gpu= on Sandbox.create and clamp timeout to ≤30 min — the kernel's own probe budget, separate from the job surface's container timeout. It's a redirect to the job surface, not a fence; you've already been handed the credential. Terminate every sandbox you create here — sb.terminate() in the same cell, in a try/finally if you exec in between (build_env's hydrate models this). Nothing reaps it mid-session: the idle timeout kills the kernel, never your sandboxes — the ≤30-min clamp only bounds how long a forgotten one bills.

The shape of a good run is: read what's already known about this workspace, decide whether the env you need exists, build it via the compute_provider kernel if it doesn't, then run the actual job through compute.create(). The two cards appear at most once each per session — and on a warm second session with a project-level grant, neither.

Workflow

Every host.compute.* call here runs via the repl tool; the compute_provider kernel is reached via the compute_provider tool. Neither is the python tool. All three share your workspace directory but not memory — pass data through files.

Start with compute_details({provider: 'byoc:modal', mode: 'read'}). Below the orientation line is the per-workspace ledger — ### env:<name>@<spec_sha> blocks recording images that already exist in this user's Modal workspace, the volumes they pair with, and any free-text notes a previous session left. The spec_sha is the content hash of the env's source file in this binary, so a ledger entry whose hash matches is one you can use without rebuilding: pass its image ref straight to compute.create() and the kernel card never appears. A mismatch means the env definition changed in this release — the old image still works but isn't what the current env file describes, so treat it as absent.

If what you need isn't in the ledger, build it in a compute_provider cell. Two helpers are pre-bound there alongside modal and app:

• list_envs() → {name: META, …, '_envs_dir': path} for every bundled env (packages, GPU tier, secrets it needs)
• build_env(name, *, hydrate=False) → builds the image, returns {'image': 'im-…', 'spec_sha', 'volumes', 'env', 'hydrate'}

To inspect what an env actually installs (the modal.Image chain) before building, read the source from the control-plane kernel (a repl cell) — it's a skill asset:

# repl tool
print(host.skills.read('remote-compute-modal', 'envs/proteomics_jax_gpu.py')['content'])

Don't find for it from bash — the path differs across builds.

# compute_provider tool, provider='modal'
need = {'transformers', 'torch'}
envs = list_envs()
print({n: m for n, m in envs.items()
       if not n.startswith('_') and need <= set(m.get('packages', []))})
# → {'proteomics_gpu': {'packages': [...], 'gpu_default': 'A100-80GB', ...}}

r = build_env('proteomics_gpu', hydrate=True)
print(r['image'], r['spec_sha'], r['volumes'])

Carry r['image'] (the im-… reference, NOT the env name) back to the control-plane kernel (a repl cell) and pass it to provider_params.modal.image — and, for a bundled env, its NAME as provider_params.modal.env: the host then reads the env's META["egress_domains"] from the SHIPPED file at every submit, so the declaration survives rebuilds (a pointer into the shipped catalog, not a power). The two kernels share a working directory but not memory, so the simplest handoff is a workspace JSON file:

# compute_provider tool — last line of the build cell
import json; json.dump(r, open('built_env.json', 'w'))

Append a ### env:<name>@<spec_sha> block to compute_details so the next session reads the im-… ref straight from there and skips the compute_provider cell entirely. Pass r['image'] (the im-… ref) to provider_params.modal.image. The adapter will resolve a bare env name from the ledger as a fallback, but that's brittle — the ledger entry can be stale or absent. Treat the build as its own task with its own validation; don't fold it into a job submission. See env-setup.md for ad-hoc patches and gated weights.

For a quick inline call (no inputs, output is just stdout), use submit_job with no inputs. The compute_done notification payload carries status / exit_code / featured_files, plus error_kind, system_hint, and deadline_fired when set — read those disclosures from the notification itself (a deadline-truncated rc-0 job announces partial outputs there). For stdout_tail, re-enter the kernel and read it from .result() after the notification arrives (call_command is SSH-only — it bails on a byoc: handle):

# repl tool — cell ① submits and RETURNS
import json
r = json.load(open('built_env.json'))
c = host.compute.create('byoc:modal', provider_params={'modal': {
    'image': r['image'],          # the im-… ref (bare name fallback brittle)
    'env': 'proteomics_gpu',      # bundled-env NAME → its shipped egress declaration applies
    'gpu': 'A100',                # timeout omitted → container gets the Settings default
    'volumes': r['volumes'],
}})
j = c.submit_job(
    intent='load esm2_t6 on GPU',
    command='python -c "from transformers import EsmModel; import torch; '
            'm = EsmModel.from_pretrained(\\"facebook/esm2_t6_8M_UR50D\\").cuda(); '
            'print(torch.cuda.get_device_name())"',
    timeout_seconds=300,          # job guard — this probe finishes in 5 min or it's hung
)
print('JOB_ID:', j.job_id)   # ← cell ends here; kernel never blocks

Then exit the cell and use wait_for_notification. When the compute_done notification arrives, its payload has status / exit_code / featured_files, plus error_kind / system_hint / deadline_fired when set. For stdout_tail, re-enter the kernel:

# repl tool — cell ② after the compute_done notification
print(c.attach_job('<JOB_ID>').result()['stdout_tail'])  # non-blocking — poller already harvested
c.close()

For a full job with inputs and harvested outputs:

# repl tool — cell ① submits and RETURNS
c = host.compute.create('byoc:modal', provider_params={'modal': {
    'image':   r['image'],               # 'im-…' from build_env(); bare-name fallback exists but is brittle
    'env':     'proteomics_jax_gpu',     # bundled-env NAME → its shipped egress declaration applies
    'gpu':     'A100-40GB',
    'cpu':     8,
    'memory':  32768,                    # MiB — Modal's unit, not GiB
    'volumes': {'/cache': 'af2-params'}, # mount path → Modal Volume name
    'timeout': 3600,                     # container lifetime, seconds — optional; omitted → Settings default; ceiling: Modal's 24 h platform limit
}})
# → tier card → Sandbox.create() under the user's workspace → handle bound to sandbox_id

job = c.submit_job(
    intent='colabfold on target.fasta — 1× A100, ~40 min',
    command='colabfold_batch target.fasta out/ --num-recycle 3 --model-type alphafold2_ptm',
    inputs=[{'src': 'target.fasta', 'dst_filename': 'target.fasta'}],
    outputs=['out/ranked_0.pdb', {'glob': 'out/*.json', 'visibility': 'featured'},
             {'glob': '*.log', 'visibility': 'hidden'}],
    timeout_seconds=2700,                # job guard — optional; default = remaining container life − harvest margin
)
print('JOB_ID:', job.job_id)   # ← cell ends here

Then wait_for_notification — the compute_done payload includes featured_files, so you can save_artifacts(payload['featured_files']) directly without re-entering the kernel. If you want the full dict (stdout_tail / stderr_tail / job_wall_s):

# repl tool — cell ② after the compute_done notification
r = c.attach_job('<JOB_ID>').result()  # non-blocking read of compute_usage.result
save_artifacts(r['featured_files'])
c.close()

r['job_wall_s'] is the seconds run.sh ran on the remote — the actual GPU/compute duration.

timeout is the container's lifetime; timeout_seconds guards one job inside it. Both are optional: timeout fills from the Settings default, timeout_seconds from the container's remaining life minus the harvest margin. Name a timeout_seconds when you know the job's budget — a hung job then costs that budget and the warm container survives. At either deadline the job is TERMed, given grace, and its outputs are staged and harvested as on success — the job lands as status: 'timed_out' (deadline-terminated, outputs harvested), not a generic failure.

inputs= stage flat into the workdir root. dst_filename is a bare filename — 'inputs/gfp.fasta' is rejected at submit (the file would land at /work/gfp.fasta, not where the command expects). src can be a path; dst_filename can't. Omit it to default to basename(src). Need a dir layout? mkdir -p it inside command=.

Sizing inputs= — and when to use a Volume instead. Inputs are stdin-streamed through a confined path at ~0.65–0.81 MiB/s effective (measured) — budget staging time from that: 1 GiB ≈ 20–26 minutes of upload before the job starts, spent from container life. The host budgets staging from input size with a planning rate it cannot verify (upload speed isn't controllable), so a slower-than-planned upload never endangers the harvest — the watchdog's absolute-clock backstop still protects the staging window; the job just gets less runtime than nominal. If an upload dies outright, the submit fails LOUDLY with a transport error and no phantom job. Practical sizing: code, configs, and small data are what inputs= is for; it is workable up to its 1 GiB per-submit cap (slow but fine — budget the ~20–26 min of staging near the cap). Anything above 1 GiB is refused at submit time, so beyond it a Volume is the only path — and the better one well before that. Volumes (Files-tab import, or a compute_provider cell; mount via provider_params.modal.volumes) persist on Modal disks across jobs and cost zero upload per submit.

Checkpoint long jobs as they run. Write progress to ./out/ (or a Volume) periodically: at either deadline the workload gets TERM, then a grace window for checkpoint-on-TERM handlers, and whatever is under out/ is staged and harvested unconditionally. A job that checkpoints loses at most one interval to a deadline — never the run. Keep ./out/ checkpoints SMALL (≈ ≤100 MB compressed): the harvest stream back to the host runs in a bounded (~2 min) window, so a multi-GB ./out/ risks harvest_failed even though staging succeeded — large checkpoints belong on a Volume, with only small summaries under ./out/.

Only ./out/ is harvested. The wrapper tars out/ + stdout.log + stderr.log — nothing else. outputs= globs are a post-harvest filter (featured/hidden), not a what-to-collect directive. If your tool writes to cwd, a Volume mount (e.g. /atlas), or $HOME, end command= with cp -r <results> out/ (or point the tool's -o/--output at out/). A successful job with an empty out/ returns only the log paths in output_files plus a system_hint telling you so.

command= is interpolated into a run.sh and run via bash run.sh. For anything beyond a single program-with-args — nested quotes, inline python -c "…", heredocs, pipelines — write the script to a workspace file and ship it via inputs=, then command='bash script.sh' or command='python script.py'. Multi-layer shell escaping inside command= is the most common cause of syntax error near unexpected token failures.

provider_params.modal uses Modal's own kwarg names — Modal's docs are the docs. The adapter resolves the strings to SDK objects (env name → its im-* from the compute_details ledger; volume name → Volume.from_name) and passes the rest through to Sandbox.create(**params). An absent timeout fills from the Settings default before the tier card renders, so the card shows the real lifetime. A timeout above Modal's 24 h platform cap or out-of-range hardware is rejected with invalid_request before any helper spawns; nothing is silently clamped.

The image-reference rule

image is always a string. A modal.Image object built in a compute_provider cell cannot cross to the control-plane kernel — they are separate Python processes under separate confinement invocations, and a live SDK handle is meaningless on the other side. So the pattern is:

# compute_provider tool — env setup (`modal` and `app` are pre-bound)
img = (modal.Image.from_registry('nvidia/cuda:12.4.1-runtime-ubuntu22.04',
                                 add_python='3.11')
       .pip_install('chai_lab==0.6.1'))
img.build(app)
print(img.object_id)          # → 'im-7f3a2e…' — read this string

# repl tool — job
c = host.compute.create('byoc:modal', provider_params={'modal': {
    'image': 'im-7f3a2e…',    # the string, not the object
    ...
}})

The adapter validates an im-* literal via Image.from_id(), which fails closed on a foreign or fabricated id, so a typo surfaces as not_found before any GPU spins. An env name is looked up in the ledger first and resolved to the recorded im-*; on a miss the adapter returns image_build_failed with copy pointing at compute-env-setup rather than triggering a build inline.

What latency to expect

Measured against real Modal during validation — set timeouts and user-status messages from these, not guesses:

So budget ~3–6 min for a fresh env's first GPU result, and ~5–10s per subsequent submit_job() on the same handle. If a first cold start is past ~90s, that's queue wait, not a hang — surface it rather than retrying.

When the user gives you a budget

A user who says "stay under eight GPUs" or "keep it to a hundred at a time" is giving you a number that the prompt alone can't enforce. You'll write it into the orchestrator's instructions, but the sub-agents you delegate to start with fresh context — they never see that line, and each one will reasonably try to use as much compute as its own task seems to warrant. Across a wide fan-out that drifts well past whatever the user had in mind, and the first sign is usually the bill.

host.compute.set_concurrency_limit(k) exists so the user's number becomes a property of the session rather than a sentence in a prompt. Call it once before delegating; the daemon stores it against the session root, counts every sub-agent's live job against the same k, and quietly holds any submit that would put the session over (the SDK retries with backoff under the hood). Sub-agent code is unchanged — the hold sits below submit_job, not in the agent.

Choosing k has one constraint beyond the user's intent: each provider also has its own ceiling, and that ceiling refuses rather than queues. A session limit above it doesn't fail, it just stops being the binding constraint — submits past the provider's own ceiling error instead of waiting. host.compute.status() returns both your k and the provider ceilings, so you can pick a value that actually queues. When the user hasn't given a number, leaving the limit unset keeps today's behaviour; set one yourself only if a fan-out is wide enough to threaten the provider cap and you'd rather queue than fail.

Warm reuse, reattachment, and close()

One handle = one container. The first submit_job() creates it; subsequent calls reuse it warm (weights/XLA cache stay hot). The poller's harvest does not terminate the container — it runs until c.close(), ~30 min of inactivity (the sandbox's PID-1 idle watchdog — a kill, not a harvest: anything unharvested goes with the container), or its container timeout, whichever comes first. Until then a finished — or failed — job's container bills idle. Every handle ends with c.close() — after its last job, not between jobs, and not while its sandbox holds the only copy of unrecovered outputs (result_rejected/harvest_failed — see "When the job fails"). Close once the final compute_done has arrived and the harvest is confirmed; if the next job on this handle is imminent, or the user asked to keep the container warm across jobs, leave it open and close when the sequence ends. Near end of life the harvest watchdog stops any running job early enough to stage its outputs, so even the last job comes back harvested. A warm container is reused only when its remaining life covers the next job's deadline plus margin; otherwise the submit transparently creates a fresh sandbox (one more cold start, same handle) and says so in a [note] line on the submit result. Sequential only: each submit wipes /work, so call job N+1's submit_job() after job N's compute_done notification arrives; for parallel jobs use separate handles. A second create() with different {gpu, cpu, memory} is a separate container and a separate card. The detached wrapper survives a Claude Science daemon restart — on restart you'll see "1 job reattached" rather than a cold start.

Multiple parallel jobs (separate handles): submit all in one repl cell, then loop wait_for_notification → act on every entry in notifications (several may arrive at once) → repeat until it returns {status:'error'} (= none left). Don't poll .result().

Before you finish the task: every handle closed (unless the user asked to keep one warm, or its sandbox holds unrecovered result_rejected/harvest_failed outputs), and no sandbox of yours still alive in the compute_provider kernel (modal.Sandbox.list(app_id=app.app_id) shows what's running). Tear down job-surface containers with c.close(), never a raw sb.terminate() — only close() consults the host's recoverable-outputs gate. Reserve sb.terminate() for sandboxes you created in the kernel, and only ones you recognise from this session — warm containers from other Claude Science installs share the same app.

When the job fails

A failed job's container stays up on purpose, so you can fix and re-submit warm — but "stays up" has a clock on it: the in-sandbox idle watchdog terminates it after ~30 min of inactivity, and reading files off it does not reset that clock. If you aren't re-submitting on this handle now, c.close() it and diagnose from the harvested tails and outputs below — except on result_rejected and harvest_failed, where the sandbox holds the only copy of the output; read those entries before you touch close(). For every other kind, closing loses only what the harvest didn't catch (a core dump, a verbose log, a residency:'remote' output) — and .download() can't save it here: scp is SSH-only and errors on a byoc: handle. Read any such file off the live sandbox from a compute_provider cell BEFORE you close: find it with modal.Sandbox.list(app_id=app.app_id, tags={"claude-science-job": "<job_id>"}) — the tag names the job whose submit cold-started the container: your failed job, unless you chained submits on a warm handle (each fresh sandbox announces itself in a [note] on its submit result) — then sb.exec("cat", "<path>") to pull the bytes.

Read r['exit_code'], r['stdout_tail'], and r['stderr_tail']. A job that hit a deadline lands as status: 'timed_out' with its partial outputs already harvested into hpc/<job_id>/ — check stdout_tail for progress and resubmit with a larger timeout_seconds (or resume from a harvested checkpoint) only if the work was actually cut short. The errors that come back as kind rather than a non-zero exit code map cleanly onto where to look:

image_build_failed — Modal's layer build errored: a yanked PyPI version, a base-image pull failure, a pin that no longer resolves. The build-log tail is in the compute_provider cell where you ran build_env(); fix the env file and rebuild there. No image reference was produced, so the ledger stays clean. If you passed an env name to create() without ever building it, the name is valid but no image is in the ledger yet — build_env() it first.

unauthorized — token rotated or revoked. The compute_provider kernel will have already exited on the first 401; the next cell respawns and re-reads ~/.modal.toml, so the user fixing their token is the whole fix.

quota_exhausted — workspace quota, concurrent-GPU limit, or spend cap. For the concurrent limit, first free what you hold: c.close() finished handles (not one holding unrecovered outputs — see below) and terminate kernel sandboxes you're done with — idle warm containers count against it. A spend cap isn't freed by closing — accrued spend stays accrued. If it still trips, surface the copy verbatim and pause; raising the limit is the user's billing decision.

rate_limited — a request-rate throttle on the token, not capacity: closing containers frees nothing here. Back off ~60s and stagger fan-out submissions, per the copy.

ownership_mismatch — another install sharing this workspace token adopted the warm sandbox first. The adapter cold-starts a fresh one; nothing for you to do beyond noting the banner.

result_rejected (compressed_cap_exceeded) — output archive larger than the consented cap. Nothing was written to the workspace and the sandbox is still warm with out.tar.gz intact; the user gets a raise-and-retry affordance that re-streams without re-running. The sandbox holds the only copy of the output — do not c.close() until it's resolved, and surface it to the user now, not at end of task: the Compute panel lists the job as recoverable for 24 h, but that listing only blocks Claude Science-side kills (a queued close() is deferred ~15 min, then executes) — the in-sandbox idle watchdog still terminates the sandbox after ~30 min of inactivity, only copy included.

harvest_failed — the output stream gave up after retries; the warm sandbox likewise holds the only copy. Same rule: no c.close() until the outputs are recovered — and the recovery path is the user's, not yours: they retrieve from the recovery list in the Compute panel (.download() errors on a byoc: handle, and reuse on it is gated). Same clocks as above: surface it immediately.

input_changed — a submit input was rewritten (or deleted/swapped) between validation and staging: the transport re-hashes the bytes it actually uploads and compares them to the sha256 captured at validation time. Nothing was uploaded and the job never started. Re-run submit_job() so the input is re-validated; if it keeps tripping, something is still writing that file — wait for the writer to finish before resubmitting.

A plain non-zero exit_code with logs is the science tool failing on inputs — same diagnosis as on SSH.

What to record

After a successful build, append the ### env:<name>@<spec_sha> block to compute_details — env-setup.md §"Recording the result" has the snippet. That ledger is the durable record; don't also write the same facts to memory. Do NOT record build timings, validated: dates, or sha256 confirmations — spec_sha already encodes staleness and the rest is noise. Gotchas you hit (a documented invocation that failed, a flag you had to add, a quota you bumped into) are worth a free-text line.

GPU tier reference

Each bundled env carries a gpu_default in its META — what its author sized for the typical workload. Start there; reach for a bigger tier when you have a concrete reason (the model is documented as needing >40 GB, the last attempt OOM'd), not as a hedge. The approval card shows the tier you picked, but the grant is keyed on provider only, so once approved for this conversation a tier change doesn't raise a new card — the user is trusting your judgment on sizing after the first approval.

Volumes

Pass volumes={"/weights": "<vol-name>"} in provider_params.modal. The mount is a symlink to /__modal/volumes/<id>/ — always reference it by the mount path, never the resolved target.

The Files tab lists byoc:modal alongside SSH hosts: at / it shows Volumes as top-level directories, and inside one you can browse, import, and download files (same pane as SSH). The path's first segment is the Volume's name, not a mount path. For scripted listing use a compute_provider cell: modal.Volume.from_name('<name>').iterdir('/', recursive=False).

Modal Environment (Settings → Compute → modal → Environment)

Optional, beside the Default app setting. A Modal Environment is the workspace namespace apps, images, volumes and sandboxes live in — with the app, one of the two coordinates every Modal name resolves under. Claude Science resolves every named object it touches (App.lookup, Volume.from_name) in the configured Environment, and the compute_provider kernel is started inside it, so build_env() / HYDRATE and an env file's own modal.Volume.from_name(...) calls land there too. Blank — the default — sends no environment at all and Modal uses its own default (normally main, or whatever ~/.modal.toml says). You cannot set or change it: it is host/UI-written (compute_providers.modal_environment), never a provider_params.modal field (the schema rejects environment / environment_name outright), and the ledger cannot reach it.

Which Environment and App am I in?

A kernel binds its coordinates at spawn — Environment, app, egress posture — and never re-reads them, while the user can change the Settings at any time; a change only reaches what gets created next. compute_provider_config() is how you find out where you actually stand:

# ── compute_provider tool ──
compute_provider_config()
# → {'provider': 'byoc:modal', 'environment': 'team-bio',
#    'app_name': 'claude-science-default', 'egress_mode': 'allowlist',
#    'config_hash': '93e1a4c20b7f'}

# …and after the user changes the Environment, Default app or Network
# Restrictions mode there, the same call:
# ComputeProviderConfigStale: compute provider config is out of date:
#   this kernel was started with config 93e1a4c20b7f but the provider
#   Settings now hash to 4be09f12aa31. Restart the compute_provider
#   kernel (close it and open a new one) to pick up the new
#   Environment/app/Network Restrictions configuration.

Call it before anything expensive. If it returns, trust every value in the dict (environment: None means Modal's workspace default; app_name is already resolved). If it raises, the Settings moved after this kernel started and there is exactly one remedy: close this compute_provider kernel and open a fresh one, which is born into the new configuration. build_env() makes the same check before building, so a stale kernel cannot spend twenty minutes on an image in a namespace the next submit_job() — which always follows the live Settings — will never look at.

Sandboxes follow the same birth rule. A fresh submit_job() takes the Settings of the moment it creates its sandbox; a warm-reuse submit keeps the Environment, app and egress fence its sandbox was born under (see Warm reuse, reattachment, and close()). So a Settings change — an egress change included — reaches your next fresh sandbox, never a warm one, and nothing ever moves: images, volumes and running sandboxes stay where they were created, reachable again the moment the setting points back.

Network egress from the job sandbox

What the job container can reach on the network is a per-provider setting (Settings → Compute → modal → Network Restrictions), not something you choose per submit. The host stamps it onto every new sandbox as Modal's outbound_domain_allowlist (or block_network). Four states:

• Not configured (NULL, the default for new connections and for any provider enabled before this setting existed) and Unrestricted — the sandbox has open internet. The byoc_submit card shows no egress row.
• Allowlist — the sandbox may only open outbound connections to a list the HOST computes at approval time: (mirror of the user's local sandbox allowlist ∪ a built-in seed of pull-only wheel indexes — download.pytorch.org, pypi.nvidia.com — when the policy mirrors) ∪ (the policy's extra domains) ∪ (the env's declared domains). A mirror:false policy is never seeded (it is the user's explicit "exactly my list"). The card shows the exact final list (Egress limited to N domains) with each part attributed; only portable domain patterns are mirrored (a local bare * or interior wildcard is dropped and disclosed, never sent to Modal where it means something else); and when there is no local allowlist to mirror (network isolation off or unavailable) the resolution is unrestricted and the card says so. Two hosts are never seeded: storage.googleapis.com (hydrate the weights volume instead) and the write-capable Hugging Face hub/CDNs — an env that pulls from HF at job time declares huggingface.co + *.hf.co itself.
• No network — the user chose to create every new job sandbox with block_network: all outbound connections refused. This is a kill switch, decided before any allowlist input is even read. The env's egress_domains / the ledger's egress: line are ignored under it — there is deliberately no channel, yours or an env's, that can open a hole in it — and the card says "all outbound network is blocked (provider policy: No network)".

You influence exactly one input to that merge: which env declaration applies. META["egress_domains"] is the list of hosts the env's documented job-time commands need (e.g. api.colabfold.com for the default ColabFold MSA server). For a bundled env, pass its NAME as provider_params.modal.env (or let the ### env: ledger block name it): the host reads the declaration from the SHIPPED file, so it survives every rebuild and can only select among shipped, reviewed sets. For an ad-hoc env (build_env(path=…)), record the egress: ledger line (see env-setup.md); that text is agent-written, so the host re-validates and caps it. Either way it is a request the user always sees, never an authority — it lands on the card as (+K declared by this env), and a standing "always allow" grant never skips a card that carries env-declared domains. Declare the exact job-time hosts only (HYDRATE runs in the compute_provider kernel, outside the fence), and you cannot set or widen the policy itself: provider_params rejects egress_allowlist / outbound_domain_allowlist outright and compute_details cannot reach the column.

Skill-driven jobs and the fence

A skill is just commands inside a fenced sandbox: every host its documented flow dials at job time either rides the merge above or dies as a connection reset. So declare an env's job-time hosts (its META["egress_domains"] / the ledger's egress: line) BEFORE the first submit, and move big one-time fetches — weights, checkpoints, sequence databases — out of the job entirely: bake them at image build or stage them from the compute_provider kernel into a Volume, neither of which is fenced (env-setup.md → "Tool recipes that survive the egress fence" has the runnable recipes). Settings → Compute → modal → Network Restrictions → Additional domains is the user-side fallback for a fetch that truly must happen inside the job.

Knowing the policy, and reading a network failure

You never have to guess what a job is allowed to reach, because the host tells you twice — three times when a compute_provider kernel is open: compute_provider_config()['egress_mode'] there names the mode the next sandbox will be created under, before any submit spends a card. At submit time the reply carries an egress line and submit_job prints it — [egress] sandbox egress at creation: allowlist (2 domains; mirror off; +0 from the env) — and the same string lives on the handle as job.egress. That line describes the sandbox for its whole life: the policy is captured when the sandbox is created, so what you see there is what every command in that job runs under, no matter what the Settings screen says five minutes later. Then, if the job's output ends up carrying connection-level failures and the sandbox was fenced, the result and the compute_done notification carry a second host-authored line starting with [egress] that names the policy, its size, and the remedy. Both lines are written by the host from the policy it actually enforced — they are evidence you can repeat to the user verbatim, not something you have to infer.

Knowing how a fence fails is what lets you attribute instead of flail. Modal's allowlist is a TLS/443 SNI filter, so it never answers at the HTTP layer; everything it refuses dies below that, and each spelling tells you which rule you hit. These are the shapes we observed live, not theory:

A 4xx/5xx, on the other hand, means the host was reachable and answered; that is an application problem, not the policy.

The fix depends on the failure class, and the [egress] hint names the suspect host (when the output names one) and the remedy. A host that simply isn't listed: ask the user to add exactly that name under Settings → Compute → modal → Network Restrictions → Additional domains (or, for an env's documented commands, declare it in META["egress_domains"]). A non-TLS / non-443 failure (apt-get, an http:// mirror, a raw socket): no allowlist edit can fix it — use HTTPS or do that install at image-build time, which is not fenced. storage.googleapis.com during a lazy weights download: hydrate the env's volume from the compute_provider kernel instead of opening the world's most general bucket host. Under No network there is nothing to add by design. Every policy is read only when a sandbox is created, so resubmit on a fresh handle — retrying in (or warm-reusing) the failed sandbox repeats the failure byte for byte.

Three moves look productive here and are not. Retrying the same command in the same sandbox cannot succeed, because nothing inside a sandbox can change its policy. Asking the user to switch to Unrestricted because one domain is missing trades their whole egress posture for a one-line allowlist edit — name the domain instead. And probing from inside the job ("let me curl a few sites to test the network") only reproduces the failure you already have; the [egress] line and job.egress already tell you the policy without spending a container on it.

Two Modal-side caveats worth knowing before you debug "the network is broken": Modal documents outbound_domain_allowlist as a Beta feature, and it filters by TLS SNI, so it applies to TLS on port 443 only — once an allowlist is set, plain-HTTP and non-443 traffic from the sandbox is refused regardless of the domain (we verified this live: http://example.com is refused even with example.com on the list). A tool that fetches over http:// will fail under any allowlist even if its host is listed; the fix is the tool's HTTPS endpoint, not a wider allowlist.

Common env gotchas

These are tool-level quirks, not architecture — things the bundled env files already handle but that bite when you're writing an ad-hoc env or debugging a job that should have worked.

• Volume.from_name(...) returns a lazy handle — vol.object_id raises AttributeError: unhydrated until the volume is mounted into a Sandbox.create(volumes={...}). Don't try to read it; record the volume name (that's what provider_params.modal.volumes takes anyway).
• rdkit (deepchem, molfeat, aizynthfinder): needs apt_install("libxrender1", "libxext6") or rdkit.Chem.Draw import fails on libXrender.so.1. The chemistry_gpu env includes these.
• dgl CUDA wheels live at data.dgl.ai, not PyPI. envs/proteomics_rfd_diffdock_gpu.py shows the find_links= pattern.
• DestVI is scvi.model.DestVI, not scvi.external.DestVI (moved in scvi-tools ≥1.0).
• Small model weights (esm2_t6_8M, bert-tiny) auto-download to /root/.cache on first use — fine for smoke; for repeated jobs hydrate a Volume once.
• Volume + TORCH_HOME path: torch.hub appends its own hub/ subdir. If you mount the volume at /weights and want torch to find checkpoints there, set TORCH_HOME=/weights (torch then reads /weights/hub/checkpoints/). Setting TORCH_HOME=/weights/hub makes it look at /weights/hub/hub/ and re-download.
• molfeat: use from molfeat.trans.fp import FPVecTransformer; FPVecTransformer(kind="ecfp:4")(smiles) — MoleculeTransformer raises on default config.